ALBERT

Beschreibung: 〈p〉Publication date: March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 75〈/p〉 〈p〉Author(s): Grigorijs Goldbergs, Stefan W. Maier, Shaun R. Levick, Andrew Edwards〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Obtaining reliable measures of tree canopy height across large areas is a central element of forest inventory and carbon accounting. Recent years have seen an increased emphasis on the use of active sensors like Radar and airborne LiDAR (light detection and scanning) systems to estimate various 3D characteristics of canopy and crown structure that can be used as predictors of biomass. However, airborne LiDAR data are expensive to acquire, and not often readily available across large remote landscapes. In this study, we evaluated the potential of stereo imagery from commercially available Very High Resolution (VHR) satellites as an alternative for estimating canopy height variables in Australian tropical savannas, using a semi-global dense matching (SGM) image-based technique. We assessed and compared the completeness and vertical accuracy of extracted canopy height models (CHMs) from GeoEye 1 and WorldView 1 VHR satellite stereo pairs and summarised the factors influencing image matching effectiveness and quality.〈/p〉 〈p〉Our results showed that stereo dense matching using the SGM technique severely underestimates tree presence and canopy height. The highest tree detection rates were achieved by using the near-infrared (NIR) band of GE1 (8–9%). WV1-GE1 cross-satellite (mixed) models did not improve the quality of extracted canopy heights. We consider these poor detection rates and height retrievals to result from: i) the clumping crown structure of the dominant Eucalyptus spp.; ii) their vertically oriented leaves (affecting the bidirectional reflectance distribution function); iii) image band radiometry and iv) wind induced crown movement affecting stereo-pair point matching. Our detailed analyses suggest that current commercially available VHR satellite data (0.5 m resolution) are not well suited to estimating canopy height variables, and therefore above ground biomass (AGB), in Eucalyptus dominated north Australian tropical savanna woodlands.〈/p〉 〈/div〉

Beschreibung: 〈p〉Publication date: March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 75〈/p〉 〈p〉Author(s): Hassan Awada, Giuseppe Ciraolo, Antonino Maltese, Giuseppe Provenzano, Miguel Angel Moreno Hidalgo, Juan Ignacio Còrcoles〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Remote sensing techniques allow monitoring the Earth surface and acquiring worthwhile information that can be used efficiently in agro-hydrological systems. Satellite images associated to computational models represent reliable resources to estimate actual evapotranspiration fluxes, 〈em〉ET〈/em〉〈sub〉a〈/sub〉, based on surface energy balance. The knowledge of 〈em〉ET〈/em〉〈sub〉a〈/sub〉 and its spatial distribution is crucial for a broad range of applications at different scales, from fields to large irrigation districts. In single plots and/or in irrigation districts, linking water volumes delivered to the plots with the estimations of remote sensed 〈em〉ET〈/em〉〈sub〉a〈/sub〉 can have a great potential to develop new cost-effective indicators of irrigation performance, as well as to increase water use efficiency. With the aim to assess the irrigation system performance and the opportunities to save irrigation water resources at the “SAT Llano Verde” district in Albacete, Castilla-La Mancha (Spain), the Surface Energy Balance Algorithm for Land (SEBAL) was applied on cloud-free Landsat 5 Thematic Mapper (TM) images, processed by cubic convolution resampling method, for three irrigation seasons (May to September 2006, 2007 and 2008). The model allowed quantifying instantaneous, daily, monthly and seasonal 〈em〉ET〈/em〉〈sub〉a〈/sub〉 over the irrigation district. The comparison between monthly irrigation volumes distributed by each hydrant and the corresponding spatially averaged 〈em〉ET〈/em〉〈sub〉a〈/sub〉, obtained by assuming an overall efficiency of irrigation network equal to 85%, allowed the assessment of the irrigation system performance for the area served by each hydrant, as well as for the whole irrigation district. It was observed that in all the investigated years, irrigation volumes applied monthly by farmers resulted generally higher than the corresponding evapotranspiration fluxes retrieved by SEBAL, with the exception of May, in which abundant rainfall occurred. When considering the entire irrigation seasons, it was demonstrated that a considerable amount of water could have been saved in the district, respectively equal to 26.2, 28.0 and 16.4% of the total water consumption evaluated in the three years.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): Xiwang Zhang, Fang Qiu, Fen Qin〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Crop acreage and its spatial distribution are a base for agriculture related works. Current research combining medium and low spatial resolution images focuses on data fusion and unmixing methods. The purpose of the former is to generate synthetic fine spatial resolution data instead of directly solving the problem. In the latter, high-resolution data is only used to provide endmembers and the result is usually an abundance map rather than the true spatial distribution data. To solve this problem, this paper designs a conceptual model which divides the study area into different types of pixels at a MODIS 250 m scale. Only three types of pixels contain winter wheat, i.e., pure winter wheat pixels (P〈sub〉A〈/sub〉), the mixed pixels comprising winter wheat and other vegetation (M〈sub〉A〈/sub〉) and the mixed pixels comprising winter wheat and other crops (M〈sub〉B〈/sub〉). Different strategies are used in processing them. (1) Within the pure cultivated land pixels, the Kullback–Leibler (KL) divergence is employed to analyze the similarity between unknown pixels and the pure winter wheat samples on the temporal change characteristics of NDVI. Further P〈sub〉A〈/sub〉 is identified. (2) For M〈sub〉A〈/sub〉, a proposed reverse unmixing method is firstly used to extract the temporal change information of cultivated land components, after which winter wheat is identified from the cultivated land components as previously described. (3) For M〈sub〉B〈/sub〉 which only appears on the border of P〈sub〉A〈/sub〉, a mask is created by expanding the P〈sub〉A〈/sub〉 and temporal difference is utilized to identify winter wheat under the mask. Finally, these three results are integrated at a TM scale with the aid of 25 m resolution land use data. We applied the proposed solution and obtained a good result in the main agricultural area of the Yiluo River Basin. The identified winter wheat planting acreage is 161,050.00 hm〈sup〉2〈/sup〉. The result is validated based on the five-hundred random validation points. Overall accuracy is 94.80% and Kappa coefficient is 0.85. This demonstrates that the temporal information reflecting crop growth is also an important indicator, and the KL divergence makes it more convenient in identifying winter wheat. This research provided a new perspective for the combination of low and medium spatial resolution remote sensing images. The proposed solution can also be effectively applied in other places and countries for the crop which has a clear temporal change characteristic that is different from others.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): Joana Cardoso-Fernandes, Ana C. Teodoro, Alexandre Lima〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Remote sensing has proved to be a powerful resource in geology capable of delineating target exploration areas for several deposit types. Only recently, these methodologies have been used for the detection of lithium (Li)-bearing pegmatites. This happened because of the growing importance and demand of Li for the construction of Li-ion batteries for electric cars. The objective of this study was to develop innovative and effective remote sensing methodologies capable of identifying Li-pegmatites through alteration mapping and through the direct identification of Li-bearing minerals. For that, cloud free Landsat-5, Landsat-8, Sentinel-2 and ASTER images with low vegetation coverage were used. The image processing methods included: RGB (red, green, blue) combinations, band ratios and selective principal component analysis (PCA). The study area of this work is the Fregeneda (Salamanca, Spain)-Almendra (Vila Nova de Foz Côa, Portugal) region, where different known types of Li-pegmatites have been mapped. This study proposes new RGB combinations, band ratios and subsets for selective PCA capable of differentiating the spectral signatures of the Li-bearing pegmatites from the spectral signatures of the host rocks. The potential and limitations of the methodologies proposed are discussed, but overall there is a great potential for the identification of Li-bearing pegmatites using remote sensing. The results obtained could be improved using sensors with a better spatial and spectral resolution.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: March 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 75〈/p〉 〈p〉Author(s): Ying Zhang, Lixin Sun〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The surface fabric of urbanized areas, (i.e. its constituent land covers and land uses) plays an essential role in the generation of the urban/rural temperature differences, i.e. the Urban Heat Island (UHI) effect. Land surface information, derived from satellite imagery, and complementary information such as demographics can be used as the basis for an understanding of the atmospheric and surface thermal variations within cities. The results of comprehensive land surface characterizations of two major Canadian urban areas, the Greater Toronto Area and Ottawa-Gatineau, are described. Spatial information, including land cover fraction maps, land use and its historic changes, population density maps are compared with intra-urban surface temperature variations derived from satellite thermal imagery. Three aspects of the impacts of land cover and land use on urban land thermal characteristics are addressed, namely, (a) the relationships between surface temperature and subpixel land cover and population density (b) intra-city seasonal temperature variations and (c) the intensification of the urban heat island effect due to urban built-up land growth.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): Huizeng Liu, Shuibo Hu, Qiming Zhou, Qingquan Li, Guofeng Wu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Accurately removing atmospheric interferences and retrieving water-leaving reflectance are decisive for subsequent ocean color applications. Over turbid waters, the black pixel assumption at the near-infrared (NIR) spectral region does not hold, and shortwave infrared (SWIR)-based atmospheric correction algorithm should be applied. Turbidity index is proposed to detect turbid waters and worked as a criterion for NIR-SWIR combined algorithm. However, studies demonstrated that turbidity index did not work well as expected. This study, using simulated data and satellite images, aimed to revisit the effectiveness of turbidity index for the switching scheme of NIR-SWIR algorithm. The simulated data were obtained from aerosol look-up tables, and the Aqua MODIS images were used. The variations of turbidity index calculated from aerosol reflectance and Rayleigh-corrected reflectance were explored. Results showed that turbidity index did not obey the assumption that it should be close to one over clear waters with negligible NIR water-leaving reflectance; its value calculated from simulated aerosol reflectance ranged from 0.7 to 2.2; and the turbidity index values varied depending on fine-mode fraction, aerosol optical thickness, relative humidity and observing geometries. Therefore, more effective switching scheme should be developed for the NIR-SWIR combined atmospheric correction algorithm.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): Kirsikka Heinilä, Miia Salminen, Sari Metsämäki, Petri Pellikka, Sampsa Koponen, Jouni Pulliainen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the 〈em〉SCAmod〈/em〉 algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms.〈/p〉 〈p〉A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI -based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth 〈 30 cm) the mean NDSI -0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values.〈/p〉 〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): Irfan A. Iqbal, Robert A. Musk, Jon Osborn, Christine Stone, Arko Lucieer〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Forest inventory operations have greatly benefitted from remotely sensed data particularly airborne laser scanning (ALS) which has become a popular technology choice for large-area forest inventories. For remote regions, for fragmented estates or for single stand-level inventories ALS may be unsuitable because of the high cost of data acquisition. Point cloud data generated from digital aerial photography (DAP) is emerging as a cost-effective alternative to ALS. In this study we compared area-based forest inventory attributes derived from point cloud datasets sourced from ALS, small-format and medium-format digital aerial photography (SFP and MFP). Non-parametric modelling approach, namely RandomForest, was employed to model forest structural attributes at both plot- and stand-levels. The results were evaluated using field data collected at 105 inventory plots. At plot-level, the maximum difference among relative RMSEs of basal area (BA), top height (H〈em〉〈sub〉top〈/sub〉〈/em〉), stocking (N) and total stem volume (TSV) of the three datasets was 2.46%, 0.55%, 13.29% and 2.53%, respectively. At stand-level, the maximum difference among relative RMSEs of BA, H〈em〉〈sub〉top〈/sub〉〈/em〉, N and TSV of the three datasets was 3.86%, 1.25%, 7.85% and 6.04%, respectively. This study demonstrates the robustness of DAP across different sensors, and thus informs forest managers planning data acquisition solutions to best suit their operational needs.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): Guangzhen Wang, Jingpu Wang, Xueyong Zou, Guoqi Chai, Mengquan Wu, Zhoulong Wang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Quantitative estimations of the fractional cover of photosynthetic vegetation (〈em〉f〈/em〉〈sub〉PV〈/sub〉), non-photosynthetic vegetation (〈em〉f〈/em〉〈sub〉NPV〈/sub〉) and bare soil (〈em〉f〈/em〉〈sub〉BS〈/sub〉) are critical for soil wind erosion, desertification, grassland grazing, grassland fire, and grassland carbon storage studies. At present, regional and large-scale 〈em〉f〈/em〉〈sub〉PV〈/sub〉, 〈em〉f〈/em〉〈sub〉NPV〈/sub〉 and 〈em〉f〈/em〉〈sub〉BS〈/sub〉 estimations have been carried out in many areas. However, few studies have used moderate resolution imaging spectroradiometer (MODIS) data to perform large-scale, long-term 〈em〉f〈/em〉〈sub〉PV〈/sub〉, 〈em〉f〈/em〉〈sub〉NPV〈/sub〉 and 〈em〉f〈/em〉〈sub〉BS〈/sub〉 estimations in the Xilingol grassland of China. The objective of this study was to quantitatively estimate the time series of 〈em〉f〈/em〉〈sub〉PV〈/sub〉, 〈em〉f〈/em〉〈sub〉NPV〈/sub〉 and 〈em〉f〈/em〉〈sub〉BS〈/sub〉 in the typical grassland region of Xilingol from MODIS image data. Field measurement spectral and coverage data from May and September 2017 were combined with the 8-day composite product (MOD09A1) acquired during 2017. We established an empirical linear model of different non-photosynthetic vegetation indices (NPVIs) and 〈em〉f〈/em〉〈sub〉NPV〈/sub〉 based on the sample scale. The linear correlation between the dead fuel index (DFI) and 〈em〉f〈/em〉〈sub〉NPV〈/sub〉 was best (R〈sup〉2〈/sup〉 = 0.60, RMSE = 0.15). A normalized difference vegetation index (NDVI)-DFI model based on MODIS data was proposed to accurately estimate the 〈em〉f〈/em〉〈sub〉PV〈/sub〉, 〈em〉f〈/em〉〈sub〉NPV〈/sub〉 and 〈em〉f〈/em〉〈sub〉BS〈/sub〉 (estimation accuracies of 44%, 71%, and 74%, respectively) in the typical grasslands of Xilingol in China. The 〈em〉f〈/em〉〈sub〉PV〈/sub〉, 〈em〉f〈/em〉〈sub〉NPV〈/sub〉 and 〈em〉f〈/em〉〈sub〉BS〈/sub〉 values for the typical grassland time series estimated by the NDVI-DFI model were consistent with the phenological characteristics of the grassland vegetation. The results show that the application of the NDVI-DFI model to the Xilingol grassland is reasonable and appropriate, and it is of great significance to the monitoring of soil wind erosion and fires in grasslands.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0303243418305907-ga1.jpg" width="242" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): B. Franch, E.F. Vermote, S. Skakun, J.C. Roger, I. Becker-Reshef, E. Murphy, C. Justice〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. Earth observation data from space can contribute to agricultural monitoring, including crop yield assessment and forecasting. In this study, we present a new crop yield model based on the Difference Vegetation Index (DVI) extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data at 1 km resolution and the un-mixing of DVI at coarse resolution to a pure wheat signal (100% of wheat within the pixel). The model was applied to estimate the national and subnational winter wheat yield in the United States and Ukraine from 2001 to 2017. The model at the subnational level shows very good performance for both countries with a coefficient of determination higher than 0.7 and a root mean square error (RMSE) of lower than 0.6 t/ha (15–18%). At the national level for the United States (US) and Ukraine the model provides a strong coefficient of determination of 0.81 and 0.86, respectively, which demonstrates good performance at this scale. The model was also able to capture low winter wheat yields during years with extreme weather events, for example 2002 in US and 2003 in Ukraine. The RMSE of the model for the US at the national scale is 0.11 t/ha (3.7%) while for Ukraine it is 0.27 t/ha (8.4%).〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): Yves Julien, José A. Sobrino〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉NDVI (Normalized Difference Vegetation Index) time series usually suffer from remaining cloud presence, even after data pre-processing. To address this issue, numerous gap-filling (or reconstruction) techniques have been developed in the literature, although their comparison has mainly been local to regional, with only two global studies to date, and has led to sometimes contradictory results. This study builds on these different comparisons, by testing different parameterizations for five NDVI temporal profile reconstruction techniques, namely HANTS (Harmonic Analysis of Time Series), IDR (iterative Interpolation for Data Reconstruction), Savitzky-Golay, Asymmetric Gaussian and Double Logistic, and then comparing them as generally parameterized, and then with the best of the tested parameterizations. These comparisons show that the HANTS reconstruction technique provides lower errors in cloud prone areas, while the IDR method works best with shorter cloud covers. However, the remaining errors in cloud prone areas are still high, and there is room for new reconstruction techniques. Although these results are only applicable to the range of the tested parameterizations, these latter have been chosen within widely used configurations, and should provide interested users with a better understanding of the different methods and the best parameterization for their needs, as well as an estimate of the expected error in the reconstruction of NDVI time series.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 77〈/p〉 〈p〉Author(s): Chiwei Xiao, Peng Li, Zhiming Feng〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Accurately monitoring rubber plantations dynamics is essential for assessing eco-environmental effects in soil, hydrology and biodiversity especially in the northern edge of the Asian tropics (e.g. Xishuangbanna, China). In this study, a novel phenology-based multiple normalization approach was firstly proposed to annually map rubber plantations between two critical phenological phase (defoliation and foliation) in Xishuangbanna during 1987–2018. It included three key steps, namely (1) Landsat non-visible bands normalization for calculating the Normalized Difference Moisture Index (NDMI) and Normalized Burn Ratio (NBR), (2) normalization of NDMI and NBR for the Normalized Vegetation Index (NVI), and (3) re-normalization of NVIs for the Re-Normalized Vegetation Index (RNVI). The NVI highlighted the temporal differences of NDMI in land surface moisture content and NBR in soil moisture as well as vegetation survival of rubber plantations during the shifting period of defoliation and foliation. The RNVI fully considered the inverse patterns of the NVIs between defoliation and foliation phase. Rubber plantations were featured by negative NVI and RNVI values within the two temporal windows, while the positive NVI and RNVI values or zero stood for other non-rubber land cover types. The average overall accuracy of five-year mature rubber plantations maps (2010, 2013, 2015, 2016, and 2018) was up to 94.7% with the average kappa coefficient of 0.88, showing the great potential of multiple normalization approach. The total area of rubber plantations increased about 5.9 times from 1987 to 2018, showing clear expansion trends from centralization to scattering in Xishuangbanna as well as continuous spread in Sino-Lao (near Luang Namtha) and Sino-Myanmar (near Mongphak of Shan) border regions on the Chinese side. In addition, annual average analysis showed that about 91.4% of rubber plantations were invariably distributed around Jinghong City and Mengla County in the past decades.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): 〈/p〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): João Gonçalves, Isabel Pôças, Bruno Marcos, C.A. Mücher, João P. Honrado〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Geographic Object-based Image Analysis (GEOBIA) is increasingly used to process high-spatial resolution imagery, with applications ranging from single species detection to habitat and land cover mapping. Image segmentation plays a key role in GEOBIA workflows, allowing to partition images into homogenous and mutually exclusive regions. Nonetheless, segmentation techniques require a robust parameterization to achieve the best results. Frequently, inappropriate parameterization leads to sub-optimal results and difficulties in comparing distinct methods.〈/p〉 〈p〉Here, we present an approach based on Genetic Algorithms (GA) to optimize image segmentation parameters by using the performance scores from object-based classification, thus allowing to assess the adequacy of a segmented image in relation to the classification problem. This approach was implemented in a new R package called 〈em〉SegOptim〈/em〉, in which several segmentation algorithms are interfaced, mostly from open-source software (〈em〉GRASS GIS, Orfeo Toolbox, RSGISLib, SAGA GIS〈/em〉, 〈em〉TerraLib〈/em〉), but also from proprietary software (〈em〉ESRI ArcGIS〈/em〉). 〈em〉SegOptim〈/em〉 also provides access to several machine-learning classification algorithms currently available in R, including Gradient Boosted Modelling, Support Vector Machines, and Random Forest.〈/p〉 〈p〉We tested our approach using very-high to high spatial resolution images collected from an Unmanned Aerial Vehicle (0.03 – 0.10 m), WorldView-2 (2 m), RapidEye (5 m) and Sentinel-2 (10 – 20 m) in six different test sites located in northern Portugal with varying environmental conditions and for different purposes, including invasive species detection and land cover mapping. The results highlight the added value of our novel comparison of image segmentation and classification algorithms. Overall classification performances (assessed through cross-validation with the Kappa index) ranged from 0.85 to 1.00. Pilot-tests show that our GA-based approach is capable of providing sound results for optimizing the parameters of different segmentation algorithms, with benefits for classification accuracy and for comparison across techniques. We also verified that no particular combination of an image segmentation and a classification algorithm is suited for all the tasks/objectives. Consequently, it is crucial to compare and optimize available methods to understand which one is more suited for a certain objective.〈/p〉 〈p〉Our approach allows a closer integration between the segmentation and classification stages, which is of high importance for GEOBIA workflows. The results from our tests confirm that this integration has benefits for comparing and optimizing both processes. We discuss some limitations of the 〈em〉SegOptim〈/em〉 approach (and potential solutions) as well as a future roadmap to expand its current functionalities.〈/p〉 〈/div〉

Beschreibung: 〈p〉Publication date: May 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 77〈/p〉 〈p〉Author(s): Mohammad Shawkat Hossain, Mazlan Hashim〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Seagrasses are rapidly losing their ability to serve ecosystem services (ESs) with the loss of global biodiversity and coastal habitat degradation over the past few decades. Monitoring ESs is therefore important for tracking subsequent decline or recovery. The development of new Earth Observation (EO) technologies and approaches involved in observing and analyzing data collected from remote sensing (RS) satellite/aircraft would make for a useful application: monitoring and mapping spatial distribution of ESs that seagrasses provide to marine ecosystems and human well-being. Unfortunately, current approaches greatly rely on spatial proxy measures to map distribution of ESs. Many of biophysical parameters are currently detectable by EO instruments, with relevance to ESs. This paper review the capabilities of advanced RS techniques for informing species diversity, growth traits, health condition, ecological processes, and water quality variables linking ESs and describe how these EO products can contribute to ES assessments. Incorporation of both the direct (seagrass extent) and indirect (water related ESs) estimates derived from EO data can now provide more direct estimates of seagrass ecosystem properties (seagrass habitat quality and biodiversity) influencing ESs than the spatial proxies presently in use and they can support in developing more mechanistic models in GIS framework and spatially explicit maps of ESs. The increasing range of EO system and data sets suitable for measuring ES indicators has potential to supporting integrated coastal land use planning. Because each ES indicator and service responds to the environment, there is no ‘one approach fits all’ solution. Selecting EO products, with required resolution to be analyzed will guide to improving mapping efforts. This work also shed lights to sensitize discussion about need of holistic methodologies, challenges, and to motivate an enhanced use of EO-based technology and data. The need for a multidisciplinary project team of ecologists, sociologists, biologists and RS experts has been suggested for proper identification of ES indicators and advanced analysis of EO data. By doing so, we anticipate rapid progress in satellite based ES assessment and characterization of ESs and, in turn, supporting conservation and management of coastal ecosystem.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): Qiang Chen, Xianwen Liu, Yijun Zhang, Jingjing Zhao, Qian Xu, Yinghui Yang, Guoxiang Liu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Satellite Interferometric Synthetic Aperture Radar (InSAR) is playing an increasingly important role in the observation of coseismic surface deformation caused by earthquakes, and has been used to invert for subsurface fault structure and reveal earthquake source mechanisms. However, the mapping of complex non-planar or curved (e.g., listric-shaped) faults still remains a challenging task due to variable dips along the underground depth and the impenetrability of the deep crust. Here, we develop a set of new inversion algorithms to determine the listric fault geometry with InSAR- and GPS-observed surface deformation as the significant constraints. The fault surface with variable dip angles is discretized into consecutive sub-fault layers along the down-dip direction. A nonlinear iteration algorithm is used to minimize the objective function to determine the dip angle for each sub-fault layer. The proposed method is first tested using synthetic data to show its effectiveness for retrieval of varying fault geometry dips, and then applied to the 2008 Mw 7.9 Wenchuan earthquake that ruptured the Yingxiu-Beichuan fault for over 320 km along the southwest-northeast strike. The inversion shows that the dip angle of the seismogenic fault is up to 76° near the surface layer, and gradually decreases along the down-dip direction. A significant decrease in dip occurs within the depths of 6–15 km with a dip of 32° at a depth of 15 km. The dip angle decreases to 2° at a depth of 20 km, and finally merges with the subparallel PengGuan fault, which is basically consistent with geological investigations and seismic waveform data inversion. Using the inferred fault geometry, the slip model associated with the event is estimated. Five high-slip concentrations along the strike of the Yingxiu-Beichuan fault are recognized. The inversion misfit of InSAR data is reduced to 7.1 cm with a significant improvement compared to previous studies.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): Mengmeng Wang, Guojin He, Zhaoming Zhang, Guizhou Wang, Zhihua Wang, Ranyu Yin, Shiai Cui, Zhijie Wu, Xiaojie Cao〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The split-window algorithm is the most commonly used method for land surface temperature (LST) retrieval from satellite data. Simplification of the Planck’s function, as an important step in developing the SWA, allows us to directly relate the radiance to the temperature toward solving the radiative transfer equation (RTE) set. In this study, Planck’s radiance relationship between two adjacent thermal infrared channels was modeled to solve the RTE set instead of simplification of the Planck’s function. A radiance-based split-window algorithm (RBSWA) was developed and applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data. The performance of the RBSWA was assessed and compared with three most common brightness temperature-based split-window algorithms (BTBSWAs) by using the simulated data and satellite measurements. Simulation analysis showed that the LST retrieval using RBSWA had a Root Mean Square Error (RMSE) of 0.5 K and achieved an improvement of 0.3 K compared with three BTBSWAs, and the LST retrieval accuracy using RBSWA was better than 1.5 K considering uncertainties in input parameters based on the sensitivity analysis. For application of RBSWA to MODIS data, the results showed that: 1) comparison between LST from MODIS LST product and LST retrieved using RBSWA showed a mean RMSE of 1.33 K for 108 groups of MODIS image covering continental US, which indicates RBSWA is reliable and robust; 2) when using the measurements from US surface radiation budget network as real values the RMSE of the RBSWA algorithm was 2.55 K and was slightly better than MODIS LST product; and 3) through the cross validation using Advanced Spaceborne Thermal Emission and Reflection Radiometer LST product, the RMSE of the RBSWA algorithm was 2.23 K and was 0.28 K less than that of MODIS LST product. We conclude that the RBSWA for LST retrieval from MODIS data can attain a better accuracy than the BTBSWA.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): R.O. Chávez, A. Moreira-Muñoz, M. Galleguillos, M. Olea, J. Aguayo, A. Latín, I. Aguilera-Betti, A.A. Muñoz, H. Manríquez〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The “blooming desert”, or the explosive development and flowering of ephemeral herbaceous and some woody desert species during years with abnormally high accumulated rainfall, is a spectacular biological phenomenon of the hyper-arid Atacama Desert (northern Chile) attracting botanists, ecologists, geo-scientists, and the general public from all over the world. However, the number of “blooming deserts”, their geographical distribution and spatio-temporal patterns have not been quantitatively assessed to date. Here, we used NDVI data from the Global Inventory Modeling and Mapping Studies (GIMMS) project to reconstruct the annual land surface phenology (LSP) of the Atacama Desert using a non-parametric statistical approach. From the reconstructed LSP, we detected the “blooming deserts” as positive NDVI anomalies and assessed three dimensions of the events: their temporal extent, intensity of “greening” and spatial extent. We identified 13 “blooming deserts” between 1981 and 2015, of which three (1997–98, 2002–03, and 2011) can be considered major events according to these metrics. The main event occurred in 2011, spanning 180 days between July and December 2011, and spread over 11,136 km〈sup〉2〈/sup〉 of Atacama dry plains. “Blooming deserts” in Atacama have been triggered by the accumulation of precipitation during a period of 2 to 12 months before and during the events. The proposed three-dimensional approach allowed us to characterize different types of “blooming deserts”: with longer episodes or larger spatial distribution or with different “greening” intensities. Its flexibility to reconstruct different LSP and detect anomalies makes this method a useful tool to study these rare phenomena in other deserts in the world also.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0303243418306202-ga1.jpg" width="348" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: April 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 76〈/p〉 〈p〉Author(s): Vidya Nahdhiyatul Fikriyah, Roshanak Darvishzadeh, Alice Laborte, Nasreen Islam Khan, Andy Nelson〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Improved rice crop and water management practices that make the sustainable use of resources more efficient are important interventions towards a more food secure future. A remote sensing-based detection of different rice crop management practices, such as crop establishment method (transplanting or direct seeding), can provide timely and cost-effective information on which practices are used as well as their spread and change over time as different management practices are adopted. Establishment method cannot be easily observed since it is a rapid event, but it can be inferred from resulting observable differences in land surface characteristics (i.e. field condition) and crop development (i.e. delayed or prolonged stages) that take place over a longer time. To examine this, we used temporal information from Synthetic Aperture Radar (SAR) backscatter to detect differences in field condition and rice growth, then related those to crop establishment practices in Nueva Ecija (Philippines). Specifically, multi-temporal, dual-polarised, C-band backscatter data at 20m spatial resolution was acquired from Sentinel-1A every 12 days over the study area during the dry season, from November 2016 to May 2017. Farmer surveys and field observations were conducted in four selected municipalities across the study area in 2017, providing information on field boundaries and crop management practices for 61 fields. Mean backscatter values were generated per rice field per SAR acquisition date. We matched the SAR acquisition dates with the reported dates for land management activities and with the estimated dates for when the crop growth stages occurred. The Mann-Whitney U test was used to identify significant differences in backscatter between the two practices during the land management activities and crop growth stages. Significant differences in cross-polarised, co-polarised and band ratio backscatter values were observed in the early growing season, specifically during land preparation, crop establishment, rice tillering and stem elongation. These findings indicate the possibility to discriminate crop establishment methods by SAR at those stages, suggesting that there is more opportunity for discrimination than has been presented in previous studies. Further testing in a wider range of environments, seasons, and management practices should be done to determine how reliably rice establishment methods can be detected. The increased use of dry and wet direct seeding has implications for many remote sensing-based rice detection methods that rely on a strong water signal (typical of transplanting) during the early season.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): H.M.A. van der Werff〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study describes a method to map shoreline indicators on a sandy beach. The hypothesis is that, on this beach, spectral albedo is predominantly determined by moisture content and water lines can, therefore, be detected as albedo contrasts. A laboratory experiment is performed to relate moisture content to image albedo, and supervised edge detection is subsequently used to map the shoreline indicators with remote sensing imagery. The algorithm is tested with data from visible, near-infrared and shortwave-infrared wavelength regions. These results are compared to shoreline indicators obtained by a field survey and a shoreline indicator derived from a digital elevation model. Both the water line present when the imagery was acquired, as well as the maximum extent of the last flood, can be detected as a single edge. Older high water lines are confused with the last high water line and appear dispersed, as there are multiple debris lines present on the beach. The low water line, usually in saturated sand, also appears dispersed due to the presence of channels and troughs. Shorelines are constant moving boundaries, which is why shoreline indicators are used as a proxy. Unlike a mathematical indicator that is based on an elevation model, our method is more sensitive to the dynamic nature of shorelines. Supervised edge-detection is a technique for generating reproducible measurements of shoreline indicator positions over time, and aids in the monitoring of coastline migration.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Touba Salehi, Majid H. Tangestani〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Discrimination of iron oxide and hydroxide mineral assemblages is a key factor in porphyry copper exploration. This paper investigates the ability of the Worldview-3 VNIR data in mapping iron oxide / hydroxide minerals associated with the porphyry copper mineralization. The study area is situated in the central part of the Urumieh–Dokhtar magmatic arc, northeastern Isfahan, Iran, which hosts a number of important porphyry copper deposits. The copper mineralization is related to the porphyry intrusive complex that ranges in composition from diorite to granodiorite. Bands 2,3,4,5, and 8 of this satellite were used to enhance the pixels containing target minerals and to mask the effects of vegetation on these pixels. The best results in terms of mapping and discriminating target materials were acquired by the band math and the matched filtering (MF) methods. The MF output results enhanced pixels with the highest fractions of the target minerals. The field investigations, X-ray diffraction (XRD) analysis, and microscopic studies showed that the distribution pattern of target minerals correspond well to ground criteria and gossan caps associated with the porphyry copper mineralization. It was concluded that Worldview-3 has the efficient spectral bands in VNIR region, which could be proposed for the iron oxide / hydroxide mineral mapping.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0303243418302022-ga1.jpg" width="500" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Ping He, Kaihua Ding, Caijun Xu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉On 25 November 2016, the Aketao, Xinjiang earthquake occurred on the Muji fault, which is located at the northernmost end of the right-lateral Karakorum Fault (KF). This event provides a rare chance to gain insights into how the stress accumulates in Pamir margin as the Indian plate collides with the Eurasian plate. Space geodetic measurements including InSAR and GPS were used to obtain coseismic surface displacements associated with this earthquake. Based on a finite fault model, the coseismic slip distribution inverted by the combined datasets indicates that the 2016 Aketao event is caused by a primary shallow strike-slip with minor normal-slip at a steep-dipping angle. To explore the real structure of Muji fault, listric fault model inferred by relocated aftershocks as well as the planar fault model, were used in our slip distribution inversion. The results suggest that the optimal fault model should be a highly-dipping planar fault with two separated asperities. The large slip zone is beneath the surface near the epicenter with a maximum slip of 1.1 m, while the small one in the east breaks the surface, in a good agreement with the field seismic geological survey. The total geodetic moment is 1.35 × 10〈sup〉19〈/sup〉〈em〉N∙m〈/em〉, equivalent to Mw 6.7. The nearly pure dextral strike-slip Aketao earthquake, and the recent 2015 Mw 7.2 sinistral strike-slip Tajikstan earthquake in this region, to some extent, manifest the extension motion is dominated in northern Pamir Plateau, in response to the northward convergence between Indian and Eurasian collision.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Grant Staben, Arko Lucieer, Peter Scarth〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Understanding ecological changes in native vegetation communities often requires information over long time periods (multiple decades). Tropical cyclones can have a major impact on woody vegetation structure across northern Australia, however understanding the impacts on woody vegetation structure is limited. Woody vegetation structural attributes such as height are used in ecological studies to identify long term changes and trends. LiDAR has been used to measure woody vegetation structure, however LiDAR datasets cover relatively small areas and historical coverage is restricted, limiting the use of this technology for monitoring long-term change. The Landsat archive spans multiple decades and is suitable for regional/continental assessment. Advances in predictive modelling using machine learning algorithms have enabled complex relationships between dependent and independent variables to be identified. The aim of this study is to develop a predictive model to estimate woody vegetation height from Landsat imagery to assist in understanding change through space and time. A LiDAR canopy height model was produced covering a range of vegetation communities in northern Australia (Darwin region) for use as the dependent variable. A random forest regression model was developed to predict mean LiDAR canopy height (30 m spatial resolution) from Landsat-5 Thematic Mapper (TM). Validation of the random forest model was undertaken on independent data (〈em〉n〈/em〉 = 30,500) resulting in an overall 〈em〉R〈/em〉〈sup〉2〈/sup〉 = 0.53, RMSE of 2.8 m. Assessment of the RMSE within four broad vegetation communities ranged from 2.5 to 3.7 m with the two dominant communities in the study area Mangrove forests and Eucalyptus communities recording an RMSE value of 2.9 m and 2.5 m respectively. The model was also applied to Landsat-7 Enhanced Thematic Mapper Plus (ETM+) resulting in an 〈em〉R〈/em〉〈sup〉2〈/sup〉 of 0.49, RMSE of 2.8 m. The model was then applied to all cloud free Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 Operational Land Imager (OLI) imagery (106/69 path/row) available between the months April, May and June for 1987 to 2016 to produce annual estimates (29 years) of canopy height. A number of time traces were produced to illustrate tree canopy height through time in the Darwin region which was severely impacted by cyclone (hurricane) Tracy on the 25〈sup〉th〈/sup〉 December 1974.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Darius Phiri, Justin Morgenroth, Cong Xu, Txomin Hermosilla〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The application of Landsat satellite imagery in land cover classification is affected by atmospheric and topographic errors, which have led to the development of different correction methods. In this study, moderate resolution atmospheric transmission (MODTRAN) and dark object subtraction (DOS) atmospheric corrections, and cosine topographic correction were evaluated individually and combined in a heterogeneous landscape in Zambia. These pre-processing methods were tested using a combination of object-based image analysis (OBIA) and Random Forests (RF) non-parametric classifier (hereafter referred to as OBIA-RF). This assessment aimed at understanding the combined effects of different pre-processing methods and the OBIA-RF classification method on the accuracy of Landsat operational land (OLI-8) imagery with different spatial resolutions. Here, we used pansharpened and standard Landsat OLI-8 images with 15 and 30 m spatial resolutions, respectively. The results showed that non pre-processed images reached a classification accuracy of 68% for pansharpened and 66% for standard Landsat OLI-8. Classification accuracy improved to 93% (pansharpened) and 86% (standard) when combined MODTRAN and cosine topographic correction pre-processing were applied. The results highlight the importance of pansharpening, as well as atmospheric and topographic corrections for Landsat OLI-8 imagery, when used as input in OBIA classification with the RF classifier.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Shanshan Yang, Le Wang, Chen Shi, Ying Lu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Mangrove forests, with high productivity, strong carbon fixation ability, and high ecological value, are a critical component of wetland ecosystems. However, in recent years, species invasion is threatening the health of mangrove forests. In order to trace the change and protect them, Light Use Efficiency (LUE) has been used as a significant parameter to estimate the vegetation productivity of mangrove forests. Recent studies have shown that the Photochemical Reflectance Index (PRI) has a strong relationship with LUE. Nevertheless, their relationships undergone significant changes under the influence of vegetation types and external environment. In this paper, we evaluated the relationship between PRI and LUE for different mangrove species (〈em〉Avicennia marina〈/em〉 and 〈em〉Aegiceras corniculatum〈/em〉) and the effects of 〈em〉Spartina alterniflora〈/em〉 invasion on the relationship. The results showed that LUE had a strong correlation with PRI. The correlation of 〈em〉Avicennia marina〈/em〉 was slightly higher than that of 〈em〉Aegiceras corniculatum〈/em〉. In addition, LUE values of mangrove forests reduced as a result of 〈em〉Spartina alterniflora〈/em〉 invasion. The mean LUE value of 〈em〉Aegiceras corniculatum〈/em〉 decreased by 27.91% which was bigger than the decrease in 〈em〉Avicennia marina〈/em〉 (23.19%). Furthermore, 〈em〉Spartina alterniflora〈/em〉 invasion weakened the LUE-PRI relationship of mangrove forests. The coefficient of determination (R〈sup〉2〈/sup〉) of the LUE-PRI relationship of 〈em〉Avicennia marina〈/em〉 and 〈em〉Aegiceras corniculatum〈/em〉 dropped by 19.48% in total, and 17% and 25.17% respectively. This research provides an effective approach to estimate the LUE of mangrove forests, which is significant for the evaluation of photosynthetic capacity and productivity of mangrove ecosystems in the future.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): B. Snapir, A. Momblanch, S.K. Jain, T.W. Waine, I.P. Holman〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Satellite Remote Sensing, with both optical and SAR instruments, can provide distributed observations of snow cover over extended and inaccessible areas. Both instruments are complementary, but there have been limited attempts at combining their measurements. We describe a novel approach to produce monthly maps of dry and wet snow areas through application of data fusion techniques to MODIS fractional snow cover and Sentinel-1 wet snow mask, facilitated by Google Earth Engine. The method is demonstrated in a 55,000 km〈sup〉2〈/sup〉 river basin in the Indian Himalayan region over a period of ∼2.5 years, although it can be applied to any areas of the world where Sentinel-1 data are routinely available. The typical underestimation of wet snow area by SAR is corrected using a digital elevation model to estimate the average melting altitude. We also present an empirical model to derive the fractional cover of wet snow from Sentinel-1. Finally, we demonstrate that Sentinel-1 effectively complements MODIS as it highlights a snowmelt phase which occurs with a decrease in snow depth but no/little decrease in snowpack area. Further developments are now needed to incorporate these high resolution observations of snow areas as inputs to hydrological models for better runoff analysis and improved management of water resources and flood risk.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Ion Sola, Alberto García-Martín, Leire Sandonís-Pozo, Jesús Álvarez-Mozos, Fernando Pérez-Cabello, María González-Audícana, Raquel Montorio Llovería〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Atmospheric correction of optical satellite imagery is an essential pre-processing for modelling biophysical variables, multi-temporal analysis, and digital classification processes. Sentinel-2 products available for users are distributed by the European Space Agency (ESA) as Top Of Atmosphere reflectance values in cartographic geometry (Level-1C product). In order to obtain Bottom Of Atmosphere reflectance images (Level-2A product) derived from this Level-1C products, ESA provides the SEN2COR module, which is implemented in the Sentinel Application Platform. Alternatively, ESA recently distributes Level-2A products processed by SEN2COR with a default configuration. On the other hand, the conversion from Level-1C to Level-2A product can be generated using alternative atmospheric correction methods, such as MAJA, 6S, or iCOR. In this context, this paper aims to evaluate the quality of Level-2A products obtained through different methods in Mediterranean shrub and grasslands by comparing data obtained from Sentinel-2 imagery with field spectrometry data. For that purpose, six plots with different land covers (asphalt, grass, shrub, pasture, and bare soil) were analyzed, by using synchronous imagery to fieldwork (from July to September 2016). The results suggest the suitability of the applied atmospheric corrections, with coefficients of determination higher than 0.90 and root mean square error lower than 0.04 achieving a relative error in bottom of atmosphere reflectance of only 2–3%. Nevertheless, minor differences were observed between the four tested methods, with slightly varying results depending on the spectral band and land cover.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Qiuyan Yu, Michael Acheampong, Ruiliang Pu, Shawn M. Landry, Wenjie Ji, Thilanki Dahigamuwa〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Urban vegetation can mitigate urban heat island (UHI) due to its ability to regulate temperature by directly or indirectly influencing water vapor transport, shading effect, and wind speed and direction. Mechanisms of effects of vegetation cover on land surface temperature (LST) have been extensively documented. Few studies, however, have examined the role of vegetation height in controlling LST. In this study, we examined the relationship between LST and vegetation height by using Light Detection and Range (LiDAR) data from the city of Tampa, Florida, USA. The results revealed that vegetation height has significant impact on LST. Additionally, we also identified the optimal height and fractional cover at which vegetation can exert the greatest influence on LST. In particular, we found that the maximum cooling effect of vegetation can only be achieved when vegetation cover is above 93.33%, an amount of which is nearly impossible to have in most of the cities. On the other hand, LST decreases at an increasing rate with vegetation height, and is optimized at 20 m. This shows that vegetation height can play an important role in regulating UHI in contributing to effect maximization with least cover possible in a city. Findings derived from this study could provide urban planners with critical insights on precise and efficient urban vegetation management in the purpose of UHI mitigation.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Iurii Shendryk, Mark Broich, Mirela G. Tulbure〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Detailed information on the number and density of trees is important for conservation and sustainable use of forest resources. In this respect, remote sensing technology is a reliable tool for deriving timely and fine-scale information on forest inventory attributes. However, to better predict and understand the functioning of the forest, fine-scale measures of tree number and density must be extrapolated to the forest plot or stand levels through upscaling. In this study, we compared and combined three sources of remotely sensed data, including low point density airborne laser scans (ALS), synthetic aperture radar (SAR) and very-high resolution WorldView-2 imagery to upscale the total number of trees to the plot level in a structurally complex eucalypt forest using random forest regression. We used information on number of trees previously derived from high point density ALS as training data for a random forest regressor and field inventory data for validation. Overall, our modelled estimates resulted in significant fits (〈em〉p〈/em〉 〈  0.05) with goodness-of-fit (〈em〉R〈sup〉2〈/sup〉〈/em〉) of 0.61, but systematically underestimated tree numbers. The ALS predictor variables (e.g. canopy cover and height) were the best for estimating tree numbers (〈em〉R〈sup〉2〈/sup〉〈/em〉 = 0.48, 〈em〉nRMSE〈/em〉 = 61%), as compared to WorldView-2 and SAR predictor variables (〈em〉R〈sup〉2〈/sup〉〈/em〉 〈 0.35). Overall, the combined use of WorldView-2, ALS and SAR predictors for estimating tree numbers showed substantial improvement in 〈em〉R〈sup〉2〈/sup〉〈/em〉 of up to 0.13 as compared to their individual use. Our findings demonstrate the potential of using low point density ALS, SAR and WorldView-2 imagery to upscale high point density ALS derived tree numbers at the plot level in a structurally complex eucalypt forest.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0303243418303155-ga1.jpg" width="474" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Javier Tomasella, Rita M. Silva Pinto Vieira, Alexandre A. Barbosa, Daniel A. Rodriguez, Marcos de Oliveira Santana, Marcelo F. Sestini〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Information about changes in land use and land cover is useful to address issues related to drylands management, as well as to support decision-making related to the sustainable use of soils. Since drylands are frequently affected by accelerated soil erosion, land degradation and desertification associated with vegetation cover losses, constant monitoring of land use and land cover changes are required. However, land use and land cover maps are often not available, making it difficult to monitor degradation. Therefore, in this work, we developed an efficient mapping method to monitor bare soil areas, which are indicative of land degradation in the case of the Northeast of Brazil, using Normalized Difference Vegetation Index images. The proposed methodology was field calibrated and applied to the region using 17-year (2000–2016) NDVI maps, with a spatial resolution of 250 m. Based on bare soil mapping, we estimated the degree of degradation using an index calculated from the persistence and frequency of bare soil during the study period. The results indicated that the degraded areas increased in the period of the study, mainly in areas of pasture and 〈em〉Caatinga〈/em〉. This expansion has been accelerated due to the severe drought that affected the region since 2011.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Xi Zhu, Andrew K. Skidmore, Roshanak Darvishzadeh, Tiejun Wang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The accurate estimation of leaf water content (LWC) and knowledge about its spatial variation are important for forest and agricultural management since LWC provides key information for evaluating plant physiology. Hyperspectral data have been widely used to estimate LWC. However, the canopy reflectance can be affected by canopy structure, thereby introducing error to the retrieval of LWC from hyperspectral data alone. Radiative transfer models (RTM) provide a robust approach to combine LiDAR and hyperspectral data in order to address the confounding effects caused by the variation of canopy structure. In this study, the INFORM model was adjusted to retrieve LWC from airborne hyperspectral and LiDAR data. Two structural parameters (i.e. stem density and crown diameter) in the input of the INFORM model that affect canopy reflectance most were replaced by canopy cover which could be directly obtained from LiDAR data. The LiDAR-derived canopy cover was used to constrain in the inversion procedure to alleviate the ill-posed problem. The models were validated against field measurements obtained from 26 forest plots and then used to map LWC in the southern part of the Bavarian Forest National Park in Germany. The results show that with the introduction of prior information of canopy cover obtained from LiDAR data, LWC could be retrieved with a good accuracy (〈em〉R〈/em〉〈sup〉2〈/sup〉 = 0.87, RMSE = 0.0022 g/cm〈sup〉2〈/sup〉, nRMSE = 0.13). The adjustment of the INFORM model facilitated the introduction of prior information over a large extent, as the estimation of canopy cover can be achieved from airborne LiDAR data.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): K. Benjamin Gustafson, Peter S. Coates, Cali L. Roth, Michael P. Chenaille, Mark A. Ricca, Erika Sanchez-Chopitea, Michael L. Casazza〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Distributional expansion and infill of pinyon (〈em〉Pinus monophylla〈/em〉) and juniper (〈em〉Juniperus osteosperma, J. occidentalis〈/em〉) trees (hereinafter, "pinyon-juniper") into sagebrush ecosystems alters the ecological function and economic viability of these ecosystems and represents a major contemporary challenge facing land and wildlife managers. Therefore, accurate and high-resolution maps of pinyon-juniper distribution and abundance across broad geographic extents would facilitate science that quantifies ecological effects of pinyon-juniper expansion and help guide land management decisions that better target areas for pinyon-juniper treatment projects. We mapped conifers at a high (1- m〈sup〉2〈/sup〉; i.e., 1 × 1-m) resolution across the majority of Nevada and northeastern California. We used digital orthophoto quad tiles from National Agriculture Imagery Program (USDA, 2013) to classify conifers using automated feature extraction (AFE) with the program Feature Analyst™ (Overwatch, 2013). Overall accuracy was 〉86% across all mapped areas for ground referencing methods. We provide five sets of full-extent maps for land managers: (1) a shapefile representing accuracy results linked to mapping subunits; (2) binary rasters representing conifer presence or absence at a 1-m〈sup〉2〈/sup〉 resolution; (3) a 900-m〈sup〉2〈/sup〉 resolution raster representing percentages of conifer canopy cover within each cell; (4) 1-m〈sup〉2〈/sup〉 resolution canopy cover classification rasters derived from a 50-m radius moving window analysis; and (5) an example map derived from our canopy cover product that prioritizes pinyon-juniper treatment by significance to sage-grouse habitat improvement. Importantly, the canopy cover maps were developed to allow user-specified flexibility based on their own objectives (i.e., develop phases of expansion). These products improve upon or complement existing conifer maps for the Western United States and will help facilitate habitat management and sagebrush ecosystem restoration through an accurate understanding of conifer distribution and abundance at multiple spatial scales.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Shanxin Guo, Bo Sun, Hankui K. Zhang, Jing Liu, Jinsong Chen, Jiujuan Wang, Xiaoli Jiang, Yan Yang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Detailed and accurate information on the spatial variation of chlorophyll-a concentration in coastal waters is a critical component of ocean ecology and environmental research. The daily MODIS chlorophyll-a products provided by NASA, with 1 km spatial resolution, are suitable for monitoring this variation globally, but these products are too coarse to apply in practice to obtain detailed information over coastal waters. Early studies have shown that spatiotemporal data fusion techniques can be used to predict higher spatial resolution land-cover data based on time-series information in MODIS and the detailed texture from Landsat. However, this technology hasn’t been tested to determine whether it can be used to predict higher spatial-resolution data in coastal waters with rapid water movement. This study aims to answer this question by providing a method to downscale the MODIS chlorophyll-a products from 1 km spatial resolution to 30 m. The spatiotemporal data fusion model U-STFM and the regression model NASA OC2M-HI were used to combine the texture and chlorophyll-a information from Landsat and MODIS. An area with rapid water movement in Bohai Bay of the Bohai Sea, northeast China, was selected for this study. Twelve matched images from MODIS in Aqua platform and Landsat 8, taken over a period of five years (2013–2017), were used to better predict detailed remote-sensing reflectance (Rrs) on the targeted days. Landsat 8 Rrs was used as ground-truth data to assess the output. The results on Mar 10th, 2016, show: 1) The downscaled results (30 m) from the U-STFM model indicate a more stable prediction of Rrs with RMSE of 0.00177 and 0.00202 and R-squared of 0.868 and 0.881 for the blue and green bands, respectively. Results from STARFM and ESTARFM fusion models are also compared in this study. 2) High correlation between log10(U-STFM Blue/ U-STFM Green) and log10(MODIS Chl) captured by OC2M-HI regression model at 1 km scale with R-squared up to 0.85 and RMSE up to 0.742 mg/m^3. This correlation was further used to predict the final chlorophyll-a concentration prediction at 30 m scale on Mar 10th, 2016; 3) The Landsat 8 chlorophyll-a product was used as reference data to evaluate the final chlorophyll-a concentration prediction (30 m) and the original MODIS chlorophyll-a product. The result shows the final prediction (30 m) maintains the accuracy of MODIS chlorophyll-a product and highly improved the local texture details near coastal waters. Predictions on nine other targeted dates with similar conclusions were also evaluated in this paper. The results in this study suggest that low spatial-resolution (1 km) daily MODIS chlorophyll-a products can be downscaled to higher resolution (30 m) products based on the U-STFM image fusion model and NASA’s OC2M-HI regression model to better understand the dynamic patterns of chlorophyll-a concentration in coastal waters.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Yanping Wang, Xinyan Mao, Wensheng Jiang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Destructive storm surges bring large waves and unusually high surges of water to coastal areas, resulting in many human casualties and significant economic loss. In this study, an unstructured grid wave-current coupled model was developed for the Bohai Sea, China, using the ADCIRC (ADvanced CIRCulation) and SWAN (Simulating WAves Nearshore) models to simulate 32 disastrous storm surge events from 1985 to 2014. The return storm surge elevation in the Bohai Sea using the Gumbel method is obtained and compared with previous results. It is found that extratropical cyclones and cold air play important roles in storm surges in the Laizhou Bay and have more influence than tropical cyclones. Moreover, the joint probabilities of surge and wave are obtained by using the Gumbel logistical model. The results show that the effect of waves in surge-wave joint probabilities on the central basin of the Bohai Sea is more significant than that on the Bohai Sea coast. By establishing a system to assess the relative risks of storm surges in the Bohai Sea, it is found that Laizhou Bay is in the greatest danger.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Rukeya Sawut, Nijat Kasim, Abdugheni Abliz, Li Hu, Ahunaji Yalkun, Balati Maihemuti, Shi Qingdong〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Spectroscopy is regarded as a quick and nondestructive method to classify and quantitatively analyze many elements of the soil. Visible and Near-infrared reflectance spectroscopy offers a conductive tool for investigating soil heavy metal pollution. The main goal of this work is to obtain spectral optimized indices (RSI, NPDI and NDSI) related to soil heavy metal Arsenic (As), to estimate the As contents in soil based on geographically weighted regression model (GWR), and to investigate the plausibility of using these spectral optimized indices to map the distribution of heavy metal Arsenic in the soil of coal mining areas. The spectral optimized indices (RSI, NPDI and NDSI) derived from the original and transformed reflectance (the reciprocal (1/R), logarithm (lg〈sup〉R〈/sup〉), logarithm-reciprocal (1/lg〈sup〉R〈/sup〉) and root mean square method (〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.gif" overflow="scroll"〉〈msqrt〉〈mi〉R〈/mi〉〈mi〉 〈/mi〉〈/msqrt〉〈/math〉) were used to construct the GWR models. Then, the variables (RSIs, NPDIs and NDIs) were applied in estimating the Arsenic (As) concentration and in the mapping of the As distribution in this study region. The NPDIs calculated by the original and transformed reflectance (〈em〉R〈/em〉, 1/〈em〉R〈/em〉, lg〈sup〉R〈/sup〉, 1/lg〈sup〉R〈/sup〉, and 〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.gif" overflow="scroll"〉〈msqrt〉〈mi〉R〈/mi〉〈mi〉 〈/mi〉〈/msqrt〉〈/math〉) indicated higher correlation coefficient values than NDSI and RSI. The highest correlation coefficient and lowest 〈em〉p〈/em〉-values (〈em〉r〈/em〉≥0.73 and 〈em〉p〈/em〉=0.001) were found in thenear-infrared (NIR, 780–1100 nm) and shortwave infrared (SWIR, 1100–1935 nm). From the 4 prediction models (GWR) performances, it can be seen that Model-a (〈em〉R〈/em〉) showed superior performance to the other three models (Model-b (1/〈em〉R〈/em〉), Model-c (〈math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.gif" overflow="scroll"〉〈msqrt〉〈mi〉R〈/mi〉〈mi〉 〈/mi〉〈/msqrt〉〈/math〉) and Model-d (lg〈em〉〈sup〉R〈/sup〉〈/em〉)), and it has the highest validation coefficients (〈em〉R〈/em〉〈sup〉2〈/sup〉 = 0.831, RMSE =4.912 μg/g, RPD=2.321) and lowest AIC (Akaike Information Criterion) value (AIC=179.96). NPDI〈sub〉1417 nm, 1246 nm〈/sub〉 is more sensitive and potential hyperspectral index for As in the study area. Thus, the two band optimized index (NPDI〈sub〉1417 nm, 1246 nm〈/sub〉) might be recommended as an indicator for estimating soil As content. The hyperspectral optimized indices may help to quickly and accurately evaluate Arsenic contents in soil, and furthermore, the results provide theoretical and data support to access the distribution of heavy metal pollution in surface soil, promoting fast and efficient investigation of mining environment pollution and sustainable development of ecology.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): T. Chakraborty, X. Lee〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉We develop a new algorithm, the simplified urban-extent (SUE) algorithm, to estimate the surface urban heat island (UHI) intensity at a global scale. We implement the SUE algorithm on the Google Earth Engine platform using Moderate Resolution Imaging Spectroradiometer (MODIS) images to calculate the UHI intensity for over 9500 urban clusters using over 15 years of data, making this one of the most comprehensive characterizations of the surface UHI to date. The results from this algorithm are validated against previous multi-city studies to demonstrate the suitability of the method. The dataset created is then filtered for elevation differentials and percentage of urban area and used to estimate the diurnal, monthly, and long-term variability in the surface UHI in different climate zones. The global mean surface UHI intensity is 0.85 °C during daytime and 0.55 °C at night. Cities in arid climate show distinct diurnal and seasonal patterns, with higher surface UHI during nighttime (compared to daytime) and two peaks throughout the year. The diurnal variability in surface UHI is highest for equatorial climate zone (0.88 °C) and lowest for arid zone (0.53 °C). The seasonality is highest in the snow climate zone and lowest for equatorial climate zone. While investigating the change in the surface UHI over a decade and a half, we find a consistent increase in the daytime surface UHI in the urban clusters of the warm temperate climate zone (0.04 °C/decade) and snow climate zone (0.05 °C/decade). Only arid climate zones show a statistically significant increase in the nighttime surface UHI intensity (0.03 °C/decade). Globally, the change is mainly seen during the daytime (0.03 °C/decade). Finally, the importance of vegetation differential between urban and rural areas on the spatiotemporal variability is examined. Vegetation has a strong control on the seasonal variability of the surface UHI and may also partly control the long-term variability. The complete UHI data are available through this website (〈a href="https://yceo.yale.edu/research/global-surface-uhi-explorer" target="_blank"〉https://yceo.yale.edu/research/global-surface-uhi-explorer〈/a〉) and allows the user to query the UHI of urban clusters using a simple interface.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Chandan Mostafiz, Ni-Bin Chang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Tasseled cap transformation (TCT) has been used to observe relationships among soil moisture, vegetation cover, and canopy condition. Time series Landsat satellite images of high resolution may provide continuous and accurate observations of the land surface, which can be further analyzed using the TCT for natural hazard events. This study explores the use of a unique dispersion phenomenon of TCT for observing the dynamics of vegetation cover and landscape changes due to a major hurricane landfall in the United States. This is based on the Landsat images taken during the Hurricane Bob event, during which the hurricane made landfall in the highly developed New England area in late August 1991. A unique comparison of the TCT time series plots illustrating the relative TCT dispersion phenomenon, which addresses the landfall’s profound impact on the Mattapoisett River watershed in 1991, reflects the interactions among biosphere, atmosphere, hydrosphere, and lithosphere in hurricane-prone regions. This analysis can be done without the use of ground truth data and can be further supported by the multitemporal and multidimensional change detection of box plots in terms of brightness and greenness to gain more biophysical interpretation. Findings unveil an inherent earth system process via the varying levels of dispersion among brightness, greenness, and wetness over the coastal watershed with environmental sustainability implications.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Lirong Ding, Ji Zhou, Xiaodong Zhang, Shaomin Liu, Ruyin Cao〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Surface air temperature (〈em〉T〈/em〉〈sub〉a〈/sub〉) is critical to the studies of radiation balance, energy budget, and water cycle. It is a necessary input for associated models. Most of the current 〈em〉T〈/em〉〈sub〉a〈/sub〉 datasets of reanalysis products have limitations at local scales due to their coarse spatial resolutions. For better modeling the radiation balance, energy budget, and water cycle over the Tibetan Plateau, this study proposes a practical method for 〈em〉T〈/em〉〈sub〉a〈/sub〉 downscaling based on the digital elevation model. This method is applied to downscale 〈em〉T〈/em〉〈sub〉a〈/sub〉 of the China regional surface meteorological feature dataset (CRSMFD) at 0.1° and the ERA-interim (ERAI) product at 0.125° to 0.01°. The daily mean 〈em〉T〈/em〉〈sub〉a〈/sub〉 and the 3-hourly instantaneous 〈em〉T〈/em〉〈sub〉a〈/sub〉 with a 0.01° are obtained. The downscaled 〈em〉T〈/em〉〈sub〉a〈/sub〉 are evaluated from the perspectives of accuracy and image quality. Results show that the daily mean 〈em〉T〈/em〉〈sub〉a〈/sub〉 downscaled from the CRSMFD product has a RMSE of 1.13 ± 1.0 K at 105 meteorological stations and RMSEs of 0.96 K to 2.34 K at three experimental stations; the instantaneous 〈em〉T〈/em〉〈sub〉a〈/sub〉 downscaled from CRSMFD has RMSEs of 1.02 K to 4.0 K at the three experimental stations. 〈em〉T〈/em〉〈sub〉a〈/sub〉 after downscaling has better agreement with the ground measured 〈em〉T〈/em〉〈sub〉a〈/sub〉 than before downscaling, especially in mountain areas. By contrast, 〈em〉T〈/em〉〈sub〉a〈/sub〉 downscaled from the ERAI product has unacceptable accuracy due to the great uncertainty of the ERAI 〈em〉T〈/em〉〈sub〉a〈/sub〉 over the Tibetan Plateau. With the proposed method, a 0.01° 〈em〉T〈/em〉〈sub〉a〈/sub〉 dataset from 2000 to 2015 over the Tibetan Plateau was generated to satisfy related studies and applications.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Faheem Iqbal, Arko Lucieer, Karen Barry〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Improved prediction of poppy capsule volume is essential for optimal management of poppy crop. In order to estimate poppy capsule volume accurately using remotely sensed imagery, the selection of most appropriate models and predictor variables is essential. Multiple spectral indices with random forest (RF) regression were tested to estimate poppy capsule volume using an Unmanned Aircraft System (UAS). Data were collected from field-based physical measurements, in-field spectral measurements and from UAS flights with multispectral sensors over two poppy crops at Cambridge and Sorell in Tasmania, Australia. Field measured spectral signatures were convolved to the multispectral bands of a UAS mounted sensor. These convolved UAS spectral signatures were used to compute multiple spectral indices to develop the RF model, and select optimal model parameters based on root mean squared error (RMSE). In addition, the RF variable importance scores were used to rank the model variables, and to identify the best performing vegetation indices. In Cambridge, an RF model based on convolved UAS spectral signatures predicted capsule volume with an RMSE values ranging from 15.60 cm〈sup〉3〈/sup〉 (10.27%) to 25.63 cm〈sup〉3〈/sup〉 (14.45%) from training and validation dataset, respectively, indicating a strong relationship between SVIs and field measured capsule volume. An RF model trained on UAS multispectral data (measure not simulated) resulted an RMSE value of 19.39 cm〈sup〉3〈/sup〉 (12.80%) based on training data set and an RMSE value of 26.85 cm〈sup〉3〈/sup〉 (17.77%) with validation dataset. The Cambridge site model parameters and optimal variables were applied to the Sorell data, which showed a significant relationship between measured and predicted capsule volume (R〈sup〉2〈/sup〉 0.72), with relative error of 26.25%. The results showed that the RF model developed using selected variables can help to predict capsule volume 2–3 weeks prior to harvest.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Agnieszka Tarko, Sytze de Bruin, Arnold K. Bregt〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Land cover identification and area quantification are key aspects in determining support payments to farmers under the European Common Agricultural Policy. Agricultural land is monitored using the Land Parcel Identification System and visual image interpretation. However, shadows covering reference parcel boundaries can hinder effective delineation. Visual interpretation of shadows is labor intensive and subjective, while automated methods give reproducible results. In this paper we compare shadow detection on satellite imagery obtained by expert photointerpretation to a proposed automated, data-driven method. The latter automated method is a thresholding approach employing both panchromatic and multispectral imagery, where the former has a finer spatial resolution than the latter. Thresholds are determined from automatically generated training data using a risk-based approach. Comparison of the total shadow area per scene showed that more pixels were labelled as shadow by the automatic procedure than by visual interpretation. However, the union of shadow area independently identified by twelve experts on a subscene was larger than the automatically determined shadow area. The limited intersection of the shadow areas identified by the experts demonstrated that experts strongly disagreed in their interpretations. The shadow area labelled by the automated method was in between the intersection and the union of the areas interpreted by experts. Furthermore, the automated shadow detection method is reproducible and reduces the interpretation effort and skill required.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Lakshmi Ram Prasath H., Kusuma K.N., Chaitanya S., Balamurugan Guru〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Kimberlite clan of rocks (KCR) comprising of mantle derived ultrabasic rocks such as Kimberlite and related Lamproites and Lamprophyres,are the primary source of diamond. Locating the KCR is first step in the diamond exploration, which is highly challenging in the field due to (i) very small spatial extent of KCR pipes (ii) high susceptibility of KCR to weathering and alteration on exposure to atmosphere, owing to their ultrabasic composition. Predictive statistical models using the geospatial data are often used to minimize the search and the present work attempts to apply the Frequency Ratio (FR) based predictive model in GIS to prepare KCR potential zone maps based on the relationship between the already explored KCR locations and the factors that favour their emplacement. Wajrakarur Kimberlite Field (WKF) in the Dharawar Craton of India, with more than 30 explored kimberlite pipes is selected as the study area. Geospatial technology has been used to generate thematic maps such as known KCR pipe locations, lineament density, lineament buffer zone, lineament intersection buffer zone, drainage anomaly buffer zone, geomorphology, and classified image showing distribution of mineral such as clay, iron oxide and calcrete, which are surface expression of KCR emplacement from various sources. Landsat 8 OLI satellite data, ASTER DEM were used in preparing the geomorphology, lineament map, and band ratio based mineral classified map. The thematic maps were converted to raster grid of 10 sq. m. FR values for each unit in each thematic map were obtained by correlating the spatial relationship between thematic map and the 25 locations of the 33 “known” KCR locations in WKF used for FR modelling. Cumulative FR value were obtained by carrying out overlay analysis of the thematic maps, which are classified into five classes by Natural Breaking method as (i)Very Low Favourable (VLF), (ii) Low Favourable (LF), (iii)Moderate favourable (MF), (iv)High Favourable (HF), and (v)Very High Favourable (VHF). The model was validated by ground verification at random sites and statistical method. During the ground visit, we observed KCR-like lithology’s at four new sites that have calcrete exposure at limited spatial extent and also some pieces of ultrabasic rocks similar to the explored sites. To ascertain their chemical composition of the samples were plotted in the MgO-K〈sub〉2〈/sub〉O-Al〈sub〉2〈/sub〉O〈sub〉3〈/sub〉 ternary diagram. All the four samples fall in the Kimberlite/Lamproite field confirming them to be KCR. The FR predictive model was also validated statistically. Total 13 locations, including 8 site out of 33 known KCR locations, one newly discovered pipe by GSI and the four locations discovered during this study were used for the validation. Statistical validation shows that 84% of model accuracy is achieved. The study reveals that Lineament Intersection, and circular drainage anomaly in 3rd order streams, lineament density are significant themes in predicting KCR emplacement zones. The study demonstrates the utility of statistical based model such as FR model in predicting the location of KCR emplacement, even with statistically insignificant distribution of KCRs and can be applied elsewhere in the world to locate the KCRs. In the process, we report discovery of four new KCR pipes in the WKF.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): François Waldner, Gregory Duveiller, Pierre Defourny〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Sentinel-2 has opened a new era for the remote sensing community where 10-m imagery is freely available with a 5-day revisit frequency and a systematic global coverage. Having both frequent and detailed observations across large geographic areas are ideal characteristics that can potentially revolutionize applications such as crop mapping and monitoring. However, such large volumes of high-resolution data pose challenges to users in terms of problem complexity, computational resources and processing time, beckoning the increasingly relevant question: at which resolution should this imagery be processed? Here, we develop a methodology to characterize resolution-dependent errors in cropland mapping and explore their behavior when we move across spatial scales and landscapes, taking special care to include the effects of the instrument's Point Spread Function (PSF). Results show how local upscaling of 10-m imagery, 〈em〉e.g.〈/em〉, from Sentinel-2, to 30 m mitigates most the adverse effects generated by the PSF when comparing it to native 30-m imagery, 〈em〉e.g.〈/em〉, from Landsat-8. Extending this logic, we demonstrate for two nationwide cases how maps can be calculated showing the optimal spatial resolution that keeps resolution-dependent errors below a user-defined threshold. Based on these maps, we estimate that 31% of Belgium and 59% of South Africa could be processed at 20 m instead of 10 m, while keeping the increase of resolution-dependent errors below 3%. These local resolution adjustments lead to a reduction in data volume and processing time by 23% and 44%, respectively.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Shanti Shrestha, Isabel Miranda, Abhishek Kumar, Maria Luisa Escobar Pardo, Subash Dahal, Taufiq Rashid, Caren Remillard, Deepak R. Mishra〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Mangroves are one of the most productive ecosystems known for provisioning of various ecosystem goods and services. They help in sequestering large amounts of carbon, protecting coastline against erosion, and reducing impacts of natural disasters such as hurricanes. Bhitarkanika Wildlife Sanctuary in Odisha harbors the second largest mangrove ecosystem in India. This study used Terra, Landsat and Sentinel-1 satellite data for spatio-temporal monitoring of mangrove forest within Bhitarkanika Wildlife Sanctuary between 2000 and 2016. Three biophysical parameters were used to assess mangrove ecosystem health: leaf chlorophyll (CHL), Leaf Area Index (LAI), and Gross Primary Productivity (GPP). A long-term analysis of meteorological data such as precipitation and temperature was performed to determine an association between these parameters and mangrove biophysical characteristics. The correlation between meteorological parameters and mangrove biophysical characteristics enabled forecasting of mangrove health and productivity for year 2050 by incorporating IPCC projected climate data. A historical analysis of land cover maps was also performed using Landsat 5 and 8 data to determine changes in mangrove area estimates in years 1995, 2004 and 2017. There was a decrease in dense mangrove extent with an increase in open mangroves and agricultural area. Despite conservation efforts, the current extent of dense mangrove is projected to decrease up to 10% by the year 2050. All three biophysical characteristics including GPP, LAI and CHL, are projected to experience a net decrease of 7.7%, 20.83% and 25.96% respectively by 2050 compared to the mean annual value in 2016. This study will help the Forest Department, Government of Odisha in managing and taking appropriate decisions for conserving and sustaining the remaining mangrove forest under the changing climate and developmental activities.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Shuang Huang, Shengbo Chen, Daming Wang, Chao Zhou, F. van der Meer, Yuanzhi Zhang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Hydrocarbon micro-seepage can result in vegetation spectral anomalies. Early detection of spectral anomalies in plants stressed by hydrocarbon micro-seepage could help reveal oil and gas resources. In this study, the origin of plant spectral anomalies affected by hydrocarbon micro-seepage was measured using indoor simulation experiments. We analyzed wheat samples grown in a simulated hydrocarbon micro-seepage environment in a laboratory setting. The leaf mesophyll structure (〈em〉N〈/em〉) values of plants in oil and gas micro-seepage regions were measured according to the content of measured biochemical parameters and spectra simulated by PROSPECT, a model for extracting hydrocarbon micro-seepage information from hyper-spectral images based on plant stress spectra. Spectral reflectance was simulated with 〈em〉N〈/em〉, chlorophyll content (〈em〉C〈sub〉ab〈/sub〉〈/em〉), water content (〈em〉C〈sub〉w〈/sub〉〈/em〉) and dry matter content (〈em〉C〈sub〉m〈/sub〉〈/em〉). Multivariate regression equations were established using varying gasoline volume as the dependent variable and spectral feature parameters exhibiting a high rate of change as the independent variables. We derived a regression equation with the highest correlation coefficient and applied it to airborne hyper-spectral data (CASI/SASI) in Qingyang Oilfield, where extracted information regarding hydrocarbon micro-seepage was matched with known oil-producing wells.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Esra Erten, Cristian Rossi〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Massive amounts of land are being reclaimed to build airports, new cities, ports, and highways. Hundreds of kilometers are added each year, as coastlines are extended further out to the sea. In this paper, this urbanization approach is monitored by Persistent Scatterer Interferometry (PSI) technique with Sentinel-1 SAR data. The study aims to explore this technology in order to support local authorities to detect and evaluate subtle terrain displacements. For this purpose, a large 3-years Sentinel-1 stack composed by 92 images acquired between 07/01/2015 to 27/01/2018 is employed and stacking techniques are chosen to assess ground motion. The test site of this study, Rize, Turkey, has been declared at high risk of collapse and radical solutions such as the relocation of the entire city in another area are been taken into consideration. A media fact-checking approach, i.e. evaluating national and international press releases on the test site, is considered for the paper and this work presents many findings in different areas of the city. For instance, alerts are confirmed by inspecting several buildings reported by the press. Critical infrastructures are monitored as well. Portions of the harbor show high displacement rates, up to 1 cm/year, proving reported warnings. Rural villages belonging to the same municipality are also investigated and a mountainous village affected by landslide is considered in the study. Sentinel-1 is demonstrated to be a suitable system to detect and monitor small changes or buildings and infrastructures for these scenarios. These changes may be highly indicative of imminent damage which can lead to the loss of the structural integrity and subsequent failure of the structure in the long-term. In Rize, only a few known motion-critical structures are monitored daily with in-situ technologies. SAR interferometry can assist to save expensive inspection and monitoring services, especially in highly critical cases such as the one studied in this paper.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Zhifeng Wang, Shuiqing Li, Sheng Dong, Kejian Wu, Huaming Yu, Linyan Wang, Wenbo Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Extreme wave climate variability in the South China Sea (SCS) was investigated using significant wave height (SWH) data simulated by the third generation wave model WAVEWATCH-III (WW-III) for the period 1976–2014. The wind forcing data was using the objective reanalysis wind dataset from the weather research and forecasting (WRF) model. The simulated SWH was well validated by observation data from satellite altimeter and in-situ buoys. A generalized extreme value (GEV) model was applied to analyze the extreme wave climate variability. Monthly significant wave height maxima is used to characterize the seasonal variability. The spatial distributions of positional and scale parameters are achieved. The positional parameter values reach the maximum in winter, while the scale parameter values are greater in summer and autumn due to the impact of typhoon. The regression analysis of harmonic functions was applied to give the annual cycle. Interannual climatology of extreme wave climate from Empirical Orthogonal Function (EOF) decomposition method and spectrum analysis method revealed a dominant 12 months period and a 22 months period, respectively. Results show that El Nino may significantly affect the extreme wave climate variability in the SCS.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Haiping Xia, Yunhao Chen, Jinling Quan〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Because waste gas from industrial burning has a significant effect on urban environment, it is important to detect industrial heat sources from remote sensing data. Given existing pyrometry, it is difficult to identify small factories with low burning temperatures. In addition, existing fire detection methods (such as the contextual algorithm) are cumbersome, complex, and contain multiple thresholds to be determined. With the purpose of detecting industrial heat sources efficiently and simply, we introduced a simple method based on the thermal anomaly index (TAI) to detect industrial heat sources. This index was constructed based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) data with a detectable temperature of 400 K, which is lower than that used in most high-temperature detection methods. By confirming with the Visible Infrared Imaging Radiometer Suite (VIIRS) Nightfire product and high-resolution images, the TAI confidently detected almost all hot spots with the VIIRS Nightfire product and detected many hot spots that were undetected by the VIIRS Nightfire product. Based on six images acquired over Tangshan, we determined that 54.52% of hot spots were undetected by the VIIRS Nightfire product, while the TAI method was able to detect these hot spots. With the MODTRAN 5 radiative transfer model, we simulated the high-temperature detection ability of the TAI. Compared with the VIIRS Nightfire product, the TAI is more sensitive when detecting hot spots below 700 K. Thus, this method can potentially detect family workshops engaged in the small-scale combustion of fuel.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Hilton Luís Ferraz da Silveira, Lênio Soares Galvão, Ieda Del’Arco Sanches, Iedo Bezerra de Sá, Tatiana Ayako Taura〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The 〈em〉Caatinga〈/em〉 is an important ecosystem in the semi-arid region of northeast Brazil and a natural laboratory for the study of plant adaptation to seasonal water stress or prolonged droughts. The soil water availability for plants depends on plant root depth and soil properties. Here, we combined for the first time the remote sensing classification of 〈em〉Caatinga〈/em〉 physiognomies with soil information derived from geostatistical analysis to relate vegetation distribution with physico-chemical attributes of soils. We evaluated the potential of multi-temporal data acquired by the MultiSpectral Instrument (MSI)/Sentinel-2 for Random Forest (RF) classification of seven physiognomies. In addition, we analyzed the contribution of airborne LiDAR metrics to improve classification accuracy compared to six vegetation indices (VIs) and 10 reflectance bands from the MSI instrument. Using a detailed soil survey, the spatial distribution of the vegetation physiognomies mapped by RF was associated with the variability of 20 physico-chemical attributes of 75 soil profiles submitted to principal components analysis (PCA) and ordinary kriging. The results showed gains in overall classification accuracy with use of the multi-temporal data over the mono-temporal observations. Gains in classification of arboreous 〈em〉Caatinga〈/em〉 were also observed after the insertion of LiDAR metrics in the analysis, especially the percentage of vegetation cover with height greater than 5 m, the terrain elevation and the standard deviation of vegetation height. Overall, the most important metrics for classification were the VIs, especially the Enhanced Vegetation Index (EVI), Normalized Difference Infrared Index (NDII-1), Optimized Soil-Adjusted Vegetation Index (OSAVI) and the Normalized Difference Vegetation Index (NDVI). The most important MSI/Sentinel-2 bands were positioned in the red-edge spectral interval. From PCA, soil attributes responsible for most of the data variance were related to soil fertility, soil depth and rock fragments in the surface horizon. The amounts of gravels and pebbles were factors of physiognomic variability with shrub and sub-shrub 〈em〉Caatinga〈/em〉 occurring preferentially over shallow and stony soils. By contrast, arboreous 〈em〉Caatinga〈/em〉 occurred over soils with total profile depth greater than 1 m. Finally, areas of sub-shrub 〈em〉Caatinga〈/em〉 had greater values of cation exchange capacity (CEC) and water retention at field capacity than areas of arboreous 〈em〉Caatinga〈/em〉. The differences were statistically significant at 95% confidence level, as indicated by Mann-Whitney U tests.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): J. Degerickx, D.A. Roberts, J.P. McFadden, M. Hermy, B. Somers〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Urban trees provide valuable ecosystem services but are at the same time under continuous pressure due to unfavorable site conditions. In order to better protect and manage our natural capital, urban green managers require frequent and detailed information on tree health at the city wide scale. In this paper we developed a workflow to monitor tree defoliation and discoloration of broadleaved trees in Brussels, Belgium, through the combined use of airborne hyperspectral and LiDAR data. Individual trees were delineated using an object-based tree detection and segmentation algorithm primarily based on LiDAR data with an average accuracy of 91%. We constructed Partial Least Squares Regression (PLSR) models to derive tree chlorophyll content (RMSE = 2.8 μg/cm²; R² = 0.77) and Leaf Area Index (LAI; RMSE = 0.5; R² = 0.66) from the average canopy spectrum. Existing spectral indices were found to perform significantly worse (RMSE 〉 7 μg/cm² and 〉1.5 respectively), mainly due to contamination of tree spectra by neighboring background materials. In the absence of local calibration data, the applicability of PLSR to other areas, sensors and tree species might be limited. Therefore, we identified the best performing/least sensitive spectral indices and proposed a simple pixel selection procedure to reduce disturbing background effects. For LAI, laser penetration metrics derived from LiDAR data attained comparable accuracies as PLSR and were suggested instead. Detection of healthy and unhealthy trees based on remotely sensed tree properties matched reasonably well with a more traditional visual tree assessment (93% and 71% respectively). If combined with early tree stress detection methods, the proposed methodology would constitute a solid basis for future urban tree health monitoring programs.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): 〈/p〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Imam Purwadi, Harald van der Werff, Caroline Lievens〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Rare Earth Elements (REEs) are indispensable in the manufacturing of renewable energy and clean technologies. Due to the high demand for REEs, mine waste, called “tailings”, are sought because they could be a possible resource for REEs. In this study, tailing samples were collected from two tin mine tailings in Bangka Island, Indonesia, and analyzed using inductively coupled plasma optical emission spectrometry, x-ray powder diffraction, and Fourier transform infrared - attenuated total reflectance. Results showed that the tailing samples were identified as quartz and contained a high amount of erbium between 111.6 and 3768.4 μg/g. Absorption features in infrared reflectance spectra were related to REE concentrations. We found a positive correlation between erbium and two absorption features that have not been reported before: One feature is centered at 500 nm with a correlation of 0.685, and one is centered at 674 nm with a correlation of 0.829.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Daniel Colson, George P. Petropoulos, Konstantinos P. Ferentinos〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The present study explores the use of the recently launched Sentinel-1 and -2 data of the Copernicus mission in wildfire mapping with a particular focus on retrieving information on burnt area, burn severity as well as in quantifying soil erosion changes. As study area, the Sierra del Gata wildfire occurred in Spain during the summer of 2015 was selected. First, diverse image processing algorithms for burnt area extraction from Sentinel-2 data were evaluated. In the next step, burn severity maps were derived from Sentinel-2 data alone, and the synergy between Sentinel-2 & Sentinel-1 for this purpose was evaluated. Finally, the impact of the wildfire to soil erodibility estimates derived from the Revised Universal Soil Loss Equation (RUSLE) model implemented to the acquired Sentinel images was explored. In overall, the Support Vector Machines (SVMs) classifier obtained the most accurate burned area mapping, with a derived accuracy of 99.38%. An object-based SVMs classification using as input both optical and radar data was the most effective approach of delineating burn severity, achieving an overall accuracy of 92.97%. Soil erosion mapping predictions allowed quantifying the impact of wildfire to soil erosion at the studied site, suggesting the method could be potentially of a wider use. Our results contribute to the understanding of wildland fire dynamics in the context of the Mediterranean ecosystem, demonstrating the usefulness of Sentinels and of their derived products in wildfire mapping and assessment.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): R. Albano, A. Sole, J. Adamowski, A. Perrone, A. Inam〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The considerable increase in flood damages in Europe in recent decades has shifted attention from flood protection to flood risk management. Assessments of expected damage provide critical information for flood risk management efforts. The evaluation of potential damages under different flood scenarios through quantification of their ability to provide relative short-, medium- and long-term risk reduction, supports decision-makers in discriminating among several alternative mitigation actions. End-users should be aware of, and knowledgeable about, the limitations and uncertainties of such analyses, as well-informed decisions regarding efficient and sustainable flood risk management will become increasingly relevant under future climate and socio-economic changes. In this context, a method was developed to identify and quantify the role of the input parameters in the uncertainty of the potential flood economic damage assessment in urban areas with low sloping/flat terrain and complex topography using a GIS-based, free and open-source software called 〈em〉Floodrisk〈/em〉. Sets of plausible input parameters for the model’s two flood loss modelling subroutines (hydraulic modelling and damage estimation) were dynamically combined to quantify the contribution of their inner parameters to the total damage assessment uncertainty. To estimate the contributions of each input to overall model uncertainty, the combination of input parameters that minimized the error in the spatial distribution assessment of the extensive damages affecting (downtown) Albenga (Italy), enumerated after the historical Centa River flood of November 5, 1994, was taken as a reference. In this specific case, a high epistemic uncertainty for the damage estimation module was noted for the specific type and form of the damage functions used. In the absence of region-specific depth-damage functions, the vulnerability curves were adapted from a range of geographic and socio-economic studies. Given the strong dependence of model uncertainty and sensitivity to local characteristics, the epistemic uncertainty associated with the risk estimate was reduced by introducing additional information into the risk analysis. Implementing newly developed site-specific curves and a more detailed classification of the construction typology of the buildings at risk, led to a substantial decrease in modelling uncertainty, along with a decrease in the sensitivity of the flood loss estimation to the uncertainty in the depth-damage function input parameter. These findings indicated the need to produce and openly disseminate data in order to develop micro-scale risk analysis through site-specific vulnerability curves. Moreover, this study highlighted the urgent need for research on the development and implementation of methods and models for the assimilation of uncertainties in decision-making processes.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Qiangqiang Sun, Ping Zhang, Danfeng Sun, Aixia Liu, Jianwang Dai〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Feedbacks between vegetation and associated habitats play a crucial role in the arid and semi-arid dryland ecosystem for maintaining the stability and sustainability. Due to the high spatial and temporal heterogeneous sparse vegetation with the associated habitats in dryland system, it is a challenge to map their interactions and feedbacks for environment management and policy decision. This paper attempted to develop an algorithm using endmembers (EMs) fraction series unmixed from Gaofen-1 (GF-1) WFV (wide field of view) finer time series images for mapping desert vegetation-habitat complexes as vegetation function groups with associated habitat. The time series of EMs, including green vegetation (GV), sand land (SL), saline land (SA), dark surface (DA) at 16 m subpixel level, derived from Multiple Endmember Spectral Mixture Analysis (MESMA), were combined to obtain classification knowledge describing the interactions and feedbacks between vegetation and habitat, and organized with decision tree (DT). According to the similarity of the interactions and feedbacks in the desert vegetation-habitat complexes, this paper further identified their potential of assessing the status of ecosystems (i.e., land degradation). The results show that the finer time series of EMs with satisfied spatial resolution can discern the sparse vegetation and the associated habitats with an overall accuracy of 83.91%, and help understanding degradation processes (i.e., sandification and local salinization) in the study area.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Haibin Luo, Zhenhong Li, Jiajun Chen, Christopher Pearson, Mingming Wang, Weicai Lv, Haiyong Ding〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Incorrect unwrapping of dense interferometric fringes caused by large gradient displacements make it difficult to measure mining subsidence using conventional Interferometric Synthetic Aperture Radar (InSAR). This paper presents a Range Split Spectrum Interferometry assisted Phase Unwrapping (R-SSIaPU) method for the first time. The R-SSIaPU method takes advantage of (i) the capability of Range Split Spectrum Interferometry of measuring surface displacements with large spatial gradients, and (ii) the capability of conventional InSAR of being sensitive to surface displacements with limited spatial gradients. Both simulated and real experiments show that the R-SSIaPU method can monitor large gradient mining-induced surface movements with high precision. In the case of the Tangjiahui mine, the R-SSIaPU method agreed with GPS with differences of approximately 4.2 cm, whilst conventional InSAR deviated from GPS with differences of nearly 1 m. The R-SSIaPU method makes phase unwrapping less challenge, especially in the cases with large surface displacements. In addition to mining subsidence, it is believed that the R-SSIaPU method can be used to monitor surface displacements caused by landslides, earthquakes, volcanic eruptions, and glacier movements.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Yu Li, Sandro Martinis, Simon Plank, Ralf Ludwig〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this paper, a two-step automatic change detection chain for rapid flood mapping based on Sentinel-1 Synthetic Aperture Radar (SAR) data is presented. First, a reference image is selected from a set of potential image candidates via a Jensen-Shannon (JS) divergence-based index. Second, saliency detection is applied on log-ratio data to derive the prior probabilities of changed and unchanged classes for initializing the following expectation-maximization (EM) based generalized Gaussian mixture model (GGMM). The saliency-guided GGMM is capable of capturing the primary pixel-based change information and handling highly imbalanced datasets. A fully-connected conditional random field (FCRF) model, which takes long-range pairwise potential connections into account, is integrated to remove the ambiguities of the saliency-guided GGMM and to achieve the final change map. The whole process chain is automatic with an efficient computation. The proposed approach was validated on flood events at the Evros River, Greece and the Wharfe River and Ouse River in York, United Kingdom. Kappa coefficients (〈em〉k〈/em〉) of 0.9238 and 0.8682 were obtained respectively. The sensitivity analysis underlines the robustness of the proposed approach for rapid flood mapping.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Wei Li, Carlos Campos-Vargas, Philip Marzahn, Arturo Sanchez-Azofeifa〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Global environmental change leads to the variation in the relative coverage of dead trees, liana-infested and non-liana-infested trees in many tropical forests. Increase in the coverage of lianas had adverse effects on forested ecosystems such as decreasing tree growth rates and increasing tree mortality. This paper proposes a classification framework that integrates unmanned aerial vehicle systems (UAVs)-derived multi-spectral images and a Deep self-encoding network (DSEN) with the goal of monitoring and quantifying the relative coverage of dead trees, liana-infested, and non-liana-infested trees at high spatial scales. Today's UAVs-derived multi-spectral images provide the much necessary high resolution/quality data to monitor ecosystem-level processes at low cost and on demand. On the other hand, DSEN, a state-of-the-art classification approach that uses multiple layers to exploit abstract, invariant features from input data, has been proved to have the ability to acquire excellent results. This new classification framework, implemented at a tropical Dry Forest site in Costa Rica, provided accurate estimations of the relative coverage of dead trees, liana-infested trees, non-liana-infested trees, and non-forests. The approach opens the door to start exploring linkages between a booming UAVS industry and machine learning/Deep learning classifiers.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Emily S. Thompson, Kirsten M. de Beurs〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Urbanization is generally understood as the process of growth in both population and developed areas. However, this perception is not reflective of the type of change that is occurring in the Rust Belt region of the United States where many urban areas reveal shrinkage instead. While much of the research surrounding these shrinking cities is in the realm of socio-economic implications, few studies have investigated how to map these shrinking areas. This research aims to contribute to the growing body of shrinkage research by examining methodology to monitor the fast removal of buildings in the Rust Belt shrinking cities with easily available open source data such as Light Detection and Ranging, aerial orthoimages, and GIS datasets. Our ultimate goal is to develop methodology for improved, generalizable mapping of shrinking cities. We applied our methods to Detroit, Michigan and Youngstown, Ohio which both show significant urban shrinkage and both have a variety of survey datasets available for validation. We map a 5-year change in Detroit as well as a 10-year and 19-year change in Youngstown to provide, in high detail, the process of building removal. For Detroit we found that 12.9% of all land parcels that contained a building in 2009 had lost this building by 2014. New builds were drastically overshadowed by the demolished structures, accounting for 〈1% of the total number of parcels in the city. We found similar results in Youngstown, were 13.1% of all parcels studied revealed that a structure was removed between 1994 and 2013, with similarly low rebuild percentages (〈1%).〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): F. Foroughnia, S. Nemati, Y. Maghsoudi, D. Perissin〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉South-west of Tehran, the capital city of Iran, is subjected to a high deformation rate due to excessive groundwater extractions. Persistent Scatterrer SAR Interferometry (PS-InSAR) technique is used to monitor Tehran’s deformation. Three time series data including two Sentinel-1A (S-1A) spanning from 2014 to 2017, and an ENVISAT-ASAR data stack spanning from 2004 to 2010, are analyzed. The PS-InSAR technique does not perform well on ENVISAT-ASAR due to poor selection of PS points induced by large perpendicular baselines and strong temporal decorrelation of the dataset. In this paper, a novel Iterative PSI method (IPSI) is proposed to increase the PS points which are lost in PS-InSAR technique because of the unsuccessful derivation of the absolute phase value due to an integer ambiguity. The method selects PS points based on simultaneous analysis of their amplitude and phase. Results demonstrate that the density of PSs has been increased by about 4.5 times. Line of Sight (LOS) velocities obtained from both S-1A and ENVISAT-ASAR data analysis are highly compatible with each other, indicating the reliability of the both applied methods. The maximum cumulative displacements are estimated as 39.6 cm and 88.4 cm for Sentinel-1A and ENVISAT-ASAR datasets respectively. Moreover, the subsidence area has grown in the period between the data acquisition time. The methods are successfully validated by subsidence rates obtained from precise leveling and GPS observations.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Margarita Huesca, David Riaño, Susan L. Ustin〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Originally developed to classify multispectral and hyperspectral images, spectral mapping methods were used to classify Light Detection and Ranging (LiDAR) data to estimate the vertical structure of vegetation for Fuel Type (FT) mapping. Three spectral mapping methods generated spatially comprehensive FT maps for Cabañeros National Park (Spain): (1) Spectral Mixture Analysis (SMA), (2) Spectral Angle Mapper (SAM), and (3) Multiple Endmember Spectral Mixture Analysis (MESMA). The Vegetation Vertical Profiles (VVPs) describe the vertical distribution of the vegetation and are used to define each FT endmember in a LiDAR signature library. Two different approaches were used to define the endmembers, one based on the field data collected in 1998 and 1999 (Approach 1) and the other on exploring spatial patterns of the singular FT discriminating factors (Approach 2). The overall accuracy is higher for Approach 2 and with best results when considering a five-FT model rather than a seven-FT model. The agreement with field data of 44% for MESMA and SMA and 40% for SAM is higher than the 38% of the official Cabañeros National Park FTs map. The principal spatial patterns for the different FTs were well captured, demonstrating the value of this novel approach using spectral mapping methods applied to LiDAR data. The error sources included the time gap between field data and LiDAR acquisition, the steep topography in parts of the study site, and the low LiDAR point density among others.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Foad Brakhasi, Aliakbar Matkan, Mohammad Hajeb, Kourosh Khoshelham〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This paper presents a new algorithm based on the support vector machine (SVM) for classifying the Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) data into classes of clean air, cloud, thin aerosol, dense aerosol, surface, subsurface and totally attenuated. The procedure is as follows: At first, the considered features based on CALIPSO data are prepared. Brightness Temperature Differences between 10 and 12 μm (BTD〈sub〉11-12〈/sub〉) is then used to better discriminate dense aerosols from clouds. The particle density feature proposed in this research is another feature participating in the classification. Training samples are automatically extracted by applying strict thresholds on the features. A wrapper feature selection is performed to rank the features based on their performance. Four post-processing steps are implemented to correct some misclassified cells e.g. edges of clouds and high-level clouds. The proposed algorithm was implemented on 4 datasets in the Middle East and North Africa (MENA), and India with various types and densities of aerosol. An accuracy assessment based on the comparison between the obtained results and ground truth samples indicated 0.94, 0.96 4, 0.92 and 0.89 kappa coefficients for the datasets. A statistical hypothesis test demonstrated that our SVM classification overcame CALIPSO vertical feature mask (VFM) product. The experimental result indicates the high accuracy of the proposed algorithm for the atmosphere scene classification using CALIPSO data.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Sivasakthy Selvakumaran, Simon Plank, Christian Geiß, Cristian Rossi, Campbell Middleton〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Scour is the removal of ground material in water bodies due to environmental changes in water flow. It particularly occurs at bridge piers and the holes formed can make bridges susceptible to collapse. The most common cause of bridge collapse is due to scour occurring during flooding, some failures causing loss of life and most resulting in significant transport disruption and economic loss. Consequently, failure of bridges due to scour is of great concern to bridge asset owners, and is currently very difficult to predict since conventional assessment methods foresee very resource-demanding monitoring efforts 〈em〉in situ〈/em〉. This paper presents evidence of how InSAR techniques can be used to monitor bridges at risk of scour, using Tadcaster Bridge, England, as a case study. Tadcaster Bridge suffered a partial collapse due to river scour on the evening of December 29th, 2015 following a period of severe rainfall and flooding. 48 TerraSAR-X scenes over the bridge from the two-year period prior to the collapse are analysed using the small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) approach. The study highlights a distinct movement in the region of the bridge where the collapse occurred prior to the actual event. This precursor to failure observed in the data over a month before actual collapse suggests the possible use of InSAR as a means of an early warning system in structural health monitoring of bridges at risk of scour.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Mariana Tiné, Liliana Perez, Roberto Molowny-Horas〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Among the most productive ecosystems around the world, wetlands support a wide range of biodiversity such as waterfowl, fish, amphibians, plants and many other species. They also provide ecosystem services that play important roles in relation to nutrient cycling, climate mitigation and adaptation, as well as food security. In this research, we examined and projected the spatiotemporal trends of change in open wetlands by coupling logistic regression, Markov chain methods and a multi-objective land allocation model into a hybrid geosimulation model. To study the changes in open wetlands we used multi-temporal land cover information interpreted from LANDSAT images (1985, 1995, and 2005). We predicted future spatial distributions of open wetlands in the administrative region of Abitibi-Témiscamingue, Quebec, Canada for 2015, 2025, 2035, 2045 and 2055. A comparison and assessment of the model’s outcomes were performed using map-comparison techniques as well as landscape metrics. Change analysis between 1985 and 2005 showed an increase of about 63% in open wetlands, while simulation results indicated that this tendency would persist into 2055 with a continuous augmentation of open wetlands in the region. The spatial distribution of predicted trends in open wetlands could provide support to local biodiversity assessments, management and conservation planning of the open wetlands in Quebec, Canada.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Sizwe Thamsanqa Hlatshwayo, Onisimo Mutanga, Romano T. Lottering, Zolo Kiala, Riyad Ismail〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Developing models for estimating aboveground biomass (AGB) in naturally growing forests is critical for climate change modelling. AGB models developed using satellite imagery varies with study area, depending on the complexity of vegetation and landscape structure, which affects the upwelling radiance. We assessed the potential of SPOT-6 imagery in predicting AGB of trees planted at different time periods, using image texture combinations. Image texture variables were computed from the SPOT6 pan-sharpened image data, which is characterised by a 1.5 m spatial resolution. In addition, we incorporated the minimal variance technique to select the optimum window sizes that best captures AGB variation in our study area. The results showed that image texture was able to detect AGB for both mature and young trees, however, models detecting mature trees were more superior, with accuracies of 〈em〉R〈/em〉〈sup〉2〈/sup〉 = 0.70 and 0.25 for 2009–2011 and 2011–2013 plantation phases, respectively. In addition, our results showed that the three band texture ratios yielded the highest accuracy (〈em〉R〈/em〉〈sup〉2〈/sup〉 = 0.88 and RMSE = 54.54 kg m〈sup〉−2〈/sup〉) compared to two texture (〈em〉R〈/em〉〈sup〉2〈/sup〉 = 0.85 and RMSE = 60.65 kg m〈sup〉−2〈/sup〉) and single texture band combinations (〈em〉R〈/em〉〈sup〉2〈/sup〉 = 0.64 and RMSE = 94.13 kg m〈sup〉−2〈/sup〉). A frequency analysis was also run to determine which bands appeared more frequently in the selected texture band models. The frequency analysis revealed that both the red and green bands appeared more frequently on the selected texture band variables, indicating that they were more sensitive to the variation of AGB in our study area. The results showed high variation in AGB within the Buffelsdraai reforestation site, especially due to varying tree plantation phases as well as topography. In essence, the study demonstrated the possibility of image texture combinations computed from the SPOT-6 image in estimating AGB.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Javier F. Calleja, Otilia Requejo Pagés, Nelson Díaz-Álvarez, Juanjo Peón, Natalia Gutiérrez, Esperanza Martín-Hernández, Alejandro Cebada Relea, David Rubio Melendi, Paulino Fernández Álvarez〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Active and passive remote sensing sensors have been applied successfully in the detection of crop marks (vegetation with a different spectral reflectance compared to its surroundings) related with buried archaeological remains. However, the detection of such crop marks depends on the sensor used, the status of the cover and the algorithm applied on the data. Moreover, buried archaeological remains generally produce microrelief marks, which can be very difficult to detect. The purpose of this work is to demonstrate that the combined use of data from the multispectral orbital sensor WorldView-2 and RGB and near infrared cameras mounted on an Unmanned Aerial Vehicle (UAV) equipped with a Global Navigation Satellite System (GNSS) can be successfully applied to the detection of buried archaeological remains. Principal Component Analysis, the Normalized Difference Vegetation Index (NDVI) and a purposely proposed band combination were obtained from WorldView-2 data to detect crop marks. The cameras carried by the UAV provide a Real Color composite, the NDVI and a high precision Digital Surface Model. The methodology developed in this work consists of searching for locations that exhibit both crop and microrelief marks with a similar shape. The WorldView-2 NDVI and the normalized Digital Surface Model of the UAV are filtered. An Archaeological Binary Map is constructed, in which pixels with both NDVI and normalized elevation above corresponding threshold values are interpreted as susceptible of containing buried archaeological remains and are given the value of one, otherwise zero. One of the locations of the Archaeological Binary Map, with a very regular pattern, is subsequently surveyed with Ground Penetrating Radar to find a buried structure, the location and shape of which match perfectly those of the Archeological Binary Map.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Alim Samat, Paolo Gamba, Sicong Liu, Zelang Miao, Erzhu Li, Jilili Abuduwaili〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In this work, fully polarized SAR (PolSAR) data are exploited to characterize halophyte plants in lakeside saline wetland environments thanks to their different scattering properties. To this aim, several polarization signatures and morphological profiles (MPs) are used as inputs to the proposed “random M5 model forest” (RM5MF) and “classification via random forest regression” (CVRFR) classifiers. The experimental results show that parameters such as pedestal height (PH), as well as 3D co-polarization and cross-polarization signature plots, are more suited than biomass index (BMI), volume scattering index (VSI), and canopy scattering index (CSI) to map halophytic plants in arid environments. When we compare the suitability of PolSAR features using RM5MF, random forest (RaF), and other five popular attribute selection approaches, all the results uniformly show that span, MPs and entropy are the most valuable features, while PH and BMI are more valuable than CSI, VSI and the radar forest degradation index (RFDI). Additionally, the diagonal elements of the coherency matrix are more valuable than are the off-diagonal elements, and double-bounce, odd-bounce and wire elements are more valuable than helix bounce and volume bounce. The study results are obtained from PolSAR L-band quad-polarimetric high-sensitivity stripmap data over two study regions by comparing RM5MF and CVRFR with more traditional algorithms (support vector machine (SVM), RaF, rotation forest (RoF), and MultiBoostAB). The RM5MF model achieves the highest accuracy value in the study regions. However, due to the binary splitting criteria in the M5 model tree, it is more computationally intensive than all the others. In contrast, the CVRFR model consumes much less time—approximately 10 times less than RM5MF, and 5 times less than RoF—but still achieves better (3%–8%) classification accuracy than SVM or RoF, and its results are comparable (less than 1% difference) to those by RaF.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Sadeepa Jayathunga, Toshiaki Owari, Satoshi Tsuyuki〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Remote sensing (RS) data are often used as a complementary data source to acquire accurate quantitative estimations of merchantable volume (V) and carbon stock in living biomass (CST), which are critical for the sustainable use of forest resources. In this study, we investigated the utility of unmanned aerial vehicles (UAVs) and the structure from motion (SfM) technique for estimating and mapping the spatial distributions of V and CST of an uneven–aged mixed conifer–broadleaf forest that had experienced major disturbances (e.g., wind damage and selection harvesting) over time. In addition to the commonly used RS structural metrics, we also calculated an image metric (broadleaf vegetation cover percentage) using a UAV–SfM orthomosaic to use as an explanatory variable. Plot level validation of UAV–SfM–estimated V revealed a root mean square error (RMSE) of 39.8 m〈sup〉3〈/sup〉 ha〈sup〉–1〈/sup〉 and a relative RMSE of 16.7%, whereas the RMSE and relative RMSE vales for UAV–SfM–estimated CST were 14.3 Mg C ha〈sup〉–1〈/sup〉 and 17.4% respectively. Our image metric had a statistically significant association with V and CST, providing additional explanatory power in the regression analysis. Nevertheless, RMSE values did not significantly change after adding the image metric into the regression analysis, e.g., %RMSE was reduced by 1.9% for V estimation, and 1.5% for CST estimation. Furthermore, the UAV–SfM estimates we obtained were comparable to light detection and ranging (LiDAR) estimates (relative RMSE of 16.4% and 16.7% for V and CST, respectively). We also successfully mapped the spatial distributions of V and CST and identified their stand– and landscape–level variations. Therefore, we confirmed the potential of UAV imagery when combined with LiDAR digital terrain model to capture the fine scale spatial variation of V and CST in uneven–aged forests subjected to silvicultural practices and natural disturbances over time.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): J.M. Ramírez-Cuesta, R.G. Allen, P.J. Zarco-Tejada, A. Kilic, C. Santos, I.J. Lorite〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The recent technical improvements in the sensors used to acquire images from land surfaces has made possible to assess the performance of the energy balance models using unprecedented spatial resolutions. Thus, the objective of this work is to evaluate the response of the different energy balance components obtained from METRIC model as a function of the input pixel size. Very high spatial resolution airborne images (≈50 cm) on three dates over olive orchards were used to aggregate different spatial resolutions, ranging from 5 m to 1 km. This study represents the first time that METRIC model has been run with such high spatial resolution imagery in heterogeneous agricultural systems, evaluating the effects caused by its aggregation into coarser pixel sizes. Net radiation and soil heat flux showed a near insensitive behavior to spatial resolution changes, reflecting that the emissivity and albedo respond linearly to pixel aggregation. However, greater discrepancies were obtained for sensible (up to 17%) and latent (up to 23%) heat fluxes at spatial resolutions coarser than 30 × 30 m due to the aggregation of non-linear components, and to the inclusion of non-agricultural areas in such aggregation. Results obtained confirm the good performance of METRIC model when used with high spatial resolution imagery, whereas they warn of some major errors in crop evapotranspiration estimation when medium or large scales are used.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Bekir Taner San, Umit Deniz Ulusar〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉This study puts forward a semi-automatic shoreline detection and future prediction with spatial uncertainty algorithm called the SLiP-SUM (Shore Line Prediction with Spatial Uncertainty Mapping), which has five main steps: (1) preprocessing of data sets (i.e. aerial photos and/or satellite images), (2) extraction or delineation of the existing shorelines with snake algorithm, 3) prediction of the future shorelines using linear regression, (4) preparation of the spatial uncertainty mapping using cokriging, and (5) producing possible shoreline with spatial uncertainty. The proposed approach was tested on the coast of Kumluca, a dynamic coastal area in Turkey, and future shoreline predictions were made for 2020, 2025, 2030 and 2035 based on remotely sensed data (aerial photos and WorldView-2 images) acquired between 1971 and 2014. A cokriging interpolation technique was adopted that takes into account both estimation errors and the spatial resolution of the source data sets for the mapping of spatial uncertainty. The results indicate that the maximum and minimum annual changes of shoreline were -2.29 m/yr (transgression) in the west-central part of the study area, and + 0.36 m/yr (regression) in the east part. The general trend of the future shoreline regime was transgressional, and the mean uncertainty values along the predicted shorelines for 2020, 2025, 2030 and 2035 were 6.78 m, 2.02 m, 6.76 m and 7.06 m, respectively.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S0303243418305737-ga1.jpg" width="500" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Elizabeth A. LaRue, Jeff W. Atkins, Kyla Dahlin, Robert Fahey, Songlin Fei, Chris Gough, Brady S. Hardiman〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Vegetation metrics derived from satellite imagery provide continuous and large spatial-scale measurements that are critical for interpreting and predicting ecosystem function. However, uncertainty still remains as to the precise structural information that could be estimated from these metrics. Landsat-derived metrics provide pixel measurements of vegetation across the landscape, whereas Light Detection and Ranging (LiDAR) provides multidimensional data on the vertical arrangement of forests. Terrestrial LiDAR metrics of structural complexity describe the arrangement of vegetation in the canopy, and could be coupled with Landsat-derived metrics through their influence on energy and light. Linking Landsat to terrestrial LiDAR estimates of canopy structure could expand the interpretation of Landsat-derived metrics and broaden the spatial scale at which structural complexity can be evaluated. Here, we examined associations between Landsat-derived metrics and terrestrial LiDAR measurements of structural complexity. Structural complexity measurements were obtained with terrestrial LiDAR from plots within eight forested NEON sites across eastern North America. Vegetation metrics (NDVI, EVI, tasseled cap metrics) were calculated for corresponding locations from Landsat 8 satellite imagery. Results showed that canopy reflectance, greenness and brightness, were linked with several measures of canopy structure. Higher levels of greenness were associated with stands having a taller canopy, greater leaf area density and variability, and a less open and porous canopy. Among greenness metrics, NDVI was most strongly correlated with structural complexity metrics (adj. 〈em〉R〈sup〉2〈/sup〉〈/em〉 = 0.52 – 0.62 for six metrics). Additionally, we found that a brighter canopy was associated with greater leaf area density and variability, canopy cover, porosity, and lower leaf clumping. Our results demonstrated the potential for large-spatial extent estimates of structural complexity using satellite imagery, and may lead to improved predictions of forest ecosystem functioning such as those predicted in “big leaf” ecosystem models.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Fariba Mohammadimanesh, Bahram Salehi, Masoud Mahdianpari, Mahdi Motagh, Brian Brisco〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Wetlands are home to a great variety of flora and fauna species and provide several unique environmental services. Knowledge of wetland species distribution is critical for sustainable management and resource assessment. In this study, multi-temporal single- and full-polarized RADARSAT-2 and single-polarized TerraSAR-X data were applied to characterize the wetland extent of a test site located in the north east of Newfoundland and Labrador, Canada. The accuracy and information content of wetland maps using remote sensing data depend on several factors, such as the type of data, input features, classification algorithms, and ecological characteristics of wetland classes. Most previous wetland studies examined the efficiency of one or two feature types, including intensity and polarimetry. Fewer investigations have examined the potential of interferometric coherence for wetland mapping. Thus, we evaluated the efficiency of using multiple feature types, including intensity, interferometric coherence, and polarimetric scattering for wetland mapping in multiple classification scenarios. An ensemble classifier, namely Random Forest (RF), and a kernel-based Support Vector Machine (SVM) were also used to determine the effect of the classifier. In all classification scenarios, SVM outperformed RF by 1.5–5%. The classification results demonstrated that the intensity features had a higher accuracy relative to coherence and polarimetric features. However, an inclusion of all feature types improved the classification accuracy for both RF and SVM classifiers. We also optimized the type and number of input features using an integration of RF variable importance and Spearman’s rank-order correlation. The results of this analysis found that, of 81 input features, 22 were the most important uncorrelated features for classification. An overall classification accuracy of 85.4% was achieved by incorporating these 22 important uncorrelated features based on the proposed classification framework.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): G. Kereszturi, L.N. Schaefer, W.K. Schleiffarth, J. Procter, R.R. Pullanagari, S. Mead, Ben Kennedy〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Optical and laser remote sensing provide resources for monitoring volcanic activity and surface hydrothermal alteration. In particular, multispectral and hyperspectral imaging can be used for detecting lithologies and mineral alterations on the surface of actively degassing volcanoes. This paper proposes a novel workflow to integrate existing optical and laser remote sensing data for geological mapping after the 2012 Te Maari eruptions (Tongariro Volcanic Complex, New Zealand). The image classification is based on layer-stacking of image features (optical and textural) generated from high-resolution airborne hyperspectral imagery, Light Detection and Ranging data (LiDAR) derived terrain models, and aerial photography. The images were classified using a Random Forest algorithm where input images were added from multiple sensors. Maximum image classification accuracy (overall accuracy = 85%) was achieved by adding textural information (e.g. mean, homogeneity and entropy) to the hyperspectral and LiDAR data. This workflow returned a total surface alteration area of ∼0.4 km〈sup〉2〈/sup〉 at Te Maari, which was confirmed by field work, lab-spectroscopy and backscatter electron imaging. Hydrothermal alteration on volcanoes forms precipitation crusts on the surface that can mislead image classification. Therefore, we also applied spectral matching algorithms to discriminate between fresh, crust altered, and completely altered volcanic rocks. This workflow confidently recognized areas with only surface alteration, establishing a new tool for mapping structurally controlled hydrothermal alteration, evolving debris flow and hydrothermal eruption hazards. We show that data fusion of remotely sensed data can be automated to map volcanoes and significantly benefit the understanding of volcanic processes and their hazards.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Narumasa Tsutsumida, Pedro Rodríguez-Veiga, Paul Harris, Heiko Balzter, Alexis Comber〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The objective of this study is to investigate spatial structures of error in the assessment of continuous raster data. The use of conventional diagnostics of error often overlooks the possible spatial variation in error because such diagnostics report only average error or deviation between predicted and reference values. In this respect, this work uses a moving window (kernel) approach to generate geographically weighted (GW) versions of the mean signed deviation, the mean absolute error and the root mean squared error and to quantify their spatial variations. Such approach computes local error diagnostics from data weighted by its distance to the centre of a moving kernel and allows to map spatial surfaces of each type of error. In addition, a GW correlation analysis between predicted and reference values provides an alternative view of local error. These diagnostics are applied to two earth observation case studies. The results reveal important spatial structures of error and unusual clusters of error can be identified through Monte Carlo permutation tests. The first case study demonstrates the use of GW diagnostics to fractional impervious surface area datasets generated by four different models for the Jakarta metropolitan area, Indonesia. The GW diagnostics reveal where the models perform differently and similarly, and found areas of under-prediction in the urban core, with larger errors in peri-urban areas. The second case study uses the GW diagnostics to four remotely sensed aboveground biomass datasets for the Yucatan Peninsula, Mexico. The mapping of GW diagnostics provides a means to compare the accuracy of these four continuous raster datasets locally. The discussion considers the relative nature of diagnostics of error, determining moving window size and issues around the interpretation of different error diagnostic measures. Investigating spatial structures of error hidden in conventional diagnostics of error provides informative descriptions of error in continuous raster data.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Rubén Ramo, Mariano García, Daniel Rodríguez, Emilio Chuvieco〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉Global burned are algorithms provide valuable information for climate modellers since fire disturbance is responsible of a significant part of the emissions and their related impact on humans. The aim of this work is to explore how four different classification algorithms, widely used in remote sensing, such as Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN) and a well-known decision tree algorithm (C5.0), for classifying burned areas at global scale through a data mining methodology using 2008 MODIS data. A training database consisting of burned and unburned pixels was created from 130 Landsat scenes. The resulting database was highly unbalanced with the burned class representing less than one percent of the total. Therefore, the ability of the algorithms to cope with this problem was evaluated.〈/p〉 〈p〉Attribute selection was performed using three filters to remove potential noise and to reduce the dimensionality of the data: Random Forest, entropy-based filter, and logistic regression. Eight out of fifty-two attributes were selected, most of them related to the temporal difference of the reflectance of the bands. Models were trained using an 80% of the database following a ten-fold approach to reduce possible overfitting and to select the optimum parameters.〈/p〉 〈p〉Finally, the performance of the algorithms was evaluated over six different regions using official statistics where they were available and benchmark burned area products, namely MCD45 (V5.1) and MCD64 (V6). Compared to official statistics, the best agreement was obtained by MCD64 (OE = 0.15, CE = 0.29) followed by RF (OE = 0.27, CE = 0.21). For the remaining three areas (Angola, Sudan and South Africa), RF (OE = 0.47, CE = 0.45) yielded the best results when compared to the reference data. NN and SVM showed the worst performance with omission and commission error reaching 0.81 and 0.17 respectively. SVM and NN showed higher sensitivity to unbalanced datasets, as in the case of burned area, with a clear bias towards the majority class. On the other hand, tree based algorithms are more robust to this issue given their own mechanisms to deal with big and unbalanced databases.〈/p〉 〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Iuliia Burdun, Valentina Sagris, Ülo Mander〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Temperature regime is one of the main controlling factors of greenhouse gas (GHG) emissions from peat bogs. Remotely sensed land surface temperature (LST) has a potential to become an efficient instrument in environmental monitoring of carbon dioxide and methane emissions from peat bogs. This paper examines the relationships between field-measured hydrometeorological variables and MODIS LST data in a hemiboreal raised bog for a period from May to September (2008–2016). The Pearson product-moment correlation was used to reveal the relationship between the field-measured parameters and LST over years and months. A multiple linear regression was chosen to model relationships between the hydrometeorological variables and LST by month. It was found that the relationships between the studied parameters and LST were year- and month-dependent. The main factor of LST was air temperature, and the correlation between LST and air temperature was the strongest during the entire period of study. This study has shown that the hydrometeorological factors of LST can explain 67%–81 % of the variance in LST in a hemiboreal raised bog. The relationships between the hydrometeorological variables and LST may be implemented in more accurate GHG emissions estimation from bogs.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Guanyuan Shuai, Jinshui Zhang, Bruno Basso, Yaozhong Pan, Xiufang Zhu, Shuang Zhu, Hongli Liu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Due to its ability to penetrate the cloud, Synthetic Aperture Radar (SAR) has been a great resource for crop mapping. Previous research has verified the applicability of SAR imagery in object-oriented crop classification, however, speckle noise limits the generation of optimal segmentation. This paper proposed an innovative SAR-based maize mapping method supported by optical image, Gaofen-1 PMS, based segmentation, named as parcel-based SAR classification assisted by optical imagery-based segmentation (os-PSC). Polarimetric decomposition was applied to extract polarimetric parameters from multi-temporal RADARSAT-2 data. One Gaofen-1 image was then used for parcel extraction, which was the basic unit for SAR image analysis. The final step was a multi-step classification for final maize mapping including: the potential maize mask extraction, pure/mixed maize parcel division and an integrated maize map production. Results showed that the overall accuracy of the os-PSC method was 89.1%, higher than those of pixel-level classification and SAR-based segmentation methods. The comparison between optical- and SAR-based segmentation demonstrated that optical-based segmentation would be better at representing maize field boundaries than the SAR-based segmentation. Moreover, the parcel- and pixel-level integrated classification will be suitable for many agricultural systems with small landownership where inter-cropping is common. Through integrating advantages of the SAR and optical data, os-PSC shows promising potentials for crop mapping.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): J.M. Ramírez-Cuesta, D. Vanella, S. Consoli, A. Motisi, M. Minacapilli〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉 〈p〉In this study, a new stand-alone satellite approach for the estimation of net surface radiation (R〈sub〉n〈/sub〉) has been implemented and validated for the Italian territory. The method uses the MODIS and MSG-SEVIRI time series products and it is independent of the use of ancillary data (i.e. ground measurements). A database of daily measurements of R〈sub〉n〈/sub〉, provided by 9 stations of the FLUXNET network, was used to validate the method in different ecological scenarios in the period 2010-12.〈/p〉 〈p〉The R〈sub〉n〈/sub〉 modelled by the proposed approach and the corresponding FLUXNET measurements were in good agreement, with RMSE and R〈sup〉2〈/sup〉 of 19.8 Wm〈sup〉−2〈/sup〉 and 0.87, respectively, at 8-days scale, and 23.3 Wm〈sup〉−2〈/sup〉 and 0.92, respectively, at daily scale.〈/p〉 〈p〉Therefore, the proposed approach can be considered effective for the estimation of spatial and temporal variability of R〈sub〉n〈/sub〉, which is a key variable related to the management of water resources, agriculture, ecology and climate change.〈/p〉 〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): A. Raechel White〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The visual analysis of remote sensing imagery is useful for the extraction of information not readily available through automated image analysis. Previous studies have shown that the replication of human reasoning about image content is difficult due to human creativity and mental flexibility. Development of automated image analysis programs continues; however, geovisual analytics suggests that it may be more beneficial to design symbiotic computer-human interpretation systems. It is imperative to understand the experiences, knowledge, and cognitive processes that image interpreters rely on. Cognitive Task Analysis (CTA) is a methodological framework developed from Cognitive Systems Engineering (CSE) where expert users are studied with the goals of explicating their needs, wants, and cognitive abilities for dealing with complex technological systems. Here we report the results of a CTA process carried out with users of a geovisual analytic tool to support forest disturbance detection and signification. These results suggest that different facets of the cognitive processes undertaken by users are not always explicit, and differences in the participant’s attentiveness to their mental processes vary greatly. Despite these differences and pathways to their final interpretations, participants were able to successfully come to similar judgments as for their peers.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): S. Bajocco, C. Ferrara, A. Alivernini, M. Bascietto, C. Ricotta〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Remotely sensed observations of seasonal greenness dynamics represent a valuable tool for studying vegetation phenology at regional and ecosystem-level scales. We investigated the seasonal variability of forests in Italy, examining the different mechanisms of phenological response to biophysical drivers. For each point of the Italian National Forests Inventory, we processed a multitemporal profile of the MODIS Enhanced Vegetation Index. Then we applied a multivariate approach for the purpose of (i) classifying the Italian forests into phenological clusters (i.e. pheno-clusters), (ii) identifying the main phenological characteristics and the forest compositions of each pheno-cluster and (iii) exploring the role of climate and physiographic variables in the phenological timing of each cluster. Results identified four pheno-clusters, following a clear elevation gradient and a distinct separation along the Mediterranean-to-temperate climatic transition of Italy. The “High-elevation coniferous” and the “High elevation deciduous” resulted mainly affected by elevation, with the former characterized by low annual productivity and the latter by high seasonality. To the contrary, the “Low elevation deciduous” showed to be mostly associated to moderate climate conditions and a prolonged growing season. Finally, summer drought was the main driving variable for the “Mediterranean evergreen”, characterized by low seasonality. The discrimination of vegetation phenology types can provide valuable information useful as a baseline framework for further studies on forests ecosystem and for management strategies.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Mohsen Azadbakht, Clive S. Fraser, Kourosh Khoshelham〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Fine scale land cover classification of urban environments is important for a variety of applications. LiDAR data has been increasingly used, separately or in conjunction with other remote sensing data, for providing land cover classification due to its high geometric accuracy as well as its additional radiometric information. An important issue in the classification of remote sensing data is the inevitable imbalance of training samples, which usually results in poor classification performance in classes with few samples (minority classes). In this paper, a synergy of sampling techniques in data mining with ensemble classifiers is proposed to address the data imbalance problem in the training datasets. Several sampling strategies, including under-sampling the majority classes, synthetic over-sampling the minority classes, hybrid-sampling, and under-sampling aggregation are examined. The results from two different datasets show superior performance of ensemble classifiers when integrated with sampling techniques. In particular, under-sampling aggregation and hybrid sampling coupled with random forests resulted in 16.7% and 5.5% improvements in the G-mean measure in two experimental datasets examined.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Cici Alexander, Amanda H. Korstjens, Graham Usher, Matthew G. Nowak, Gabriella Fredriksson, Ross A. Hill〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Tropical rainforests support a large proportion of the Earth’s plant and animal species within a restricted global distribution, and play an important role in regulating the Earth’s climate. However, the existing knowledge of forest types or habitats is relatively poor and there are large uncertainties in the quantification of carbon stock in these forests. Airborne Laser Scanning, using LiDAR, has advantages over other remote sensing techniques for describing the three-dimensional structure of forests. With respect to the habitat requirements of different species, forest structure can be defined by canopy height, canopy cover and vertical arrangement of biomass. In this study, forest patches were identified based on classification and hierarchical merging of a LiDAR-derived Canopy Height Model in a tropical rainforest in Sumatra, Indonesia. Attributes of the identified patches were used as inputs for k-medoids clustering. The clusters were then analysed by comparing them with identified forest types in the field. There was a significant association between the clusters and the forest types identified in the field, to which arang forests and mixed agro-forests contributed the most. The topographic attributes of the clusters were analysed to determine whether the structural classes, and potentially forest types, were related to topography. The tallest clusters occurred at significantly higher elevations (〉850 m) and steeper slopes (〉26°) than the other clusters. These are likely to be remnants of undisturbed primary forests and are important for conservation and habitat studies and for carbon stock estimation. This study showed that LiDAR data can be used to map tropical forest types based on structure, but that structural similarities between patches of different floristic composition or human use histories can limit habitat separability as determined in the field.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Eva Marino, Fernando Montes, José Luis Tomé, José Antonio Navarro, Carmen Hernando〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Vertical fuel structure is critical for fire hazard assessment in forest ecosystems. Forest stands with ladder fuels are more prone to crown fires because of canopy fuel continuity. However, characterization of ladder fuels is difficult in the field and few studies have developed explicit measurement procedures to account for these hazardous fuel situations. This study compares vertical profiles derived from airborne laser scanning (ALS) data and stereoscopic hemispherical images obtained in 〈em〉Pinus sylvestris〈/em〉 stands in central Spain to test their ability to detect the presence or absence of vertical fuel continuity (VFC). Vertical fuel profiles based on canopy cover fraction estimations at different height strata were assessed at plot level and compared with field observations. The quadratic form distance (QFD) was the metric used to quantify the similarity between histogram distributions defined by the vertical profiles from different datasets. Logistic regression analysis was tested to discriminate areas with and without VFC from ALS data at two threshold levels (15% and 30%). The vertical fuel profiles of canopy cover showed a different level of correspondence depending on the relative amount of ladder fuels. Significant logistic models were found (p 〈 0.0001, c-index〉090) for different combination of ALS metrics, with low percentiles (up to P30), canopy relief ratio (CRR) and the percentage of returns normalized by height strata (PRN) up to 8 m as the best predictors to identify the presence of VFC. Results indicated that both datasets were useful in retrieving variability of forest fuel distribution, but further methodological improvements (e.g. understory segmentation in stereoscopic images, new algorithms to better account for occlusions, or ground calibration for laser attenuation in ALS) are needed to increase accuracy in highly continuous areas.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): David Rodríguez-Rodríguez, Javier Martínez-Vega, Pilar Echavarría〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Spain has experienced massive recent socioeconomic changes that have had an influence on biodiversity and landscapes through land use-land cover (LULC) changes. Protected areas (PAs) seek to conserve biodiversity by establishing a legal and, sometimes, managerial regime that forbids or restricts LULC changes that are damaging to biodiversity. Here, we used CORINE Land Cover (CLC) data between 1987 and 2006 to assess differences in LULC changes and processes of change as metrics of effectiveness in four PA networks of clear legal and managerial characteristics in Spain: Nature reserves (NRs), Nature parks (NPs), Sites of Community Importance (SCIs) and Special Protection Areas (SPAs). We also compared LULC changes and processes of change around each PA network applying a modified Before-After-Control-Impact (BACI) research design with two increasingly distant control areas and two models of increased validity. The four PA networks were more environmentally sustainable than their surrounding areas although an effectiveness gradient was shown: NRs 〉 SCIs 〉 SPAs 〉 NPs, suggesting little influence of PA management on LULC changes overall. Another gradient of environmental sustainability of control areas was evident: SCIs 〉 SPAs 〉 NPs 〉 NRs. Proximal controls were more sustainable than distant ones. The main LULC increases inside PAs affected agro-forestry areas and transitional woodland-shrub, whereas artificial surfaces, permanently irrigated lands and burned areas prevailed in the proximal and distant controls. Three main LULC processes of change inside and around Spanish PAs outstood: forest succession, land development, and new irrigated areas, the two former chiefly affecting surrounding areas and posing serious threats to effective biodiversity conservation.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Luojia Hu, Wenyu Li, Bing Xu〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Reliable information of national-level mangrove forest change in China is urgently needed for Chinese government to make appropriate policies of mangrove forest conservation. Yet, employing traditional methods (all based on single-date remotely sensed imagery) to accurately map mangrove forest in China is relatively difficult, given the influence of tide variability on the spectrum of a large proportion of mangrove forest and the spectral similarity between mangrove forest, cropland, and natural terrestrial vegetation. However, the temporal profile of spectrum for mangrove forest is likely to be distinctive, due to the influence of tide variability on mangrove forest spectrum. Therefore, in this study, we investigated the potential of using some robust spectral-temporal variability metrics (quantiles), capturing characteristics of temporal profiles for different land cover types, to reliably separate mangrove forest. We also mapped mangrove forest in China for 6 periods (1986–1992, 1993–1997, 1998–2002, 2003–2007, 2008–2012, and 2013–2017) and analyzed mangrove forest change over past decades using all available Landsat imagery. Producer’s and user’s accuracies of the land cover type “mangrove forest” for all periods are high (〉90%), indicating the effectiveness of our method. We found that mangrove forest in China has significantly increased, from 10774 ha in the period 1986–1992 to 19220 ha in the period 2013–2017. There is also a potential for employing our method to map global mangrove forest around 2015.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Weiheng Xu, Yuanwei Qin, Xiangming Xiao, Guangzhi Di, Russell B. Doughty, Yuting Zhou, Zhenhua Zou, Lei Kong, Quanfu Niu, Weili Kou〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉High demand for tea has driven the expansion of tea plantations in the tropical and subtropical regions over the past few decades. Tea plant cultivation promotes economic development and creates job opportunities, but tea plantation expansion has significant impacts on biodiversity, carbon and water cycles, and ecosystem services. Mapping the spatial distribution and extent of tea plantations in a timely fashion is crucial for land use management and policy making. In this study, we mapped tea plantation expansion in Menghai County, Yunnan Province, China. We analyzed the structure and features of major land cover types in this tropical and subtropical region using (1) the HH and HV gamma-naught imagery from the Advanced Land Observation Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) and (2) time series Landsat TM/ETM+/OLI imagery. Tea plantation maps for 2010 and 2015 were generated using the pixel-based support vector machine (SVM) approach at 30 m resolution, which had high user/producer accuracies of 83.58%/91.67% and 87.50%/90.83%, respectively. The resultant maps show that tea plantation area increased by 33.56% (∼9335 ha), from ∼27,817 ha in 2010 to ∼37,152 ha in 2015. The additional tea plantation area was mainly converted from forest (32.50%) and cropland (67.50%). The results showed that the combination of PALSAR and optical data performed better in tea plantation mapping than using optical data only. This study provides a promising new approach to identify and map tea plantations in complex tropical landscapes at high spatial resolution.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Sven Hemmelder, Wouter Marra, Henk Markies, Steven M. De Jong〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉River morphology dynamics and river bank erosion were mapped using multi-temporal images acquired in June 2014 and June 2015 by an Unmanned Airborne Vehicle (UAV). The selected study sites are located in two dynamic parts of the floodplain of the river Buëch in the Hautes-Alpes province in south-eastern France. The images were processed using the Structure from Motion algorithm into high spatial resolution OrthoMosaics of 5 cm pixel size and DEMs (Digital Elevation Model) of 10 cm pixel size. The positional and vertical accuracy of the UAV products were evaluated using Real Time Kinematic GPS (RTK-GPS) points measurements of markers laid out in the floodplain during image acquisition. Obtained accuracies are centimeters to decimeters. River morphology such as channel displacements, gravel bank displacement and avulsions were evaluated using the OrthoMosaics. The Buëch river shows mainly braided river properties but at locations some meandering river properties were observed. Bank erosion volume calculations were made by comparing the high spatial resolution DEMs of 2014 and 2015. Accuracy of bank erosion assessment was evaluated using RTK-GPS transects of known sites of bank erosion. Bank retreat could be mapped at centimeter to decimeter detail but sometimes hampered by overhanging vegetation, water glitter and shadows. Our conclusions are that time-series of high spatial resolution UAV images can be acquired in a flexible and easy way, the individual images can nowadays be processed in a straightforward way into suitable products i.e. OrthoMosaics and DEMs which are valuable products for land administrators having a responsibility to survey and monitor rivers and control them. Accuracy of the UAV products is high in XYZ direction and sufficient for river monitoring purposes and for designing management measures.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Jizhe Xia, Chaowei Yang, Qingquan Li〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉With the rapid advancement of Earth Observation systems, Earth Observation data has been collected and accumulated at an unprecedented fast rate. Earth Observation Big Data emerged with new opportunities for human to better understand the Earth systems, but also pose a tremendous challenge for efficiently transforming Big Data into Earth Observation Big Value. Targeting on this challenge, a well-organized data index is a key to enhance the “Data-Value” transformation by accelerating the access to data. Although various data indexing approaches have been proposed with different optimization objectives, literature shows that there are still apparent limitations for Earth Observation data indexing. This paper aims to build a spatiotemporal indexing for Earth Observation Big Data. Specifically, a) to support various Earth Observation Data Infrastructures, we adopt an indexing framework to efficiently retrieve data with various textual, spatial and temporal requirements; b) a distributed indexing structure is designed to improve the index scalability; c) data access pattern is integrated to the indexing algorithm for both spatial and workload balancing. The results show that our indexing approach outperforms traditional indexing approaches and accelerates the access to Earth Observation data. We envision that data indexing will become a key technology that drives fundamental Earth Observation advancements in the Big Data era.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): S. Homayouni, H. McNairn, M. Hosseini, X. Jiao, J. Powers〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Monitoring crop condition using optical satellite indices has a legacy of several decades. Early warning of variances in crop production is vital in mitigating regional and global food insecurity. Adoption of optical vegetation indices for this purpose is widespread, yet cloud cover impedes the acquisition of these data. Although early research using scatterometers and aircraft hinted at the sensitivity of Synthetic Aperture Radar (SAR) responses to crop development, the implementation of satellite SAR observations in operational crop condition monitoring is limited. In the research presented here, volume-to-surface (V/S) scattering ratios derived from C-band RADARSAT-2 quad and simulated compact polarimetric (QP and CP) imagery are assessed for their potential to monitor crop growth. Both V/S ratios were strongly correlated with optical vegetation indices, including the widely adopted Normalized Difference and Soil Adjusted Vegetation Indices. The changes in the ratio of volume to surface scattering were correlated with variations in crop biomass. The results support the potential of a SAR scattering ratio for crop condition monitoring. In particular, encouraging results were reported for compact polarimetry, a mode that can be implemented to deliver broader swath coverage conducive to regional and national monitoring.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Lynda Khiali, Mamoudou Ndiath, Samuel Alleaume, Dino Ienco, Kenji Ose, Maguelonne Teisseire〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The expansion of satellite technologies makes remote sensing data abundantly available. While the access to such data is no longer an issue, the analysis of this kind of data is still challenging and time consuming. In this paper, we present an object-oriented methodology designed to handle multi-annual Satellite Image Time Series (SITS). This method has the objective to automatically analyse a SITS to depict and characterize the dynamic of the areas (the way that the land cover of the areas evolve over time). First, it identifies the spatio-temporal entities (reference objects) to be tracked. Second, the evolution of such entities is described by means of a graph structure and finally it groups together spatio-temporal entities that evolve similarly. The analysis were performed on three study areas to highlight inter (among the study areas) and intra (inside a study area) similarity by following the evolution of the underlying phenomena. The analysis demonstrate the benefits of our methodology. Moreover, we also stress how an expert can exploit the extracted knowledge to pinpoint relevant landscape evolutions in the multi-annual time series and how to make connections among different study areas.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Mohamed Abdelkareem, Gamal M. Kamal El-Din, Ibrahim Osman〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Observations of the Earth from space either by satellites or aircrafts are significant approaches for mineral exploration, because of their capability of revealing hydrothermal alteration minerals and sensing the surface/subsurface fracture/fault zones. A study area that is situated in the Pan-African belt of Egypt is tested for targeting potential area of mineral resources involved the hydrothermal system using satellite imagery combined with aeromagnetic, geochemical and field data. Extracted alteration layers using ASTER data and major structures (fault/fracture zones) from DEM, ALOS/PALSAR and aeromagnetic data were prepared and integrated using Knowledge-driven technique in multicriteria-decision making tools for producing mineral prospect map. The results revealed five predictive areas of expected mineral occurrences ranging from excellent to very low. Spectral analysis using ASTER data allowed defining the key-hydrothermal minerals which revealed three successive zones of alterations e.g., argillic, phyllic and propylitic. Plausible areas of minerals fitting to the ore body representing the center of the extensive alterations. Field, geochemical, and ore microscopic investigations validated the results of integrated data. Field data revealed that the mineralization zones extend along NNE-SSW thrust trend that later modified by NW-SE, N-S and NE-SW strike slip faults. Hydrothermal processes related to later magmatic stages probably were responsible for destruction and remobilization of the primary minerals of the host metavolcanics. Microscopic examination revealed that Fe-Cu〈em〉-Zn-〈/em〉Pb sulfide minerals are associated with auriferous quartz veins. Plausible areas of prospective interest for possible mineralization also were characterized using geochemical analysis. This study successfully displays the key role of integration approach for exploring mineral resources in arid regions.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Muluken N. Bazezew, Yousif A. Hussin, E.H. Kloosterman〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Parameters of individual trees can be measured from LiDAR data provided that the laser points are dense enough to distinguish tree crowns. Retrieving tree parameters for above-ground biomass (AGB) valuation of the complex biophysical tropical forests using LiDAR technology is a major undertaking, and yet needs vital effort. Integration of Airborne LiDAR Scanner (ALS) and Terrestrial Laser Scanner (TLS) data for estimation of tree AGB at a single-tree level has been investigated in part of the tropical forest of Malaysia. According to the complete tree-crown detection potential of ALS and TLS, the forest canopy was cross-sectioned into upper and lower canopy layers. In a first step, multiresolution segmentation of the ALS canopy height model (CHM) was deployed to delineate upper canopy tree crowns. Results showed a 73% segmentation accuracy and permissible to detect 57% of field-measured trees. Two-way tree height validations were executed, viz. ALS-based upper and TLS-based lower canopy tree heights. The root mean square error (RMSE) for upper canopy trees height was 3.24 m (20.18%), and the bias was –1.20 m (–7.45%). For lower canopy trees height, RMSE of 1.45 m (14.77%) and bias of 0.42 m (4.29%) were obtained. In a second step, diameter at breast height (DBH) of individual tree stems detected from TLS data was measured. The RMSE obtained was 1.30 cm (6.52%), which was as nearly accurate as manually measured-DBH. In a third step, ALS-detected trees were co-registered and linked with the corresponding tree stems detected by TLS for DBH use. Lastly, an empirical regression model was developed for AGB estimated from a field-based method using an independent variable derived from ALS and TLS data. The result suggests that traditional field-methods underestimate AGB or carbon with the bias –0.289 (–3.53%) Mg, according for approximately 11%. Conversely, integrative use of ALS and TLS can enhance the capability of estimating more accurately AGB or carbon stock of the tropical forests.〈/p〉〈/div〉 〈h5〉Graphical abstract〈/h5〉 〈div〉〈p〉〈figure〉〈img src="https://ars.els-cdn.com/content/image/1-s2.0-S030324341830727X-ga1.jpg" width="338" alt="Graphical abstract for this article" title=""〉〈/figure〉〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Huapeng Li, Ce Zhang, Shuqing Zhang, Peter M. Atkinson〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Spatial and temporal information on plant and soil conditions is needed urgently for monitoring of crop productivity. Remote sensing has been considered as an effective means for crop growth monitoring due to its timely updating and complete coverage. In this paper, we explored the potential of L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data for crop monitoring and classification. The study site was located in the Sacramento Valley, in California where the cropping system is relatively diverse. Full season polarimetric signatures, as well as scattering mechanisms, for several crops, including almond, walnut, alfalfa, winter wheat, corn, sunflower, and tomato, were analyzed with linear polarizations (HH, HV, and VV) and polarimetric decomposition (Cloude–Pottier and Freeman–Durden) parameters, respectively. The separability amongst crop types was assessed across a full calendar year based on both linear polarizations and decomposition parameters. The unique structure-related polarimetric signature of each crop was provided by multitemporal UAVSAR data with a fine temporal resolution. Permanent tree crops (almond and walnut) and alfalfa demonstrated stable radar backscattering values across the growing season, whereas winter wheat and summer crops (corn, sunflower, and tomato) presented drastically different patterns, with rapid increase from the emergence stage to the peak biomass stage, followed by a significant decrease during the senescence stage. In general, the polarimetric signature was heterogeneous during June and October, while homogeneous during March-to-May and July-to-August. The scattering mechanisms depend heavily upon crop type and phenological stage. The primary scattering mechanism for tree crops was volume scattering (〉40%), while surface scattering (〉40%) dominated for alfalfa and winter wheat, although double-bounce scattering (〉30%) was notable for alfalfa during March-to-September. Surface scattering was also dominant (〉40%) for summer crops across the growing season except for sunflower and tomato during June and corn during July-to-October when volume scattering (〉40%) was the primary scattering mechanism. Crops were better discriminated with decomposition parameters than with linear polarizations, and the greatest separability occurred during the peak biomass stage (July-August). All crop types were completely separable from the others when simultaneously using UAVSAR data spanning the whole growing season. The results demonstrate the feasibility of L-band SAR for crop monitoring and classification, without the need for optical data, and should serve as a guideline for future research.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Jia Jin, Quan Wang〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Hyperspectral indices have proven to be useful for quantifying various plant parameters including a number of biochemical components. However, it remains a great challenge to unveil the physical and physiological mechanisms of wavelengths used by these statistically/empirically identified indices, and this unveiling is a critical step towards developing generally applicable indices. In this study, we have introduced a dummy variable to the well-accepted leaf scale radiative transfer model (PROSPECT-4) and have conducted a series of virtual experiments by tweaking different absorption features (intensity, peak wavelength, and half-width) of the dummy variable for investigating the underlying mechanisms of those wavelengths used by efficient indices. Results clearly indicated that the informative bands used in efficient indices, instead of the target parameter’s absorption peaks, are generally concentrated among wavelengths that the targeted parameter has relatively high specific absorption coefficients compared with other biochemical components. This finding has also been validated by replacing the dummy variable with carotenoids based on PROSPECT-5B simulated dataset. Further analysis reveals that although the concentrations of chlorophyll are higher than other pigments in most leaves, the commonly recommended wavelength of 550 nm may be disturbed by other pigments and may only be applicable when anthocyanins are minor. Results obtained in this study have largely explained why specific wavelengths are used in efficient indices, and thus this should lay a basis for understanding the underlying mechanisms and help to develop robust indices for estimating vegetation biochemical parameters.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Kersten Clauss, Marco Ottinger, Patrick Leinenkugel, Claudia Kuenzer〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Rice is the most important food crop in Asia and rice exports can significantly contribute to a country's GDP. Vietnam is the third largest exporter and fifth largest producer of rice, the majority of which is grown in the Mekong Delta. The cultivation of rice plants is important, not only in the context of food security, but also contributes to greenhouse gas emissions, provides man-made wetlands as an ecosystem, sustains smallholders in Asia and influences water resource planning and run-off water management. Rice growth can be monitored with Synthetic Aperture Radar (SAR) time series due to the agronomic flooding followed by rapid biomass increase affecting the backscatter signal. With the advent of Sentinel-1 a wealth of free and open SAR data is available to monitor rice on regional or larger scales and limited data availability should not be an issue from 2015 onwards. We used Sentinel-1 SAR time series to estimate rice production in the Mekong Delta, Vietnam, for three rice seasons centered on the year 2015. Rice production for each growing season was estimated by first classifying paddy rice area using superpixel segmentation and a phenology based decision tree, followed by yield estimation using random forest regression models trained on in situ yield data collected by surveying 357 rice farms. The estimated rice production for the three rice growing seasons 2015 correlates well with data at the district level collected from the province statistics offices with 〈em〉R〈/em〉〈sup〉2〈/sup〉s of 0.93 for the Winter–Spring, 0.86 for the Summer–Autumn and 0.87 for the Autumn–Winter season.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Timothy G. Whiteside, Renée E. Bartolo〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Revegetation success is a key element of mine site rehabilitation. A number of criteria related to mine site close-out are associated with revegetation. The monitoring of mine site revegetation efforts have traditionally been undertaken using field-based plot or transect methods. Often the sampling design for this monitoring is limited due to resource constraints, therefore reducing the statistical power of the data and missing information over most of the mine site. The recent advances in Remotely Piloted Aircraft Systems (RPAS) technology for remote sensing enables the collection of appropriate scale data over entire mine sites reducing the need for sampling and eliminating potential bias. This paper describes an object-based technique for extracting woody cover and estimating proportional woody cover from RPAS imagery over the rehabilitated Jabiluka mine site located in the tropical north of Australia. The technique was tested on three data sets that covered three different dates, two different sensors, and two different processing methods. Overall woody cover detection accuracies from each data set were over 95%. Proportional woody cover derived from the technique showed strong linear relationships with manually estimated cover (〈em〉r〈/em〉〈sup〉2〈/sup〉 〉 0.88). This study shows that the technique is robust and works with a range of RPAS data sets and enables at scale analysis of woody cover change between dates. The technique will be an important component of ongoing monitoring of mine site revegetation in the region.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Priscilla Addison, Thomas Oommen〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The increasing knowledge in the capabilities of satellite imagery to hazard applications is especially useful in emergency situations where timing and ability to cover large areas are of the essence. For optical imagery, cloud coverage can corrupt an image rendering it unusable for intended emergency analyses. This study proposes the use of Synthetic Aperture Radar (SAR) imagery for burn severity analysis for western United States sites, as an alternative to its optical based counterpart, differenced normalized burn ratio (dNBR). Unlike optical sensors, the radar sensor is an active sensor that is able to penetrate clouds and smoke, an attribute that is crucial in emergency situations where immediate burn severity data are needed to assess the vulnerability of fire affected areas to post-fire hazards. Using C5 decision tree algorithm we developed a SAR-based metric that attempts to classify burn severities of fire affected locations in the western USA. We then compared the performance of this developed metric to that obtained by the existing dNBR metric, to determine if there is any merit to its adoption as an alternative for the western USA landscape. The results showed the SAR approach to produce higher validation metrics in comparison to the dNBR. It had an overall accuracy and kappa of 60% and 0.35, respectively, in comparison to the 35% and 0.1 of the dNBR approach. This shows an improved ability to quickly obtain burn severity data and make better informed decisions in emergency situations.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: February 2019〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 74〈/p〉 〈p〉Author(s): Yosef Mengistu Darge, Binyam Tesfaw Hailu, Ameha Atnafu Muluneh, Tesfaye Kidane〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉Despite the high geothermal potential of the Main Ethiopian Rift (MER), risks associated with the industry and the difficulty of identifying possible targets using ground surveys alone continue to impede the development of geothermal power diligence in Ethiopia. In this paper, we investigate the geothermal potential of the Tulu Moye prospect area in the MER using Landsat 8, which is an important and cost-effective method of detecting geothermal anomalies. Data with a path/row of 168/054 were obtained from the Landsat 8 Operational Land Imager (OLI) and Thermal Infrared (TIR) sensors for October 17, 2014. Based on radiometric calibration, atmospheric correction (with the 6S model) and an NDVI-based threshold method for calculating land surface emissivity, a split-window algorithm was applied to retrieve the land surface temperature (LST) of the study area. Results show LST values ranging from 292.2 to 315.8 K, with the highest values found in barren lands. A comparison of LST between the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 shows a maximum difference of 1.47 K. Anomalous areas were also discovered, where LST was about 3-9 K higher than the background area. We identified seven of these as areas of high geothermal activity in the Tulu Moye prospective geothermal area. Auxiliary data and overlay analysis tools eliminated any non-geothermal influences. The research reveals that the distribution of highy prospective geothermal areas is consistent with the development and distribution of faults in the study area. Magmatism is the thermal source and faults provide conduits for the heat to flow from earth’s interior to the surface, facilitating the presence of geothermal anomalies. Finally, TIR remote sensing methods prove to be a robust and cost-effective technique for detecting LST anomalies in the geologically active area of MER. Moreover, combining TIR remote sensing with knowledge of the structural geology of the area and geothermal mechanisms is an efficient approach to detecting geothermal areas.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): J.J. van der Sanden, N.H. Short, H. Drouin〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉TanDEM-X bistatic and pursuit monostatic InSAR coherence offer different utility for lake ice extent mapping. Both facilitate ice-water discrimination largely independent of the SAR incidence angle but only pursuit monostatic coherence does so under all wind conditions. Relative to backscatter intensity—the basis for most existing mapping approaches—pursuit monostatic coherence offers enhanced utility. Our automated mapping approach combines basic interferometric processing, ice-water classification using a 0.3 coherence threshold and geospatial analysis to separate lakes from land. The approach is developed and demonstrated using TanDEM-X pursuit monostatic data acquired during freeze-up but should also be of use for the mapping of lake ice breakup. Early in the freeze-up season, the extent of lake ice is underestimated due to the commission of new ice—estimated age ≤ 5 days—in the water class. An evaluation of coherence for sample regions reveals three principal—surface cover and cooperative mode dependent—decorrelation sources: temporal change, additive noise and multiplicative noise. TanDEM-X operated in its pursuit monostatic mode for a limited time in support of scientific studies. The introduction of a system with a fully operational capacity to acquire near-simultaneous InSAR images would benefit operational users concerned with lake ice extent mapping and—we expect—many others that deal with water or other rapidly changing features.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Cong Xu, Bruce Manley, Justin Morgenroth〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉In New Zealand, 30% of plantation forests are small-scale (〈1000 ha) and knowledge of these forests, especially those less than 100 ha, is limited. These forests are expected to comprise more than 40% of the total harvest volume by 2020, so it is critical to understand the small-scale forest resource in order to plan effectively for marketing, harvesting, logistics and transport capacity. A remote sensing solution to small-scale forest description is necessary because conducting a comprehensive ground-based survey of those patchy forests is impractical. However, the utility of remote sensing prediction techniques for application in small-scale forests is unknown. This research evaluated two parametric models (multiple linear regression and seemingly unrelated regression) and two non-parametric models (k-Nearest Neighbour and Random Forest) models to predict stand variables (mean top height, basal area, volume and stand age) using model inputs including RapidEye-derived metrics and LiDAR-derived metrics. LiDAR-derived metrics were better at predicting all forest stand variables relative to RapidEye metrics. Combining LiDAR metrics with RapidEye metrics did not improve variable prediction results (on average 0.2% reduction in RMSE). Non-parametric models and parametric models performed similarly. Of all approaches tested in this study, multiple linear regression (MLR) using LiDAR-derived metrics was deemed to be the best performing modelling approach for predicting stand variables for small-scale plantation forests in New Zealand. MLR predicted mean top height (MTH) with a root-mean-square-error (RMSE) of 1.81 m, basal area (BA) with an RMSE of 9.92 m〈sup〉2〈/sup〉  ha〈sup〉−1〈/sup〉, stand volume with an RMSE of 94.93 m〈sup〉3〈/sup〉  ha〈sup〉−1〈/sup〉 and age with an RMSE of 2.17 years.〈/p〉〈/div〉

Beschreibung: 〈p〉Publication date: December 2018〈/p〉 〈p〉〈b〉Source:〈/b〉 International Journal of Applied Earth Observation and Geoinformation, Volume 73〈/p〉 〈p〉Author(s): Nafiseh Ghasemi, Valentyn Tolpekin, Alfred Stein〈/p〉 〈h5〉Abstract〈/h5〉 〈div〉〈p〉The RMoG (Random-Motion-over-Ground) model is commonly used to obtain tree height values from PolInSAR images. The RMoG model borrows its structure function from conventional RVoG (Random-Volume-over-Ground) model which is limited for modelling structural variety in canopy layer. This paper extends the RMoG model to improve tree height estimation accuracy by using a Fourier-Legendre polynomial as the structure function. The new model is denoted by the RMoG〈sub〉〈em〉L〈/em〉〈/sub〉 model. The proposed modification makes height estimation less prone to errors by enabling more flexibility in representing the vertical structure of the vegetation layer. We applied the RMoG〈sub〉〈em〉L〈/em〉〈/sub〉 model on airborne P- and L-band PolInSAR images from the Remingstorp test site in southern Sweden. We compared it with the RMoG and the conventional RVoG models using Lidar height map and field data for validation. For P-band, the relative error was equal to 37.5% for the RVoG model, to 23.7% for the RMoG model, and to 18.5% for the RMoG〈sub〉〈em〉L〈/em〉〈/sub〉 model. For L-band it was equal to 30.54% for the RVoG model, to 20.02% for the RMoG model, and to 21.63% for the RMoG〈sub〉〈em〉L〈/em〉〈/sub〉. We concluded that the RMoG〈sub〉〈em〉L〈/em〉〈/sub〉 model estimates tree height more accurately in P-band, while in L-band the RMoG model was equally good. The RMoG〈sub〉〈em〉L〈/em〉〈/sub〉 model is of a great value for future SAR sensors that are more focused than before on tree height and biomass estimation.〈/p〉〈/div〉