ALBERT

Description: Remote Sensing, Vol. 10, Pages 1327: Comparing the Performance of Neural Network and Deep Convolutional Neural Network in Estimating Soil Moisture from Satellite Observations Remote Sensing doi: 10.3390/rs10091327 Authors: Lingling Ge Renlong Hang Yi Liu Qingshan Liu Soil moisture (SM) plays an important role in hydrological cycle and weather forecasting. Satellite provides the only viable approach to regularly observe large-scale SM dynamics. Conventionally, SM is estimated from satellite observations based on the radiative transfer theory. Recent studies have demonstrated that the neural network (NN) method can retrieve SM with comparable accuracy as conventional methods. Here, we are interested in whether the NN model with more complex structures, namely deep convolutional neural network (DCNN), can bring about further improvement in SM retrievals when compared with the NN model used in recent studies. To achieve this objective, the same input data are used for the DCNN and NN models, including L-band Soil Moisture and Ocean Salinity (SMOS) brightness temperature (TB), C-band Advanced Scatterometer (ASCAT) backscattering coefficients, Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and soil temperature. The target SM used to train the DCNN and NN models is the European Center for Medium-range Weather Forecasts Re-Analysis Interim (ERA-Interim) product. The experiment consists of two phases: the learning phase from 1 January to 31 December 2015 and the testing phase from 1 January to 31 December 2016. In the learning phase, we train the DCNN and NN models using the ERA-Interim SM. When evaluation between DCNN and NN against in situ measurements in the testing phase, we find that the temporal correlations between DCNN SM and in situ measurements are higher than those between NN SM and in situ measurements by 6 . 2 % and 2 . 5 % on ascending and descending orbits, respectively. In addition, from the perspective of temporal and spatial dynamics, the simulated SM values by DCNN/NN and the ERA-Interim SM agree relatively well at a global scale. Results suggest that both NN and DCNN models are effective in estimating SM from satellite observations, and DCNN can achieve slightly better performance than NN.

Description: Remote Sensing, Vol. 10, Pages 1344: Evaluating the Potential of ALS Data to Increase the Efficiency of Aboveground Biomass Estimates in Tropical Peat–Swamp Forests Remote Sensing doi: 10.3390/rs10091344 Authors: Paul Magdon Eduardo González-Ferreiro César Pérez-Cruzado Edwine Setia Purnama Damayanti Sarodja Christoph Kleinn Estimates of aboveground biomass (AGB) in forests are critically required by many actors including forest managers, forest services and policy makers. Because the AGB of a forest cannot be observed directly, models need to be employed. Allometric models that predict the AGB of a single tree as a function of diameter at breast height (DBH) are commonly used in forest inventories that use a probability selection scheme to estimate total AGB. However, for forest areas with limited accessibility, implementing such a field-based survey can be challenging. In such cases, models that use remotely sensed information may support the biomass assessment if useful predictor variables are available and statistically sound estimators can be derived. Airborne laser scanning (ALS) has become a prominent auxiliary data source for forest biomass assessments and is even considered to be one of the most promising technologies for AGB assessments in forests. In this study, we combined ALS and forest inventory data from a logged-over tropical peat swamp forest in Central Kalimantan, Indonesia to estimate total AGB. Our objective was to compare the precision of AGB estimates from two approaches: (i) from a field-based inventory only and, (ii) from an ALS-assisted approach where ALS and field inventory data were combined. We were particularly interested in analyzing whether the precision of AGB estimates can be improved by integrating ALS data under the particular conditions. For the inventory, we used a standard approach based on a systematic square sample grid. For building a biomass-link model that relates the field based AGB estimates to ALS derived metrics, we used a parametric nonlinear model. From the field-based approach, the estimated mean AGB was 241.38 Mgha − 1 with a standard error of 11.17 Mgha − 1 (SE% = 4.63%). Using the ALS-assisted approach, we estimated a similar mean AGB of 245.08 Mgha − 1 with a slightly smaller standard error of 10.57 Mgha − 1 (SE% = 4.30%). Altogether, this is an improvement of precision of estimation, even though the biomass-link model we found showed a large Root Mean Square Error (RMSE) of 47.43 Mgha − 1 . We conclude that ALS data can support the estimation of AGB in logged-over tropical peat swamp forests even if the model quality is relatively low. A modest increase in precision of estimation (from 4.6% to 4.3%), as we found it in our study area, will be welcomed by all forest inventory planners as long as ALS data and analysis expertise are available at low or no cost. Otherwise, it gives rise to a challenging economic question, namely whether the cost of the acquisition of ALS data is reasonable in light of the actual increase in precision.

Description: Remote Sensing, Vol. 10, Pages 1346: Estimation of Terrestrial Global Gross Primary Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux Data Remote Sensing doi: 10.3390/rs10091346 Authors: Joanna Joiner Yasuko Yoshida Yao Zhang Gregory Duveiller Martin Jung Alexei Lyapustin Yujie Wang Compton J. Tucker We estimate global terrestrial gross primary production (GPP) based on models that use satellite data within a simplified light-use efficiency framework that does not rely upon other meteorological inputs. Satellite-based geometry-adjusted reflectances are from the MODerate-resolution Imaging Spectroradiometer (MODIS) and provide information about vegetation structure and chlorophyll content at both high temporal (daily to monthly) and spatial (∼1 km) resolution. We use satellite-derived solar-induced fluorescence (SIF) to identify regions of high productivity crops and also evaluate the use of downscaled SIF to estimate GPP. We calibrate a set of our satellite-based models with GPP estimates from a subset of distributed eddy covariance flux towers (FLUXNET 2015). The results of the trained models are evaluated using an independent subset of FLUXNET 2015 GPP data. We show that variations in light-use efficiency (LUE) with incident PAR are important and can be easily incorporated into the models. Unlike many LUE-based models, our satellite-based GPP estimates do not use an explicit parameterization of LUE that reduces its value from the potential maximum under limiting conditions such as temperature and water stress. Even without the parameterized downward regulation, our simplified models are shown to perform as well as or better than state-of-the-art satellite data-driven products that incorporate such parameterizations. A significant fraction of both spatial and temporal variability in GPP across plant functional types can be accounted for using our satellite-based models. Our results provide an annual GPP value of ∼140 Pg C year - 1 for 2007 that is within the range of a compilation of observation-based, model, and hybrid results, but is higher than some previous satellite observation-based estimates.

Description: IJGI, Vol. 7, Pages 348: Nesting Patterns of Loggerhead Sea Turtles (Caretta caretta): Development of a Multiple Regression Model Tested in North Carolina, USA ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090348 Authors: Joanne N. Halls Alyssa L. Randall Numerous environmental conditions may influence when a female Loggerhead sea turtle (Caretta caretta) selects a nesting site. Limited research has used Geographic Information Systems (GIS) and statistical analysis to study sea turtle spatial patterns and temporal trends. Therefore, the goals of this research were to identify areas that were most prevalent for nesting and to test social and environmental variables to create a nesting suitability predictive model. Data were analyzed at all barrier island beaches in North Carolina, USA (515 km) and several variables were statistically significant: distance to hardened structures, beach nourishment, house density, distance to inlets, and beach elevation, slope, and width. Interestingly, variables that were not significant were population density, proximity to the Gulf Stream, and beach aspect. Several statistical techniques were tested and Negative Binomial Distribution produced good regional results while Geographically Weighted Regression models successfully predicted the number of nests with an average of 75% of the variance explained. Therefore, the combination of traditional and spatial statistics provided insightful predictive modeling results that may be incorporated into management strategies and may have important implications for the designation of critical Loggerhead nesting habitats.

Description: Remote Sensing, Vol. 10, Pages 1357: Integration of Low-Resolution ALS and Ground-Based SfM Photogrammetry Data. A Cost-Effective Approach Providing an ‘Enhanced 3D Model’ of the Hound Tor Archaeological Landscapes (Dartmoor, South-West England) Remote Sensing doi: 10.3390/rs10091357 Authors: Lukáš Holata Jindřich Plzák Radek Světlík João Fonte Airborne laser scanning (ALS) data is increasingly distributed freely for ever larger territories, albeit usually in only low resolution. This data source is extensively used in archaeology; however, various remains of past human activities are not recorded in sufficient detail, or are missing completely. The main purpose of this paper is to present a cost-effective approach providing reliable and accurate 3D documentation of the deserted medieval settlement of Hound Tor, a complex site consisting of preserved stone building walls and field system remains. The proposed procedure integrates ALS data with structure from motion (SfM) photogrammetry into a single data source (point cloud). Taking advantage of the benefits of both techniques (reclassified ALS data documents the hinterland, while SfM records the residential area in high detail), an enhanced 3D model has been created surpassing the available ALS data and reflecting the actual state of preserved features. The final outputs will help with the management of the site, its presentation to the general public, and also to enrich understanding of it. As both data sources are currently easily accessible and the proposed procedure has only limited budget requirements, it can be easily adopted and applied extensively (e.g., for virtual preservation of threatened complex sites and areas).

Description: Remote Sensing, Vol. 10, Pages 1356: Airborne and Terrestrial Laser Scanning Data for the Assessment of Standing and Lying Deadwood: Current Situation and New Perspectives Remote Sensing doi: 10.3390/rs10091356 Authors: Niccolò Marchi Francesco Pirotti Emanuele Lingua LiDAR technology is finding uses in the forest sector, not only for surveys in producing forests but also as a tool to gain a deeper understanding of the importance of the three-dimensional component of forest environments. Developments of platforms and sensors in the last decades have highlighted the capacity of this technology to catch relevant details, even at finer scales. This drives its usage towards more ecological topics and applications for forest management. In recent years, nature protection policies have been focusing on deadwood as a key element for the health of forest ecosystems and wide-scale assessments are necessary for the planning process on a landscape scale. Initial studies showed promising results in the identification of bigger deadwood components (e.g., snags, logs, stumps), employing data not specifically collected for the purpose. Nevertheless, many efforts should still be made to transfer the available methodologies to an operational level. Newly available platforms (e.g., Mobile Laser Scanner) and sensors (e.g., Multispectral Laser Scanner) might provide new opportunities for this field of study in the near future.

Description: IJGI, Vol. 7, Pages 357: Space–Time Analysis of Vehicle Theft Patterns in Shanghai, China ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090357 Authors: Yuanyuan Mao Shenzhi Dai Jiajun Ding Wei Zhu Can Wang Xinyue Ye To identify and compare the space–time patterns of vehicle thefts and the effects of associated environmental factors, this paper conducts a case study of the Pudong New Area (PNA), a major urban district in Shanghai, China’s largest city. Geographic information system (GIS)-based analysis indicated that there was a stable pattern of vehicle theft over time. Hotspots of vehicle theft across different time periods were identified. These data provide clues for how law enforcement can prioritize the deployment of limited patrol and investigative resources. Vehicle thefts, especially those of non-motor vehicles, tend to be concentrated in the central-western portion of the PNA, which experienced a dramatic rate of urbanization and has a high concentration of people and vehicles. Important factors contributing to vehicle thefts include a highly mobile and transitory population, a large population density, and high traffic volume.

Description: IJGI, Vol. 7, Pages 354: On the Risk Assessment of Terrorist Attacks Coupled with Multi-Source Factors ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090354 Authors: Xun Zhang Min Jin Jingying Fu Mengmeng Hao Chongchong Yu Xiaolan Xie Terrorism has wreaked havoc on today’s society and people. The discovery of the regularity of terrorist attacks is of great significance to the global counterterrorism strategy. In this study, we improve the traditional location recommendation algorithm coupled with multi-source factors and spatial characteristics. We used the data of terrorist attacks in Southeast Asia from 1970 to 2016, and comprehensively considered 17 influencing factors, including socioeconomic and natural resource factors. The improved recommendation algorithm is used to build a spatial risk assessment model of terrorist attacks, and the effectiveness is tested. The model trained in this study is tested with precision, recall, and F-Measure. The results show that, when the threshold is 0.4, the precision is as high as 88%, and the F-Measure is the highest. We assess the spatial risk of the terrorist attacks in Southeast Asia through experiments. It can be seen that the southernmost part of the Indochina peninsula and the Philippines are high-risk areas and that the medium-risk and high-risk areas are mainly distributed in the coastal areas. Therefore, future anti-terrorism measures should pay more attention to these areas.

Description: IJGI, Vol. 7, Pages 353: Novel Method for Virtual Restoration of Cultural Relics with Complex Geometric Structure Based on Multiscale Spatial Geometry ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090353 Authors: Miaole Hou Su Yang Yungang Hu Yuhua Wu Lili Jiang Sizhong Zhao Putong Wei Because of the age of relics and the lack of historical data, the geometric forms of missing parts can only be judged by the subjective experience of repair personnel, which leads to varying restoration effects when the geometric structure of the complex relic is reconstructed. Therefore, virtual repair effects cannot fully reflect the historical appearance of cultural relics. In order to solve this problem, this paper presents a virtual restoration method based on the multiscale spatial geometric features of cultural relics in the case of complex construction where the geometric shape of the damaged area is unknown, using the Dazu Thousand-Hand Bodhisattva statue in China as an example. In this study, the global geometric features of the three-dimensional (3D) model are analyzed in space to determine the geometric shape of the damaged parts of cultural relics. The local geometric features are represented by skeleton lines based on regression analysis, and a geometric size prediction model of the defective parts is established, which is used to calculate the geometric dimensions of the missing parts. Finally, 3D surface reconstruction technology is used to quantitate virtual restoration of the defective parts. This method not only provides a new idea for the virtual restoration of artifacts with complex geometric structure, but also may play a vital role in the protection of cultural relics.

Description: Remote Sensing, Vol. 10, Pages 1371: A Prediction Smooth Method for Blending Landsat and Moderate Resolution Imagine Spectroradiometer Images Remote Sensing doi: 10.3390/rs10091371 Authors: Detang Zhong Fuqun Zhou Landsat images have been widely used in support of responsible development of natural resources, disaster risk management (e.g., forest fire, flooding etc.), agricultural production monitoring, as well as environmental change studies due to its medium spatial resolution and rich spectral information. However, its availability and usability are largely constrained by its low revisit frequency. On the other hand, MODIS (Moderate Resolution Imaging Spectroradiometer) images for land studies have much more frequent coverage but with a lower spatial resolution of 250–500 m. To take advantages of the two sensors and expand their availability and usability, during the last decade, a number of image fusion methods have been developed for generating Landsat-like images from MODIS observations to supplement clear-sky Landsat imagery. However, available methods are typically effective or applicable for certain applications. For a better result, a new Prediction Smooth Reflectance Fusion Model (PSRFM) for blending Landsat and MODIS images is proposed. PSRFM consists of a dynamic prediction model and a smoothing filter. The dynamic prediction model generates synthetic Landsat images from a pair of Landsat and MODIS images and another MODIS image, either forward or backward in time. The smoothing filter combines the forward and backward predictions by weighted average based on elapsed time or on the estimated prediction uncertainty. Optionally, the smooth filtering can be applied with constraints based on Normalized Difference Snow Index (NDSI) or Normalized Difference Vegetation Index (NDVI). In comparison to some published reflectance fusion methods, PSRFM shows the following desirable characteristics: (1) it can deal with one pair or two pairs of Landsat and MODIS images; (2) it can incorporate input image uncertainty during prediction and estimate prediction uncertainty; (3) it can track gradual vegetation phenological changes and deal with abrupt land-cover type changes; and (4) for predictions using two pairs of input images, the results can be further improved through the constrained smoothing filter based on NDSI or NDVI for certain applications. We tested PSRFM to generate a Landsat-like image time series by using Landsat 8 OLI and MODIS (MOD09GA) images and compared it to two reflectance fusion algorithms: STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) and ESTARFM (Enhanced version of STARFM). The results show that the proposed PSRFM is effective and outperforms STARFM and ESTARFM both visually and quantitatively.

Description: Remote Sensing, Vol. 10, Pages 1369: On the Very High-Resolution Radar Image Statistics of the Exponentially Correlated Rough Surface: Experimental and Numerical Studies Remote Sensing doi: 10.3390/rs10091369 Authors: Ming Jin Kun-Shan Chen Dengfeng Xie The aim of this study is to investigate, by means of experimental measurements and full-wave simulations, the dominant factors for the very high-resolution (VHR) radar image speckles from exponentially correlated rough surfaces. A Ka-band radar system was used to collect the return signal from such a surface sample fabricated by 3D printing and that signal was further processed into images at different resolution scales, where the image samples were obtained by horizontally turning around the surface sample. To cross-validate the results and to further discuss the VHR speckle properties, full wave simulations by full 3D Finite Difference Time Domain (FDTD) method were conducted with 1600 realizations for the speckle analysis. At the considered very high resolution, speckle statistics show divergence from the fully developed Rayleigh distribution. The factors that impact on the high-resolution speckle properties from exponentially correlated rough surface, are analyzed in views of the equivalent number of scatterers theory and scattering scales, respectively. From the data results and extended discussions, it is evident that both of the above factors matter for VHR speckle of backscattering, from the exponentially correlated rough surface as a good representative for the ground surface.

Description: Remote Sensing, Vol. 10, Pages 1368: Estimating Fire Background Temperature at a Geostationary Scale—An Evaluation of Contextual Methods for AHI-8 Remote Sensing doi: 10.3390/rs10091368 Authors: Bryan Hally Luke Wallace Karin Reinke Simon Jones Chermelle Engel Andrew Skidmore An integral part of any remotely sensed fire detection and attribution method is an estimation of the target pixel’s background temperature. This temperature cannot be measured directly independent of fire radiation, so indirect methods must be used to create an estimate of this background value. The most commonly used method of background temperature estimation is through derivation from the surrounding obscuration-free pixels available in the same image, in a contextual estimation process. This method of contextual estimation performs well in cloud-free conditions and in areas with homogeneous landscape characteristics, but increasingly complex sets of rules are required when contextual coverage is not optimal. The effects of alterations to the search radius and sample size on the accuracy of contextually derived brightness temperature are heretofore unexplored. This study makes use of imagery from the AHI-8 geostationary satellite to examine contextual estimators for deriving background temperature, at a range of contextual window sizes and percentages of valid contextual information. Results show that while contextual estimation provides accurate temperatures for pixels with no contextual obscuration, significant deterioration of results occurs when even a small portion of the target pixel’s surroundings are obscured. To maintain the temperature estimation accuracy, the use of no less than 65% of a target pixel’s total contextual coverage is recommended. The study also examines the use of expanding window sizes and their effect on temperature estimation. Results show that the accuracy of temperature estimation decreases significantly when expanding the examined window, with a 50% increase in temperature variability when using a larger window size than 5 × 5 pixels, whilst generally providing limited gains in the total number of temperature estimates (between 0.4%–4.4% of all pixels examined). The work also presents a number of case study regions taken from the AHI-8 disk in more depth, and examines the causes of excess temperature variation over a range of topographic and land cover conditions.

Description: Remote Sensing, Vol. 10, Pages 1367: An Empirical Algorithm to Retrieve Significant Wave Height from Sentinel-1 Synthetic Aperture Radar Imagery Collected under Cyclonic Conditions Remote Sensing doi: 10.3390/rs10091367 Authors: Weizeng Shao Yuyi Hu Jingsong Yang Ferdinando Nunziata Jian Sun Huan Li Juncheng Zuo In this study, an empirical algorithm is proposed to retrieve significant wave height (SWH) from dual-polarization Sentinel-1 (S-1) synthetic aperture radar (SAR) imagery collected under cyclonic conditions. The retrieval scheme is based on the well-known CWAVE empirical function that is here updated to deal with multi-polarization S-1 SAR measurements collected using the interferometric wide (IW) and the Extra Wide-Swath (EW) imaging modes, under cyclonic conditions. First, a training dataset that consists of six S-1 SAR images collected under cyclonic conditions is exploited to both tune the retrieval function and to check the soundness of the retrievals against the co-located WAVEWATCH-III (WW3) numerical simulations. The comparison of simulation from the WW3 model and measurements from altimeter Jason-2 shows a 0.29m root mean square error (RMSE) of significant wave height (SWH). Then, a testing data-set that consists of two S-1 SAR images is exploited to provide a preliminary validation. The results, verified against both WW3 and European Centre for Medium-Range Weather Forecasts (ECMWF) data, show the soundness of the herein approach.

Description: Remote Sensing, Vol. 10, Pages 1365: Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review Remote Sensing doi: 10.3390/rs10091365 Authors: Jacinta Holloway Kerrie Mengersen Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to a growing interaction between the earth science and statistical domains. With this in mind, we reviewed the literature on statistical machine learning methods commonly applied to remote sensing data. We focus particularly on applications related to the United Nations World Bank Sustainable Development Goals, including agriculture (food security), forests (life on land), and water (water quality). We provide a review of useful statistical machine learning methods, how they work in a remote sensing context, and examples of their application to these types of data in the literature. Rather than prescribing particular methods for specific applications, we provide guidance, examples, and case studies from the literature for the remote sensing practitioner and applied statistician. In the supplementary material, we also describe the necessary steps pre and post analysis for remote sensing data; the pre-processing and evaluation steps.

Description: Remote Sensing, Vol. 10, Pages 1363: Analysis Ready Data: Enabling Analysis of the Landsat Archive Remote Sensing doi: 10.3390/rs10091363 Authors: John L. Dwyer David P. Roy Brian Sauer Calli B. Jenkerson Hankui K. Zhang Leo Lymburner Data that have been processed to allow analysis with a minimum of additional user effort are often referred to as Analysis Ready Data (ARD). The ability to perform large scale Landsat analysis relies on the ability to access observations that are geometrically and radiometrically consistent, and have had non-target features (clouds) and poor quality observations flagged so that they can be excluded. The United States Geological Survey (USGS) has processed all of the Landsat 4 and 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) archive over the conterminous United States (CONUS), Alaska, and Hawaii, into Landsat ARD. The ARD are available to significantly reduce the burden of pre-processing on users of Landsat data. Provision of pre-prepared ARD is intended to make it easier for users to produce Landsat-based maps of land cover and land-cover change and other derived geophysical and biophysical products. The ARD are provided as tiled, georegistered, top of atmosphere and atmospherically corrected products defined in a common equal area projection, accompanied by spatially explicit quality assessment information, and appropriate metadata to enable further processing while retaining traceability of data provenance.

Description: Remote Sensing, Vol. 10, Pages 1364: Cross-Pol Transponder with Frequency Shifter for Bistatic Ground-Based Synthetic Aperture Radar Remote Sensing doi: 10.3390/rs10091364 Authors: Massimiliano Pieraccini Lapo Miccinesi Ground-based synthetic aperture radar (GBSAR) systems are popular remote sensing instruments for detecting the ground changes of landslides, glaciers, and open pits as well as for detecting small displacements of large structures, such as bridges and dams. Recently (2017), a novel mono/bistatic GBSAR configuration was proposed to acquire two different components of displacement of the targets in the field of view. This bistatic configuration relies on a transponder that consists—in its basic implementation—of just two antennas and an amplifier. The aim of this article was to design and experimentally test an improved transponder with cross-polarized antennas and frequency shifter that is able to prevent possible oscillations even at very high gain, as required in long-range applications. The transponder was successfully field-tested, and its measured gain was 91 dB gain.

Description: IJGI, Vol. 7, Pages 360: Share Our Cultural Heritage (SOCH): Worldwide 3D Heritage Reconstruction and Visualization via Web and Mobile GIS ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090360 Authors: Hari K. Dhonju Wen Xiao Jon P. Mills Vasilis Sarhosis Despite being of paramount importance to humanity, tangible cultural heritage is often at risk from natural and anthropogenic threats worldwide. As a result, heritage discovery and conservation remain a huge challenge for both developed and developing countries, with heritage sites often inadequately cared for, be it due to a lack of resources, nonrecognition of the value by local people or authorities, human conflict, or some other reason. This paper presents an online geo-crowdsourcing system, termed Share Our Cultural Heritage (SOCH), which can be utilized for large-scale heritage documentation and sharing. Supported by web and mobile GIS, cultural heritage data such as textual stories, locations, and images can be acquired via portable devices. These data are georeferenced and presented to the public via web-mapping. Using photogrammetric modelling, acquired images are used to reconstruct heritage structures or artefacts into 3D digital models, which are then visualized on the SOCH web interface to enable public interaction. This end-to-end system incubates an online virtual community to encourage public engagement, raise awareness, and stimulate cultural heritage ownership. It also provides valuable resources for cultural heritage exploitation, management, education, and monitoring over time.

Description: IJGI, Vol. 7, Pages 358: Spatial-Temporal Analysis of Human Dynamics on Urban Land Use Patterns Using Social Media Data by Gender ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090358 Authors: Chengcheng Lei An Zhang Qingwen Qi Huimin Su Jianghao Wang The relationship between urban human dynamics and land use types has always been an important issue in the study of urban problems in China. This paper used location data from Sina Location Microblog (commonly known as Weibo) users to study the human dynamics of the spatial-temporal characteristics of gender differences in Beijing’s Olympic Village in June 2014. We applied mathematical statistics and Local Moran’s I to analyze the spatial-temporal distribution of Sina Microblog users in 100 m × 100 m grids and land use patterns. The female users outnumbered male users, and the sex ratio ( S R varied under different land use types at different times. Female users outnumbered male users regarding residential land and public green land, but male users outnumbered female users regarding workplace, especially on weekends, as the S R on weekends ( S R was 120.5) was greater than that on weekdays ( S R was 118.8). After a Local Moran’s I analysis, we found that High–High grids are primarily distributed across education and scientific research land and residential land; these grids and their surrounding grids have more female users than male users. Low–Low grids are mainly distributed across sports centers and workplaces on weekdays; these grids and their surrounding grids have fewer female users than male users. The average number of users on Saturday was the highest value and, on weekends, the number of female and male users both increased in commercial land, but male users were more active than female users ( S R was 110).

Description: Remote Sensing, Vol. 10, Pages 1382: “Regression-then-Fusion” or “Fusion-then-Regression”? A Theoretical Analysis for Generating High Spatiotemporal Resolution Land Surface Temperatures Remote Sensing doi: 10.3390/rs10091382 Authors: Haiping Xia Yunhao Chen Yutong Zhao Zixuan Chen The trade-off between spatial and temporal resolutions in satellite sensors has inspired the development of numerous thermal sharpening methods. Specifically, regression and spatiotemporal fusion are the two main strategies used to generate high-resolution land surface temperatures (LSTs). The regression method statically downscales coarse-resolution LSTs, whereas the spatiotemporal fusion method can dynamically downscale LSTs; however, the resolution of downscaled LSTs is limited by the availability of the fine-resolution LSTs. Few studies have combined these two methods to generate high spatiotemporal resolution LSTs. This study proposes two strategies for combining regression and fusion methods to generate high spatiotemporal resolution LSTs, namely, the “regression-then-fusion” (R-F) and “fusion-then-regression” (F-R) methods, and discusses the criteria used to determine which strategy is better. The R-F and F-R have several advantages: (1) they fully exploit the information in the available data on the visible and near infrared (VNIR) and thermal infrared (TIR) bands; (2) they downscale the LST time series to a finer resolution corresponding to that of VNIR data; and (3) they inherit high spatial reconstructions from the regression method and dynamic temporal reconveyance from the fusion method. The R-F and F-R were tested with different start times and target times using Landsat 8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer data. The results showed that the R-F performed better than the F-R when the regression error at the start time was smaller than that at the target time, and vice versa.

Description: Remote Sensing, Vol. 10, Pages 1379: Monitoring of the 2015 Villarrica Volcano Eruption by Means of DLR’s Experimental TET-1 Satellite Remote Sensing doi: 10.3390/rs10091379 Authors: Simon Plank Michael Nolde Rudolf Richter Christian Fischer Sandro Martinis Torsten Riedlinger Elisabeth Schoepfer Doris Klein Villarrica Volcano is one of the most active volcanoes in the South Andes Volcanic Zone. This article presents the results of a monitoring of the time before and after the 3 March 2015 eruption by analyzing nine satellite images acquired by the Technology Experiment Carrier-1 (TET-1), a small experimental German Aerospace Center (DLR) satellite. An atmospheric correction of the TET-1 data is presented, based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Database (GDEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor data with the shortest temporal baseline to the TET-1 acquisitions. Next, the temperature, area coverage, and radiant power of the detected thermal hotspots were derived at subpixel level and compared with observations derived from MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) data. Thermal anomalies were detected nine days before the eruption. After the decrease of the radiant power following the 3 March 2015 eruption, a stronger increase of the radiant power was observed on 25 April 2015. In addition, we show that the eruption-related ash coverage of the glacier at Villarrica Volcano could clearly be detected in TET-1 imagery. Landsat-8 imagery was analyzed for comparison. The information extracted from the TET-1 thermal data is thought be used in future to support and complement ground-based observations of active volcanoes.

Description: Remote Sensing, Vol. 10, Pages 1375: Evaluation of PROBA-V Collection 1: Refined Radiometry, Geometry, and Cloud Screening Remote Sensing doi: 10.3390/rs10091375 Authors: Carolien Toté Else Swinnen Sindy Sterckx Stefan Adriaensen Iskander Benhadj Marian-Daniel Iordache Luc Bertels Grit Kirches Kerstin Stelzer Wouter Dierckx Lieve Van den Heuvel Dennis Clarijs Fabrizio Niro PROBA-V (PRoject for On-Board Autonomy–Vegetation) was launched in May-2013 as an operational continuation to the vegetation (VGT) instruments on-board the Système Pour l’Observation de la Terre (SPOT)-4 and -5 satellites. The first reprocessing campaign of the PROBA-V archive from Collection 0 (C0) to Collection 1 (C1) aims at harmonizing the time series, thanks to improved radiometric and geometric calibration and cloud detection. The evaluation of PROBA-V C1 focuses on (i) qualitative and quantitative assessment of the new cloud detection scheme; (ii) quantification of the effect of the reprocessing by comparing C1 to C0; and (iii) evaluation of the spatio-temporal stability of the combined SPOT/VGT and PROBA-V archive through comparison to METOP/advanced very high resolution radiometer (AVHRR). The PROBA-V C1 cloud detection algorithm yields an overall accuracy of 89.0%. Clouds are detected with very few omission errors, but there is an overdetection of clouds over bright surfaces. Stepwise updates to the visible and near infrared (VNIR) absolute calibration in C0 and the application of degradation models to the SWIR calibration in C1 result in sudden changes between C0 and C1 Blue, Red, and NIR TOC reflectance in the first year, and more gradual differences for short-wave infrared (SWIR). Other changes result in some bias between C0 and C1, although the root mean squared difference (RMSD) remains well below 1% for top-of-canopy (TOC) reflectance and below 0.02 for the normalized difference vegetation index (NDVI). Comparison to METOP/AVHRR shows that the recent reprocessing campaigns on SPOT/VGT and PROBA-V have resulted in a more stable combined time series.

Description: Remote Sensing, Vol. 10, Pages 1377: Transferability and Upscaling of Fuzzy Classification for Shoreline Change over 30 Years Remote Sensing doi: 10.3390/rs10091377 Authors: Ratna Sari Dewi Wietske Bijker Alfred Stein Muh Aris Marfai Local authorities require information on shoreline change for land use decision making. Monitoring shoreline changes is useful for updating shoreline maps used in coastal planning and management. By analysing data over a period of time, where and how fast the coast has changed can be determined. Thereby, we can prevent any development in high risk areas. This study investigated the transferability of a fuzzy classification of shoreline changes and to upscale towards a larger area. Using six sub areas, three strategies were used: (i) Optimizing two FCM (fuzzy c-means) parameters based on the predominant land use/cover of the reference subset, (ii) adopting the class mean and number of classes resulting from the classification of reference subsets to perform FCM on target subsets, and (iii) estimating the optimal level of fuzziness of target subsets. This approach was applied to a series of images to identify shoreline positions in a section of the northern Central Java Province, Indonesia which experienced a severe change of shoreline position over three decades. The extent of shoreline changes was estimated by overlaying shoreline images. Shoreline positions were highlighted to infer the erosion and accretion area along the coast, and the shoreline changes were calculated. From the experimental results, we obtained m (level of fuzziness) values in the range from 1.3 to 1.9 for the seven land use/cover classes that were analysed. Furthermore, for ten images used in this research, we obtained the optimal m = 1.8. For a similar coastal characteristic, this m value can be adopted and the relation between land use/cover and two FCM parameters can shorten the time required to optimise parameters. The proposed method for upscaling and transferring the classification method to a larger, or different, areas is promising showing κ (kappa) values > 0.80. The results also show an agreement of water membership values between the reference and target subsets indicated by κ > 0.82. Over the study period, the area exhibited both erosion and accretion. The erosion was indicated by changes into water and changes from non-water into shoreline were observed for approximately 78 km2. Accretion was due to changes into non-water and changes from water into shoreline for 19.5 km2. Erosion was severe in the eastern section of the study area, whereas the middle section gained land through reclamation activities. These erosion and accretion processes played an active role in the changes of the shoreline. We conclude that the method is applicable to the current study area. The relation between land use/cover classes and the value of FCM parameters produced in this study can be adopted.

Description: IJGI, Vol. 7, Pages 361: Automatic Seam-Line Detection in UAV Remote Sensing Image Mosaicking by Use of Graph Cuts ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090361 Authors: Ming Li Deren Li Bingxuan Guo Lin Li Teng Wu Weilong Zhang Image mosaicking is one of the key technologies in data processing in the field of computer vision and digital photogrammetry. For the existing problems of seam, pixel aliasing, and ghosting in mosaic images, this paper proposes and implements an optimal seam-line search method based on graph cuts for unmanned aerial vehicle (UAV) remote sensing image mosaicking. This paper first uses a mature and accurate image matching method to register the pre-mosaicked UAV images, and then it marks the source of each pixel in the overlapped area of adjacent images and calculates the energy value contributed by the marker by using the target energy function of graph cuts constructed in this paper. Finally, the optimal seam-line can be obtained by solving the minimum value of target energy function based on graph cuts. The experimental results show that our method can realize seamless UAV image mosaicking, and the image mosaic area transitions naturally.

Description: Remote Sensing, Vol. 10, Pages 1393: Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay Remote Sensing doi: 10.3390/rs10091393 Authors: Nicole M. DeLuca Benjamin F. Zaitchik Frank C. Curriero Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate.

Description: Remote Sensing, Vol. 10, Pages 1392: Monitoring and Analysis of Surface Deformation in Mining Area Based on InSAR and GRACE Remote Sensing doi: 10.3390/rs10091392 Authors: Meinan Zheng Kazhong Deng Hongdong Fan Sen Du To determine the relationship between underground mining, groundwater storage change, and surface deformation, we first used two sets of ENVISAT data and one set of Sentinel-1A data to obtain surface deformation in eastern Xuzhou coalfield based on the temporarily coherent point interferometric synthetic aperture radar (TCPInSAR) technique. By comparison with underground mining activities, it indicated that the surface subsidence is mainly related to mine exploitation and residual subsidence in the goaf, while the surface uplift is mainly related to restoration of the groundwater level. The average groundwater storage change in the eastern Xuzhou coalfield from January 2005 to June 2017 was obtained through the Gravity Recovery and Climate Experiment (GRACE) data, and the results indicated that the groundwater storage changed nonlinearly with time. The reliability of the groundwater monitoring results was qualitatively validated by using measured well data from April 2009 to April 2010. Combining with time of mining and mine closing analysis, groundwater storage change within the research area had a strong correlation with drainage activity of underground mining. An analysis was finally conducted on the surface deformation and the groundwater storage change within the corresponding time. The results indicated that the groundwater storage variation in the research area has a great influence on the surface deformation after the mine closed.

Description: IJGI, Vol. 7, Pages 362: Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090362 Authors: Li Chen Qing Zhu Xiao Xie Han Hu Haowei Zeng Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, disturbances (e.g., vehicles, shadows of trees, and buildings), the necessity of finding weak road edges while avoiding noise, and the fast-acquisition requirement of road information for crisis response. To solve these difficulties, a two-stage method combining edge information and region characteristics is presented. In the first stage, convolutions are executed by applying Gabor wavelets in the best scale to detect Gabor features with location and orientation information. The features are then merged into one response map for connection analysis. In the second stage, highly complete, connected Gabor features are used as edge constraints to facilitate stable object segmentation and limit region growing. Finally, segmented objects are evaluated by some fundamental shape features to eliminate nonroad objects. The results indicate the validity and superiority of the proposed method to efficiently extract accurate road targets from VHR remote-sensing images.

Description: Remote Sensing, Vol. 10, Pages 1399: Satellite-Based Water Consumption Dynamics Monitoring in an Extremely Arid Area Remote Sensing doi: 10.3390/rs10091399 Authors: Shen Tan Bingfang Wu Nana Yan Hongwei Zeng Evapotranspiration (ET) involves actual water consumption directly from the land surface; however, regional ET maps are usually neglected during water management and allocation. In this study, an integrated satellite-based ET monitoring approach with two spatial resolutions is proposed over an extremely arid basin in China that has experienced crop area expansion and has been the focus of a water-saving project since 2012. The proposed ETWatch approach combined with an empirical downscaling strategy based on vegetation condition was employed to produce monthly ET maps. This method achieves satisfactory accuracy and is validated by its reasonable spatial and temporal pattern results. Yearly results exhibit an increasing ET trend before 2012, which subsequently gradually decrease. This trend fits well with the dynamics of the basin-wide vegetation condition, indicating that there is a stronger correlation between water consumption and vegetation than between other environmental indicators. The average ET over three main crop types in the region (grape, cotton, and melon) decreased by approximately 5% due to optimizations of the irrigation timeline during the project, while 13% of the water savings can be attributed to the fallowing of crop areas. Based on the irrigation distribution in 2012, a comparison between drip and border irrigation that achieves water savings of 3.6% from grape and 5.8% from cotton is conducted. However, an afforestation project that involved planting young trees led to an approximate 25% increase in water consumption. Overall, since 2012, the water-saving project has achieved satisfactory performance regarding excessive groundwater withdrawal, showing a reduction trend of 3 million m3/year and an increase in Lake Aiding water levels since 2011. The results reveal the potential of the ET monitoring strategy as a basis for basin-scale water management.

Description: Remote Sensing, Vol. 10, Pages 1398: MiRTaW: An Algorithm for Atmospheric Temperature and Water Vapor Profile Estimation from ATMS Measurements Using a Random Forests Technique Remote Sensing doi: 10.3390/rs10091398 Authors: Francesco Di Paola Elisabetta Ricciardelli Domenico Cimini Angela Cersosimo Arianna Di Paola Donatello Gallucci Sabrina Gentile Edoardo Geraldi Salvatore Larosa Saverio T. Nilo Ermann Ripepi Filomena Romano Paolo Sanò Mariassunta Viggiano A new algorithm for the estimation of atmospheric temperature (T) and water vapor (WV) vertical profiles in nonprecipitating conditions is presented. The microwave random forest temperature and water vapor (MiRTaW) profiling algorithm is based on the random forest (RF) technique and it uses microwave (MW) sounding from the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite. Three different data sources were chosen for both training and validation purposes, namely, the ERA-Interim from the European Centre for Medium-Range Weather Forecasts (ECMWF), the Infrared Atmospheric Sounding Interferometer Atmospheric Temperature Water Vapour and Surface Skin Temperature (IASI L2 v6) from the Meteorological Operational satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the radiosonde observations from the Integrated Global Radiosonde Archive (IGRA). The period from 2012 to 2016 was considered in the training dataset; particular attention was paid to the instance selection procedure, in order to reduce the full training dataset with negligible information loss. The out-of-bag (OOB) error was computed and used to select the optimal RF parameters. Different RFs were trained, one for each vertical level: 32 levels for T (within 10–1000 hPa) and 23 levels for WV (200–1000 hPa). The validation of the MiRTaW profiling algorithm was conducted on a dataset from 2017. The mean bias error (MBE) of T vertical profiles ranges within about (−0.4–0.4) K, while for the WV mixing ratio, the MBE starts at ~0.5 g/kg near the surface and decreases to ~0 g/kg at 200 hPa level, in line with the expectations.

Description: Remote Sensing, Vol. 10, Pages 1397: UAV Remote Sensing for Biodiversity Monitoring: Are Forest Canopy Gaps Good Covariates? Remote Sensing doi: 10.3390/rs10091397 Authors: Martin B. Bagaram Diego Giuliarelli Gherardo Chirici Francesca Giannetti Anna Barbati Forest canopy gaps are important to ecosystem dynamics. Depending on tree species, small canopy openings may be associated with intra-crown porosity and with space among crowns. Yet, literature on the relationships between very fine-scaled patterns of canopy openings and biodiversity features is limited. This research explores the possibility of: (1) mapping forest canopy gaps from a very high spatial resolution orthomosaic (10 cm), processed from a versatile unmanned aerial vehicle (UAV) imaging platform, and (2) deriving patch metrics that can be tested as covariates of variables of interest for forest biodiversity monitoring. The orthomosaic was imaged from a test area of 240 ha of temperate deciduous forest types in Central Italy, containing 50 forest inventory plots each of 529 m2 in size. Correlation and linear regression techniques were used to explore relationships between patch metrics and understory (density, development, and species diversity) or forest habitat biodiversity variables (density of micro-habitat bearing trees, vertical species profile, and tree species diversity). The results revealed that small openings in the canopy cover (75% smaller than 7 m2) can be faithfully extracted from UAV red, green, and blue bands (RGB) imagery, using the red band and contrast split segmentation. The strongest correlations were observed in the mixed forests (beech and turkey oak) followed by intermediate correlations in turkey oak forests, followed by the weakest correlations in beech forests. Moderate to strong linear relationships were found between gap metrics and understory variables in mixed forest types, with adjusted R2 from linear regression ranging from 0.52 to 0.87. Equally strong correlations in the same forest types were observed for forest habitat biodiversity variables (with adjusted R2 ranging from 0.52 to 0.79), with highest values found for density of trees with microhabitats and vertical species profile. In conclusion, this research highlights that UAV remote sensing can potentially provide covariate surfaces of variables of interest for forest biodiversity monitoring, conventionally collected in forest inventory plots. By integrating the two sources of data, these variables can be mapped over small forest areas with satisfactory levels of accuracy, at a much higher spatial resolution than would be possible by field-based forest inventory solely.

Description: Remote Sensing, Vol. 10, Pages 1402: Analyzing the Effect of Fluorescence Characteristics on Leaf Nitrogen Concentration Estimation Remote Sensing doi: 10.3390/rs10091402 Authors: Jian Yang Shalei Song Lin Du Shuo Shi Wei Gong Jia Sun Biwu Chen Leaf nitrogen concentration (LNC) is a significant indicator of crops growth status, which is related to crop yield and photosynthetic efficiency. Laser-induced fluorescence is a promising technology for LNC estimation and has been widely used in remote sensing. The accuracy of LNC monitoring relies greatly on the selection of fluorescence characteristics and the number of fluorescence characteristics. It would be useful to analyze the performance of fluorescence intensity and ratio characteristics at different wavelengths for LNC estimation. In this study, the fluorescence spectra of paddy rice excited by different excitation light wavelengths (355 nm, 460 nm, and 556 nm) were acquired. The performance of the fluorescence intensity and fluorescence ratio of each band were analyzed in detail based on back-propagation neural network (BPNN) for LNC estimation. At 355 nm and 460 nm excitation wavelengths, the fluorescence characteristics related to LNC were mainly located in the far-red region, and at 556 nm excitation wavelength, the red region being an optimal band. Additionally, the effect of the number of fluorescence characteristics on the accuracy of LNC estimation was analyzed by using principal component analysis combined with BPNN. Results demonstrate that at least two fluorescence spectral features should be selected in the red and far-red regions to estimate LNC and efficiently improve the accuracy of LNC estimation.

Description: Remote Sensing, Vol. 10, Pages 1400: Source Model and Stress Disturbance of the 2017 Jiuzhaigou Mw 6.5 Earthquake Constrained by InSAR and GPS Measurements Remote Sensing doi: 10.3390/rs10091400 Authors: Shunying Hong Xin Zhou Kui Zhang Guojie Meng Yanfang Dong Xiaoning Su Lei Zhang Shuai Li Keliang Ding Seismogenic fault geometry, especially for a blind fault, is usually difficult to derive, based only on the distribution of aftershocks and interference fringes of Interferometric Synthetic Aperture Radar (InSAR). To better constrain the fault geometry of the 2017 Jiuzhaigou Mw 6.5 earthquake, we first carried out a nonlinear inversion for a single fault source using multi-peak particle swarm optimization (MPSO), Monte Carlo (MC), and Markov Chain Monte Carlo (MCMC) algorithms, respectively, with constraints of InSAR data in multiple SAR viewing geometries. The fault geometry models retrieved with different methods were highly consistent and mutually verifiable, showing that a blind faulting with a strike of ~154° and a dip angle of ~77° was responsible for the Jiuzhaigou earthquake. Based on the optimal fault geometry model, the fault slip distribution jointly inverted from the InSAR and Global Positioning System (GPS) data by the steepest descent method (SDM) and the MC method showed that the slip was mainly concentrated at the depth of 1–15 km, and only one slip center appeared at the depth of 5–9 km with a maximum slip of about 1.06 m, some different from previous studies. Taking the shear modulus of μ = 32 GPa, the seismic moment derived from the distributed slip model was about 7.85 × 1018 Nm, equivalent to Mw 6.54, which was slightly larger than that from the focal mechanism solutions. The fault spatial geometry and slip distribution could be further validated with the spatial patterns of the immediate aftershocks. Most of the off-fault aftershocks with the magnitude > M2 within one year after the mainshock occurred in the stress positive stress change area, which coincided with the stress triggering theory. The static Coulomb stress, triggered by the mainshock, significantly increased at the Tazang fault (northwest to the epicenter), and at the hidden North Huya fault, and partial segments of the Minjiang fault (west of the epicenter).

Description: Remote Sensing, Vol. 10, Pages 1405: Investigations on the Coregistration of Sentinel-1 TOPS with the Conventional Cross-Correlation Technique Remote Sensing doi: 10.3390/rs10091405 Authors: Yuxiao Qin Daniele Perissin Jing Bai In Sentinel-1 TOPS mode, the antenna sweeps in the azimuth direction for the purpose of illuminating the targets with the entire azimuth antenna pattern (AAP). This azimuth sweeping introduces an extra high-frequency Doppler term into the impulse response function (IRF), which poses a more strict coregistration accuracy for the interferometric purpose. A 1/1000 pixel coregistration accuracy is required for the interferometric phase error to be negligible, and the enhanced spectral diversity (ESD) method is applied for achieving such accuracy. However, since ESD derives miscoregistration from cross-interferometric phase, and phase is always wrapped to [ − π , π ) , an initial coregistration method with enough accuracy is required to resolve the phase ambiguity in ESD. The mainstream for initial coregistration that meets this requirement is the geometrical approach, which accuracy mainly depends on the accuracy of orbits. In this article, the authors propose to investigate the feasibility of using the conventional coregistration approach, namely the cross-correlation-and-rigid-transformation, as the initial coregistration method. The aim is to quantify the coregistration accuracy for cross-correlation-and-rigid-transformation using the Cramér-Rao lower bound (CRLB) and determine whether this method could eventually help to resolve the phase ambiguities of ESD. In addition, we studied the feasibility and robustness of the cross-correlation plus ESD under different conditions. For validation, we checked whether the cross-correlation plus ESD approach could reach the same coregistration accuracy as geometrical plus ESD approach. In general, for large areas with enough coherence and little topography variance, the cross-correlation method could be used as an alternative to the geometrical approach. The interferogram from the two different approaches (with ESD applied afterward) shows a negligible difference under such circumstances.

Description: IJGI, Vol. 7, Pages 370: Raising Semantics-Awareness in Geospatial Metadata Management ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090370 Authors: Cristiano Fugazza Monica Pepe Alessandro Oggioni Paolo Tagliolato Paola Carrara Geospatial metadata are often encoded in formats that either are not aimed at efficient retrieval of resources or are plainly outdated. Particularly, the quantum leap represented by the Semantic Web did not induce so far a consistent, interlinked baseline in the geospatial domain. Datasets, scientific literature related to them, and ultimately the researchers behind these products are only loosely connected; the corresponding metadata intelligible only to humans, duplicated in different systems, seldom consistently. We address these issues by relating metadata items to resources that represent keywords, institutes, researchers, toponyms, and virtually any RDF data structure made available over the Web via SPARQL endpoints. Essentially, our methodology fosters delegated metadata management as the entities referred to in metadata are independent, decentralized data structures with their own life cycle. Our example implementation of delegated metadata envisages: (i) editing via customizable web-based forms (including injection of semantic information); (ii) encoding of records in any XML metadata schema; and (iii) translation into RDF. Among the semantics-aware features that this practice enables, we present a worked-out example focusing on automatic update of metadata descriptions. Our approach, demonstrated in the context of INSPIRE metadata (the ISO 19115/19119 profile eliciting integration of European geospatial resources) is also applicable to a broad range of metadata standards, including non-geospatial ones.

Description: IJGI, Vol. 7, Pages 369: GIS-Assisted Prediction and Risk Zonation of Wildlife Attacks in the Chitwan National Park in Nepal ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090369 Authors: Aleš Ruda Jaromír Kolejka Thakur Silwal Population growth forces the human community to expand into the natural habitats of wild animals. Their efforts to use natural sources often collide with wildlife attacks. These animals do not only protect their natural environment, but in the face of losing the potential food sources, they also penetrate in human settlements. The research was situated in the Chitwan National Park (CNP) in Nepal, and the aim of this study was to investigate possible geospatial connections between attacks of all kinds of animals on humans in the CNP and its surroundings between 2003 and 2013. The patterns of attacks were significantly uneven across the months, and 89% of attacks occurred outside the park. In total, 74% attacks occurred in the buffer zone forests and croplands within 1 km from the park. There was a strong positive correlation among the number of victims for all attacking animals with a maximum of one victim per 4 km2, except elephant and wild boar. The density of bear victims was higher where the tiger and rhino victims were lower, e.g., in the Madi valley. The data collected during this period did not show any signs of spatial autocorrelation. The calculated magnitude per unit area using the kernel density, together with purpose-defined land use groups, were used to determine five risk zones of wildlife attacks. In conclusion, it was found that the riskiest areas were locations near the forest that were covered by agricultural land and inhabited by humans. Our research results can support any local spatial decision-making processes for improving the co-existence of natural protection in the park and the safety of human communities living in its vicinity.

Description: IJGI, Vol. 7, Pages 368: Critical Review of Methods to Estimate PM2.5 Concentrations within Specified Research Region ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090368 Authors: Guangyuan Zhang Xiaoping Rui Yonglei Fan Obtaining PM2.5 data for the entirety of a research region underlies the study of the relationship between PM2.5 and human spatiotemporal activity. A professional sampler with a filter membrane is used to measure accurate values of PM2.5 at single points in space. However, there are numerous PM2.5 sampling and monitoring facilities that rely on data from only representative points, and which cannot measure the data for the whole region of research interest. This provides the motivation for researching the methods of estimation of particulate matter in areas having fewer monitors at a special scale, an approach now attracting considerable academic interest. The aim of this study is to (1) reclassify and particularize the most frequently used approaches for estimating the PM2.5 concentrations covering an entire research region; (2) list improvements to and integrations of traditional methods and their applications; and (3) compare existing approaches to PM2.5 estimation on the basis of accuracy and applicability.

Description: Remote Sensing, Vol. 10, Pages 1444: Method Combining Probability Integration Model and a Small Baseline Subset for Time Series Monitoring of Mining Subsidence Remote Sensing doi: 10.3390/rs10091444 Authors: Hongdong Fan Lu Lu Yahui Yao Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) has high accuracy for monitoring slow surface subsidence. However, in the case of a large-scale mining subsidence areas, the monitoring capabilities of TS-InSAR are poor, owing to temporal and spatial decorrelation. To monitor mining subsidence effectively, a method known as Probability Integration Model Small Baseline Set (PIM-SBAS) was applied. In this method, mining subsidence with a large deformation gradient was simulated by a PIM. After simulated deformation was transformed into a wrapped phase, the residual wrapped phase was obtained by subtracting the simulated wrapped phase from the actual wrapped phase. SBAS was used to calculate the residual subsidence. Finally, the mining subsidence was determined by adding the simulated deformation to the residual subsidence. The time series subsidence of the Nantun mining area was derived from 10 TerraSAR-X (TSX) images for the period 25 December 2011 to 2 April 2012. The Zouji highway above the 9308 workface was the target for study. The calculated maximum mining subsidence was 860 mm. The maximum subsidence for the Zouji highway was about 145 mm. Compared with the SBAS method, PIM-SBAS alleviates the difficulty of phase unwrapping, and may be used to monitor large-scale mining subsidence.

Description: Remote Sensing, Vol. 10, Pages 1442: An Improved Digital Elevation Model of the Lunar Mons Rümker Region Based on Multisource Altimeter Data Remote Sensing doi: 10.3390/rs10091442 Authors: Fei Li Chang Zhu Weifeng Hao Jianguo Yan Mao Ye Jean-Pierre Barriot Qing Cheng Tao Sun Mons Rümker is the primary candidate region for the lunar landing mission of Chang’E-5. We propose a data processing method that combines multisource altimeter data and we developed an improved digital elevation model (DEM) of the Mons Rümker region with a horizontal resolution of 256 pixels per degree. The lunar orbiter laser altimeter (LOLA) onboard the lunar reconnaissance orbiter (LRO) acquired 884 valid orbital benchmark data with a high precision. A special crossover adjustment of 156 orbital profiles from the Chang’E-1 laser altimeter (LAM) and 149 orbital profiles from the SELenological and ENgineering Explorer (SELENE) laser altimeter (LALT) was applied. The radial residual root mean square (RMS) of the LAM was reduced from 154.83 ± 43.60 m to 14.29 ± 27.84 m and that of the LALT was decreased from 3.50 ± 5.0 m to 2.75 ± 4.4 m. We used the adjusted LAM and LALT data to fill the LOLA gaps and created the merged LOLA + LAM and LOLA + LALT DEMs. The merged LOLA + LAM DEM showed distortions because of the horizontal geolocation errors in the LAM data. The merged LOLA + LALT DEM was closer to the ground truth than the LOLA-only DEM when validated with the images of the LRO camera (LROC).

Description: Remote Sensing, Vol. 10, Pages 1445: Remote Sensing of Antarctic Glacier and Ice-Shelf Front Dynamics—A Review Remote Sensing doi: 10.3390/rs10091445 Authors: Celia A. Baumhoer Andreas J. Dietz Stefan Dech Claudia Kuenzer The contribution of Antarctica’s ice sheet to global sea-level rise depends on the very dynamic behavior of glaciers and ice shelves. One important parameter of ice-sheet dynamics is the location of glacier and ice-shelf fronts. Numerous remote sensing studies on Antarctic glacier and ice-shelf front positions exist, but no long-term record on circum-Antarctic front dynamics has been established so far. The article outlines the potential of remote sensing to map, extract, and measure calving front dynamics. Furthermore, this review provides an overview of the spatial and temporal availability of Antarctic calving front observations for the first time. Single measurements are compiled to a circum-Antarctic record of glacier and ice shelf retreat/advance. We find sufficient frontal records for the Antarctic Peninsula and Victoria Land, whereas on the West Antarctic Ice Sheet (WAIS), measurements only concentrate on specific glaciers and ice sheets. Frontal records for the East Antarctic Ice Sheet exist since the 1970s. Studies agree on the general retreat of calving fronts along the Antarctic Peninsula. East Antarctic calving fronts also showed retreating tendencies between 1970s until the early 1990s, but have advanced since the 2000s. Exceptions of this general trend are Victoria Land, Wilkes Land, and the northernmost Dronning Maud Land. For the WAIS, no clear trend in long-term front fluctuations could be identified, as observations of different studies vary in space and time, and fronts highly fluctuate. For further calving front analysis, regular mapping intervals as well as glacier morphology should be included. We propose to exploit current and future developments in Earth observations to create frequent standardized measurements for circum-Antarctic assessments of glacier and ice-shelf front dynamics in regard to ice-sheet mass balance and climate forcing.

Description: Remote Sensing, Vol. 10, Pages 1446: Exploring the Inclusion of Small Regenerating Trees to Improve Above-Ground Forest Biomass Estimation Using Geospatial Data Remote Sensing doi: 10.3390/rs10091446 Authors: Anh V. Le David J. Paull Amy L. Griffin Research on the contribution of understory components to the total above ground biomass (AGB) has to date received very little attention because most prior biomass estimation studies have ignored small regenerating trees beneath the main canopy with the assumption that their contribution to biomass is generally negligible. Only a few biomass studies have emphasized a considerable contribution to biomass of understory components in forest ecosystems. However, this study of native, tropical, deciduous forest biomass in the Central Highlands of Vietnam was able to explore the contribution of small regenerating trees to total biomass by exploiting a large field inventory of hundreds to thousands of individually-counted small regenerating trees per hectare. Thus, this study investigated the influence of small regenerating tree biomass on models of the relationship between total AGB and remote sensing data. These analyses were trained with and without topographic variables derived from ASTER-GDEM. Our results demonstrate that the inclusion of small regenerating understory trees (R2 = 0.42, NRMSE or %RMSE = 30.5%) provides a quantifiable improvement in total estimated AGB compared to using only large woody canopy trees (R2 = 0.21, NRMSE or %RMSE = 36.6%) when correlating field-based biomass measurements with optical image-derived variables. All analyses show that the inclusion of terrain factors made an important contribution to biomass modeling. This study suggests that for young, open forests where there are many small regenerating trees, the contribution of understory biomass should be taken into consideration to improve total AGB estimation.

Description: Remote Sensing, Vol. 10, Pages 1441: Long-Term Changes in Water Clarity in Lake Liangzi Determined by Remote Sensing Remote Sensing doi: 10.3390/rs10091441 Authors: Xuan Xu Xiaolong Huang Yunlin Zhang Dan Yu Water clarity (via the Secchi disk depth, SDD) is an important indicator of water quality and lake ecosystem health. Monitoring long-term SDD change is vital for water quality assessment and lake management. In this study, we developed and validated an empirical model for estimating the SDD based on Landsat ETM+ and OLI data using the combination of band ratio of the near-infrared (NIR) band to the blue band and the NIR band. Time series data of remotely estimated SDD in Lake Liangzi were retrieved from 2007 to 2016 using the proposed models based on forty Landsat images. The results of the Mann–Kendall test (p = 0.002) and linear regression (R2 = 0.352, p < 0.001) indicated that the SDD in Lake Liangzi demonstrated a significant decreasing trend during the study period. The annual mean SDD in Lake Liangzi was significantly negatively correlated with the population (R2 = 0.530, p = 0.017) and gross domestic product (R2 = 0.619, p = 0.007) of the Lake Liangzi basin. In addition, water level increase and the flood have an important effect on SDD decrease. Our study revealed that anthropogenic activities may be driving factors for the long-term declining trend in the SDD. Additionally, floods and heavy precipitation may decrease the SDD over the short term in Lake Liangzi. A declining trend in the SDD in Lake Liangzi may continue under future intense anthropogenic activities and climate change such as the extreme heavy precipitation event increase.

Description: Remote Sensing, Vol. 10, Pages 1443: Evaluation of Semi-Analytical Algorithms to Retrieve Particulate and Dissolved Absorption Coefficients in Gulf of California Optically Complex Waters Remote Sensing doi: 10.3390/rs10091443 Authors: Stella Patricia Betancur-Turizo Adriana González-Silvera Eduardo Santamaría-del-Ángel Jing Tan Robert Frouin Two semi-analytical algorithms, Generalized Inherent Optical Property (GIOP) and Garver-Siegel-Maritorena (GSM), were evaluated in terms of how well they reproduced the absorption coefficient of phytoplankton (aph(λ)) and dissolved and detrital organic matter (adg(λ)) at three wavelengths (λ of 412, 443, and 488 nm) in a zone with optically complex waters, the Upper Gulf of California (UGC) and the Northern Gulf of California (NGC). In the UGC, detritus determines most of the total light absorption, whereas, in the NGC, chromophoric dissolved organic material (CDOM) and phytoplankton dominate. Upon comparing the results of each model with a database assembled from four cruises done from spring to summer (March through September) between 2011 and 2013, it was found that GIOP is a better estimator for aph(λ) than GSM, independently of the region. However, both algorithms underestimate in situ values in the NGC, whereas they overestimate them in the UGC. Errors are associated with the following: (a) the constant a*ph(λ) value used by GSM and GIOP (0.055 m2 mgChla−1) is higher than the most frequent value observed in this study’s data (0.03 m2 mgChla−1), and (b) satellite-derived chlorophyll a concentration (Chla) is biased high compared with in situ Chla. GIOP gave also better results for the adg(λ) estimation than GSM, especially in the NGC. The spectral slope Sdg was identified as an important parameter for estimating adg(λ), and this study’s results indicated that the use of a fixed input value in models was not adequate. The evaluation confirms the lack of generality of algorithms like GIOP and GSM, whose reflectance model is too simplified to capture expected variability. Finally, a greater monitoring effort is suggested in the study area regarding the collection of in situ reflectance data, which would allow explaining the effects that detritus and CDOM may have on the semi-analytical reflectance inversions, as well as isolating the possible influence of the atmosphere on the satellite-derived water reflectance and Chla.

Description: IJGI, Vol. 7, Pages 371: An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090371 Authors: Bin Feng Qing Zhu Mingwei Liu Yun Li Junxiao Zhang Xiao Fu Yan Zhou Maosu Li Huagui He Weijun Yang Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.

Description: IJGI, Vol. 7, Pages 373: Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090373 Authors: Liang Lyu Qing Xu Chaozhen Lan Qunshan Shi Wanjie Lu Yang Zhou Yinghao Zhao The implementation of increased space exploration missions reduces the distance between human beings and outer space. Although it is impossible for everyone to enter the remote outer space, virtual environments could provide computer-based digital spaces that we can observe, participate in, and experience. In this study, Sino-InSpace, a digital simulation platform, was developed to support the construction of virtual space environments. The input data are divided into two types, the environment element and the entity object, that are then supported by the unified time-space datum. The platform adopted the pyramid model and octree index to preprocess the geographic and space environment data, which ensured the efficiency of data loading and browsing. To describe objects perfectly, they were abstracted and modeled based on four aspects including attributes, ephemeris, geometry, and behavior. Then, the platform performed the organization of a visual scenario based on logical modeling and data modeling; in addition, it ensured smooth and flexible visual scenario displays using efficient data and rendering engines. Multilevel modes (application directly, visualization development, and scientific analysis) were designed to support multilevel applications for users from different grades and fields. Each mode provided representative case studies, which also demonstrated the capabilities of the platform for data integration, visualization, process deduction, and auxiliary analysis. Finally, a user study with human participants was conducted from multiple views (usability, user acceptance, presence, and software design). The results indicate that Sino-InSpace performs well in simulation for virtual space environments, while a virtual reality setup is beneficial for promoting the experience.

Description: IJGI, Vol. 7, Pages 372: Digital Image Correlation (DIC) Analysis of the 3 December 2013 Montescaglioso Landslide (Basilicata, Southern Italy): Results from a Multi-Dataset Investigation ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090372 Authors: Paolo Caporossi Paolo Mazzanti Francesca Bozzano Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset.

Description: Remote Sensing, Vol. 10, Pages 1438: Estimation of Methane Emissions from Rice Paddies in the Mekong Delta Based on Land Surface Dynamics Characterization with Remote Sensing Remote Sensing doi: 10.3390/rs10091438 Authors: Hironori Arai Wataru Takeuchi Kei Oyoshi Lam Dao Nguyen Kazuyuki Inubushi In paddy soils in the Mekong Delta, soil archaea emit substantial amounts of methane. Reproducing ground flux data using only satellite-observable explanatory variables is a highly transparent method for evaluating regional emissions. We hypothesized that PALSAR-2 (Phased Array type L-band Synthetic Aperture RADAR) can distinguish inundated soil from noninundated soil even if the soil is covered by rice plants. Then, we verified the reproducibility of the ground flux data with satellite-observable variables (soil inundation and cropping calendar) and with hierarchical Bayesian models. Furthermore, inundated/noninundated soils were classified with PALSAR-2. The model parameters were successfully converged using the Hamiltonian–Monte Carlo method. The cross-validation of PALSAR-2 land surface water coverage (LSWC) with several inundation indices of MODIS (Moderate Resolution Imaging Spectroradiometer) and AMSR-2 (Advanced Microwave Scanning Radiometer-2) data showed that (1) high PALSAR-2-LSWC values were detected even when MODIS and AMSR-2 inundation index values (MODIS-NDWI and AMSR-2-NDFI) were low and (2) low values of PALSAR-2-LSWC tended to be less frequently detected as the MODIS-NDWI and AMSR-2-NDFI increased. These findings indicate the potential of PALSAR-2 to detect inundated soils covered by rice plants even when MODIS and AMSR-2 cannot, and show the similarity between PALSAR-2-LSWC and the other two indices for nonvegetated areas.

Description: Remote Sensing, Vol. 10, Pages 1447: Quantitative Responses of Satellite-Derived Nighttime Lighting Signals to Anthropogenic Land-Use and Land-Cover Changes across China Remote Sensing doi: 10.3390/rs10091447 Authors: Ting Ma Remotely sensed artificial lighting radiances at night can provide spatially explicit proxy measures of the magnitude of human activity. Satellite-derived nighttime light images, mainly provided by the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB), have been increasingly used to study demographic and socioeconomic activities for a wide range of issues—for instance, human population dynamics, economic growth, and urbanization process—at multiple scales. In practice, the lack of texture information regarding man-made surfaces would usually lead to substantial difficulty in delineating the spatial dynamics in human settlements due to the diverse distributions of artificial nocturnal lighting sources, which are closely related to the predominant land-use/land-cover (LULC) types and their evolutions. An understanding of how nighttime lighting signals respond to synchronous anthropogenic LULC changes, therefore, is crucially important for the spatiotemporal investigations of human settlement dynamics. In this study, we used DMSP-derived nighttime light (NTL) data and Landsat-derived LULC maps to quantitatively estimate the pixel-level responses of NTL signals to different types of human-induced LULC conversions between 1995 and 2010 across China. Our results suggest that the majority (>70%) of pixel-level LULC conversions into artificial lands (including urban, rural, and built-up lands) might show a statistically significant increase in nighttime brightness with an average >20 (in digital number, DN) step change in nighttime lights (dNTL), both of which are distinctly higher than that in the LULC conversions into non-man-made surfaces on the whole. A receiver operating characteristic (ROC) curve-based analysis implies that we might have an average chance of ~90% to identify the nationwide LULC conversions into man-made surfaces from all types of conversions through the observed changes in artificial nocturnal luminosity signals. Moreover, ROC curve-based analyses also yield two nation-level optimal dNTL thresholds of 4.8 and 7.8 DN for recognizing newly emerged three types of artificial lands and urban lands between 1995 and 2010 across the entire country, respectively. In short, our findings reveal fundamental insights into the quantitative connections between the anthropogenic LULC changes and the corresponding responses of synchronous nightlight signals at the pixel-level, which are generally essential for further applications of satellite-derived nocturnal luminosity data in the spatiotemporal investigations of human settlement dynamics.

Description: IJGI, Vol. 7, Pages 377: Application of Industrial Risk Management Practices to Control Natural Hazards, Facilitating Risk Communication ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090377 Authors: Jongook Lee Dong Kun Lee Establishing a comprehensive management framework to manage the risk from natural hazards is challenging because of the extensive affected areas, uncertainty in predictions of natural disasters, and the involvement of various stakeholders. Applying risk management practices proven in the industrial sector can assist systematic hazard identification and quantitative risk assessment for natural hazards, thereby promoting interactive risk communication to the public. The objective of this study is to introduce methods of studying risk commonly used in the process industry, and to suggest how such methods can be applied to manage natural disasters. In particular, the application of Hazard and Operability (HAZOP), Safety Integrated Level (SIL), and Quantitative Risk Analysis (QRA) was investigated, as these methods are used to conduct key studies in industry. We present case studies of the application of HAZOP to identify climate-related natural hazards, and of SIL and QRA studies that were performed to provide quantitative risk indices for landslide risk management. The analyses presented in this study can provide a useful framework for improving the risk management of natural hazards through establishing a more systematic context and facilitating risk communication.

Description: Remote Sensing, Vol. 10, Pages 1475: The Contribution of Terrestrial Laser Scanning to the Analysis of Cliff Slope Stability in Sugano (Central Italy) Remote Sensing doi: 10.3390/rs10091475 Authors: Paolo Mazzanti Luca Schilirò Salvatore Martino Benedetta Antonielli Elisa Brizi Alessandro Brunetti Claudio Margottini Gabriele Scarascia Mugnozza In this work, we describe a comprehensive approach aimed at assessing the slope stability conditions of a tuff cliff located below the village of Sugano (Central Italy) starting from remote geomechanical analysis on high-resolution 3D point clouds collected by terrestrial laser scanner (TLS) surveys. Firstly, the identification of the main joint systems has been made through both manual and automatic analyses on the 3D slope model resulting from the surveys. Afterwards, the identified joint sets were considered to evaluate the slope stability conditions by attributing safety factor (SF) values to the typical rock blocks whose kinematic was proved as compatible with tests for toppling under two independent triggering conditions: hydrostatic water pressure within the joints and seismic action. The results from the remote investigation of the cliff slope provide geometrical information of the blocks more susceptible to instability and pointed out that limit equilibrium condition can be achieved for potential triggering scenarios in the whole outcropping slope.

Description: Remote Sensing, Vol. 10, Pages 1483: Evaluation of SMAP Freeze/Thaw Retrieval Accuracy at Core Validation Sites in the Contiguous United States Remote Sensing doi: 10.3390/rs10091483 Authors: Simon Kraatz Jennifer M. Jacobs Ronny Schröder Eunsang Cho Michael Cosh Mark Seyfried John Prueger Stan Livingston Seasonal freeze-thaw (FT) impacts much of the northern hemisphere and is an important control on its water, energy, and carbon cycle. Although FT in natural environments extends south of 45°N, FT studies using the L-band have so far been restricted to boreal or greater latitudes. This study addresses this gap by applying a seasonal threshold algorithm to Soil Moisture Active Passive (SMAP) data (L3_SM_P) to obtain a FT product south of 45°N (‘SMAP FT’), which is then evaluated at SMAP core validation sites (CVS) located in the contiguous United States (CONUS). SMAP landscape FT retrievals are usually in good agreement with 0–5 cm soil temperature at SMAP grids containing CVS stations (>70%). The accuracy could be further improved by taking into account specific overpass time (PM), the grid-specific seasonal scaling factor, the data aggregation method, and the sampling error. Annual SMAP FT extent maps compared to modeled soil temperatures derived from the Goddard Earth Observing System Model Version 5 (GEOS-5) show that seasonal FT in CONUS extends to latitudes of about 35–40°N, and that FT varies substantially in space and by year. In general, spatial and temporal trends between SMAP and modeled FT were similar.

Description: Remote Sensing, Vol. 10, Pages 1482: Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8 Remote Sensing doi: 10.3390/rs10091482 Authors: Marcel Urban Christian Berger Tami E. Mudau Kai Heckel John Truckenbrodt Victor Onyango Odipo Izak P. J. Smit Christiane Schmullius During the southern summer season of 2015 and 2016, South Africa experienced one of the most severe meteorological droughts since the start of climate recording, due to an exceptionally strong El Niño event. To investigate spatiotemporal dynamics of surface moisture and vegetation structure, data from ESA’s Copernicus Sentinel-1/-2 and NASA’s Landsat-8 for the period between March 2015 and November 2017 were utilized. In combination, these radar and optical satellite systems provide promising data with high spatial and temporal resolution. Sentinel-1 C-band data was exploited to derive surface moisture based on a hyper-temporal co-polarized (vertical-vertical—VV) radar backscatter change detection approach, describing dynamics between dry and wet seasons. Vegetation information from a TLS (Terrestrial Laser Scanner)-derived canopy height model (CHM), as well as the normalized difference vegetation index (NDVI) from Sentinel-2 and Landsat-8, were utilized to analyze vegetation structure types and dynamics with respect to the surface moisture index (SurfMI). Our results indicate that our combined radar–optical approach allows for a separation and retrieval of surface moisture conditions suitable for drought monitoring. Moreover, we conclude that it is crucial for the development of a drought monitoring system for savanna ecosystems to integrate land cover and vegetation information for analyzing surface moisture dynamics derived from Earth observation time series.

Description: Remote Sensing, Vol. 10, Pages 1484: Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape Remote Sensing doi: 10.3390/rs10091484 Authors: Liang Wan Yijian Li Haiyan Cen Jiangpeng Zhu Wenxin Yin Weikang Wu Hongyan Zhu Dawei Sun Weijun Zhou Yong He Remote estimation of flower number in oilseed rape under different nitrogen (N) treatments is imperative in precision agriculture and field remote sensing, which can help to predict the yield of oilseed rape. In this study, an unmanned aerial vehicle (UAV) equipped with Red Green Blue (RGB) and multispectral cameras was used to acquire a series of field images at the flowering stage, and the flower number was manually counted as a reference. Images of the rape field were first classified using K-means method based on Commission Internationale de l’Éclairage (CIE) L*a*b* space, and the result showed that classified flower coverage area (FCA) possessed a high correlation with the flower number (r2 = 0.89). The relationships between ten commonly used vegetation indices (VIs) extracted from UAV-based RGB and multispectral images and the flower number were investigated, and the VIs of Normalized Green Red Difference Index (NGRDI), Red Green Ratio Index (RGRI) and Modified Green Red Vegetation Index (MGRVI) exhibited the highest correlation to the flower number with the absolute correlation coefficient (r) of 0.91. Random forest (RF) model was developed to predict the flower number, and a good performance was achieved with all UAV variables (r2 = 0.93 and RMSEP = 16.18), while the optimal subset regression (OSR) model was further proposed to simplify the RF model, and a better result with r2 = 0.95 and RMSEP = 14.13 was obtained with the variable combination of RGRI, normalized difference spectral index (NDSI (944, 758)) and FCA. Our findings suggest that combining VIs and image classification from UAV-based RGB and multispectral images possesses the potential of estimating flower number in oilseed rape.

Description: Remote Sensing, Vol. 10, Pages 1493: Potential of Cost-Efficient Single Frequency GNSS Receivers for Water Vapor Monitoring Remote Sensing doi: 10.3390/rs10091493 Authors: Andreas Krietemeyer Marie-claire ten Veldhuis Hans van der Marel Eugenio Realini Nick van de Giesen Dual-frequency Global Navigation Satellite Systems (GNSSs) enable the estimation of Zenith Tropospheric Delay (ZTD) which can be converted to Precipitable Water Vapor (PWV). The density of existing GNSS monitoring networks is insufficient to capture small-scale water vapor variations that are especially important for extreme weather forecasting. A densification with geodetic-grade dual-frequency receivers is not economically feasible. Cost-efficient single-frequency receivers offer a possible alternative. This paper studies the feasibility of using low-cost receivers to increase the density of GNSS networks for retrieval of PWV. We processed one year of GNSS data from an IGS station and two co-located single-frequency stations. Additionally, in another experiment, the Radio Frequency (RF) signal from a geodetic-grade dual-frequency antenna was split to a geodetic receiver and two low-cost receivers. To process the single-frequency observations in Precise Point Positioning (PPP) mode, we apply the Satellite-specific Epoch-differenced Ionospheric Delay (SEID) model using two different reference network configurations of 50–80 km and 200–300 km mean station distances, respectively. Our research setup can distinguish between the antenna, ionospheric interpolation, and software-related impacts on the quality of PWV retrievals. The study shows that single-frequency GNSS receivers can achieve a quality similar to that of geodetic receivers in terms of RMSE for ZTD estimations. We demonstrate that modeling of the ionosphere and the antenna type are the main sources influencing the ZTD precision.

Description: Remote Sensing, Vol. 10, Pages 1510: Diurnal Response of Sun-Induced Fluorescence and PRI to Water Stress in Maize Using a Near-Surface Remote Sensing Platform Remote Sensing doi: 10.3390/rs10101510 Authors: Shan Xu Zhigang Liu Liang Zhao Huarong Zhao Sanxue Ren Sun-induced Fluorescence (SIF) and Photochemical Reflectance Index (PRI) data were collected in the field over maize to study their diurnal responses to different water stresses at the canopy scale. An automated field spectroscopy system was used to obtain continuous and long-term measurements of maize canopy in four field plots with different irrigation treatments. This system collects visible to near-infrared spectra with a spectrometer, which provides a sub-nanometer spectral resolution in the spectral range of 480~850 nm. The red SIF (FR) and far red SIF (FFR) data were retrieved by Spectral Fitting Methods (SFM) in the O 2 -A band and O 2 -B band, respectively. In addition to PRI, Δ PRI values were derived from PRI by subtracting an early morning PRI value. Photosynthetic active radiation (PAR) data, the canopy fraction of absorbed PAR (fPAR), and the air/canopy temperature and photosystem II operating efficiency (YII) at the leaf scale were collected concurrently. In this paper, the diurnal dynamics of each parameter before and after watering at the jointing stage were compared. The results showed that (i) both FR and FFR decreased under water stress, but FR always peaked at noon, and the peak of FFR advanced with the increase in stress. Leaf folding and the increase in Non-photochemical Quenching (NPQ) are the main reasons for this trend. Leaf YII gradually decreased from 8:00 to 14:00 and then recovered. In drought, leaf YII was smaller and decreased more rapidly. Therefore, the fluorescence yield at both the leaf and canopy scale responded to water stress. (ii) As good indicators of changes in NPQ, diurnal PRI and Δ PRI data also showed specific decreases due to water stress. Δ PRI can eliminate the impact of canopy structure. Under water stress, Δ PRI decreased rapidly from 8:00 to 13:00, and the maximum range of this decrease was approximately 0.05. After 13:00, their values started to increase but could not recover to their morning level. (iii) Higher canopy-air temperature differences ( Δ T ) indicate that stomatal closure leads to an increase in leaf temperature, which maintains a higher state in the afternoon. In summary, to cope with water stress, both leaf folding and changes in physiology are activated. To monitor drought, SIF performs best around midday, and PRI is better after noon.

Description: Remote Sensing, Vol. 10, Pages 1509: Google Earth Engine Applications Since Inception: Usage, Trends, and Potential Remote Sensing doi: 10.3390/rs10101509 Authors: Lalit Kumar Onisimo Mutanga The Google Earth Engine (GEE) portal provides enhanced opportunities for undertaking earth observation studies. Established towards the end of 2010, it provides access to satellite and other ancillary data, cloud computing, and algorithms for processing large amounts of data with relative ease. However, the uptake and usage of the opportunity remains varied and unclear. This study was undertaken to investigate the usage patterns of the Google Earth Engine platform and whether researchers in developing countries were making use of the opportunity. Analysis of published literature showed that a total of 300 journal papers were published between 2011 and June 2017 that used GEE in their research, spread across 158 journals. The highest number of papers were in the journal Remote Sensing, followed by Remote Sensing of Environment. There were also a number of papers in premium journals such as Nature and Science. The application areas were quite varied, ranging from forest and vegetation studies to medical fields such as malaria. Landsat was the most widely used dataset; it is the biggest component of the GEE data portal, with data from the first to the current Landsat series available for use and download. Examination of data also showed that the usage was dominated by institutions based in developed nations, with study sites mainly in developed nations. There were very few studies originating from institutions based in less developed nations and those that targeted less developed nations, particularly in the African continent.

Description: Remote Sensing, Vol. 10, Pages 1508: Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations Remote Sensing doi: 10.3390/rs10101508 Authors: Yelu Zeng Baodong Xu Gaofei Yin Shengbiao Wu Guoqing Hu Kai Yan Bin Yang Wanjuan Song Jing Li This paper presents a simple radiative transfer model based on spectral invariant properties (SIP). The canopy structure parameters, including the leaf angle distribution and multi-angular clumping index, are explicitly described in the SIP model. The SIP model has been evaluated on its bidirectional reflectance factor (BRF) in the angular space at the radiation transfer model intercomparison platform, and in the spectrum space by the PROSPECT+SAIL (PROSAIL) model. The simulations of BRF by SIP agreed well with the reference values in both the angular space and spectrum space, with a root-mean-square-error (RMSE) of 0.006. When compared with the widely-used Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model on fPAR, the RMSE was 0.006 and the R2 was 0.99, which shows a high accuracy. This study also suggests the newly proposed vegetation index, the near-infrared (NIR) reflectance of vegetation (NIRv), was a good linear approximation of the canopy structure parameter, the directional area scattering factor (DASF), with an R2 of 0.99. NIRv was not influenced much by the soil background contribution, but was sensitive to the leaf inclination angle. The sensitivity of NIRv to canopy structure and the robustness of NIRv to the soil background suggest NIRv is a promising index in future biophysical variable estimations with the support of the SIP model, especially for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) observations near the hot spot directions.

Description: Remote Sensing, Vol. 10, Pages 1505: Evaluation of Manning’s n Roughness Coefficient in Arid Environments by Using SAR Backscatter Remote Sensing doi: 10.3390/rs10101505 Authors: Yuval Sadeh Hai Cohen Shimrit Maman Dan G. Blumberg The prediction of arid region flash floods (magnitude and frequency) is essential to ensure the safety of human life and infrastructures and is commonly based on hydrological models. Traditionally, catchment characteristics are extracted using point-based measurements. A considerable improvement of point-based observations is offered by remote sensing technologies, which enables the determination of continuous spatial hydrological parameters and variables, such as surface roughness, which significantly influence runoff velocity and depth. Hydrological models commonly express the surface roughness using Manning’s roughness coefficient (n) as a key variable. The objectives were thus to determine surface roughness by exploiting a new high spatial resolution spaceborne synthetic aperture radar (SAR) technology and to examine the correlation between radar backscatter and Manning’s roughness coefficient in an arid environment. A very strong correlation (R2 = 0.97) was found between the constellation of small satellites for Mediterranean basin observation (COSMO)-SkyMed SAR backscatter and surface roughness. The results of this research demonstrate the feasibility of using an X-band spaceborne sensor with high spatial resolution for the evaluation of surface roughness in flat arid environments. The innovative method proposed to evaluate Manning’s n roughness coefficient in arid environments with sparse vegetation cover using radar backscatter may lead to improvements in the performance of hydrological models.

Description: Remote Sensing, Vol. 10, Pages 1504: A Common “Stripmap-Like” Interferometric Processing Chain for TOPS and ScanSAR Wide Swath Mode Remote Sensing doi: 10.3390/rs10101504 Authors: Yuxiao Qin Daniele Perissin Jing Bai This article describes the technical implementation of a “stripmap-like” interferometric processing flow that could be used for both Terrain Observation with Progressive Scans (TOPS) and ScanSAR. In this “stripmap-like” approach, the discontinuous bursts of wide swath mode for the same subswath are stitched into a continuous single look complex (SLC) image at the very beginning of the processing chain. For users who wish not to get into the complexity behind the wide swath mode and simply want to use the interferometric products, this implementation provides the identical processing steps and output products to the stripmap case. This implementation also features a user-friendly processing interface, where all the wide-swath-related processes are hidden under the hood. In addition, this approach makes the best use of an existing standard InSAR processing software. In this article, the complete common processing chain for TOPS and ScanSAR is elaborated and some key issues are discussed, starting from stitching, deramping to coregistration and enhanced spectral diversity (ESD). The authors also introduce a quick implementation of fine coregistration during the ESD step that does not require resampling the slave image using the conventional method.

Description: Remote Sensing, Vol. 10, Pages 1506: The Role of Earth Observation, with a Focus on SAR Interferometry, for Sinkhole Hazard Assessment Remote Sensing doi: 10.3390/rs10101506 Authors: Andre Theron Jeanine Engelbrecht Sinkholes are global phenomena with significant consequences on the natural- and built environment. Significant efforts have been devoted to the assessment of sinkhole hazards to predict the spatial and temporal occurrence of future sinkholes as well as to detect small-scale deformation prior to collapse. Sinkhole hazard maps are created by considering the distribution of past sinkholes in conjunction with their geomorphic features, controlling conditions and triggering mechanisms. Quantitative risk assessment then involves the statistical analysis of sinkhole events in relation to these conditions with the aim of identifying high risk areas. Remote sensing techniques contribute to the field of sinkhole hazard assessment by providing tools for the population of sinkhole inventories and lend themselves to the monitoring of precursory deformation prior to sinkhole development. In this paper, we outline the background to sinkhole formation and sinkhole hazard assessment. We provide a review of earth observation techniques, both for the compilation of sinkhole inventories as well as the monitoring of precursors to sinkhole development. We discuss the advantages and limitations of these approaches and conclude by highlighting the potential role of radar interferometry in the early detection of sinkhole-induced instability resulting in a potential decrease in the risk to human lives and infrastructure by enabling proactive remediation.

Description: Remote Sensing, Vol. 10, Pages 1503: Suspended Sediment Concentration Estimation from Landsat Imagery along the Lower Missouri and Middle Mississippi Rivers Using an Extreme Learning Machine Remote Sensing doi: 10.3390/rs10101503 Authors: Kyle T. Peterson Vasit Sagan Paheding Sidike Amanda L. Cox Megan Martinez Monitoring and quantifying suspended sediment concentration (SSC) along major fluvial systems such as the Missouri and Mississippi Rivers provide crucial information for biological processes, hydraulic infrastructure, and navigation. Traditional monitoring based on in situ measurements lack the spatial coverage necessary for detailed analysis. This study developed a method for quantifying SSC based on Landsat imagery and corresponding SSC data obtained from United States Geological Survey monitoring stations from 1982 to present. The presented methodology first uses feature fusion based on canonical correlation analysis to extract pertinent spectral information, and then trains a predictive reflectance–SSC model using a feed-forward neural network (FFNN), a cascade forward neural network (CFNN), and an extreme learning machine (ELM). The trained models are then used to predict SSC along the Missouri–Mississippi River system. Results demonstrated that the ELM-based technique generated R2 > 0.9 for Landsat 4–5, Landsat 7, and Landsat 8 sensors and accurately predicted both relatively high and low SSC displaying little to no overfitting. The ELM model was then applied to Landsat images producing quantitative SSC maps. This study demonstrates the benefit of ELM over traditional modeling methods for the prediction of SSC based on satellite data and its potential to improve sediment transport and monitoring along large fluvial systems.

Description: Remote Sensing, Vol. 10, Pages 1501: Optimization of Airborne Antenna Geometry for Ocean Surface Scatterometric Measurements Remote Sensing doi: 10.3390/rs10101501 Authors: Alexey Nekrasov Alena Khachaturian Evgeny Abramov Dmitry Popov Oleg Markelov Viktor Obukhovets Vladimir Veremyev Mikhail Bogachev We consider different antenna configurations, ranging from simple X-configuration to multi-beam star geometries, for airborne scatterometric measurements of the wind vector near the ocean surface. For all geometries, track-stabilized antenna configurations, as well as horizontal transmitter and receiver polarizations, are considered. The wind vector retrieval algorithm is generalized here for an arbitrary star geometry antenna configuration and tested using the Ku-Band geophysical model function. Using Monte Carlo simulations for the fixed total measurement time, we show explicitly that the relative wind speed estimation accuracy barely depends on the chosen antenna geometry, while the maximum wind direction retrieval error reduces moderately with increasing angular resolution, although at the cost of increased retrieval algorithm computational complexity, thus, limiting online analysis options with onboard equipment. Remarkably, the simplest X-configuration, while the simplest in terms of hardware implementation and computational time, appears an outlier, yielding considerably higher maximum retrieval errors when compared to all other configurations. We believe that our results are useful for the optimization of both hardware and software design for modern airborne scatterometric measurement systems based on tunable antenna arrays especially, those requiring online data processing.

Description: Remote Sensing, Vol. 10, Pages 1502: Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data Remote Sensing doi: 10.3390/rs10101502 Authors: Evan B. Brooks Randolph H. Wynne Valerie A. Thomas The continued development of algorithms using multitemporal Landsat data creates opportunities to develop and adapt imputation algorithms to improve the quality of that data as part of preprocessing. One example is de-striping Enhanced Thematic Mapper Plus (ETM+, Landsat 7) images acquired after the Scan Line Corrector failure in 2003. In this study, we apply window regression, an algorithm that was originally designed to impute low-quality Moderate Resolution Imaging Spectroradiometer (MODIS) data, to Landsat Analysis Ready Data from 2014–2016. We mask Operational Land Imager (OLI; Landsat 8) image stacks from five study areas with corresponding ETM+ missing data layers, using these modified OLI stacks as inputs. We explored the algorithm’s parameter space, particularly window size in the spatial and temporal dimensions. Window regression yielded the best accuracy (and moderately long computation time) with a large spatial radius (a 7 × 7 pixel window) and a moderate temporal radius (here, five layers). In this case, root mean square error for deviations from the observed reflectance ranged from 3.7–7.6% over all study areas, depending on the band. Second-order response surface analysis suggested that a 15 × 15 pixel window, in conjunction with a 9-layer temporal window, may produce the best accuracy. Compared to the neighborhood similar pixel interpolator gap-filling algorithm, window regression yielded slightly better accuracy on average. Because it relies on no ancillary data, window regression may be used to conveniently preprocess stacks for other data-intensive algorithms.

Description: Remote Sensing, Vol. 10, Pages 1519: Edge Dependent Chinese Restaurant Process for Very High Resolution (VHR) Satellite Image Over-Segmentation Remote Sensing doi: 10.3390/rs10101519 Authors: Hong Tang Xuejun Zhai Wei Huang Image over-segmentation aims to partition an image into spatially adjacent and spectrally homogeneous regions. It could reduce the complexity of image representation and enhance the efficiency of subsequent image processing. Previously, many methods for image over-segmentation have been proposed, but almost of them need to assign model parameters in advance, e.g., the number of segments. In this paper, a nonparametric clustering model is employed to the over-segmentation of Very High Resolution (VHR) satellite images, in which the number of segments can automatically be inferred from the observed data. The proposed model is called the Edge Dependent Chinese restaurant process (EDCRP), which extends the distance dependent Chinese restaurant process to make full use of local image structure information, i.e., edges. Experimental results show that the presented methods outperform state of the art methods for image over-segmentation in terms of both metrics based direct evaluation and classification based indirect evaluation.

Description: Remote Sensing, Vol. 10, Pages 1520: Validation of the First Years of GPM Operation over Cyprus Remote Sensing doi: 10.3390/rs10101520 Authors: Adrianos Retalis Dimitris Katsanos Filippos Tymvios Silas Michaelides Global Precipitation Measurement (GPM) high-resolution product is validated against rain gauges over the island of Cyprus for a three-year period, starting from April 2014. The precipitation estimates are available in both high temporal (half hourly) and spatial (10 km) resolution and combine data from all passive microwave instruments in the GPM constellation. The comparison performed is twofold: first the GPM data are compared with the precipitation measurements on a monthly basis and then the comparison focuses on extreme events, recorded throughout the first 3 years of GPM’s operation. The validation is based on ground data from a dense and reliable network of rain gauges, also available in high temporal (hourly) resolution. The first results show very good correlation regarding monthly values; however, the correspondence of GPM in extreme precipitation varies from “no correlation” to “high correlation”, depending on case. This study aims to verify the GPM rain estimates, since such a high-resolution dataset has numerous applications, including the assimilation in numerical weather prediction models and the study of flash floods with hydrological models.

Description: Remote Sensing, Vol. 10, Pages 1524: Using Satellite Remote Sensing to Study the Impact of Climate and Anthropogenic Changes in the Mesopotamian Marshlands, Iraq Remote Sensing doi: 10.3390/rs10101524 Authors: Reyadh Albarakat Venkat Lakshmi Compton J. Tucker The Iraqi Marshes in Southern Iraq are considered one of the most important wetlands in the world. From 1982 to the present, their area has varied between 10,500 km2 and 20,000 km2. The marshes support a variety of plants, such as reeds and papyrus, and are home to many species of birds. These marshes are Al-Hammar, Central or Al-Amarah, and Al-Huwaiza. Freshwater supplies to the marshes come from the Tigris and Euphrates rivers in Iraq and from the Karkha River from Iran. For this analysis, we used the Land Long-Term Data Record Version 5 (LTDR V5) Normalized Difference Vegetation Index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) sensor dataset. This dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution to monitor the spatial and temporal variability of vegetation along with other hydrological variables such as land surface temperature, precipitation, and evapotranspiration. In our analysis, we considered three time periods: 1982–1992; 1993–2003; and 2004–2017 due to anthropogenic activities and climate changes. Furthermore, we examined the relationships between various water cycle variables through the investigation of vegetation and water coverage changes, and studied the impacts of climate change and anthropogenic activities on the Iraqi Marshes and considered additional ground observations along with the satellite datasets. Statistical analyses over the last 36 years show significant deterioration in the vegetation: 68.78%, 98.73, and 83.71% of the green biomass has declined for Al-Hammar, The Central marshes, and Al-Huwaiza, respectively. The AVHRR and Landsat images illustrate a decrease in water and vegetation coverage, which in turn has led to an increase in barren lands. Unfortunately, statistical analyses show that marshland degradation is mainly induced by human actions. The shrinkage in water supplies taken by Iraq’s local neighbors (i.e., Turkey, Syria, and Iran) has had a sharp impact on water levels. The annual discharge of the Tigris declined from ~2500–3000 m3/s to ~500 m3/s, and the annual discharge of the Euphrates River declined from ~1500 m3/s to less than 500 m3/s.

Description: Remote Sensing, Vol. 10, Pages 1522: Machine Learning-Based Slum Mapping in Support of Slum Upgrading Programs: The Case of Bandung City, Indonesia Remote Sensing doi: 10.3390/rs10101522 Authors: Gina Leonita Monika Kuffer Richard Sliuzas Claudio Persello The survey-based slum mapping (SBSM) program conducted by the Indonesian government to reach the national target of “cities without slums” by 2019 shows mapping inconsistencies due to several reasons, e.g., the dependency on the surveyor’s experiences and the complexity of the slum indicators set. By relying on such inconsistent maps, it will be difficult to monitor the national slum upgrading program’s progress. Remote sensing imagery combined with machine learning algorithms could support the reduction of these inconsistencies. This study evaluates the performance of two machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF), for slum mapping in support of the slum mapping campaign in Bandung, Indonesia. Recognizing the complexity in differentiating slum and formal areas in Indonesia, the study used a combination of spectral, contextual, and morphological features. In addition, sequential feature selection (SFS) combined with the Hilbert–Schmidt independence criterion (HSIC) was used to select significant features for classifying slums. Overall, the highest accuracy (88.5%) was achieved by the SVM with SFS using contextual, morphological, and spectral features, which is higher than the estimated accuracy of the SBSM. To evaluate the potential of machine learning-based slum mapping (MLBSM) in support of slum upgrading programs, interviews were conducted with several local and national stakeholders. Results show that local acceptance for a remote sensing-based slum mapping approach varies among stakeholder groups. Therefore, a locally adapted framework is required to combine ground surveys with robust and consistent machine learning methods, for being able to deal with big data, and to allow the rapid extraction of consistent information on the dynamics of slums at a large scale.

Description: Remote Sensing, Vol. 10, Pages 1523: Geocoding Error Correction for InSAR Point Clouds Remote Sensing doi: 10.3390/rs10101523 Authors: Sina Montazeri Fernando Rodríguez González Xiao Xiang Zhu Persistent Scatterer Interferometry (PSI) is an advanced multitemporal InSAR technique that is capable of retrieving the 3D coordinates and the underlying deformation of time-coherent scatterers. Various factors degrade the localization accuracy of PSI point clouds in the geocoding process, which causes problems for interpretation of deformation results and also making it difficult for the point clouds to be compared with or integrated into data from other sensors. In this study, we employ the SAR imaging geodesy method to perform geodetic corrections on SAR timing observations and thus improve the positioning accuracy in the horizontal components. We further utilize geodetic stereo SAR to extract large number of highly precise ground control points (GCP) from SAR images, in order to compensate for the unknown height offset of the PSI point cloud. We demonstrate the applicability of the approach using TerraSAR-X high resolution spotlight images over the city of Berlin, Germany. The corrected results are compared with a reference LiDAR point cloud of Berlin, which confirms the improvement in the geocoding accuracy.

Description: Remote Sensing, Vol. 10, Pages 1521: A Novel Index for Impervious Surface Area Mapping: Development and Validation Remote Sensing doi: 10.3390/rs10101521 Authors: Yugang Tian Hui Chen Qingju Song Kun Zheng The distribution and dynamic changes in impervious surface areas (ISAs) are crucial to understanding urbanization and its impact on urban heat islands, earth surface energy balance, hydrological cycles, and biodiversity. Remotely sensed data play an essential role in ISA mapping, and numerous methods have been developed and successfully applied for ISA extraction. However, the heterogeneity of ISA spectra and the high similarity of the spectra between ISA and soil have not been effectively addressed. In this study, we selected data from the US Geological Survey (USGS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral libraries as samples and used blue and near-infrared bands as characteristic bands based on spectral analysis to propose a novel index, the perpendicular impervious surface index (PISI). Landsat 8 operational land imager data in four provincial capital cities of China (Wuhan, Shenyang, Guangzhou, and Xining) were selected as test data to examine the performance of the proposed PISI in four different environments. Threshold analysis results show that there is a significant positive correlation between PISI and the proportion of ISA, and threshold can be adjusted according to different needs with different accuracy. Furthermore, comparative analyses, which involved separability analysis and extraction precision analysis, were conducted among PISI, biophysical composition index (BCI), and normalized difference built-up index (NDBI). Results indicate that PISI is more accurate and has better separability for ISA and soil as well as ISA and vegetation in the ISA extraction than the BCI and NDBI under different conditions. The accuracy of PISI in the four cities is 94.13%, 96.50%, 89.51%, and 93.46% respectively, while BCI and NDBI showed accuracy of 77.53%, 93.49%, 78.02%, and 84.03% and 58.25%, 57.53%, 77.77%, and 64.83%, respectively. In general, the proposed PISI is a convenient index to extract ISA with higher accuracy and better separability for ISA and soil as well as ISA and vegetation. Meanwhile, as PISI only uses blue and near-infrared bands, it can be used in a wider variety of remote sensing images.

Description: IJGI, Vol. 7, Pages 382: Incremental Road Network Generation Based on Vehicle Trajectories ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7100382 Authors: Zhongyi Ni Lijun Xie Tian Xie Binhua Shi Yao Zheng Nowadays, most vehicles are equipped with positioning devices such as GPS which can generate a tremendous amount of trajectory data and upload them to the server in real time. The trajectory data can reveal the shape and evolution of the road network and therefore has an important value for road planning, vehicle navigation, traffic analysis, and so on. In this paper, a road network generation method is proposed based on the incremental learning of vehicle trajectories. Firstly, the input vehicle trajectory data are cleaned by a preprocess module. Then, the original scattered positions are clustered and mapped to the representation points which stand for the feature points of the real roads. After that, the corresponding representation points are connected based on the original connection information of the trajectories. Finally, all representation points are connected by a Delaunay triangulation network and the real road segments are found by a shortest path searching approach between the connected representation point pairs. Experiments show that this method can build the road network from scratch and refine it with the input data continuously. Both the accuracy and timeliness of the extracted road network can continuously be improved with the growth of real-time trajectory data.

Description: IJGI, Vol. 7, Pages 383: Care, Indifference and Anxiety—Attitudes toward Location Data in Everyday Life ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7100383 Authors: Michal Rzeszewski Piotr Luczys Modern mobile devices are replete with advanced sensors that expand the array of possible methods of locating users. This can be used as a tool to gather and use spatial information, but it also brings with it the specter of “geosurveillance” in which the “location” becomes a product in itself. In the realm of software developers, space/place has been reduced and discretized to a set of coordinates, devoid of human experiences and meanings. To function in such digitally augmented realities, people need to adopt specific attitudes, often marked with anxiety. We explored attitudes toward location data collection practices using qualitative questionnaire surveys (n = 280) from Poznan and Edinburgh. The prevailing attitude that we identified is neutral with a strong undertone of resignation—surrendering personal location is viewed as a form of digital currency. A smaller number of people had stronger, emotional views, either very positive or very negative, based on uncritical technological enthusiasm or fear of privacy violation. Such a wide spectrum of attitudes is not only produced by interaction with technology but can also be a result of different values associated with space and place itself. Those attitudes can bring additional bias into spatial datasets that is not related to demographics.

Description: IJGI, Vol. 7, Pages 319: BIM-GIS Integration as Dedicated and Independent Course for Geoinformatics Students: Merits, Challenges, and Ways Forward ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080319 Authors: Ihab Hijazi Andreas Donaubauer Thomas H. Kolbe Information mined from building information models as well as associated geographical data and Geographic Information System (GIS) analyses can increase the success of construction processes and asset management, including buildings, roads, and public facilities. The integration of information from both domains requires high expertise in both spheres. The existing B.Sc and M.Sc. programs linked to the built environment at the Technical University of Munich offer courses for the Building Information Model (BIM) and GIS that are distributed among study programs in Civil Engineering, Architecture, and Geomatics. Students graduating as professionals in one of these domains rarely know how to solve pre-defined technical problems associated with the integration of information from BIM and GIS. Students in such programs seldom practice skills needed for the integration of information from BIM and GIS at a level that is needed in working life. Conversely, the technologies in both domains create artificial boundaries that do not exist in reality—for example, water and electricity would not be of use if the utilities terminated in front of buildings. To bring a change and bridge the gap between BIM and GIS, a change in the teaching methods of BIM/GIS needs to be considered. The Technical University of Munich (TUM) has developed a master’s course (M.Sc. course) for students in Geoinformatics which focuses on competencies required to achieve BIM/GIS integration. This paper describes the course development process and provides a unique perspective on the curriculum and subjects. It also presents the course objective, course development, the selection and development of learning materials, and the assessment of the intended learning outcome of the course. The developed course is validated through a questionnaire, and feedback is provided by participants of the BIM/GIS integration workshop representing a panel of experts in the domain.

Description: Remote Sensing, Vol. 10, Pages 1248: Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images Remote Sensing doi: 10.3390/rs10081248 Authors: Hua Sun Qing Wang Guangxing Wang Hui Lin Peng Luo Jiping Li Siqi Zeng Xiaoyu Xu Lanxiang Ren Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons-kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas.

Description: Remote Sensing, Vol. 10, Pages 1247: Depth from Satellite Images: Depth Retrieval Using a Stereo and Radiative Transfer-Based Hybrid Method Remote Sensing doi: 10.3390/rs10081247 Authors: Simon Collings Elizabeth J. Botha Janet Anstee Norm Campbell Satellite imagery is increasingly being used to provide estimates of bathymetry in near-coastal (shallow) areas of the planet, as a more cost-effective alternative to traditional methods. In this paper, the relative accuracy of radiative-transfer and photogrammetric stereo methods applied to World View 2 imagery are examined, using LiDAR bathymetry and towed video as ground truth, and it is demonstrated, with a case study, that these methods are complementary; where one method might have limited accuracy, the other method often has improved accuracy. The depths of uniform, highly-reflective (sand) sea bed are better estimated with a radiative transfer-based method, while areas where there is high visual contrast in the scene, as identified by using a local standard deviation measure, are better estimated using the photogrammetric technique. In this paper, it is shown that a hybrid method can give a potential improvement in accuracy of more than 50% (from 2.84 m to 1.38 m RMSE in the ideal case) compared to either of the two methods alone. Metrics are developed that can be used to characterize regions of the scene where each technique is superior, realizing an improved overall depth accuracy over either method alone of between 16.9% and 19.7% (demonstrating a realised RMSE of 2.36 m).

Description: IJGI, Vol. 7, Pages 321: Extracting Indoor Space Information in Complex Building Environments ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080321 Authors: Yueyong Pang Chi Zhang Liangchen Zhou Bingxian Lin Guonian Lv Indoor space information extraction is an important aspect of reconstruction for building information modeling and a necessary process for geographic information system from outdoor to indoor. Entity model extracting methods provide advantages in terms of accuracy for building indoor spaces, as compared with network and grid model methods, and the extraction results can be converted into a network or grid model. However, existing entity model extracting methods based on a search loop do not consider the complex indoor environment of a building, such as isolated columns and walls or cross-floor spaces. In this study, such complex indoor environments are analyzed in detail, and a new approach for extracting buildings’ indoor space information is proposed. This approach is based on indoor space boundary calculation, the Boolean difference for single-floor space extraction, relationship reconstruction, and cross-floor space extraction. The experimental results showed that the proposed method can accurately extract indoor space information from the complex indoor environment of a building with geometric, semantic, and relationship information. This study is theoretically important for better understanding the complexity of indoor space extraction and practically important for improving the modeling accuracy of buildings.

Description: IJGI, Vol. 7, Pages 323: Analyzing the Tagging Quality of the Spanish OpenStreetMap ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080323 Authors: Jesús M. Almendros-Jiménez Antonio Becerra-Terón In this paper, a framework for the assessment of the quality of OpenStreetMap is presented, comprising a batch of methods to analyze the quality of entity tagging. The approach uses Taginfo as a reference base and analyses quality measures such as completeness, compliance, consistence, granularity, richness and trust . The framework has been used to analyze the quality of OpenStreetMap in Spain, comparing the main cities of Spain. Also a comparison between Spain and some major European cities has been carried out. Additionally, a Web tool has been also developed in order to facilitate the same kind of analysis in any area of the world.

Description: IJGI, Vol. 7, Pages 322: Comparison of Communication Viewsheds Derived from High-Resolution Digital Surface Models Using Line-of-Sight, 2D Fresnel Zone, and 3D Fresnel Zone Analysis ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080322 Authors: Jieun Baek Yosoon Choi We compared three methods for deriving communication viewsheds, which indicate the coverage areas for transmitter points from high-resolution digital surface models. Communication viewsheds were analyzed with a novel 3D Fresnel zone method, as well as line-of-sight (LOS) analysis and 2D Fresnel zone analysis, using high-resolution digital surface models (DSM) from a topographical survey. A LOS analysis calculates a visibility index by comparing the profile elevations of landforms between the transmitter and the receiver, using LOS elevations. A 2D Fresnel zone analysis calculates a 2D Fresnel index by comparing the profile elevations of landforms with the transverse plane elevations of the Fresnel zone. A 3D Fresnel zone analysis quantitatively analyzes communication stability by calculating a 3D Fresnel index, obtained by comparing the elevations of every terrain cell in a Fresnel zone with the total altitude of the Fresnel zone. The latter produced the most accurate results. Indexes derived by applying different transmitter offset heights, signal frequencies, and DSM resolutions for each of the three methods were then quantitatively analyzed. As both the offset height of the transmitter and the signal frequency decreased, the differences between the results derived from each method increased significantly. Moreover, larger DSM cells generated less accurate results.

Description: Remote Sensing, Vol. 10, Pages 1254: Quality Assurance Framework Development Based on Six New ECV Data Products to Enhance User Confidence for Climate Applications Remote Sensing doi: 10.3390/rs10081254 Authors: Joanne Nightingale Klaas Folkert Boersma Jan-Peter Muller Steven Compernolle Jean-Christopher Lambert Simon Blessing Ralf Giering Nadine Gobron Isabelle De Smedt Pierre Coheur Maya George Jörg Schulz Alexander Wood Data from Earth observation (EO) satellites are increasingly used to monitor the environment, understand variability and change, inform evaluations of climate model forecasts, and manage natural resources. Policymakers are progressively relying on the information derived from these datasets to make decisions on mitigating and adapting to climate change. These decisions should be evidence based, which requires confidence in derived products, as well as the reference measurements used to calibrate, validate, or inform product development. In support of the European Union’s Earth Observation Programmes Copernicus Climate Change Service (C3S), the Quality Assurance for Essential Climate Variables (QA4ECV) project fulfilled a gap in the delivery of climate quality satellite-derived datasets, by prototyping a generic system for the implementation and evaluation of quality assurance (QA) measures for satellite-derived ECV climate data record products. The project demonstrated the QA system on six new long-term, climate quality ECV data records for surface albedo, leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), nitrogen dioxide (NO2), formaldehyde (HCHO), and carbon monoxide (CO). The provision of standardised QA information provides data users with evidence-based confidence in the products and enables judgement on the fitness-for-purpose of various ECV data products and their specific applications.

Description: IJGI, Vol. 7, Pages 324: An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080324 Authors: Jian Chen Gang Ou Ao Peng Lingxiang Zheng Jianghong Shi Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m.

Description: Remote Sensing, Vol. 10, Pages 1257: Integrating Drone Imagery into High Resolution Satellite Remote Sensing Assessments of Estuarine Environments Remote Sensing doi: 10.3390/rs10081257 Authors: Patrick C. Gray Justin T. Ridge Sarah K. Poulin Alexander C. Seymour Amanda M. Schwantes Jennifer J. Swenson David W. Johnston Very high-resolution satellite imagery (≤5 m resolution) has become available on a spatial and temporal scale appropriate for dynamic wetland management and conservation across large areas. Estuarine wetlands have the potential to be mapped at a detailed habitat scale with a frequency that allows immediate monitoring after storms, in response to human disturbances, and in the face of sea-level rise. Yet mapping requires significant fieldwork to run modern classification algorithms and estuarine environments can be difficult to access and are environmentally sensitive. Recent advances in unoccupied aircraft systems (UAS, or drones), coupled with their increased availability, present a solution. UAS can cover a study site with ultra-high resolution (<5 cm) imagery allowing visual validation. In this study we used UAS imagery to assist training a Support Vector Machine to classify WorldView-3 and RapidEye satellite imagery of the Rachel Carson Reserve in North Carolina, USA. UAS and field-based accuracy assessments were employed for comparison across validation methods. We created and examined an array of indices and layers including texture, NDVI, and a LiDAR DEM. Our results demonstrate classification accuracy on par with previous extensive fieldwork campaigns (93% UAS and 93% field for WorldView-3; 92% UAS and 87% field for RapidEye). Examining change between 2004 and 2017, we found drastic shoreline change but general stability of emergent wetlands. Both WorldView-3 and RapidEye were found to be valuable sources of imagery for habitat classification with the main tradeoff being WorldView’s fine spatial resolution versus RapidEye’s temporal frequency. We conclude that UAS can be highly effective in training and validating satellite imagery.

Description: Remote Sensing, Vol. 10, Pages 1275: Focusing High-Resolution Airborne SAR with Topography Variations Using an Extended BPA Based on a Time/Frequency Rotation Principle Remote Sensing doi: 10.3390/rs10081275 Authors: Chunhui Lin Shiyang Tang Linrang Zhang Ping Guo With the increasing requirement for resolution, the negligence of topography variations causes serious phase errors, which leads to the degradation of the focusing quality of the synthetic aperture (SAR) imagery, and geometric distortion. Hence, a precise and fast algorithm is necessary for high-resolution airborne SAR. In this paper, an extended back-projection (EBP) algorithm is proposed to compensate the phase errors caused by topography variations. Three-dimensional (3D) variation will be processed in the time-domain for high-resolution airborne SAR. Firstly, the quadratic phase error (QPE) brought by topography variations is analyzed in detail for high-resolution airborne SAR. Then, the key operation, a time-frequency rotation operation, is applied to decrease the samplings in the azimuth time-domain. Just like the time-frequency rotation of the conventional two-step approach, this key operation can compress data in an azimuth time-domain and it reduces the computational burden of the conventional back-projection algorithm, which is applied lastly in the time-domain processing. The results of the simulations validate that the proposed algorithm, including frequency-domain processing and time-domain processing can obtain good focusing performance. At the same time, it has strong practicability with a small amount of computation, compared with the conventional algorithm.

Description: IJGI, Vol. 7, Pages 325: Journey-to-Crime Distances of Residential Burglars in China Disentangled: Origin and Destination Effects ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080325 Authors: Luzi Xiao Lin Liu Guangwen Song Stijn Ruiter Suhong Zhou Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western societies, and little is known about how the distinct features of communities in major Chinese cities shape residential burglars’ travel patterns. To fill this gap, we apply a cross-classified multilevel regression model on data of 3763 burglary trips in ZG City, one of the bustling metropolises in China. This allows us to gain insight into how residential burglars’ journey-to-crime distances are shaped by their individual-level characteristics as well as those of their home and target communities. Results show that the characteristics of the home community have larger effects than those of target communities, while individual-level features are most influential. Older burglars travel over longer distances to commit their burglaries than the younger ones. Offenders who commit their burglaries in groups tend to travel further than solo offenders. Burglars who live in communities with a higher average rent, a denser road network and a higher percentage of local residents commit their burglaries at shorter distances. Communities with a denser road network attract burglars from a longer distance, whereas those with a higher percentage of local residents attract them from shorter by.

Description: Remote Sensing, Vol. 10, Pages 1271: Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs Remote Sensing doi: 10.3390/rs10081271 Authors: Feng Gao Qun Wang Junyu Dong Qizhi Xu Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods.

Description: Remote Sensing, Vol. 10, Pages 1270: Greening and Browning of the Hexi Corridor in Northwest China: Spatial Patterns and Responses to Climatic Variability and Anthropogenic Drivers Remote Sensing doi: 10.3390/rs10081270 Authors: Qingyu Guan Liqin Yang Ninghui Pan Jinkuo Lin Chuanqi Xu Feifei Wang Zeyu Liu The arid region of northwest China provides a unique terrestrial ecosystem to identify the response of vegetation activities to natural and anthropogenic changes. To reveal the influences of climate and anthropogenic factors on vegetation, the Normalized Difference Vegetation Index (NDVI), climate data, and land use and land cover change (LUCC) maps were used for this study. We analyzed the spatiotemporal change of NDVI during 2000–2015. A partial correlation analysis suggested that the contribution of precipitation (PRE) and temperature (TEM) on 95.43% of observed greening trends was 47% and 20%, respectively. The response of NDVI in the eastern section of the Qilian Mountains (ESQM) and the western section of the Qilian Mountains (WSQM) to PRE and TEM showed opposite trends. The multiple linear regressions used to quantify the contribution of anthropogenic activity on the NDVI trend indicated that the ESQM and oasis areas were mainly affected by anthropogenic activities (26%). The observed browning trend in the ESQM was attributed to excessive consumption of natural resources. A buffer analysis and piecewise regression methods were further applied to explore the influence of urbanization on NDVI and its change rate. The study demonstrated that urbanization destroys the vegetation cover within the developed city areas and extends about 4 km beyond the perimeter of urban areas and the NDVI of buffer cities (counties) in the range of 0–4 km (0–3 km) increased significantly. In the range of 5–15 (4–10) km (except for Jiayuguan), climate factors were the major drivers of a slight downtrend in the NDVI. The relationship of land use change and NDVI trends showed that construction land, urban settlement, and farmland expanded sharply by 171.43%, 60%, and 10.41%, respectively. It indicated that the rapid process of urbanization and coordinated urban-rural development shrunk ecosystem services.

Description: Remote Sensing, Vol. 10, Pages 1283: Flood Prevention and Emergency Response System Powered by Google Earth Engine Remote Sensing doi: 10.3390/rs10081283 Authors: Cheng-Chien Liu Ming-Chang Shieh Ming-Syun Ke Kung-Hwa Wang This paper reviews the efforts made and experiences gained in developing the Flood Prevention and Emergency Response System (FPERS) powered by Google Earth Engine, focusing on its applications at the three stages of floods. At the post-flood stage, FPERS integrates various remote sensing imageries, including Formosat-2 optical imagery to detect and monitor barrier lakes, synthetic aperture radar imagery to derive an inundation map, and high-spatial-resolution photographs taken by unmanned aerial vehicles to evaluate damage to river channels and structures. At the pre-flood stage, a huge amount of geospatial data are integrated in FPERS and are categorized as typhoon forecast and archive, disaster prevention and warning, disaster events and analysis, or basic data and layers. At the during-flood stage, three strategies are implemented to facilitate the access of the real-time data: presenting the key information, making a sound recommendation, and supporting the decision-making. The example of Typhoon Soudelor in August of 2015 is used to demonstrate how FPERS was employed to support the work of flood prevention and emergency response from 2013 to 2016. The capability of switching among different topographic models and the flexibility of managing and searching data through a geospatial database are also explained, and suggestions are made for future works.

Description: Remote Sensing, Vol. 10, Pages 1282: Crop Classification in a Heterogeneous Arable Landscape Using Uncalibrated UAV Data Remote Sensing doi: 10.3390/rs10081282 Authors: Jonas E. Böhler Michael E. Schaepman Mathias Kneubühler Land cover maps are indispensable for decision making, monitoring, and management in agricultural areas, but they are often only available after harvesting. To obtain a timely crop map of a small-scale arable landscape in the Swiss Plateau, we acquired uncalibrated, very high-resolution data, with a spatial resolution of 0.05 m and four spectral bands, using a consumer-grade camera on an unmanned aerial vehicle (UAV) in June 2015. We resampled the data to different spatial and spectral resolutions, and evaluated the method using textural features (first order statistics and mathematical morphology), a random forest classifier for best performance, as well as number and size of the structuring elements. Our main findings suggest the overall best performing data consisting of a spatial resolution of 0.5 m, three spectral bands (RGB—red, green, and blue), and five different sizes of the structuring elements. The overall accuracy (OA) for the full set of crop classes based on a pixel-based classification is 66.7%. In case of a merged set of crops, the OA increases by ~7% (74.0%). For an object-based classification based on individual field parcels, the OA increases by ~20% (OA of 86.3% for the full set of crop classes, and 94.6% for the merged set, respectively). We conclude the use of UAV to be most relevant at 0.5 m spatial resolution in heterogeneous arable landscapes when used for crop classification.

Description: Remote Sensing, Vol. 10, Pages 1280: Drivers of Landscape Changes in Coastal Ecosystems on the Yukon-Kuskokwim Delta, Alaska Remote Sensing doi: 10.3390/rs10081280 Authors: M. Torre Jorgenson Gerald V. Frost Dorte Dissing The Yukon-Kuskokwim Delta (YKD) is the largest delta in western North America and its productive coastal ecosystems support globally significant populations of breeding birds and a large indigenous population. To quantify past landscape changes as a guide to assessing future climate impacts to the YKD and how indigenous society may adapt to change, we photo-interpreted ecotypes at 600 points within 12 grids in a 2118 km2 area along the central YKD coast using a time-series of air photos from 1948–1955 and 1980 and satellite images from 2007–2008 (IKONOS) and 2013–2016 (WorldView). We found that ecotype classes changed 16.2% (342 km2) overall during the ~62 years. Ecotypes changed 6.0% during 1953–1980, 7.2% during 1980–2007 and 3.8% during 2007–2015. Lowland Moist Birch-Ericaceous Low Scrub (−5.0%) and Coastal Saline Flat Barrens (−2.3%) showed the greatest decreases in area, while Lowland Water Sedge Meadow (+1.7%) and Lacustrine Marestail Marsh (+1.3%) showed the largest increases. Dominant processes affecting change were permafrost degradation (5.3%), channel erosion (3.0%), channel deposition (2.2%), vegetation colonization (2.3%) and lake drainage (1.5%), while sedimentation, water-level fluctuations, permafrost aggradation and shoreline paludification each affected <0.5% of the area. Rates of change increased dramatically in the late interval for permafrost degradation (from 0.06 to 0.26%/year) and vegetation colonization (from 0.03 to 0.16%/year), while there was a small decrease in channel deposition (from 0.05 to 0.0%/year) due largely to barren mudflats being colonized by vegetation. In contrast, rates of channel erosion remained fairly constant. The increased permafrost degradation coincided with increasing storm frequency and air temperatures. We attribute increased permafrost degradation and vegetation colonization during the recent interval mostly to the effects of a large storm in 2005, which caused extensive salt-kill of vegetation along the margins of permafrost plateaus and burial of vegetation on active tidal flats by mud that was later recolonized. Due to the combination of extremely flat terrain, sea-level rise, sea-ice reduction that facilitates more storm flooding and accelerating permafrost degradation, we believe the YKD is the most vulnerable region in the Arctic to climate warming.

Description: Remote Sensing, Vol. 10, Pages 1279: A New Method for Mapping Aquatic Vegetation Especially Underwater Vegetation in Lake Ulansuhai Using GF-1 Satellite Data Remote Sensing doi: 10.3390/rs10081279 Authors: Qi Chen Ruihong Yu Yanling Hao Linhui Wu Wenxing Zhang Qi Zhang Xunan Bu It is difficult to accurately identify and extract bodies of water and underwater vegetation from satellite images using conventional vegetation indices, as the strong absorption of water weakens the spectral feature of high near-infrared (NIR) reflected by underwater vegetation in shallow lakes. This study used the shallow Lake Ulansuhai in the semi-arid region of China as a research site, and proposes a new concave–convex decision function to detect submerged aquatic vegetation (SAV) and identify bodies of water using Gao Fen 1 (GF-1) multi-spectral satellite images with a resolution of 16 meters acquired in July and August 2015. At the same time, emergent vegetation, “Huangtai algae bloom”, and SAV were classified simultaneously by a decision tree method. Through investigation and verification by field samples, classification accuracy in July and August was 92.17% and 91.79%, respectively, demonstrating that GF-1 data with four-day short revisit period and high spatial resolution can meet the standards of accuracy required by aquatic vegetation extraction. The results indicated that the concave–convex decision function is superior to traditional classification methods in distinguishing water and SAV, thus significantly improving SAV classification accuracy. The concave–convex decision function can be applied to waters with SAV coverage greater than 40% above 0.3 m and SAV coverage 40% above 0.1 m under 1.5 m transparency, which can provide new methods for the accurate extraction of SAV in other regions.

Description: Remote Sensing, Vol. 10, Pages 1278: SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager Remote Sensing doi: 10.3390/rs10081278 Authors: Jean-François Rysman Giulia Panegrossi Paolo Sanò Anna Cinzia Marra Stefano Dietrich Lisa Milani Mark S. Kulie This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10−3 kg·m−2) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m−2. Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70°S–70°N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI.

Description: Remote Sensing, Vol. 10, Pages 1277: Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico Remote Sensing doi: 10.3390/rs10081277 Authors: Mikhail Urbazaev Felix Cremer Mirco Migliavacca Markus Reichstein Christiane Schmullius Christian Thiel Information on the spatial distribution of forest structure parameters (e.g., aboveground biomass, vegetation height) are crucial for assessing terrestrial carbon stocks and emissions. In this study, we sought to assess the potential and merit of multi-temporal dual-polarised L-band observations for vegetation height estimation in tropical deciduous and evergreen forests of Mexico. We estimated vegetation height using dual-polarised L-band observations and a machine learning approach. We used airborne LiDAR-based vegetation height for model training and for result validation. We split LiDAR-based vegetation height into training and test data using two different approaches, i.e., considering and ignoring spatial autocorrelation between training and test data. Our results indicate that ignoring spatial autocorrelation leads to an overoptimistic model’s predictive performance. Accordingly, a spatial splitting of the reference data should be preferred in order to provide realistic retrieval accuracies. Moreover, the model’s predictive performance increases with an increasing number of spatial predictors and training samples, but saturates at a specific level (i.e., at 12 dual-polarised L-band backscatter measurements and at around 20% of all training samples). In consideration of spatial autocorrelation between training and test data, we determined an optimal number of L-band observations and training samples as a trade-off between retrieval accuracy and data collection effort. In summary, our study demonstrates the merit of multi-temporal ScanSAR L-band observations for estimation of vegetation height at a larger scale and provides a workflow for robust predictions of this parameter.

Description: Remote Sensing, Vol. 10, Pages 1281: Reconstruction of Three-Dimensional (3D) Indoor Interiors with Multiple Stories via Comprehensive Segmentation Remote Sensing doi: 10.3390/rs10081281 Authors: Lin Li Fei Su Fan Yang Haihong Zhu Dalin Li Xinkai Zuo Feng Li Yu Liu Shen Ying The fast and stable reconstruction of building interiors from scanned point clouds has recently attracted considerable research interest. However, reconstructing long corridors and connected areas across multiple floors has emerged as a substantial challenge. This paper presents a comprehensive segmentation method for reconstructing a three-dimensional (3D) indoor structure with multiple stories. With this method, the over-segmentation that usually occurs in the reconstruction of long corridors in a complex indoor environment is overcome by morphologically eroding the floor space to segment rooms and by overlapping the segmented room-space with partitioned cells via extracted wall lines. Such segmentation ensures both the integrity of the room-space partitions and the geometric regularity of the rooms. For spaces across floors in a multistory building, a peak-nadir-peak strategy in the distribution of points along the z-axis is proposed in order to extract connected areas across multiple floors. A series of experimental tests while using seven real-world 3D scans and eight synthetic models of indoor environments show the effectiveness and feasibility of the proposed method.

Description: Remote Sensing, Vol. 10, Pages 1293: Using Near-Infrared-Enabled Digital Repeat Photography to Track Structural and Physiological Phenology in Mediterranean Tree–Grass Ecosystems Remote Sensing doi: 10.3390/rs10081293 Authors: Yunpeng Luo Tarek S. El-Madany Gianluca Filippa Xuanlong Ma Bernhard Ahrens Arnaud Carrara Rosario Gonzalez-Cascon Edoardo Cremonese Marta Galvagno Tiana W. Hammer Javier Pacheco-Labrador M. Pilar Martín Gerardo Moreno Oscar Perez-Priego Markus Reichstein Andrew D. Richardson Christine Römermann Mirco Migliavacca Tree–grass ecosystems are widely distributed. However, their phenology has not yet been fully characterized. The technique of repeated digital photographs for plant phenology monitoring (hereafter referred as PhenoCam) provide opportunities for long-term monitoring of plant phenology, and extracting phenological transition dates (PTDs, e.g., start of the growing season). Here, we aim to evaluate the utility of near-infrared-enabled PhenoCam for monitoring the phenology of structure (i.e., greenness) and physiology (i.e., gross primary productivity—GPP) at four tree–grass Mediterranean sites. We computed four vegetation indexes (VIs) from PhenoCams: (1) green chromatic coordinates (GCC), (2) normalized difference vegetation index (CamNDVI), (3) near-infrared reflectance of vegetation index (CamNIRv), and (4) ratio vegetation index (CamRVI). GPP is derived from eddy covariance flux tower measurement. Then, we extracted PTDs and their uncertainty from different VIs and GPP. The consistency between structural (VIs) and physiological (GPP) phenology was then evaluated. CamNIRv is best at representing the PTDs of GPP during the Green-up period, while CamNDVI is best during the Dry-down period. Moreover, CamNIRv outperforms the other VIs in tracking growing season length of GPP. In summary, the results show it is promising to track structural and physiology phenology of seasonally dry Mediterranean ecosystem using near-infrared-enabled PhenoCam. We suggest using multiple VIs to better represent the variation of GPP.

Description: Remote Sensing, Vol. 10, Pages 1294: Fractal-Based Local Range Slope Estimation from Single SAR Image with Applications to SAR Despeckling and Topographic Mapping Remote Sensing doi: 10.3390/rs10081294 Authors: Gerardo Di Martino Alessio Di Simone Daniele Riccio In this paper, we propose a range slope estimation procedure from single synthetic aperture radar (SAR) images with both methodological and applicative innovations. The retrieval algorithm is based on an analytical linearized direct model, which relates the SAR intensity data to the range local slopes and encompasses both a surface model and an electromagnetic scattering model. Scene topography is described via fractal geometry, whereas the Small Perturbation Method is adopted to represent the scattering behavior of the surface. The range slope map is then used to estimate the surface topography and the local incidence angle map. For topographic mapping applications, also referred to as shape from shading, a regularization procedure is derived to recover the azimuth local slope and reduce distortions. Then we present a new intriguing application of the inversion procedure in the field of SAR despeckling. Proposed techniques and high-level products are tested in a wide series of experiments, where the algorithms are applied to both simulated (canonical) and actual SAR images. It is proved that the proposed range slope retrieval technique can (1) provide an estimate of the surface shape, with overall better performance w.r.t. typical models used in this field and (2) be useful in advanced despeckling techniques.

Description: Remote Sensing, Vol. 10, Pages 1289: Multiscale and Multifeature Segmentation of High-Spatial Resolution Remote Sensing Images Using Superpixels with Mutual Optimal Strategy Remote Sensing doi: 10.3390/rs10081289 Authors: Zhongliang Fu Yangjie Sun Liang Fan Yutao Han High spatial resolution (HSR) image segmentation is considered to be a major challenge for object-oriented remote sensing applications that have been extensively studied in the past. In this paper, we propose a fast and efficient framework for multiscale and multifeatured hierarchical image segmentation (MMHS). First, the HSR image pixels were clustered into a small number of superpixels using a simple linear iterative clustering algorithm (SLIC) on modern graphic processing units (GPUs), and then a region adjacency graph (RAG) and nearest neighbors graph (NNG) were constructed based on adjacent superpixels. At the same time, the RAG and NNG successfully integrated spectral information, texture information, and structural information from a small number of superpixels to enhance its expressiveness. Finally, a multiscale hierarchical grouping algorithm was implemented to merge these superpixels using local-mutual best region merging (LMM). We compared the experiments with three state-of-the-art segmentation algorithms, i.e., the watershed transform segmentation (WTS) method, the mean shift (MS) method, the multiresolution segmentation (MRS) method integrated in commercial software, eCognition9, on New York HSR image datasets, and the ISPRS Potsdam dataset. Computationally, our algorithm was dozens of times faster than the others, and it also had the best segmentation effect through visual assessment. The supervised and unsupervised evaluation results further proved the superiority of the MMHS algorithm.

Description: Remote Sensing, Vol. 10, Pages 1285: Synergetic Use of Sentinel-1 and Sentinel-2 Data for Soil Moisture Mapping at Plot Scale Remote Sensing doi: 10.3390/rs10081285 Authors: Reza Attarzadeh Jalal Amini Claudia Notarnicola Felix Greifeneder This paper presents an approach for retrieval of soil moisture content (SMC) by coupling single polarization C-band synthetic aperture radar (SAR) and optical data at the plot scale in vegetated areas. The study was carried out at five different sites with dominant vegetation cover located in Kenya. In the initial stage of the process, different features are extracted from single polarization mode (VV polarization) SAR and optical data. Subsequently, proper selection of the relevant features is conducted on the extracted features. An advanced state-of-the-art machine learning regression approach, the support vector regression (SVR) technique, is used to retrieve soil moisture. This paper takes a new look at soil moisture retrieval in vegetated areas considering the needs of practical applications. In this context, we tried to work at the object level instead of the pixel level. Accordingly, a group of pixels (an image object) represents the reality of the land cover at the plot scale. Three approaches, a pixel-based approach, an object-based approach, and a combination of pixel- and object-based approaches, were used to estimate soil moisture. The results show that the combined approach outperforms the other approaches in terms of estimation accuracy (4.94% and 0.89 compared to 6.41% and 0.62 in terms of root mean square error (RMSE) and R2), flexibility on retrieving the level of soil moisture, and better quality of visual representation of the SMC map.

Description: Remote Sensing, Vol. 10, Pages 1286: Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data Remote Sensing doi: 10.3390/rs10081286 Authors: Viktoriya Tsyganskaya Sandro Martinis Philip Marzahn Ralf Ludwig The C-band Sentinel-1 satellite constellation enables the continuous monitoring of the Earth's surface within short revisit times. Thus, it provides Synthetic Aperture Radar (SAR) time series data that can be used to detect changes over time regardless of daylight or weather conditions. Within this study, a time series classification approach is developed for the extraction of the flood extent with a focus on temporary flooded vegetation (TFV). This method is based on Sentinel-1 data, as well as auxiliary land cover information, and combines a pixel-based and an object-oriented approach. Multi-temporal characteristics and patterns are applied to generate novel times series features, which represent a basis for the developed approach. The method is tested on a study area in Namibia characterized by a large flood event in April 2017. Sentinel-1 times series were used for the period between September 2016 and July 2017. It is shown that the supplement of TFV areas to the temporary open water areas prevents the underestimation of the flood area, allowing the derivation of the entire flood extent. Furthermore, a quantitative evaluation of the generated flood mask was carried out using optical Sentinel-2 images, whereby it was shown that overall accuracy increased by 27% after the inclusion of the TFV.

Description: Remote Sensing, Vol. 10, Pages 1284: Road Centerline Extraction from Very-High-Resolution Aerial Image and LiDAR Data Based on Road Connectivity Remote Sensing doi: 10.3390/rs10081284 Authors: Zhiqiang Zhang Xinchang Zhang Ying Sun Pengcheng Zhang The road networks provide key information for a broad range of applications such as urban planning, urban management, and navigation. The fast-developing technology of remote sensing that acquires high-resolution observational data of the land surface offers opportunities for automatic extraction of road networks. However, the road networks extracted from remote sensing images are likely affected by shadows and trees, making the road map irregular and inaccurate. This research aims to improve the extraction of road centerlines using both very-high-resolution (VHR) aerial images and light detection and ranging (LiDAR) by accounting for road connectivity. The proposed method first applies the fractal net evolution approach (FNEA) to segment remote sensing images into image objects and then classifies image objects using the machine learning classifier, random forest. A post-processing approach based on the minimum area bounding rectangle (MABR) is proposed and a structure feature index is adopted to obtain the complete road networks. Finally, a multistep approach, that is, morphology thinning, Harris corner detection, and least square fitting (MHL) approach, is designed to accurately extract the road centerlines from the complex road networks. The proposed method is applied to three datasets, including the New York dataset obtained from the object identification dataset, the Vaihingen dataset obtained from the International Society for Photogrammetry and Remote Sensing (ISPRS) 2D semantic labelling benchmark and Guangzhou dataset. Compared with two state-of-the-art methods, the proposed method can obtain the highest completeness, correctness, and quality for the three datasets. The experiment results show that the proposed method is an efficient solution for extracting road centerlines in complex scenes from VHR aerial images and light detection and ranging (LiDAR) data.

Description: Remote Sensing, Vol. 10, Pages 1291: Distributed Fiber Optic Sensors for the Monitoring of a Tunnel Crossing a Landslide Remote Sensing doi: 10.3390/rs10081291 Authors: Aldo Minardo Ester Catalano Agnese Coscetta Giovanni Zeni Lei Zhang Caterina Di Maio Roberto Vassallo Roberto Coviello Giuseppe Macchia Luciano Picarelli Luigi Zeni This work reports on the application of a distributed fiber-optic strain sensor for long-term monitoring of a railway tunnel affected by an active earthflow. The sensor has been applied to detect the strain distribution along an optical fiber attached along the two walls of the tunnel. The experimental results, relative to a two-year monitoring campaign, demonstrate that the sensor is able to detect localized strains, identify their location along the tunnel walls, and follow their temporal evolution.

Description: Remote Sensing, Vol. 10, Pages 1290: Sentinel-2 Image Fusion Using a Deep Residual Network Remote Sensing doi: 10.3390/rs10081290 Authors: Frosti Palsson Johannes R. Sveinsson Magnus O. Ulfarsson Single sensor fusion is the fusion of two or more spectrally disjoint reflectance bands that have different spatial resolution and have been acquired by the same sensor. An example is Sentinel-2, a constellation of two satellites, which can acquire multispectral bands of 10 m, 20 m and 60 m resolution for visible, near infrared (NIR) and shortwave infrared (SWIR). In this paper, we present a method to fuse the fine and coarse spatial resolution bands to obtain finer spatial resolution versions of the coarse bands. It is based on a deep convolutional neural network which has a residual design that models the fusion problem. The residual architecture helps the network to converge faster and allows for deeper networks by relieving the network of having to learn the coarse spatial resolution part of the inputs, enabling it to focus on constructing the missing fine spatial details. Using several real Sentinel-2 datasets, we study the effects of the most important hyperparameters on the quantitative quality of the fused image, compare the method to several state-of-the-art methods and demonstrate that it outperforms the comparison methods in experiments.

Description: Remote Sensing, Vol. 10, Pages 1287: Building Detection from VHR Remote Sensing Imagery Based on the Morphological Building Index Remote Sensing doi: 10.3390/rs10081287 Authors: Yongfa You Siyuan Wang Yuanxu Ma Guangsheng Chen Bin Wang Ming Shen Weihua Liu Automatic detection of buildings from very high resolution (VHR) satellite images is a current research hotspot in remote sensing and computer vision. However, many irrelevant objects with similar spectral characteristics to buildings will cause a large amount of interference to the detection of buildings, thus making the accurate detection of buildings still a challenging task, especially for images captured in complex environments. Therefore, it is crucial to develop a method that can effectively eliminate these interferences and accurately detect buildings from complex image scenes. To this end, a new building detection method based on the morphological building index (MBI) is proposed in this study. First, the local feature points are detected from the VHR remote sensing imagery and they are optimized by the saliency index proposed in this study. Second, a voting matrix is calculated based on these optimized local feature points to extract built-up areas. Finally, buildings are detected from the extracted built-up areas using the MBI algorithm. Experiments confirm that our proposed method can effectively and accurately detect buildings in VHR remote sensing images captured in complex environments.

Description: Remote Sensing, Vol. 10, Pages 1292: Symmetric Double-Eye Structure in Hurricane Bertha (2008) Imaged by SAR Remote Sensing doi: 10.3390/rs10081292 Authors: Guosheng Zhang William Perrie Internal dynamical processes play a critical role in hurricane intensity variability. However, our understanding of internal storm processes is less well established, partly because of fewer observations. In this study, we present an analysis of the hurricane double-eye structure imaged by the RADARSAT-2 cross-polarized synthetic aperture radar (SAR) over Hurricane Bertha (2008). SAR has the capability of hurricane monitoring because of the ocean surface roughness induced by surface wind stress. Recently, the C-band cross-polarized SAR measurements appear to be unsaturated for the high wind speeds, which makes SAR suitable for studies of the hurricane internal dynamic processes, including the double-eye structure. We retrieve the wind field of Hurricane Bertha (2008), and then extract the closest axisymmetric double-eye structure from the wind field using an idealized vortex model. Comparisons between the axisymmetric model extracted wind field and SAR observed winds demonstrate that the double-eye structure imaged by SAR is relatively axisymmetric. Associated with airborne measurements using a stepped-frequency microwave radiometer, we investigate the hurricane internal dynamic process related to the double-eye structure, which is known as the eyewall replacement cycle (ERC). The classic ERC theory was proposed by assuming an axisymmetric storm structure. The ERC internal dynamic process of Hurricane Bertha (2008) related to the symmetric double-eye structure here, which is consistent with the classic theory, is observed by SAR and aircraft.

Description: Remote Sensing, Vol. 10, Pages 1288: Improvement in Surface Solar Irradiance Estimation Using HRV/MSG Data Remote Sensing doi: 10.3390/rs10081288 Authors: Filomena Romano Domenico Cimini Angela Cersosimo Francesco Di Paola Donatello Gallucci Sabrina Gentile Edoardo Geraldi Salvatore Larosa Saverio T. Nilo Elisabetta Ricciardelli Ermann Ripepi Mariassunta Viggiano The Advanced Model for the Estimation of Surface Solar Irradiance (AMESIS) was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) to derive surface solar irradiance from SEVIRI radiometer on board the MSG geostationary satellite. The operational version of AMESIS has been running continuously at IMAA-CNR over all of Italy since 2017 in support to the monitoring of photovoltaic plants. The AMESIS operative model provides two different estimations of the surface solar irradiance: one is obtained considering only the low-resolution channels (SSI_VIS), while the other also takes into account the high-resolution HRV channel (SSI_HRV). This paper shows the difference between these two products against simultaneous ground-based observations from a network of 63 pyranometers for different sky conditions (clear, overcast and partially cloudy). Comparable statistical scores have been obtained for both AMESIS products in clear and cloud situation. In terms of bias and correlation coefficient over partially cloudy sky, better performances are found for SSI_HRV (0.34 W/m2 and 0.995, respectively) than SSI_VIS (−33.69 W/m2 and 0.862) at the expense of the greater run-time necessary to process HRV data channel.