ALBERT

Description: The new wave of modern rail transit and the proposal of the Belt and Road Initiative (BRI) have complicated the business patterns of the rolling stock manufacturing industry (RSMI) and the export of rolling stock products, especially in the case of countries participating in the BRI. Based on the analysis of trade patterns—which focuses on the evolution of trade links, community structures, and intraregional export competitiveness—this study aims to explore the changes in the RSMI within the BRI region from 2003 to 2017. Sequential clustering was applied to the creation of a three-phase timeline. The network models of the cumulative trade of the rolling stock products and trades of two typical categories of products were constructed in each phase for the evolution analysis. Social network analysis methods, such as the analysis of network indices and community detection, were also applied. The results show that from 2003 to 2017, the connectivity of the rolling stock trade in this region significantly increased. China was the largest exporter, with increasing trade influence and technological strength. Ukraine and Russia were less competitive and highly mutually dependent. Czechia and Austria’s competitiveness remained prominent, but compared with China they lacked expansive vitality. South Korea was also an active and competitive country with strong technological prowess. These countries accounted for the majority of the exports, and were always at the center of their own separate communities, over which they maintained a sphere of influence. The grouping of countries far from any such spheres of influence changed frequently.

Description: The world’s most extensive tropical peatlands occur in the Cuvette Centrale depression in the Congo Basin, which stores 30.6 petagrams of carbon (95% CI, 6.3–46.8). Improving our understanding of the genesis, development and functioning of these under-studied peatlands requires knowledge of their topography and, in particular, whether the peat surface is domed, as this implies a rain-fed system. Here we use a laser altimeter mounted on an unmanned airborne vehicle (UAV) to measure peat surface elevation along two transects at the edges of a peatland, in the northern Republic of Congo, to centimetre accuracy and compare the results with an analysis of nearby satellite LiDAR data (ICESat and ICESat-2). The LiDAR elevations on both transects show an upward slope from the peatland edge, suggesting a surface elevation peak of around 1.8 m over ~20 km. While modest, this domed shape is consistent with the peatland being rainfed. In-situ peat depth measurements and our LiDAR results indicate that this peatland likely formed at least 10,000 years BP in a large shallow basin ~40 km wide and ~3 m deep.

Description: The Direct Broadcast Network (DBNet) provides near-real-time delivery of low-earth-orbiting (LEO) meteorological satellites to operational numerical weather prediction (NWP) systems that need short data cut-off times to allow for the assimilation of the most recent satellite measurements. The NWP model requires timely delivery of observations including atmospheric temperature, humidity, and surface wind vectors. The World Meteorological Organization (WMO) Space Program (WSP) recommends the data latency of no more than 20 min for the satellite measurements. Currently, not all DBNet stations are delivering satellite data within the 20-min time frame. In this study, the forecast impact of the observations of LEO satellite sounders with data latency of 20 min or less was evaluated using the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). Reducing the data latency up to 5 min increases the number of LEO infrared (IR) and microwave (MW) sounder observations delivered to the NCEP GFS data assimilation system by more than 20%. Overall, this study demonstrates a positive impact on the global weather forecasts when the IR and MW sounder data are delivered by 20 min anywhere in the world. Additional forecast benefits are not obvious for shorter data latency. Results from this study support the WSP recommendation of 20–minute data latency.

Description: Drought is a natural phenomenon that originates from the absence of precipitation over a certain period and is capable of causing damage to societal development. With the advent of orbital remote sensing, rainfall estimates from satellites have appeared as viable alternatives to monitor natural hazards in ungauged basins and complex areas of the world; however, the accuracies of these orbital products still need to be verified. Thus, this work aims to evaluate the performance of Tropical Rainfall Measuring Mission (TRMM) satellite rainfall estimates in monitoring the spatiotemporal behavior of droughts at multiple temporal scales over Paraíba State based on the standardized precipitation index (SPI) over 20 years (1998–2017). For this purpose, rainfall data from 78 rain gauges and 187 equally spaced TRMM cell grids throughout the region are used, and accuracy analyses are performed at the single-gauge level and in four mesoregions at eight different time scales based on 11 statistical metrics calculations divided into three different categories. The results show that in the mesoregions close to the coast, the satellite-based product is less accurate in capturing the drought behavior regardless of the evaluated statistical metrics. At the temporal scale, the TRMM is more accurate in identifying the pattern of medium-term droughts; however, there is considerable spatial variation in the accuracy of the product depending on the performance index. Therefore, it is concluded that rainfall estimates from the TRMM satellite are a valuable source of data to identify drought behavior in a large part of Paraíba State at different time scales, and further multidisciplinary studies should be conducted to monitor these phenomena more accurately based on satellite data.

Description: The megacity of Tehran, the capital of Iran, is subjected to a high earthquake risk. Located at the central part of the Alpine–Himalayan seismic belt, Tehran is surrounded by several active faults that show some M7+ historical earthquake records. The high seismic hazard in combination with a dense population distribution and several vulnerability factors mean Tehran is one of the top 20 worldwide megacities at a high earthquake risk. This article aims to prepare an assessment of the present-day earthquake risk in Tehran. First, the earthquake risk components including hazard, exposure, and vulnerability are evaluated based on some accessible GIS-based datasets (e.g., seismicity, geology, active faults, population distribution, land use, urban fabric, buildings’ height and occupancy, structure types, and ages, as well as the vicinity to some critical infrastructures). Then, earthquake hazard maps in terms of PGA are prepared using a probabilistic approach as well as a surface rupture width map. Exposure and vulnerability maps are also provided deterministically in terms of population density and hybrid physical vulnerability, respectively. Finally, all these components are combined in a spatial framework and an earthquake risk map is provided for Tehran.

Description: In this paper, we applied the re-analysis data cobe-SST (cobe-sea surface temperature) and Global Land Data Assimilation System (GLDAS) surface soil moisture (SM) data from 1961 to 2011 by using regional correlation analysis and time series causality analysis to trace annual variations in and identify the abnormal relationship of sea surface temperature (SST) in the eastern China Sea and SM in eastern China (EC). We also used satellite Moderate Resolution Imaging Spectroradiometer (MODIS) SST and AMSR-E SM data to examine the correlation of SST and SM in EC from 2004–2009. The results show that the SST in the eastern China Sea has experienced a warming trend since 1987, whereas the SM in EC has shown a drying trend since 1978. Before 1967 and after 1997, SST and SM changed during opposite phases, whereas from 1967 to 1997 they changed during the same phase. The differences between them may result from the abnormal summer precipitation causing abnormal SM. According to the regional correlation analysis, SST of the East China Sea is significantly related to SM in the southeast coastal area, and temporal sequence causality analysis shows that SST is correlated with and has higher influence on SM than vice versa. SM during spring and autumn shows a similar correlation with SST during the four seasons, so that SM in spring and autumn is positively correlated with SST in autumn and negatively correlated with SST in other seasons. SM in summer and winter correlated with SST in the four seasons, contradicting the foregoing conclusions. All these findings indicate that the thermodynamic state of the eastern China Sea has affected SM in EC.

Description: Archaeologists engaging with Airborne Laser Scanning (ALS) data rely heavily on manual inspection of various derived visualizations. However, manual inspection of ALS data is extremely time-consuming and as such presents a major bottleneck in the data analysis workflow. We have therefore set out to learn and test a deep neural network model for classifying from previously manually annotated ancient Maya structures of the Chactún archaeological site in Campeche, Mexico. We considered several variations of the VGG-19 Convolutional Neural Network (CNN) to solve the task of classifying visualized example structures from previously manually annotated ALS images of man-made aguadas, buildings and platforms, as well as images of surrounding terrain (four classes and over 12,000 anthropogenic structures). We investigated how various parameters impact model performance, using: (a) six different visualization blends, (b) two different edge buffer sizes, (c) additional data augmentation and (d) architectures with different numbers of untrainable, frozen layers at the beginning of the network. Many of the models learned under the different scenarios exceeded the overall classification accuracy of 95%. Using overall accuracy, terrain precision and recall (detection rate) per class of anthropogenic structure as criteria, we selected visualization with slope, sky-view factor and positive openness in separate bands; image samples with a two-pixels edge buffer; Keras data augmentation; and five frozen layers as the optimal combination of building blocks for learning our CNN model.

Description: To meet the needs of coastline efficient extraction and dynamic monitoring, this paper proposes a new method for coastline extraction by combining the tidal level and the digital elevation model (DEM) of the coastal zone from tilt photography. Firstly, the DEM of coastal zone was obtained by using unmanned aerial vehicle (UAV) tilt photography; at the same time, the accuracy of aerial triangulation(AT) is improved referencing to the constraint of water boundary points, and then the mean high water spring tide was obtained by combining tidal harmonic analysis and Global Navigation Satellite System (GNSS) tidal level. Finally, the coastline and the dynamic water-surface line are extracted from the DEM of the coastal zone by tracking the contour lines with the elevation of the mean high water springs (MHWS) and the instantaneous sea-surface elevation, respectively. The experiments carried out in the coastal zones of Liaoning Province, China, proved the proposed method and achieved better than 0.2 m of horizontal position accuracy and 0.1 m of the vertical accuracy.

Description: This paper focuses on landslide susceptibility prediction in Nanchuan, a high-risk landslide disaster area. The evidential belief function (EBF)-based function tree (FT), logistic regression (LR), and logistic model tree (LMT) were applied to Nanchuan District, China. Firstly, an inventory with 298 landslides was compiled and separated into two parts (70%: 209; 30%: 89) as training and validation datasets. Then, based on the EBF method, the Bel values of 16 conditioning factors related to landslide occurrence were calculated, and these Bel values were used as input data for building other models. The receiver operating characteristic (ROC) curve and the values of the area under the ROC curve (AUC) were used to evaluate and compare the prediction ability of the four models. All the models achieved good results and performed well. In particular, the LMT model had the best performance (0.847 and 0.765, obtained from the training and validation datasets, respectively). This paper also demonstrates the superiority of integration and optimization of models in landslide susceptibility evaluation. Finally, the best classification method was selected to draw landslide susceptibility maps, which may be helpful for government administrators and engineers to carry out land design and planning.

Description: Climate observations and their applications require measurements with high stability and low uncertainty in order to detect and assess climate variability and trends. The difficulty with space-based observations is that it is generally not possible to trace them to standard calibration references when in orbit. In order to overcome this problem, it has been proposed to deploy space-based radiometric reference systems which intercalibrate measurements from multiple satellite platforms. Such reference systems have been strongly recommended by international expert teams. This paper describes the Chinese Space-based Radiometric Benchmark (CSRB) project which has been under development since 2014. The goal of CSRB is to launch a reference-type satellite named LIBRA in around 2025. We present the roadmap for CSRB as well as requirements and specifications for LIBRA. Key technologies of the system include miniature phase-change cells providing fixed-temperature points, a cryogenic absolute radiometer, and a spontaneous parametric down-conversion detector. LIBRA will offer measurements with SI traceability for the outgoing radiation from the Earth and the incoming radiation from the Sun with high spectral resolution. The system will be realized with four payloads, i.e., the Infrared Spectrometer (IRS), the Earth-Moon Imaging Spectrometer (EMIS), the Total Solar Irradiance (TSI), and the Solar spectral Irradiance Traceable to Quantum benchmark (SITQ). An on-orbit mode for radiometric calibration traceability and a balloon-based demonstration system for LIBRA are introduced as well in the last part of this paper. As a complementary project to the Climate Absolute Radiance and Refractivity Observatory (CLARREO) and the Traceable Radiometry Underpinning Terrestrial- and Helio- Studies (TRUTHS), LIBRA is expected to join the Earth observation satellite constellation and intends to contribute to space-based climate studies via publicly available data.

Description: Soil properties variability is a factor that greatly influences cereals crops production and interacts with a proper assessment of crop nutritional status, which is fundamental to support site-specific management able to guarantee a sustainable crop production. Several management strategies of precision agriculture are now available to adjust the nitrogen (N) input to the actual crop needs. Many of the methods have been developed for proximal sensors, but increasing attention is being given to satellite-based N management systems, many of which rely on the assessment of the N status of crops. In this study, the reliability of the crop nutritional status assessment through the estimation of the nitrogen nutrition index (NNI) from Sentinel-2 (S2) satellite images was examined, focusing of the impact of soil properties variability for crop nitrogen deficiency monitoring. Vegetation indices (VIs) and biophysical variables (BVs), such as the green area index (GAI_S2), leaf chlorophyll content (Cab_S2), and canopy chlorophyll content (CCC_S2), derived from S2 imagery, were used to investigate plant N status and NNI retrieval, in the perspective of its use for guiding site-specific N fertilization. Field experiments were conducted on maize and on durum wheat, manipulating 4 groups of plots, according to soil characteristics identified by a soil map and quantified by soil samples analysis, with different N treatments. Field data collection highlighted different responses of the crops to N rate and soil type in terms of NNI, biomass (W), and nitrogen concentration (Na%). For both crops, plots in one soil class (FOR1) evidenced considerably lower values of BVs and stress conditions with respect to others soil classes even for high N rates. Soil samples analyses showed for FOR1 soil class statistically significant differences for pH, compared to the other soil classes, indicating that this property could be a limiting factor for nutrient absorption, hence crop growth, regardless of the amount of N distributed to the crop. The correlation analysis between measured crop related BVs and satellite-based products (VIs and S2_BVs) shows that it is possible to: (i) directly derive NNI from CCC_S2 (R2 = 0.76) and either normalized difference red edge index (NDRE) for maize (R2 = 0.79) or transformed chlorophyll absorption ratio index (TCARI) for durum wheat (R2 = 0.61); (ii) indirectly estimate NNI as the ratio of plant nitrogen uptake (PNUa) and critical plant nitrogen uptake (PNUc) derived using CCC_S2 (R2 = 0.77) and GAI_S2 (R2 = 0.68), respectively. Results of this study confirm that NNI is a good indicator to monitor plants N status, but also highlights the importance of linking this information to soil properties to support N site-specific fertilization in the precision agriculture framework. These findings contribute to rational agro-practices devoted to avoid N fertilization excesses and consequent environmental losses, bringing out the real limiting factors for optimal crop growth.

Description: The Advanced Microwave Sounding Unit (AMSU)-A/Advanced Technology Microwave Sounder (ATMS) onboard the National Oceanic Atmospheric Administration (NOAA)-18/-19, MetOp-A/-B, and Suomi National Polar-orbiting Partnership satellites provide global observations of the cloud Liquid Water Path (LWP) almost 10 times a day. This study explores the possibility of capturing the diurnal cycle of the LWP. An inter-satellite cross-calibration is first carried out using a double-difference method. A remapping is then used to obtain the AMSU-A-like LWP to account for beam shape discrepancies between the ATMS and AMSU-A. We finally examine the diurnal cycle of the LWP over the Southeast Pacific Ocean using the ATMS and AMSU-A data from the five satellites mentioned above. Results show that the remapped ATMS results agree well with the AMSU-A results at the same local time over a stratocumulus region. LWP retrievals from multiple satellite cross-track microwave radiometers can well reproduce the diurnal variation characteristics of LWP in 2015 over the East Pacific Ocean, including the seasonal variation of the diurnal variation. This study presents the first step toward merging LWP data from all ATMS and AMSU-A radiometers and will be of interest to many researchers studying LWP-related weather and climate changes, especially considering the possible loss of higher-resolution microwave-frequency conical-scanning sensors in the coming years.

Description: The main objective of this research is to explore the cognitive processes of expert and novice map users during the retrieval of map-related information, within varying difficulty levels (i.e., easy, moderate, hard), by using eye tracking and electroencephalogram (EEG). In this context, we present a spatial memory experiment consisting of a large number of stimuli to study the effect of task difficulty on map users’ behavior through cognitive load measurements. Next to the reaction time and success rate, we used fixation and saccade related eye tracking metrics (i.e., average fixation duration, the number of fixations per second, saccade amplitude and saccade velocity), and EEG power spectrum (i.e., event-related changes in alpha and theta frequency bands) to identify the cognitive load. While fixation metrics indicated no statistically significant difference between experts and novices, saccade metrics proved the otherwise. EEG power spectral density analysis, on the other side, suggested an increase in theta (i.e., event-related synchronization) and a decrease in alpha (except moderate tasks) (i.e., event-related desynchronization) at all difficulty levels of the task for both experts and novices, which is an indicator of cognitive load. Although no significant difference emerged between two groups, we found a significant difference in their overall performances when the participants were classified as good and relatively bad learners. Triangulating EEG results with the recorded eye tracking data and the qualitative analysis of focus maps indeed provided a detailed insight on the differences of the individuals’ cognitive processes during this spatial memory task.

Description: In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work was to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud.) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophyte communities with all sensor combinations. In all combinations, the model-based producer’s accuracy (PA) and user’s accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L.

Description: The Guizhou Plateau has an extremely fragile ecological environment with prominent soil and water losses. Since 2000, conservation policies and ecological restoration projects, e.g., the Grain for Green Project (GGP), have been implemented on the Guizhou Plateau to control soil/water losses which have achieved notable accomplishments. Using the Revised Universal Soil Loss Equation (RUSLE) to estimate the soil conservation service (SCS) on the Guizhou Plateau, this study analyzed the dynamic characteristics of its spatiotemporal variation based on multiyear (2000–2018) meteorological and remote sensing data to determine its driving mechanisms. Residual analysis of the meteorological and remote sensing data was used to evaluate the effect of anthropogenic activities. Results showed a clear upward trend (1.39 t ha−1 yr−1) of SCS on the Guizhou Plateau during 2000–2018, and areas with a highly improved positive effect on SCS were distributed primarily in karst landform regions. Precipitation and vegetation fractional coverage (VFC) were found to be positively correlated with SCS on the Guizhou Plateau. Specifically, the highest proportion of significant positive correlation between precipitation and SCS was related to the Wildlife Conservation Nature Reserve (WCNR), and the highest proportion of significant positive correlation between VFC and SCS was related to the GGP, i.e., 76.59% and 53.02%, respectively. Residual analysis revealed a significant positive role of anthropogenic activity on SCS improvement via ecological engineering in areas with a poor ecological background, e.g., the GGP in western areas where the ecological environment is fragile and the problem of water/soil loss is serious. In areas with a more robust ecological background, e.g., the engineering area of the WCNR, the effect of anthropogenic activity has had a largely negative effect on SCS. The findings of this study could make an important contribution to the development of ecological management projects and the work to control soil/water losses on the Guizhou Plateau.

Description: Correction of spectral reflectance for shadow and topography in optical remote sensing data is challenging. Here, we corrected for canopy self-shadowing in an evergreen conifer forest in mountainous terrain using a three-dimensional (3D) point cloud. In our approach, the surface was modeled from structure-from-motion processed images provided by an unmanned aerial vehicle; then, the relationship between the observed spectral reflectance of the Sentinel-2A/B multispectral instrument, and the simulated sunlit fraction (the percentage of the solar-illuminated area within a Sentinel-2 pixel) was determined based on a ray-tracing scheme using a 3D point cloud. Scene-based and pixel-based linear regressions were applied to remove canopy-shadow and topographic effects from satellite-observed reflectance. Scene-based regression resulted in large seasonal changes that caused overcorrection in winter. Pixel-based regression generated stable seasonal profiles in both the red and near-infrared reflectance values over the conifer canopy; however, excessively smoothed seasonal changes were implemented over deciduous vegetation. In both correction methods, the reflection of incident light by the canopy likely improved the correction accuracy by decreasing the contrast between illuminated and shaded pixels in summer and increasing it in winter. The results were also visually compared with those from the Sun-Canopy-Sensor with C (SCS+C) method and the Sentinel-2 Level-2A product. This research demonstrates the effectiveness of using a 3D point cloud to correct for self-shadowing and topographic effects on remotely sensed reflectance.

Description: Plantations of fast-growing Eucalyptus trees have become a common sight in the western Iberian peninsula where they are planted to exploit their economic potential. Negative side-effects of large scale plantations including the invasive behavior of Eucalyptus trees outside of regular plantations have become apparent. This study uses medium resolution, multi-spectral imagery of the Sentinel 2 satellites to map Eucalyptus across Portugal and parts of Spain with a focus on Natura 2000 areas inside Portugal, that are protected under the European birds and habitats directives. This method enables the detection of small incipient as well as mixed populations outside of regular plantations. Ground truth maps were compiled using field surveys as well as high resolution satellite imagery and were used to train Feedforward Neural Networks. These models predict Eucalyptus tree cover with a sensitivity of up to 75.7% as well as a specificity of up to 95.8%. The overall accuracy of the prediction is 92.5%. A qualitative assessment of Natura 2000 areas in Portugal has been performed and 15 areas have been found to be affected by Eucalyptus of which 9 are strongly affected. This study demonstrates the applicability of multi-spectral imagery for tree-species classification and invasive species control. It provides a probability-map of Eucalyptus tree cover for the western Iberian peninsula with 10 m spatial resolution and shows the need for monitoring of Eucalyptus in protected areas.

Description: The BeiDou navigation satellite system (BDS) currently has 41 satellites in orbits and will reach its full constellation following the launch of the last BDS satellite in June 2020 to provide navigation, positioning, and timing (PNT) services for global users. In this contribution, we investigate the characteristics of inter-system bias (ISB) between BDS-2 and BDS-3 and verify whether an additional ISB parameter should be introduced for the BDS-2 and BDS-3 precise point positioning (PPP). The results reveal that because of different clock references applied for BDS-2 and BDS-3 in the International GNSS Service (IGS) precise satellites clock products and the inconsistent code hardware delays of BDS-2 and BDS-3 for some receiver types, an ISB parameter needs to be introduced for BDS-2 and BDS-3 PPP. Further, the results show that the ISB can be regarded as a constant within a day, the value of which is closely related to the receiver type. The ISB values of the stations with the same receiver type are similar to each other, but a great difference may be presented for different receiver types, up to several meters. In addition, the impact of ISB on PPP has also been studied, which demonstrates that the performance of kinematic PPP could be improved when ISB is introduced.

Description: The field of data science has had a significant impact in both academia and industry, and with good reason [...]

Description: The vertically distributed aerosol optical properties are investigated over Pakistan utilizing the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 products from 2007 to 2014. For a better understanding of the spatiotemporal characteristics of vertical aerosol layers, the interannual and seasonal variations of nine selected aerosol parameters such as the AOD of the lowest aerosol layer (AODL), the base height of the lowest aerosol layer (HL), the top height of the highest aerosol layer (HH), the volume depolarization ratio of the lowest aerosol layer (DRL), the color ratio of the lowest aerosol layer (CRL), total AOD of all the aerosol layers (AODT), the number of aerosol feature layers (N), the thickness of the lowest aerosol layer (TL), the AOD proportion for the lowest aerosol layer (PAODL) for both day and night times are analyzed. The results show AODT increased slightly from 2007 to 2014 over Pakistan, and relatively high AODT exists over the Punjab and Sindh (southern region), which might be owing to the high level of economic development, frequent dust storms, and profound agricultural activities (anthropogenic emissions). AODT increases from north to south. The reason may be that the southern region is rapidly urbanized and is near the desert. The northern region is dominated by agricultural land, and cities are usually semi-urbanized. The highest AODT appears in summer compared to the other seasons, and during daytime compared to nighttime. The HL and HH vary significantly, owing to the topography of Pakistan. The N is relatively large over Punjab and Sindh compared to the other regions, which might be caused by relatively stronger atmospheric convections. The spatial distribution of the TL showed an inverse relationship with the topography as lower values are observed over elevated regions such as Gilgit-Baltistan and Jammu-Kashmir. The value of the PAODL indicates that 77% of the total aerosols are mainly concentrated in the lowest layer of the atmosphere over Pakistan. The higher values of DRL and CRL indicate non-spherical and large particles over Balochistan and Sindh, which might be related to the proximity to the desert. This study provides very useful information about vertically distributed aerosol optical properties which could help researchers and policymakers to regulate and mitigate air pollution issues of Pakistan.

Description: The existing deep learning methods for point cloud classification are trained using abundant labeled samples and used to test only a few samples. However, classification tasks are diverse, and not all tasks have enough labeled samples for training. In this paper, a novel point cloud classification method for indoor components using few labeled samples is proposed to solve the problem of the requirement for abundant labeled samples for training with deep learning classification methods. This method is composed of four parts: mixing samples, feature extraction, dimensionality reduction, and semantic classification. First, the few labeled point clouds are mixed with unlabeled point clouds. Next, the mixed high-dimensional features are extracted using a deep learning framework. Subsequently, a nonlinear manifold learning method is used to embed the mixed features into a low-dimensional space. Finally, the few labeled point clouds in each cluster are identified, and semantic labels are provided for unlabeled point clouds in the same cluster by a neighborhood search strategy. The validity and versatility of the proposed method were validated by different experiments and compared with three state-of-the-art deep learning methods. Our method uses fewer than 30 labeled point clouds to achieve an accuracy that is 1.89–19.67% greater than existing methods. More importantly, the experimental results suggest that this method is not only suitable for single-attribute indoor scenarios but also for comprehensive complex indoor scenarios.

Description: Gridded passive microwave brightness temperatures (TB) from special sensor microwave imager and sounder (SSMIS) instruments on three different satellite platforms are compared in different years to investigate the consistency between the sensors over time. The orbits of the three platforms have drifted over their years of operation, resulting in changing relative observing times that could cause biases in TB estimates and near-real-time sea ice concentrations derived from the NASA Team algorithm that are produced at the National Snow and Ice Data Center. Comparisons of TB histograms and concentrations show that there are small mean differences between sensors, but variability within an individual sensor is much greater. There are some indications of small changes due to orbital drift, but these are not consistent across different frequencies. Further, the overall effect of the drift, while not definitive, is small compared to the intra- and interannual variability in individual sensors. These results suggest that, for near-real-time use, the differences in the sensors are not critical. However, for long-term time series, even the small biases should be corrected for. The strong day-to-day, seasonal, and interannual variability in TB distributions indicate that time-varying algorithm coefficients in the NASA team algorithm would lead to improved, more consistent sea ice concentration estimates.

Description: Uncertainty in satellite-derived burned area estimates are especially high in grassland systems, which are some of the most frequently burned ecosystems in the world. In this study, we compare differences in predicted burned area estimates for a region with the highest fire activity in North America, the Flint Hills of Kansas, USA, using the moderate resolution imaging spectroradiometer (MODIS) MCD64A1 burned area product and a customization of the MODIS MCD64A1 product using a major ground-truthing effort by the Kansas Department of Health and Environment (KDHE-MODIS customization). Local-scale ground-truthing and the KDHE-MODIS product suggests MODIS burned area estimates under predicted fire occurrence by 28% over a 19-year period in the Flint Hills ecoregion. Between 2001 and 2019, MODIS product indicated

Description: Solar-induced chlorophyll fluorescence (SIF) provides a new and direct way of monitoring photosynthetic activity. However, current SIF products are limited by low spatial resolution or sparse sampling. In this paper, we present a data-driven method of generating a global, spatially continuous TanSat SIF product. Firstly, the key explanatory variables for modelling canopy SIF were investigated using in-situ and satellite observations. According to theoretical and experimental analysis, the solar radiation intensity was found to be a dominant driving environmental variable for the SIF yield at both the canopy and global scales; this has, however, been neglected in previous research. The cosine value of the solar zenith angle at noon (cos (SZA0)), a proxy for solar radiation intensity, was found to be a dominant abiotic factor for the SIF yield. Next, a Random Forest (RF) approach was employed for SIF prediction based on Moderate Resolution Imaging Spectroradiometer (MODIS) visible-to-NIR reflectance data, the normalized difference vegetation (NDVI), cos (SZA0), and air temperature. The machine learning model performed well at predicting SIF, giving R2 values of 0.73, an RMSE of 0.30 mW m−2 nm−1 sr−1 and a bias of 0.22 mW m−2 nm−1 sr−1 for 2018. If cos (SZA0) was not included, the accuracy of the RF model decreased: the R2 value was then 0.65, the RMSE 0.34 mW m−2 nm−1 sr−1 and an bias of 0.26 mW m−2 nm−1 sr−1, further verifying the importance of cos (SZA0). Finally, the globally continuous TanSat SIF product was developed and compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIF data. The results showed that the globally continuous TanSat SIF product agreed well with the TROPOMI SIF data, with an R2 value of 0.73. Thus, this paper presents an improved approach to modelling satellite SIF that has a better accuracy, and the study also generated a global, spatially continuous TanSat SIF product with a spatial resolution of 0.05°.

Description: In this paper, we present a Remote Sens. approach to localize parking cars in a city in order to enable the development of parking space availability models. We propose to use high-resolution stereo satellite images for this problem, as they provide enough details to make individual cars recognizable and the time interval between the stereo shots allows to reason about the moving or static condition of a car. Consequently, we describe a complete processing pipeline where raw satellite images are georeferenced, ortho-rectified, equipped with a digital surface model and an inclusion layer generated from Open Street Model vector data, and finally analyzed for parking cars by means of an adapted Faster R-CNN oriented bounding box detector. As a test site for the proposed approach, a new publicly available dataset of the city of Barcelona labeled with parking cars is presented. On this dataset, a Faster R-CNN model directly trained on the two ortho-rectified stereo images achieves an average precision of 0.65 for parking car detection. Finally, an extensive empirical and analytical evaluation shows the validity of our idea, as parking space occupancy can be broadly derived in fully visible areas, whereas moving cars are efficiently ruled out. Our evaluation also includes an in-depth analysis of the stereo occlusion problem in view of our application scenario as well as the suitability of using a reconstructed Digital Surface Model (DSM) as additional data modality for car detection. While an additional adoption of the DSM in our pipeline does not provide a beneficial cue for the detection task, the stereo images provide essentially two views of the dynamic scene at different timestamps. Therefore, for future studies, we recommend a satellite image acquisition geometry with smaller incidence angles, to decrease occlusions by buildings and thus improve the results with respect to completeness.

Description: The vertical distribution of aerosols is important for accurate surface PM2.5 retrieval and initial modeling forecasts of air pollution, but the observation of aerosol profiles on the regional scale is usually limited. Therefore, in this study, an approach to aerosol extinction profile fitting is proposed to improve surface PM2.5 retrieval from satellite observations. Owing to the high similarity of the single-peak extinction profile in the distribution pattern, the log-normal distribution is explored for the fitting model based on a decadal dataset (3248 in total) from Micro Pulse LiDAR (MPL) measurements. The logarithmic mean, standard deviation, and the height of peak extinction near-surface (Mode) are manually derived as the references for model construction. Considering the seasonal impacts on the planetary boundary layer height (PBLH), Mode, and the height of the surface layer, the extinction profile is then constructed in terms of the planetary boundary layer height (PBLH) and the total column aerosol optical depth (AOD). A comparison between fitted profiles and in situ measurements showed a high level of consistency in terms of the correlation coefficient (0.8973) and root-mean-square error (0.0415). The satellite AOD is subsequently applied for three-dimensional aerosol extinction, and the good agreement of the extinction coefficient with the PM2.5 within the surface layer indicates the good performance of the proposed fitting approach and the potential of satellite observations for providing accurate PM2.5 data on a regional scale.

Description: Suspended Particulate Matter (SPM) is a major constituent in coastal waters, involved in processes such as light attenuation, pollutant propagation, and waterways blockage. The spatial distribution of SPM is an indicator of deposition and erosion patterns in estuaries and coastal zones and a necessary input to estimate the material fluxes from the land through rivers to the sea. In-situ methods to estimate SPM provide limited spatial data in comparison to the coverage that can be obtained remotely. Ocean color remote sensing complements field measurements by providing estimates of the spatial distributions of surface SPM concentration in natural waters, with high spatial and temporal resolution. Existing methods to obtain SPM from remote sensing vary between purely empirical ones to those that are based on radiative transfer theory together with empirical inputs regarding the optical properties of SPM. Most algorithms use a single satellite band that is switched to other bands for different ranges of turbidity. The necessity to switch bands is due to the saturation of reflectance as SPM concentration increases. Here we propose a multi-band approach for SPM retrievals that also provides an estimate of uncertainty, where the latter is based on both uncertainties in reflectance and in the assumed optical properties of SPM. The approach proposed is general and can be applied to any ocean color sensor or in-situ radiometer system with red and near-infra-red bands. We apply it to six globally distributed in-situ datasets of spectral water reflectance and SPM measurements over a wide range of SPM concentrations collected in estuaries and coastal environments (the focus regions of our study). Results show good performance for SPM retrieval at all ranges of concentration. As with all algorithms, better performance may be achieved by constraining empirical assumptions to specific environments. To demonstrate the flexibility of the algorithm we apply it to a remote sensing scene from an environment with highly variable sediment concentrations.

Description: Forward logistic regression and conditional analysis have been compared to assess landslide susceptibility across the whole territory of the Sicilian region (about 25,000 km2) using previously existing data and a nested tiered approach. These approaches were aimed at singling out a statistical correlation between the spatial distribution of landslides that have affected the Sicilian region in the past, and a set of controlling factors: outcropping lithology, rainfall, landform classification, soil use, and steepness. The landslide inventory used the proposal of building the models like the official one obtained in the PAI (hydro geologic asset plan) project, amounting to more than 33,000 events. The 11 types featured in PAI were grouped into 4 macro-typologies, depending on the inherent conditions believed to generate various kinds of failures and their kinematic evolution. The study has confirmed that it is possible to carry out a regional landslide susceptibility assessment based solely on existing data (i.e., factor maps and the landslide archive), saving a considerable amount of time and money. For scarp landslides, where the selected factors (steepness, landform classification, and lithology) are more discriminate, models show excellent performance: areas under receiver operating characteristic (ROC) (AUCs) average 〉 0.9, while hillslope landslide results are highly satisfactory (average AUCs of about 0.8). The stochastic approach makes it possible to classify the Sicilian territory depending on its propensity to landslides in order to identify those municipalities which are most susceptible at this level of study, and are potentially worthy of more specific studies, as required by European-level protocols.

Description: This paper uses the technology acceptance model (TAM) framework for the research of economic and geography students’ attitudes towards interdisciplinary knowledge. Based on the SmartPLS Structural equation modelling SEM variance-based method, research results were gained through analysis of survey data of economic and geography students. They participated in the Spationomy project in the period of 2017–2019. Online questionnaires were fulfilled before and after students’ participation in the project and their future behavioural intention to use interdisciplinary knowledge was analysed. Based on the research, we can confirm that the Spationomy project has achieved its purpose, as both groups of students (economic and geography students) have acquired interdisciplinary knowledge and students intend to use it also in the future. Therefore, we can argue that the students included in the project in practice gained recognition of systems thinking about the importance of mutual interdisciplinary cooperation towards achieving synergies. The results also show that TAM can be successfully implemented to analyse how students of economics and geography accept the use of interdisciplinary knowledge in the learning process, which is an important implication for management and education as well as from the theoretical implications viewpoint. While effective analysis using TAM has been used successfully and relatively frequently in economics and business field, we have not found relevant examples of its implementation in the broader field of geography. However, the acceptance of geographic information system (GIS) or other information technologies/information software (IT/IS) tool-based approaches of analysis in the geography field may be of most importance. Therefore, also, this represents an important implication for the research area.

Description: Under the trend of increasingly informationalized military operations and the increasing maneuverability of combat units, military commanders have put forward higher requirements for the accuracy and promptness of information on battlefield situation maps. Based on the sea battlefield, this paper studies the pros and cons of the color matching of military symbols on sea situation maps. Fifteen colors, where each Hue had five colors, were chosen using the Munsell Color System according to Chroma axis and the Value axis on a span of 2 and 4. By collecting and analyzing the P300 EEG data, reaction time data, and accuracy data of 20 subjects, a better color matching selection of military symbols on pure color (L = 85, a = −10, and b = −23) sea situation maps is put forward, and the conclusions are as follows: (1) the different colors all cause the P300 component in EEG experiment. Among them, the P300 amplitude that is caused by military symbols with lower Chroma is smaller and the latency is shorter, indicating that the user experience and efficiency of low Chroma color symbols will be better than those with high Chroma color symbols. (2) High Value color map military symbols cause higher P300 amplitude and longer latency. According to the results above, this paper puts forward three optimized colors, namely, blue (L = 39, a = 20, and b = −49), green (L = 80, a = −72, and b = 72), and red (L = 20, a = 41, and b = 28). Additionally, three map interfaces were designed to confirm the validity of these colors. By means of applying the NASA-TLX (Task Load Index) scale to evaluate the task load of the confirmation interfaces, it can be concluded that these three optimized colors are preferred by users who are skilled in GIS and interface design. Therefore, the research conclusion of this paper can provide important reference values for military map design, which is helpful in shortening the identification and judgment time during the use of situation maps and it can improve users’ operation performance.

Description: The unprecedented slowdown in China during the COVID-19 period of November 2019 to April 2020 should have reduced pollution in smog-laden cities. However, moderate resolution imaging spectrometer (MODIS) satellite retrievals of aerosol optical depth (AOD) show a marked increase in aerosols over the Beijing–Tianjin–Hebei (BHT) region and most of Northeast and Central China, compared with the previous winter. Fine particulate (PM2.5) data from ground monitoring stations show an increase of 19.5% in Beijing during January and February 2020, and no reduction for Tianjin. In March and April 2020, a different spatial pattern emerges, with very high AOD levels observed over 50% of the Chinese mainland, and including peripheral regions in the northwest and southwest. At the same time, ozone monitoring instrument (OMI) satellite-derived NO2 concentrations fell drastically across China. The increase in PM2.5 while NO2 decreased in BTH and across China is likely due to enhanced production of secondary particulates. These are formed when reductions in NOx result in increased ozone formation, thus increasing the oxidizing capacity of the atmosphere. Support for this explanation is provided by ground level air quality data showing increased volume of fine mode aerosols throughout February and March 2020, and increased levels of PM2.5, relative humidity (RH), and ozone during haze episodes in the COVID-19 lockdown period. Backward trajectories show the origin of air masses affecting industrial centers of North and East China to be local. Other contributors to increased atmospheric particulates may include inflated industrial production in peripheral regions to compensate loss in the main population and industrial centers, and low wind speeds. Satellite monitoring of the extraordinary atmospheric conditions resulting from the COVID-19 shutdown could enhance understanding of smog formation and attempts to control it.

Description: The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), infected (I) and dry (D) leaves of apple trees. Analysis of variance was employed in order to determine which hyperspectral narrow spectral bands exhibited the most significant differences. Spectral signatures for the range of 400–2500 nm were acquired with Thermo Scientific Evolution 220 and iS50NIR spectrometers. The selected spectral bands were then used to evaluate several RS indices, including ARI (Anthocyanin Reflectance Index), RDVI (Renormalized Difference Vegetation Index), MSR (Modified Simple Ratio) and NRI (Nitrogen Reflectance Index), for Fire Blight detection in apple tree leaves. Furthermore, a new index was proposed, namely QFI. The spectral indices were tested on apple trees infected by Fire Blight in a quarantine greenhouse. Results indicated that the short-wavelength infrared (SWIR) band located at 1450 nm was able to distinguish (I) and (H) leaves, while the SWIR band at 1900 nm differentiated all three leaf types. Moreover, tests using the Pearson correlation indicated that ARI, MSR and QFI exhibited the highest correlations with the infection progress. Our results prove that our hyperspectral remote sensing technique is able to differentiate (H), (I) and (D) leaves of apple trees for the reliable and precise detection of Fire Blight.

Description: Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change.

Description: The transport of dangerous goods by rail carries a high risk of accident and every effort should be made to ensure that such transport is carried out under the best possible safety conditions. The research objective was to analyze and identify the main risks associated with the transport of dangerous goods by rail as well as to identify and assess the main factors of safe transport in order to reduce the risk of accident. For this purpose, analysis of the literature, systematization, generalization, and evaluation by experts was applied. The article states that in order to ensure the safe transport of dangerous goods by rail, it is necessary to comply with the rules for loading and unloading dangerous goods, the established requirements and instructions, and technical conditions of wagons and their labelling as well as preventive measures to reduce the risk. Recommendations are provided on how to reduce accidents and incidents in the transport of dangerous goods by rail.

Description: Fengyun-3C (FY-3C) is the first Chinese satellite that is capable of using the Radio Occultation (RO) technique to retrieve atmospheric profiles. This research evaluates the quality of FY-3C RO profiles including refractivity, temperature, and specific humidity by comparing with corresponding information from the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim) data over the period of 2015–2018. The evaluation is carried out by calculating and analyzing mean systematic differences between FY-3C and ERA-Interim profiles and corresponding standard deviations over a selected spatial and temporal domain. Results show that the FY-3C RO profiles are overall with good agreements with the ERA-Interim data. Global mean refractivity systematic differences are within ±0.2% from 5 to 30 km altitude range with relative standard deviations of less than 2%. Global temperature mean systematic differences vary within ±0.2 K from a 10- to 20-km altitude range with standard deviations of less than 2 K. Global mean specific humidity differences are found to be within ±0.2 g/kg from 2 to 20 km with standard deviations of less than 1 g/kg. FY-3C profiles show visible latitudinal and altitudinal variations, while the seasonal variations are minor. Sampling errors of refractivity and temperature are also found to be larger at higher latitudinal regions due to RO events being less sampled in the polar region.

Description: Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure. As the only in-operation hyperspectral satellite launched by China, GF-5 is potentially useful for accurate LAI estimation. However, there is no research focus on evaluating GF-5 data for LAI estimation. Hyperspectral remote sensing data contains abundant information about the reflective characteristics of vegetation canopies, but these abound data also easily result in a dimensionality curse. Therefore, feature selection (FS) is necessary to reduce data redundancy to achieve more reliable estimations. Currently, machine learning (ML) algorithms have been widely used for FS. Moreover, the same ML algorithm is usually conducted for both FS and regression in LAI estimation. However, no evidence suggests that this is the optimal solution. Therefore, this study focuses on evaluating the capacity of GF-5 spectral reflectance for estimating LAI and the performances of different combination of FS and ML algorithms. Firstly, the PROSAIL model, which coupled leaf optical properties model PROSPECT and the scattering by arbitrarily inclined leaves (SAIL) model, was used to generate simulated GF-5 reflectance data under different vegetation and soil conditions, and then three FS methods, including random forest (RF), K-means clustering (K-means) and mean impact value (MIV), and three ML algorithms, including random forest regression (RFR), back propagation neural network (BPNN) and K-nearest neighbor (KNN) were used to develop nine LAI estimation models. The FS process was conducted twice using different strategies: Firstly, three FS methods were conducted to search the lowest dimension number, which maintained the estimation accuracy of all bands. Then, the sequential backward selection (SBS) method was used to eliminate the bands having minimal impact on LAI estimation accuracy. Finally, three best estimation models were selected and evaluated using reference LAI. The results showed that although the RF_RFR model (RF used for feature selection and RFR used for regression) achieved reliable LAI estimates (coefficient of determination (R2) = 0.828, root mean square error (RMSE) = 0.839), the poor performance (R2 = 0.763, RMSE = 0.987) of the MIV_BPNN model (MIV used for feature selection and BPNN used for regression) suggested using feature selection and regression conducted by the same ML algorithm could not always ensure an optimal estimation. Moreover, RF selection preserved the most informative bands for LAI estimation so that each ML regression method could achieve satisfactory estimation results. Finally, the results indicated that the RF_KNN model (RF used as feature selection and KNN used for regression) with seven GF-5 spectral band reflectance achieved the better estimation results than others when validated by simulated data (R2 = 0.834, RMSE = 0.824) and actual reference LAI (R2 = 0.659, RMSE = 0.697).

Description: Recent developments in hyperspectral satellites have dramatically promoted the wide application of large-scale quantitative remote sensing. As an essential part of preprocessing, cloud detection is of great significance for subsequent quantitative analysis. For Gaofen-5 (GF-5) data producers, the daily cloud detection of hundreds of scenes is a challenging task. Traditional cloud detection methods cannot meet the strict demands of large-scale data production, especially for GF-5 satellites, which have massive data volumes. Deep learning technology, however, is able to perform cloud detection efficiently for massive repositories of satellite data and can even dramatically speed up processing by utilizing thumbnails. Inspired by the outstanding learning capability of convolutional neural networks (CNNs) for feature extraction, we propose a new dual-branch CNN architecture for cloud segmentation for GF-5 preview RGB images, termed a multiscale fusion gated network (MFGNet), which introduces pyramid pooling attention and spatial attention to extract both shallow and deep information. In addition, a new gated multilevel feature fusion module is also employed to fuse features at different depths and scales to generate pixelwise cloud segmentation results. The proposed model is extensively trained on hundreds of globally distributed GF-5 satellite images and compared with current mainstream CNN-based detection networks. The experimental results indicate that our proposed method has a higher F1 score (0.94) and fewer parameters (7.83 M) than the compared methods.

Description: Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral camera—installed on the ground or possibly a drone—and detection based on spectral signatures. We introduce a novel automatic method for annotating spectral signatures based on a combination of state-of-the-art deep learning methods. After we collected millions of samples with our method, we used a deep learning approach to train a classifier to detect people and cars. Our results show that, based only on spectral signature classification, we can achieve an Matthews Correlation Coefficient of 0.83. We evaluate our classification method in areas with varying vegetation and discuss the limitations and constraints that the current hyperspectral imaging technology has. We conclude that spectral signature classification is possible with high accuracy in uncontrolled outdoor environments. Nevertheless, even with state-of-the-art compact passive hyperspectral imaging technology, high dynamic range of illumination and relatively low image resolution continue to pose major challenges when developing object detection algorithms for areas of dense vegetation.

Description: Scientific exploration of seabed substrata has significantly progressed in the last few years. Hydroacoustic methods of seafloor investigation, including multibeam echosounder measurements, allow us to map large areas of the seabed with unprecedented precision. Through time-series of hydroacoustic measurements, it was possible to determine areas with distinct characteristics in the inlets of the Lagoon of Venice, Italy. Their temporal variability was investigated. Monitoring the changes was particularly relevant, considering the presence at the channel inlets of mobile barriers of the Experimental Electromechanical Module (MoSE) project installed to protect the historical city of Venice from flooding. The detection of temporal and spatial changes was performed by comparing seafloor maps created using object-based image analysis and supervised classifiers. The analysis included extraction of 25 multibeam echosounder bathymetry and backscatter features. Their importance was estimated using an objective approach with two feature selection methods. Moreover, the study investigated how the accuracy of classification could be affected by the scale of object-based segmentation. The application of the classification method at the proper scale allowed us to observe habitat changes in the tidal inlet of the Venice Lagoon, showing that the sediment substrates located in the Chioggia inlet were subjected to very dynamic changes. In general, during the study period, the area was enriched in mixed and muddy sediments and was depleted in sandy deposits. This study presents a unique methodological approach to predictive seabed sediment composition mapping and change detection in a very shallow marine environment. A consistent, repeatable, logical site-specific workflow was designed, whose main assumptions could be applied to other seabed mapping case studies in both shallow and deep marine environments, all over the world.

Description: Nighttime light data have been proven to be valuable for socioeconomic studies. However, they are not only affected by anthropogenic factors but also by physical factors, and previous studies have rarely examined these diverse variables in a systematic way that explains differences in nighttime lights across different cities. In this paper, hierarchical linear models at two levels of city and province were developed to investigate the nighttime lights effect on cross-level factors. An experiment was conducted for 281 prefecture cities in Mainland China using orbital satellite data in 2016. (1) There exist significant differences among city average lights, of which 49.9% is caused at the provincial level, indicating the factors at the provincial level cannot be ignored. (2) Economy-energy-infrastructure and demography factors have a significant positive lights effect. Meanwhile, industry-information and living-standard factors at the provincial level can further significantly increase these differences by 18.30% and 29.01%, respectively. (3) The natural-greenness factor displayed a significant negative lights effect, and its interaction with natural-ecology will continue to decrease city lights by 11.99%. However, artificial-greenness is an unreliable city-level factor explaining lights variations. (4) As for the negative lights effect of elevation and latitude, these become significant in a multivariate context and contribute lights indirectly. (5) The two-level hierarchical linear models are statistically significant at the level of 10%, and compared with the null model, the explained variances on city lights can be improved by 70% at the city level and 90% at the provincial level in the final mixed effect model.

Description: This Special Issue intended to probe the impact of the adoption of advanced machine learning methods in remote sensing applications including those considering recent big data analysis, compression, multichannel, sensor and prediction techniques. In principal, this edition of the Special Issue is focused on time series data processing for remote sensing applications with special emphasis on advanced machine learning platforms. This issue is intended to provide a highly recognized international forum to present recent advances in time series remote sensing. After review, a total of eight papers have been accepted for publication in this issue.

Description: Urban land-use information is important for urban land-resource planning and management. However, current methods using traditional surveys cannot meet the demand for the rapid development of urban land management. There is an urgent need to develop new methods to overcome the shortcomings of conventional methods. To address the issue, this study used the random forest (RF), support vector machine (SVM), and artificial neural network (ANN) models to build machine-leaning methods for urban land-use classification. Taking Hangzhou as an example, these machine-leaning methods could all successfully classify the essential urban land use into 6 Level I classes and 13 Level II classes based on the semantic features extracted from Sentinel-2A images, multi-source features of types of points of interest (POIs), land surface temperature, night lights, and building height. The validation accuracy of the RF model for the Level I and Level II land use was 79.88% and 71.89%, respectively, performing better compared to SVM (78.40% and 68.64%) and ANN models (71.30% and 63.02%). However, the variations of the user accuracy among the methods depended on the urban land-use level. For the Level I land-use classification, the user accuracy was high, except for the transportation land by all methods. In general, the RF and SVM models performed better than the ANN model. For the Level II land-use classification, the user accuracy of different models was quite distinct. With the RF model, the user accuracy of educational and medical land was above 80%. Moreover, with the SVM model, the user accuracy of the business office and educational land classification was above 75%. However, the user accuracy of the ANN model on the Level II land-use classification was poor. Our results showed that the RF model performs best, followed by SVM model, and ANN model was relatively poor in the essential urban land-use classification. The results proved that the use of machine-learning methods can quickly extract land-use types with high accuracy, and provided a better method choice for urban land-use information acquisition.

Description: Making use of dual-frequency (DF) global navigation satellite system (GNSS) observations and good dynamic models, the precise orbit determination (POD) for the satellites on low earth orbits has been intensively investigated in the last decades and has achieved an accuracy of centimeters. With the rapidly increasing number of the CubeSat missions in recent years, the POD of CubeSats were also attempted with combined dynamic models and GNSS DF observations. While comprehensive dynamic models are allowed to be used in the postprocessing mode, strong constraints on the data completeness, continuity, and restricted resources due to the power and size limits of CubeSats still hamper the high-accuracy POD. An analysis of these constraints and their impact on the achievable orbital accuracy thus needs to be considered in the planning phase. In this study, with the focus put on the use of DF GNSS data in postprocessing CubeSat POD, a detailed sensitivity analysis of the orbital accuracy was performed w.r.t. the data continuity, completeness, observation sampling interval, latency requirements, availability of the attitude information, and arc length. It is found that the overlapping of several constraints often causes a relatively large degradation in the orbital accuracy, especially when one of the constraints is related to a low duty-cycle of, e.g., below 40% of time. Assuming that the GNSS data is properly tracked except for the assumed constraints, and using the International GNSS Service (IGS) final products or products from the IGS real-time service, the 3D orbital accuracy for arcs of 6 h to 24 h should generally be within or around 1 dm, provided that the limitation on data is not too severe, i.e., with a duty-cycle not lower than 40% and an observation sampling interval not larger than 60 s.

Description: We present the new Precipitation REtrieval covering the TIbetan Plateau (PRETIP) as a feasibility study using the two geostationary (GEO) satellites Elektro-L2 and Insat-3D with reference to the GPM (Global Precipitation Measurement Mission) IMERG (Integrated Multi-satellitE Retrievals for GPM) product. The present study deals with the assignment of the rainfall rate. For precipitation rate assignment, the best-quality precipitation estimates from the gauge calibrated microwave (MW) within the IMERG product were combined with the GEO data by Random Forest (RF) regression. PRETIP was validated with independent MW precipitation information not considered for model training and revealed a good performance on 30 min and 11 km spatio-temporal resolution with a correlation coefficient of R = 0.59 and outperforms the validation of the independent MW precipitation with IMERG’s IR only product (R = 0.18). A comparison of PRETIP precipitation rates in 4 km resolution with daily rain gauge measurements from the Chinese Ministry of Water Resources revealed a correlation of R = 0.49. No differences in the performance of PRETIP for various elevation ranges or between the rainy (July, August) and the dry (May, September) season could be found.

Description: Insights into flood dynamics, rather than solely flood extent, are critical for effective flood disaster management, in particular in the context of emergency relief and damage assessment. Although flood dynamics provide insight in the spatio-temporal behaviour of a flood event, to date operational visualization tools are scarce or even non-existent. In this letter, we distil a flood dynamics map from a radar satellite image time series (SITS). For this, we have upscaled and refined an existing design that was originally developed on a small area, describing flood dynamics using an object-based approach and a graph-based representation. Two case studies are used to demonstrate the operational value of this method by visualizing flood dynamics which are not visible on regular flood extent maps. Delineated water bodies are grouped into graphs according to their spatial overlap on consecutive timesteps. Differences in area and backscatter are used to quantify the amount of variation, resulting in a global variation map and a temporal profile for each water body, visually describing the evolution of the backscatter and number of polygons that make up the water body. The process of upscaling led us to applying a different water delineation approach, a different way of ensuring the minimal mapping unit and an increased code efficiency. The framework delivers a new way of visualizing floods, which is straightforward and efficient. Produced global variation maps can be applied in a context of data assimilation and disaster impact management.

Description: The photochemical reflectance index (PRI) has been suggested as an indicator of light use efficiency (LUE), and for use in the improvement of estimating gross primary production (GPP) in LUE models. Over the last two decades, solar-induced fluorescence (SIF) observations from remote sensing have been used to evaluate the distribution of GPP over a range of spatial and temporal scales. However, both PRI and SIF observations have been decoupled from photosynthesis under a variety of non-physiological factors, i.e., sun-view geometry and environmental variables. These observations are important for estimating GPP but rarely reported in the literature. In our study, multi-angle PRI and SIF observations were obtained during the 2018 growing season in a maize field. We evaluated a PRI-based LUE model for estimating GPP, and compared it with the direct estimation of GPP using concurrent SIF measurements. Our results showed that the observed PRI varied with view angles and that the averaged PRI from the multi-angle observations exhibited better performance than the single-angle observed PRI for estimating LUE. The PRI-based LUE model when compared to SIF, demonstrated a higher ability to capture the diurnal dynamics of GPP (the coefficient of determination (R2) = 0.71) than the seasonal changes (R2 = 0.44), while the seasonal GPP variations were better estimated by SIF (R2 = 0.50). Based on random forest analyses, relative humidity (RH) was the most important driver affecting diurnal GPP estimation using the PRI-based LUE model. The SIF-based linear model was most influenced by photosynthetically active radiation (PAR). The SIF-based linear model did not perform as well as the PRI-based LUE model under most environmental conditions, the exception being clear days (the ratio of direct and diffuse sky radiance 〉 2). Our study confirms the utility of multi-angle PRI observations in the estimation of GPP in LUE models and suggests that the effects of changing environmental conditions should be taken into account for accurately estimating GPP with PRI and SIF observations.

Description: Active school commuting provides a convenient opportunity to promote physical activity for children, while also reducing car dependence and its associated environmental impacts. School–home distance is a critical factor in school commuting mode choice, and longer distances have been proven to increase the likelihood of driving. In this study, we combine open-access data acquired from Baidu Map application programming interface (API) with GIS (Geographic Information System) technology to estimate the extent to which the present school–home distances can be reduced for public middle schools in Jianye District, Nanjing, China. Based on the policies for school planning and catchment allocation, we conduct a scenario analysis of school catchment reassignment whereby residences are reassigned to the nearest school. The results show that, despite the government’s ‘attending nearby school’ policy, some students in the study area are subjected to excess school–home distances, and the overall journey-to-school trips can be reduced by 20.07%, accounting for 330.8 km. This excess distance indicates the extent to which the need for vehicle travel can be potentially reduced in favor of active school commuting and a low-carbon lifestyle. Therefore, these findings provide important insights into school siting and school catchment assignment policies seeking to facilitate active school commuting, achieve educational spatial equity and reduce car dependence.

Description: Ocean bottom seismometer (OBS) can record both pressure and displacement data by modern marine seismic acquisitions with four-component (4C) sensors. Elastic full-waveform inversion (EFWI) has shown to recover high-accuracy parameter models from multicomponent seismic data. However, due to limitation of the standard elastic wave equation, EFWI can hardly simulate and utilize the pressure components. To remedy this problem, we propose an elastic full-waveform inversion method based on a modified acoustic-elastic coupled (AEC) equation. Our method adopts a new misfit function to account for both 1C pressure and 3C displacement data, which can easily adjust the weight of different data components and eliminate the differences in the order of magnitude. Owing to the modified AEC equation, our method can simultaneously generate pressure and displacement records and avoid explicit implementation of the boundary condition at the seabed. Besides, we also derive a new preconditioned truncated Gauss–Newton algorithm to consider the Hessian associated with ocean bottom seismic 4C data. We analyze the multiparameter sensitivity kernels of pressure and displacement components and use two numerical experiments to demonstrate that the proposed method can provide more accurate multiparameter inversions with higher resolution and convergence rate.

Description: The increasing drought severities and consequent devastating impacts on society over the Indian semi-arid regions demand better drought monitoring and early warning systems. Operational agricultural drought assessment methods in India mainly depend on a single input parameter such as precipitation and are based on a sparsely located in-situ measurements, which limits monitoring precision. The overarching objective of this study is to address this need through the development of an integrated agro-climatological drought monitoring approach, i.e., combined drought indicator for Marathwada (CDI_M), situated in the central part of Maharashtra, India. In this study, satellite and model-based input parameters (i.e., standardized precipitation index (SPI-3), land surface temperature (LST), soil moisture (SM), and normalized difference vegetation index (NDVI)) were analyzed at a monthly scale from 2001 to 2018. Two quantitative methods were tested to combine the input parameters for developing the CDI_M. These methods included an expert judgment-based weight of each parameter (Method-I) and principle component analysis (PCA)-based weighting approach (Method-II). Secondary data for major types of crop yields in Marathwada were utilized to assess the CDI_M results for the study period. CDI_M maps depict moderate to extreme drought cases in the historic drought years of 2002, 2009, and 2015–2016. This study found a significant increase in drought intensities (p ≤ 0.05) and drought frequency over the years 2001–2018, especially in the Latur, Jalna, and Parbhani districts. In comparison to Method-I (r ≥ 0.4), PCA-based (Method-II) CDI_M showed a higher correlation (r ≥ 0.60) with crop yields in both harvesting seasons (Kharif and Rabi). In particular, crop yields during the drier years showed a greater association (r 〉 6.5) with CDI_M over Marathwada. Hence, the present study illustrated the effectiveness of CDI_M to monitor agricultural drought in India and provide improved information to support agricultural drought management practices.

Description: Satellite remote sensing of sea surface salinity (SSS) in the recent decade (2010–2019) has proven the capability of L-band (1.4 GHz) measurements to resolve SSS spatiotemporal variability in the tropical and subtropical oceans. However, the fidelity of SSS retrievals in cold waters at mid-high latitudes has yet to be established. Here, four SSS products derived from two satellite missions were evaluated in the subpolar North Atlantic Ocean in reference to two in situ gridded products. Harmonic analysis of annual and semiannual cycles in in situ products revealed that seasonal variations of SSS are dominated by an annual cycle, with a maximum in March and a minimum in September. The annual amplitudes are larger (〉0.3 practical salinity scale (pss)) in the western basin where surface waters are colder and fresher, and weaker (~0.06 pss) in the eastern basin where surface waters are warmer and saltier. Satellite SSS products have difficulty producing the right annual cycle, particularly in the Labrador/Irminger seas where the SSS seasonality is dictated by the influx of Arctic low-salinity waters along the boundary currents. The study also found that there are basin-scale, time-varying drifts in the decade-long SMOS data records, which need to be corrected before the datasets can be used for studying climate variability of SSS.

Description: In the development of geographic information-based applications for mobile devices, achieving better access speed and visual effects is the main research aim. In this paper, we propose a new geographic data display method based on Geohash, namely GeohashTile, to improve the performance of traditional geographic data display methods in data indexing, data compression, and the projection of different granularities. First, we use the Geohash encoding system to represent coordinates, as well as to partition and index large-scale geographic data. The data compression and tile encoding is accomplished by Geohash. Second, to realize a direct conversion between Geohash and screen-pixel coordinates, we adopt the relative position projection method. Finally, we improve the calculation and rendering efficiency by using the intermediate result caching method. To evaluate the GeohashTile method, we have implemented the client and the server of the GeohashTile system, which is also evaluated in a real-world environment. The results show that Geohash encoding can accurately represent latitude and longitude coordinates in vector maps, while the GeohashTile framework has obvious advantages when requesting data volume and average load time compared to the state-of-the-art GeoTile system.

Description: Based on 26 years of satellite altimetry, this study reveals the presence of a persistent east–west pattern in the sea level of the Red Sea, which is visible throughout the years when considering the east–west difference in sea level. This eastern–western (EW) difference is positive during winter when a higher sea level is observed at the eastern coast of the Red Sea and the opposite occurs during summer. May and October are transition months that show a mixed pattern in the sea level difference. The EW difference in the southern Red Sea has a slightly higher range compared to that of the northern region during summer, by an average of 0.2 cm. Wavelet analysis shows a significant annual cycle along with other signals of lower magnitude for both the northern and southern Red Sea. Removing the annual cycle reveals two energy peaks with periodicities of

Description: Industry 4.0 comprises a wide spectrum of developmental processes within the management of manufacturing and chain production. Presently, there is a huge effort to automate manufacturing and have automatic control of the production. This intention leads to the increased need for high-quality methods for digitization and object reconstruction, especially in the area of reverse engineering. Commonly used scanning software based on well-known algorithms can correctly process smooth objects. Nevertheless, they are usually not applicable for complex-shaped models with sharp features. The number of the points on the edges is extremely limited due to the principle of laser scanning and sometimes also low scanning resolution. Therefore, a correct edge reconstruction problem occurs. The same problem appears in many other laser scanning applications, i.e., in the representation of the buildings from airborne laser scans for 3D city models. We focus on a method for preservation and reconstruction of sharp features. We provide a detailed description of all three key steps: point cloud segmentation, edge detection, and correct B-spline edge representation. The feature detection algorithm is based on the conventional region-growing method and we derive the optimal input value of curvature threshold using logarithmic least square regression. Subsequent edge representation stands on the iterative algorithm of B-spline approximation where we compute the weighted asymmetric error using the golden ratio. The series of examples indicates that our method gives better or comparable results to other methods.

Description: Globalization has become a major issue of focus as rapid urban populations and urbanization effects are on the rise. A critical need arises for effective urban planning for Istanbul in relation to the use of a hybrid approach integrating AHP-Entropy and GIS for emergency facility planning. In this paper, the combination of AHP and Entropy methods was used for evaluating criterion weights subjectively and objectively. These techniques were utilized with regard to the assessment of suitable areas for planning new urban emergency facilities for Istanbul province which experiences increasing urban fire-related emergencies. AHP and Entropy have been used to evaluate the weights of determined criteria from expert preference judgments and GIS for processing, analysis and visualization of the model result in the form of a suitability map for new urban emergency facilities. Validation of the model was performed on the criteria with the strongest influence in the decision outcome and spatially visualized using the sensitivity analysis (SA) method of one-at-a-time (OAT). From the findings, it was estimated that 28.1% of the project area, accounting for a third of it, is likely to be exposed to the risk of urban fires and therefore immediate planning of new urban emergency facilities is recommended for adequate fire service coverage and protection.

Description: This paper discusses change detection in SAR time-series. First, several statistical properties of the coefficient of variation highlight its pertinence for change detection. Subsequently, several criteria are proposed. The coefficient of variation is suggested to detect any kind of change. Furthermore, several criteria that are based on ratios of coefficients of variations are proposed to detect long events, such as construction test sites, or point-event, such as vehicles. These detection methods are first evaluated on theoretical statistical simulations to determine the scenarios where they can deliver the best results. The simulations demonstrate the greater sensitivity of the coefficient of variation to speckle mixtures, as in the case of agricultural plots. Conversely, they also demonstrate the greater specificity of the other criteria for the cases addressed: very short event or longer-term changes. Subsequently, detection performance is assessed on real data for different types of scenes and sensors (Sentinel-1, UAVSAR). In particular, a quantitative evaluation is performed with a comparison of our solutions with baseline methods. The proposed criteria achieve the best performance, with reduced computational complexity. On Sentinel-1 images containing mainly construction test sites, our best criterion reaches a probability of change detection of 90% for a false alarm rate that is equal to 5%. On UAVSAR images containing boats, the criteria proposed for short events achieve a probability of detection equal to 90% of all pixels belonging to the boats, for a false alarm rate that is equal to 2%.

Description: Falling mixed-phase virga from a thin supercooled liquid layer cloud base were observed on 20 occasions at altitudes of 2.3–9.4 km with ground-based lidars at Wuhan (30.5 °N, 114.4 °E), China. Polarization lidar profile (3.75-m) analysis reveals some ubiquitous features of both falling mixed-phase virga and their liquid parent cloud layers. Each liquid parent cloud had a well-defined base height where the backscatter ratio R was ~7.0 and the R profile had a clear inflection point. At an altitude of ~34 m above the base height, the depolarization ratio reached its minimum value (~0.04), indicating a liquid-only level therein. The thin parent cloud layers tended to form on the top of a broad preexisting aerosol/liquid water layer. The falling virga below the base height showed firstly a significant depolarization ratio increase, suggesting that most supercooled liquid drops in the virga were rapidly frozen into ice crystals (via contact freezing). After reaching a local maximum value of the depolarization ratio, both the values of the backscatter ratio and depolarization ratio for the virga exhibited an overall decrease with decreasing height, indicating sublimated ice crystals. The diameters of the ice crystals in the virga were estimated based on an ice particle sublimation model along with the lidar and radiosonde observations. It was found that the ice crystal particles in these virga cases tended to have smaller mean diameters and narrower size distributions with increasing altitude. The mean diameter value is 350 ± 111 µm at altitudes of 4–8.5 km.

Description: Freshwater lakes supply a large amount of inland water resources to sustain local and regional developments. However, some lake systems depend upon great fluctuation in water surface area. Poyang lake, the largest freshwater lake in China, undergoes dramatic seasonal and interannual variations. Timely monitoring of Poyang lake surface provides essential information on variation of water occurrence for its ecosystem conservation. Application of histogram-based image segmentation in radar imagery has been widely used to detect water surface of lakes. Still, it is challenging to select the optimal threshold. Here, we analyze the advantages and disadvantages of a segmentation algorithm, the Otsu Method, from both mathematical and application perspectives. We implement the Otsu Method and provide reusable scripts to automatically select a threshold for surface water extraction using Sentinel-1 synthetic aperture radar (SAR) imagery on Google Earth Engine, a cloud-based platform that accelerates processing of Sentinel-1 data and auto-threshold computation. The optimal thresholds for each January from 2017 to 2020 are − 14.88 , − 16.93 , − 16.96 and − 16.87 respectively, and the overall accuracy achieves 92 % after rectification. Furthermore, our study contributes to the update of temporal and spatial variation of Poyang lake, confirming that its surface water area fluctuated annually and tended to shrink both in the center and boundary of the lake on each January from 2017 to 2020.

Description: GNSS positioning accuracy can be degraded in areas where the surrounding object geometry and morphology interacts with the GNSS signals. Specifically, urban environments pose challenges to precise GNSS positioning because of signal interference or interruptions. Also, non-GNSS surveying methods, including total stations and laser scanners, involve time consuming practices in the field and costly equipment. The present study proposes the use of an Unmanned Aerial Vehicle (UAV) for autonomous rapid mapping that resolves the problem of localization for the drone itself by acquiring location information of characteristic points on the ground in a local coordinate system using simultaneous localization and mapping (SLAM) and vision algorithms. A common UAV equipped with a camera and at least a single known point, are enough to produce a local map of the scene and to estimate the relative coordinates of pre-defined ground points along with an additional arbitrary point cloud. The resulting point cloud is readily measurable for extracting and interpreting geometric information from the area of interest. Under two novel optimization procedures performing line and plane alignment of the UAV-camera-measured point geometries, a set of experiments determines that the localization of a visual point in distances reaching 15 m from the origin, delivered a level of accuracy under 50 cm. Thus, a simple UAV with an optical sensor and a visual marker, prove quite promising and cost-effective for rapid mapping and point localization in an unknown environment.

Description: Adversarial training has demonstrated advanced capabilities for generating image models. In this paper, we propose a deep neural network, named a classified adversarial network (CAN), for multi-spectral image change detection. This network is based on generative adversarial networks (GANs). The generator captures the distribution of the bitemporal multi-spectral image data and transforms it into change detection results, and these change detection results (as the fake data) are input into the discriminator to train the discriminator. The results obtained by pre-classification are also input into the discriminator as the real data. The adversarial training can facilitate the generator learning the transformation from a bitemporal image to a change map. When the generator is trained well, the generator has the ability to generate the final result. The bitemporal multi-spectral images are input into the generator, and then the final change detection results are obtained from the generator. The proposed method is completely unsupervised, and we only need to input the preprocessed data that were obtained from the pre-classification and training sample selection. Through adversarial training, the generator can better learn the relationship between the bitemporal multi-spectral image data and the corresponding labels. Finally, the well-trained generator can be applied to process the raw bitemporal multi-spectral images to obtain the final change map (CM). The effectiveness and robustness of the proposed method were verified by the experimental results on the real high-resolution multi-spectral image data sets.

Description: Indoor environments can be very complex. Due to the challenges in these environments in combination with the absence of mobile wayfinding aids, a great need exists for innovative research on indoor wayfinding. In this explorative study, a game was developed in Unity to investigate whether the concept of gamification could be used in studies on indoor wayfinding so as to provide useful information regarding the link between wayfinding performance, personal characteristics, and building layout. Results show a significant difference between gamers and non-gamers as the complexity of the player movement has an important impact on the navigation velocity in the game. However, further analysis reveals that the architectural layout also has an impact on the navigation velocity and that wrong turns in the game are influenced by the landmarks at the decision points: navigating at deeper decision points in convex spaces is slower and landmarks of the categories pictograms and infrastructural were more effective in this particular building. Therefore, this explorative study, which provides an approach for the use of gamification in indoor wayfinding research, has shown that serious games could be successfully used as a medium for data acquisition related to indoor wayfinding in a virtual environment.

Description: Having updated knowledge of cropland extent is essential for crop monitoring and food security early warning. Previous research has proposed different methods and adopted various datasets for mapping cropland areas at regional to global scales. However, most approaches did not consider the characteristics of farming systems and apply the same classification method in different agroecological zones (AEZs). Furthermore, the acquisition of in situ samples for classification training remains challenging. To address these knowledge gaps and challenges, this study applied a zone-specific classification by comparing four classifiers (random forest, the support vector machine (SVM), the classification and regression tree (CART) and minimum distance) for cropland mapping over four different AEZs in the Zambezi River basin (ZRB). Landsat-8 and Sentinel-2 data and derived indices were used and synthesized to generate thirty-five layers for classification on the Google Earth Engine platform. Training samples were derived from three existing landcover datasets to minimize the cost of sample acquisitions over the large area. The final cropland map was generated at a 10 m resolution. The performance of the four classifiers and the viability of training samples were analysed. All classifiers presented higher accuracy in cool AEZs than in warm AEZs, which may be attributed to field size and lower confusion between cropland and grassland classes. This indicates that agricultural landscape may impact classification results regardless of the classifiers. Random forest was found to be the most stable and accurate classifier across different agricultural systems, with an overall accuracy of 84% and a kappa coefficient of 0.67. Samples extracted over the full agreement areas among existing datasets reduced uncertainty and provided reliable calibration sets as a replacement of costly in situ measurements. The methodology proposed by this study can be used to generate periodical high-resolution cropland maps in ZRB, which is helpful for the analysis of cropland extension and abandonment as well as intensity changes in response to the escalating population and food insecurity.

Description: Mapping wetlands with high spatial and thematic accuracy is crucial for the management and monitoring of these important ecosystems. Wetland maps in New Brunswick (NB) have traditionally been produced by the visual interpretation of aerial photographs. In this study, we used an alternative method to produce a wetland map for southern New Brunswick, Canada, by classifying a combination of Landsat 8 OLI, ALOS-1 PALSAR, Sentinel-1, and LiDAR-derived topographic metrics with the Random Forests (RF) classifier. The images were acquired in three seasons (spring, summer, and fall) with different water levels and during leaf-off/on periods. The resulting map has eleven wetland classes (open bog, shrub bog, treed bog, open fen, shrub fen, freshwater marsh, coastal marsh, shrub marsh, shrub wetland, forested wetland, and aquatic bed) plus various non-wetland classes. We achieved an overall accuracy classification of 97.67%. We compared 951 in-situ validation sites to the classified image and both the 2106 and 2019 reference maps available through Service New Brunswick. Both reference maps were produced by photo-interpretation of RGB-NIR digital aerial photographs, but the 2019 NB reference also included information from LiDAR-derived surface and ecological metrics. Of these 951 sites, 94.95% were correctly identified on the classified image, while only 63.30% and 80.02% of these sites were correctly identified on the 2016 and 2019 NB reference maps, respectively. If only the 489 wetland validation sites were considered, 96.93% of the sites were correctly identified as a wetland on the classified image, while only 58.69% and 62.17% of the sites were correctly identified as a wetland on the 2016 and 2019 NB reference maps, respectively.

Description: Coral reefs are declining worldwide as a result of the effects of multiple natural and anthropogenic stressors, including regional-scale temperature-induced coral bleaching. Such events have caused significant coral mortality, leading to an evident structural collapse of reefs and shifts in associated benthic communities. In this scenario, reasonable mapping techniques and best practices are critical to improving data collection to describe spatial and temporal patterns of coral reefs after a significant bleaching impact. Our study employed the potential of a consumer-grade drone, coupled with structure from motion and object-based image analysis to investigate for the first time a tool to monitor changes in substrate composition and the associated deterioration in reef environments in a Maldivian shallow-water coral reef. Three key substrate types (hard coral, coral rubble and sand) were detected with high accuracy on high-resolution orthomosaics collected from four sub-areas. Multi-temporal acquisition of UAV data allowed us to compare the classified maps over time (February 2017, November 2018) and obtain evidence of the relevant deterioration in structural complexity of flat reef environments that occurred after the 2016 mass bleaching event. We believe that our proposed methodology offers a cost-effective procedure that is well suited to generate maps for the long-term monitoring of changes in substrate type and reef complexity in shallow water.

Description: Airborne interferometric data, obtained from the Cirrus Coupled Cloud-Radiation Experiment (CIRCCREX) and from the PiknMix-F field campaign, are used to test the ability of a machine learning cloud identification and classification algorithm (CIC). Data comprise a set of spectral radiances measured by the Tropospheric Airborne Fourier Transform Spectrometer (TAFTS) and the Airborne Research Interferometer Evaluation System (ARIES). Co-located measurements of the two sensors allow observations of the upwelling radiance for clear and cloudy conditions across the far- and mid-infrared part of the spectrum. Theoretical sensitivity studies show that the performance of the CIC algorithm improves with cloud altitude. These tests also suggest that, for conditions encompassing those sampled by the flight campaigns, the additional information contained within the far-infrared improves the algorithm’s performance compared to using mid-infrared data only. When the CIC is applied to the airborne radiance measurements, the classification performance of the algorithm is very high. However, in this case, the limited temporal and spatial variability in the measured spectra results in a less obvious advantage being apparent when using both mid- and far-infrared radiances compared to using mid-infrared information only. These results suggest that the CIC algorithm will be a useful addition to existing cloud classification tools but that further analyses of nadir radiance observations spanning the infrared and sampling a wider range of atmospheric and cloud conditions are required to fully probe its capabilities. This will be realised with the launch of the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission, ESA’s 9th Earth Explorer.

Description: In recent years, the smart car sector has been increasing enormously in the Internet of Things (IoT) market. Furthermore, the number of smart cars seems set to increase over the next few years. This goal will be achieved because the application of recent IoT technologies to the automotive sector opens up innovative opportunities for the mobility of the future, in which connected cars will be more and more prominent in smart cities. This paper aims to provide an overview of the current status and future perspectives of smart cars, taking into account technological, transport, and social features. An analysis concerning the approaches to making smart a generic car, the possible evolutions that could occur in the coming decades, the characteristics of 5G, ADAS (advanced driver assistance systems), and the power sources is carried out in this paper.

Description: UV cameras have been used for over a decade in order to remotely sense SO2 emission rates from active volcanoes, and to thereby enhance our understanding of processes related to active and passive degassing. Whilst SO2 column density retrievals can be more accurate/sophisticated using alternative techniques (e.g., Differential Optical Absorption Spectrometer (DOAS), Correlation Spectrometer (COSPEC)), due to their higher spectral resolutions, UV cameras provide the advantage of high time-resolution emission rates, a much greater spatial resolution, and the ability to simultaneously retrieve plume speeds. Nevertheless, the relatively high costs have limited their uptake to a limited number of research groups and volcanic observatories across the planet. One recent intervention in this regard has been the introduction of the PiCam UV camera, which has considerably lowered instrumental cost. Here we present the first data obtained with the PiCam system from seven persistently degassing volcanoes in northern Chile, demonstrating robust field operation in challenging conditions and over an extended period of time, hence adding credence to the potential of these units for more widespread dissemination to the international volcanic gas measurement community. Small and weak plumes, as well as strongly degassing plumes were measured at distances ranging 0.6–10.8 km from the sources, resulting in a wide range of SO2 emission rates, varying from 3.8 ± 1.8 to 361 ± 31.6 td−1. Our acquired data are discussed with reference to previously reported emission rates from other ground-based remotely sensed techniques at the same volcanoes, in particular considering: resolution of single plume emissions in multi-plume volcanoes, light dilution, plume geometry, seasonal effects, and the applied plume speed measurement methodology. The main internal/external factors that influence positive/negative PiCam measurements include camera shake, light dilution, and the performance of the OpenCV and control points post processing methods. A simple reprocessing method is presented in order to correct the camera shake. Finally, volcanoes were separated into two distinct groups: low and moderate SO2 emission rates systems. These groups correlate positively with their volcanological characteristics, especially with the fluid compositions from fumaroles.

Description: There are a number of pavement management systems, but most of them are limited in providing pavement design and pavement design sensitivity information. This paper presents efforts towards the integrated pavement design and management system, by developing smart pavement design sensitivity analysis software. In this paper, the sensitivity analyses of critical design input parameters have been performed to identify input parameters which have the most significant impacts on the pavement thickness. Based on the existing pavement design procedures and their sensitivity analysis results, a smart pavement design sensitivity analysis (PDSA) software package was developed, to allow a user to retrieve the most appropriate pavement thickness and immediately perform pavement design sensitivity analysis. The PDSA software is a useful tool for managing pavements, by allowing a user to instantaneously retrieve a pavement design for a given condition from the database and perform a design sensitivity analysis without running actual pavement design programs. The proposed smart PDSA software would result in the most efficient pavement management system, by incorporating the optimum pavement thickness as part of the pavement management process.

Description: Filling in the void between forest ecology and remote sensing through monitoring biodiversity variables is of great interest. In this study, we utilized imaging spectroscopy data from the ISRO–NASA Airborne Visible InfraRed Imaging Spectrometer—Next Generation (AVIRIS-NG) India campaign to investigate how the measurements of biodiversity attributes of forests over wide areas can be augmented by synchronous field- and spectral-metrics. Three sites, Shoolpaneshwar Wildlife Sanctuary (SWS), Vansda National Park (VNP), and Mudumalai Tiger Reserve (MTR), spread over a climatic gradient (rainfall and temperature), were selected for this study. Abundant species maps of three sites were produced using a support vector machine (SVM) classifier with a 76–80% overall accuracy. These maps are a valuable input for forest resource management. Convex hull volume (CHV) is computed from the first three principal components of AVIRIS-NG spectra and used as a spectral diversity metric. It was observed that CHV increased with species numbers showing a positive correlation between species and spectral diversity. Additionally, it was observed that the abundant species show higher spectral diversity over species with lesser spread, provisionally revealing their functional diversity. This could be one of the many reasons for their expansive reach through adaptation to local conditions. Higher rainfall at MTR was shown to have a positive impact on species and spectral diversity as compared to SWS and VNP. Redundancy analysis explained 13–24% of the variance in abundant species distribution because of climatic gradient. Trends in spectral CHVs observed across the three sites of this study indicate that species assemblages may have strong local controls, and the patterns of co-occurrence are largely aligned along climatic gradient. Observed changes in species distribution and diversity metrics over climatic gradient can help in assessing these forests’ responses to the projected dynamics of rainfall and temperature in the future.

Description: The adaptation of modeled solar radiation data with coincident ground measurements has become a standard practice of the industry, typically requested by financial institutions in the detailed solar resource assessments of solar projects. This practice mitigates the risk of solar projects, enhancing the adequate solar plant design and reducing the uncertainty of its yield estimates. This work presents a procedure for improving the accuracy of modeled solar irradiance series through site-adaptation with coincident ground-based measurements relying on the use of a regression preprocessing followed by an empirical quantile mapping (eQM) correction. It was tested at nine sites in a wide range of latitudes and climates, resulting in significant improvements of statistical indicators of dispersion, distribution similarity and overall performance: relative bias is reduced on average from −1.8% and −2.3% to 0.1% and 0.3% for GHI and DNI, respectively; relative root mean square deviation is reduced on average from 17.9% and 34.9% to 14.6% and 29.8% for GHI and DNI, respectively; the distribution similarity is also improved after the site-adaptation (KSI is 3.5 and 3.9 times lower for GHI and DNI at hourly scale, respectively). The methodology is freely available as supplementary material and downloadable as R-package from SiteAdapt.

Description: Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean Intersection over Union (IoU) and inference speed. It was found that the Faster RCNN model with 200 box proposals had similar good weed detection performance to the SSD model in terms of precision, recall, f1 score, and IoU, as well as a similar inference time. The precision, recall, f1 score and IoU were 0.65, 0.68, 0.66 and 0.85 for Faster RCNN with 200 proposals, and 0.66, 0.68, 0.67 and 0.84 for SSD, respectively. However, the optimal confidence threshold of the SSD model was found to be much lower than that of the Faster RCNN model, which indicated that SSD might have lower generalization performance than Faster RCNN for mid- to late-season weed detection in soybean fields using UAV imagery. The performance of the object detection model was also compared with patch-based CNN model. The Faster RCNN model yielded a better weed detection performance than the patch-based CNN with and without overlap. The inference time of Faster RCNN was similar to patch-based CNN without overlap, but significantly less than patch-based CNN with overlap. Hence, Faster RCNN was found to be the best model in terms of weed detection performance and inference time among the different models compared in this study. This work is important in understanding the potential and identifying the algorithms for an on-farm, near real-time weed detection and management.

Description: Land subsidence threatens the stable operation of urban rail transit, including subways. Obtaining deformation information during the entire life-cycle of a subway becomes a necessary means to guarantee urban safety. Restricted by sensor life and cost, the single-sensor Multi-temporal Interferometric Synthetic Aperture Radar (MTI) technology has been unable to meet the needs of long-term sequence, high-resolution deformation monitoring, especially of linear objects. The multi-sensor MTI time-series fusion (MMTI-TSF) techniques has been proposed to solve this problem, but rarely mentioned. In this paper, an improved MMTI-TSF is systematically explained and its limitations are discussed. Taking the Beijing Subway Network (BSN) as a case study, through cross-validation and timing verification, we find that the improved MMTI-TSF results have higher accuracy (R2 of 98% and, Root Mean Squared Error (RMSE) of 4mm), and compared with 38 leveling points, the fitting effect of the time series is good. We analyzed the characteristics of deformation along the BSN over a 15-year periods. The results suggest that there is a higher risk of instability in the eastern section of Beijing Subway Line 6 (L6). The land subsidence characteristics along the subway lines are related to its position from the subsidence center, and the edge of the subsidence center presents a segmented feature.

Description: We investigate the applicability of offshore geoelectrical profiling in the littoral zone, e.g., for archaeological prospection, sediment classification and investigations on coastal ground water upwelling. We performed field measurements with a 20 m long multi-electrode streamer in inverse Schlumberger configuration, which we used to statistically evaluate measurement uncertainty and the reproducibility of offshore electric resistivity tomography. We compared floating and submerged electrodes, as well as stationary and towed measurements. We found out that apparent resistivity values can be determined with an accuracy of 1% to 5% (1σ) depending on the measurement setup under field conditions. Based on these values and focusing on typical meter-scale targets, we used synthetic resistivity models to theoretically investigate the tomographic resolution and depth penetration achievable near-beach underneath a column of brackish water of about 1 m depth. From the analysis, we conclude that offshore geoelectric sounding allows the mapping of archaeological stone settings. The material differentiation of low-porosity rock masses 〈 15% is critical. Submerged wooden objects show a significant resistivity contrast to sand and rocks. Distinguishing brine-saturated sandy sediments from cohesive silty-clayey sediments is difficult due to their equal or reversed resistivity contrasts. Submarine freshwater discharges in sandy aquifers can be localized well, though difficulties may occur if the seafloor encounters massive low-porosity rock masses. As to the measurement setups, submerged and floating electrodes differ in their spatial resolution. Whereas stone settings of 0.5 to 1 m can still be located with submerged electrodes within the uppermost 4 m underneath the seafloor, they have to be 〉2 m if floating electrodes are used. Therefore, we recommend using submerged electrodes, especially in archaeological prospection. Littoral geological and hydrogeological mapping is also feasible with floating electrodes in a more time-saving way.

Description: With the rapid development of large-scale satellite constellations and the increasing demand for rapid communication and emergency rescue using global satellite-based Internet, there have been new requirements for efficient algorithms for inter-communication between satellites. As the constellations of low-orbit satellites become larger, the complexities of real-time inter-satellite calculation and path planning are becoming more complicated and are increasing geometrically. To address the bottlenecks in large-scale node space computing, we introduced a global space grid. Based on this grid, an efficient calculation method of spatial inter-connection between satellite constellations is proposed, according to the concept of “storage for computing” and the high computational efficiency of the spatial grid model. This strategy includes the following parts: (1) the introduction of the GeoSOT-3D global grid model into aerospace and the construction of the aerospace grid indexing BigTable; (2) a set of algorithms for satellite visibility analysis according to the visible grid look-up table and the secondary grid index; and (3) planning inter-satellite routing by querying the grid’s inherent visibility. The idea at the basis of this method is to employ the “space for time” concept to convert the high-dimensional floating operations into one-dimensional matching operations by querying the inherent “visible” attribute of the grid. In our study, we simulated thousands of satellites, discretized their trajectories into grids, and pre-calculated the visibility between grid cells to plan the routing path for the ground data transmission. The theoretical analysis and experimental verification show that the algorithm is feasible and efficient, and it can significantly improve the computational efficiency of inter-satellite connection. We hope that the method can be used in emergency communications, disaster warning, and maritime rescue, and can contribute to the next generation of satellite internet and “satellite-ground” integrated networks.

Description: Remote sensing technology plays an increasingly important role in land surface temperature (LST) research. However, various remote sensing data have spatial–temporal scales contradictions. In order to address this problem in LST research, the current study downscaled LST based on three different models (multiple linear regression (MLR), thermal sharpen (TsHARP) and random forest (RF)) from 1 km to 100 m to analyze surface urban heat island (SUHI) in daytime (10:30 a.m.) and nighttime (10:30 p.m.) of four seasons, based on Moderate Resolution Imaging Spectroradiometer (MODIS)/LST products and Landsat 8 Operational Land Imager (OLI). This research used an area (25 × 25 km) of Hangzhou with high spatial heterogeneity as the study area. R2 and RMSE were introduced to evaluate the conversion accuracy. Finally, we compared with similarly retrieved LST to verify the feasibility of the method. The results indicated the following. (1) The RF model was the most suitable to downscale MODIS/LST. The MLR model and the TsHARP model were not applicable for downscaling studies in highly heterogeneous regions. (2) From the time dimension, the prediction precision in summer and winter was clearly higher than that in spring and autumn, and that at night was generally higher than during the day. (3) The SUHI range at night was smaller than that during the day, and was mainly concentrated in the urban center. The SUHI of the research region was strongest in autumn and weakest in winter. (4) The validation results of the error distribution histogram indicated that the MODIS/LST downscaling method based on the RF model is feasible in highly heterogeneous regions.

Description: This paper proposes a combined approach comprising a set of methods for the high-precision mapping of soil moisture in a study area located in Jiangsu Province of China, based on the Chinese C-band synthetic aperture radar data of GF-3 and high spatial-resolution optical data of GF-1, in situ experimental datasets and background knowledge. The study was conducted in three stages: First, in the process of eliminating the effect of vegetation canopy, an empirical vegetation water content model and a water cloud model with localized parameters were developed to obtain the bare soil backscattering coefficient. Second, four commonly used models (advanced integral equation model (AIEM), look-up table (LUT) method, Oh model, and the Dubois model) were coupled to acquire nine soil moisture retrieval maps and algorithms. Finally, a simple and effective optimal solution method was proposed to select and combine the nine algorithms based on classification strategies devised using three types of background knowledge. A comprehensive evaluation was carried out on each soil moisture map in terms of the root-mean-square-error (RMSE), Pearson correlation coefficient (PCC), mean absolute error (MAE), and mean bias (bias). The results show that for the nine individual algorithms, the estimated model constructed using the AIEM (mv1) was significantly more accurate than those constructed using the other models (RMSE = 0.0321 cm³/cm³, MAE = 0.0260 cm³/cm³, and PCC = 0.9115), followed by the Oh model (m_v5) and LUT inversion method under HH polarization (mv2). Compared with the independent algorithms, the optimal solution methods have significant advantages; the soil moisture map obtained using the classification strategy based on the percentage content of clay was the most satisfactory (RMSE = 0.0271 cm³/cm³, MAE = 0.0225 cm³/cm³, and PCC = 0.9364). This combined method could not only effectively integrate the optical and radar satellite data but also couple a variety of commonly used inversion models, and at the same time, background knowledge was introduced into the optimal solution method. Thus, we provide a new method for the high-precision mapping of soil moisture in areas with a complex underlying surface.

Description: The growing amount of openly available, meter-scale geospatial vertical aerial imagery and the need of the OpenStreetMap (OSM) project for continuous updates bring the opportunity to use the former to help with the latter, e.g., by leveraging the latest remote sensing data in combination with state-of-the-art computer vision methods to assist the OSM community in labeling work. This article reports our progress to utilize artificial neural networks (ANN) for change detection of OSM data to update the map. Furthermore, we aim at identifying geospatial regions where mappers need to focus on completing the global OSM dataset. Our approach is technically backed by the big geospatial data platform Physical Analytics Integrated Repository and Services (PAIRS). We employ supervised training of deep ANNs from vertical aerial imagery to segment scenes based on OSM map tiles to evaluate the technique quantitatively and qualitatively.

Description: Underground mining can produce subsidence phenomena, especially if orebodies are surficial or occur in soft rocks. In some countries, illegal mining is a big problem for environmental, social and economic reasons. However, when unauthorized excavation is conducted underground, it is even more dangerous because it can produce unexpected surficial collapses in areas not adequately monitored. For this reason, it is important to find quick and economic techniques able to give information about the spatial and temporal development of uncontrolled underground activities in order to improve the risk management. In this work, the differential interferometric synthetic aperture radar (DInSAR) technique, implemented in the SUBSOFT software, has been used to study terrain deformation related to illegal artisanal mining in Ecuador. The study area is located in Zaruma (southeast of El Oro province), a remarkable site for Ecuadorian cultural heritage where, at the beginning of the 2017, a local school collapsed, due to sinkhole phenomena that occurred around the historical center. The school, named “Inmaculada Fe y Alegria”, was located in an area where mining activity was forbidden. For this study, the surface deformations that occurred in the Zaruma area from 2015 to 2019 were detected by using the Sentinel-1 data derived from the Europe Space Agency of the Copernicus Program. Deformations of the order of five centimeters were revealed both in correspondence of known exploitation tunnels, but also in areas where the presence of tunnels had not been verified. In conclusion, this study allowed to detect land surface movements related to underground mining activity, confirming that the DInSAR technique can be applied for monitoring mining-related subsidence.

Description: In high-precision GPS precise point positioning (PPP) time transfer, errors caused by the effect of ionosphere delay have to be corrected. Usually the ionosphere-free combinations of the pseudo code and the carrier phase is used in GPS PPP data processing, and it effectively eliminates the effect of the first-order ionospheric delay. This study quantitatively analyzes the errors caused by higher-order ionospheric (Ion2+) delays in precise PPP time transfer. Data of two 7-day test periods, including low and moderate ionospheric conditions, from 20 stations located in middle- and low-latitude, were analyzed. The difference in clock solution with and without the Ion2+ correction, including the receiver clock solution and time-link clock solution, was deeply analyzed and discussed. The difference sequence shows a constant bias plus some variations with a diurnal variation. For the difference of the receiver clock solutions, the mean standard deviation of the variations is 3.92 ps in low-latitude, which is much larger than that of 2.59 ps in mid-latitude due to the influence of the larger ionospheric electron density on the low-latitude. The maximum constant bias reached more than 15 ps and was negative at most stations in the northern hemisphere, while it was positive at most stations located in the southern hemisphere. The difference in the time-link solutions correlates not only with time and region, but also with the length of the time-links. The largest difference in the long time-link SYDN-PTBB, BJNM-SYDN, AMC2-SYDN, etc., reaches more than 25 ps, while that of the short time-link IENG-PTBB, BRUX-PTBB, etc., is less than 3.5 ps. Therefore, the Ion2+ correction is necessary for high-precision PPP time transfer over long time-links, especially time-links made by one station located in the northern hemisphere and another located in the south hemisphere; however, it could be ignored for short time-links.

Description: The increasing efforts in developing smart city concepts are often coupled with three-dimensional (3D) modeling of envisioned designs. Such conceptual designs and planning are multi-disciplinary in their nature. Realistic implementations must include existing urban structures for proper planning. The development of a participatory planning and presentation platform has several challenges from scene reconstruction to high-performance visualization, while keeping the fidelity of the designs. This study proposes a framework for the integrated representation of existing urban structures in CityGML LoD2 combined with a future city model in LoD3. The study area is located in Sahinbey Municipality, Gaziantep, Turkey. Existing city parts and the terrain were reconstructed using high-resolution aerial images, and the future city was designed in a CAD (computer-aided design) environment with a high level of detail. The models were integrated through a high-resolution digital terrain model. Various 3D modeling approaches together with model textures and semantic data were implemented and compared. A number of performance tuning methods for efficient representation and visualization were also investigated. The study shows that, although the object diversity and the level of detail in the city models increase, automatic reconstruction, dynamic updating, and high-performance web-based visualization of the models remain challenging.

Description: Monitoring uncontained built-up area expansion remains a complex challenge for the development and implementation of a sustainable planning system. In this regard, proper planning requires accurate monitoring tools and up-to-date information on rapid territorial transformations. The purpose of the study was to assess built-up area expansion, comparing two freely available and widely used datasets, respectively, Corine Land Cover and Landsat, to each other, as well as the ground truth, with the goal of identifying the most cost-effective and reliable tool. The analysis was based on the largest post-socialist city in the European Union, the capital of Romania, Bucharest, and its neighboring Ilfov County, from 1990 to 2018. This study generally represents a new approach to measuring the process of urban expansion, offering insights about the strengths and limitations of the two datasets through a multi-level territorial perspective. The results point out discrepancies between the datasets, both at the macro-scale level and at the administrative unit’s level. On the macro-scale level, despite the noticeable differences, the two datasets revealed the spatiotemporal magnitude of the expansion of the built-up area and can be a useful tool for supporting the decision-making process. On the smaller territorial scale, detailed comparative analyses through five case-studies were conducted, indicating that, if used alone, limitations on the information that can be derived from the datasets would lead to inaccuracies, thus significantly limiting their potential to be used in the development of enforceable regulation in urban planning.

Description: This article presents the methodology and results of pilot field illuminance measurements using an unmanned aerial vehicle (UAV). The main goal of the study was to quantify the luminous flux emitted in the upper hemisphere (toward the sky) based on obtained measurement data. The luminous flux emitted toward the sky is the source of undesirable light pollution. For test purposes, a height-adjustable mobile park lantern was constructed, at the top of which any type of luminaire can be installed. In the pilot measurements, two real opal sphere-type luminaires were considered. The lantern was situated in an open area located away from a large city agglomeration. To determine the unusable luminous flux, illuminance was measured, placing the necessary measuring equipment on board a UAV. The measurements were supplemented with the registration of illuminance on the ground upon which the lantern was installed. Based on these data, the useful luminous flux was calculated. The findings show that UAVs may be successfully used for the assessment of the influence of lighting on the light pollution effect.

Description: The importance of monitoring and modelling the impact of climate change on crop phenology in a given ecosystem is ever-growing. For example, these procedures are useful when planning various processes that are important for plant protection. In order to proactively monitor the olive (Olea europaea)’s phenological response to changing environmental conditions, it is proposed to monitor the olive orchard with moving or stationary cameras, and to apply deep learning algorithms to track the timing of particular phenophases. The experiment conducted for this research showed that hardly perceivable transitions in phenophases can be accurately observed and detected, which is a presupposition for the effective implementation of integrated pest management (IPM). A number of different architectures and feature extraction approaches were compared. Ultimately, using a custom deep network and data augmentation technique during the deployment phase resulted in a fivefold cross-validation classification accuracy of 0.9720 ± 0.0057. This leads to the conclusion that a relatively simple custom network can prove to be the best solution for a specific problem, compared to more complex and very deep architectures.

Description: Accurately mapping forest effective leaf area index (LAIe) at the landscape level is a crucial step to better simulate various ecological and physiological processes such as photosynthesis, respiration, transpiration, and precipitation interception. The LAIe products obtained from two-dimensional (2-D) remotely sensed optical imageries are usually biased due to their inability to identify the vertical forest structure and eliminate the effects of forest background (i.e., shrubs, grass, snow, and bare earth). In this study, we first stratified the forest overstory and background layers and generated a forest background mask layer based on the structural information implicitly contained within the aerial laser scanning (ALS) data. We improved the retrieval accuracy of LAIe by combining light detection and ranging (Lidar)-based three dimensional (3-D) structural and 2-D spectral information. Then, we obtained the improved final LAIe estimation result by masking the forest background pixels from the optical remotely sensed imageries. Our results showed that: (1) Removing forest background information could effectively (R2 increase from 20% to 30%) improve the estimation accuracy of optical-based forest LAIe depending on forest structure characteristics. (2) The forest background in the forest stands with low canopy cover showed more apparent effects on LAIe estimation compared with the forest stands with a high canopy cover. (3) The combination of ALS and optical remotely sensed data could produce the best LAIe retrieval result effectively by removing the forest background information.

Description: Forest damage due to storms causes economic loss and requires a fast response to prevent further damage such as bark beetle infestations. By using Convolutional Neural Networks (CNNs) in conjunction with a GIS, we aim at completely streamlining the detection and mapping process for forest agencies. We developed and tested different CNNs for rapid windthrow detection based on PlanetScope satellite data and high-resolution aerial image data. Depending on the meteorological situation after the storm, PlanetScope data might be rapidly available due to its high temporal resolution, while the acquisition of high-resolution airborne data often takes weeks to a month and is, therefore, used in a second step for more detailed mapping. The study area is located in Bavaria, Germany (ca. 165 km2), and labels for damaged areas were provided by the Bavarian State Institute of Forestry (LWF). Modifications of a U-Net architecture were compared to other approaches using transfer learning (e.g., VGG19) to find the most efficient architecture for the task on both datasets while keeping the computational time low. A custom implementation of U-Net proved to be more accurate than transfer learning, especially on medium (3 m) resolution PlanetScope imagery (intersection over union score (IoU) 0.55) where transfer learning completely failed. Results for transfer learning based on VGG19 on high-resolution aerial image data are comparable to results from the custom U-Net architecture (IoU 0.76 vs. 0.73). When using both architectures on a dataset from a different area (located in Hesse, Germany), however, we find that the custom implementations have problems generalizing on aerial image data while VGG19 still detects most damage in these images. For PlanetScope data, VGG19 again fails while U-Net achieves reasonable mappings. Results highlight the potential of Deep Learning algorithms to detect damaged areas with an IoU of 0.73 on airborne data and 0.55 on Planet Dove data. The proposed workflow with complete integration into ArcGIS is well-suited for rapid first assessments after a storm event that allows for better planning of the flight campaign followed by detailed mapping in a second stage.

Description: We aim at verifying whether the use of high-resolution coherency functionals could improve the signal-to-noise ratio (S/N) of Ground-Penetrating Radar data by introducing a variable and precisely picked velocity field in the migration process. After carrying out tests on synthetic data to schematically simulate the problem, assessing the types of functionals most suitable for GPR data analysis, we estimated a varying velocity field relative to a real dataset. This dataset was acquired in an archaeological area where an excavation after a GPR survey made it possible to define the position, type, and composition of the detected targets. Two functionals, the Complex Matched Coherency Measure and the Complex Matched Analysis, turned out to be effective in computing coherency maps characterized by high-resolution and strong noise rejection, where velocity picking can be done with high precision. By using the 2D velocity field thus obtained, migration algorithms performed better than in the case of constant or 1D velocity field, with satisfactory collapsing of the diffracted events and moving of the reflected energy in the correct position. The varying velocity field was estimated on different lines and used to migrate all the GPR profiles composing the survey covering the entire archaeological area. The time slices built with the migrated profiles resulted in a higher S/N than those obtained from non-migrated or migrated at constant velocity GPR profiles. The improvements are inherent to the resolution, continuity, and energy content of linear reflective areas. On the basis of our experience, we can state that the use of high-resolution coherency functionals leads to migrated GPR profiles with a high-grade of hyperbolas focusing. These profiles favor better imaging of the targets of interest, thereby allowing for a more reliable interpretation.

Description: In recent years, regional algorithms have shown great potential in the field of synthetic aperture radar (SAR) image segmentation. However, SAR images have a variety of landforms and a landform with complex texture is difficult to be divided as a whole. Due to speckle noise, traditional over-segmentation algorithm may cause mixed superpixels with different labels. They are usually located adjacent to two areas or contain more noise. In this paper, a new semantic segmentation method of SAR images based on texture complexity analysis and key superpixels is proposed. Texture complexity analysis is performed and on this basis, mixed superpixels are selected as key superpixels. Specifically, the texture complexity of the input image is calculated by a new method. Then a new superpixels generation method called neighbourhood information simple linear iterative clustering (NISLIC) is used to over-segment the image. For images with high texture complexity, the complex areas are first separated and key superpixels are selected according to certain rules. For images with low texture complexity, key superpixels are directly extracted. Finally, the superpixels are pre-segmented by fuzzy clustering based on the extracted features and the key superpixels are processed at the pixel level to obtain the final result. The effectiveness of this method has been successfully verified on several kinds of images. Comparing with the state-of-the-art algorithms, the proposed algorithm can more effectively distinguish different landforms and suppress the influence of noise, so as to achieve semantic segmentation of SAR images.

Description: Accurate and timely access to the production area of crop seeds allows the seed market and secure seed supply to be monitored. Seed maize and common maize production fields typically share similar phenological development profiles with differences in the planting patterns, which makes it challenging to separate these fields from decametric-resolution satellite images. In this research, we proposed a method to identify seed maize production fields as early as possible in the growing season using a time series of remote sensing images in the Liangzhou district of Gansu province, China. We collected Sentinel-2 and GaoFen-1 (GF-1) images captured from March to September. The feature space for classification consists of four original bands, namely red, green, blue, and near-infrared (nir), and eight vegetation indexes. We analyzed the timeliness of seed maize identification using Sentinel-2 time series of different time spans and identified the earliest time frame for reasonable classification accuracy. Then, the earliest time series that met the requirements of regulatory accuracy were compared and analyzed. Four machine/deep learning algorithms were tested, including K-nearest neighbor (KNN), support vector classification (SVC), random forest (RF), and long short-term memory (LSTM). The results showed that using Sentinel-2 images from March to June, the RF and LSTM algorithms achieve over 88% accuracy, with the LSTM performing the best (90%). In contrast, the accuracy of KNN and SVC was between 82% and 86%. At the end of June, seed maize mapping can be carried out in the experimental area, and the precision can meet the basic requirements of monitoring for the seed industry. The classification using GF-1 images were less accurate and reliable; the accuracy was 85% using images from March to June. To achieve near real-time identification of seed maize fields early in the growing season, we adopted an automated sample generation approach for the current season using only historical samples based on clustering analysis. The classification accuracy using new samples extracted from historical mapping reached 74% by the end of the season (September) and 63% by the end of July. This research provides important insights into the classification of crop fields cultivated with the same crop but different planting patterns using remote sensing images. The approach proposed by this study enables near-real time identification of seed maize production fields within the growing season, which could effectively support large-scale monitoring of the seed supply industry.

Description: Leaf chlorophyll content (LCC) is a pivotal parameter in the monitoring of agriculture and carbon cycle modeling at regional and global scales. ENVISAT MERIS and Sentinel-3 OLCI data are suitable for use in the global monitoring of LCC because of their spectral specifications (covering red-edge bands), wide field of view and short revisit times. Generally, remote sensing approaches for LCC retrieval consist of statistically- and physically-based models. The physical approaches for LCC estimation require the use of radiative transfer models (RTMs), which are more robust and transferrable than empirical models. However, the operational retrieval of LCC at large scales is affected by the large variability in canopy structures and soil backgrounds. In this study, we proposed an improved look-up-table (LUT) approach to retrieve LCC by combining multiple canopy structures and soil backgrounds to deal with the ill-posed inversion problem caused by the lack of prior knowledge on canopy structure and soil-background reflectance. Firstly, the PROSAIL-D model was used to simulate canopy spectra with diverse imaging gometrics, canopy structures, soil backgrounds and leaf biochemical contents, and the canopy spectra were resampled according to the spectral response functions of ENVISAT MERIS and Sentinel-3 OLCI instruments. Then, an LUT that included 25 sub-LUTs corresponding to five types of canopy structure and five types of soil background was generated for LCC estimation. The mean of the best eight solutions, rather than the single best solution with the smallest RMSE value, was selected as the retrieval of each sub-LUT. The final inversion result was obtained by calculating the mean value of the 25 sub-LUTs. Finally, the improved LUT approach was tested using simulations, field measurements and ENVISAT MERIS satellite data. A simulation using spectral bands from the MERIS and Sentinel-3 OLCI simulation datasets yielded an R2 value of 0.81 and an RMSE value of 10.1 μg cm−2. Validation performed well with field-measured canopy spectra and MERIS imagery giving RMSE values of 9.9 μg cm−2 for wheat and 9.6 μg cm−2 for soybean using canopy spectra and 8.6 μg cm−2 for soybean using MERIS data. The comparison with traditional chlorophyll-sensitive indices showed that our improved LUT approach gave the best performance for all cases. Therefore, these promising results are directly applicable to the use of ENVISAT MERIS and Sentinel-3 OLCI data for monitoring of crop LCC at a regional or global scale.

Description: A new method to identify short-rotation eucalyptus plantations by exploring both the changing pattern of vegetation indices due to tree crop rotation and spectral characteristics of eucalyptus in the red-edge region is presented. It can be adopted to produce eucalyptus maps of high spatial resolution (30 m) at large scales, with the use of open remote sensing images from Landsat 8 Operational Land Imager (OLI), MODerate resolution Imaging Spectroradiometer (MODIS), and Sentinel-2 MultiSpectral Instrument (MSI), as well as a free cloud computing platform, Google Earth Engine (GEE). The method is composed of three main steps. First, a time series of Enhanced Vegetation Index (EVI) is constructed from Landsat data for each pixel, and a statistical hypothesis testing is followed to determine whether the pixel belongs to a tree plantation or not based on the idea that tree crops should be harvested in a specific period. Then, a broadleaf/needleleaf classification is applied to distinguish eucalyptus from coniferous trees such as pine and fir using the red-edge bands of Sentinel-2 data. Refinements based on superpixel are performed at last to remove the salt-and-pepper effects resulted from per-pixel detection. The proposed method allows gaps in the time series that are very common in tropical and subtropical regions by employing time series segmentation and statistical hypothesis testing, and could capture forest disturbances such as conversion of natural forest or agricultural lands to eucalyptus plantations emerged in recent years by using a short observing time. The experiment in Guangxi province of China demonstrated that the method had an overall accuracy of 87.97%, with producer’s accuracy of 63.85% and user’s accuracy of 66.89% for eucalyptus plantations.

Description: In the last few years, the occurrence and abundance of tree-related microhabitats and habitat trees have gained great attention across Europe as indicators of forest biodiversity. Nevertheless, observing microhabitats in the field requires time and well-trained staff. For this reason, new efficient semiautomatic systems for their identification and mapping on a large scale are necessary. This study aims at predicting microhabitats in a mixed and multi-layered Mediterranean forest using Airborne Laser Scanning data through the implementation of a Machine Learning algorithm. The study focuses on the identification of LiDAR metrics useful for detecting microhabitats according to the recent hierarchical classification system for Tree-related Microhabitats, from single microhabitats to the habitat trees. The results demonstrate that Airborne Laser Scanning point clouds support the prediction of microhabitat abundance. Better prediction capabilities were obtained at a higher hierarchical level and for some of the single microhabitats, such as epiphytic bryophytes, root buttress cavities, and branch holes. Metrics concerned with tree height distribution and crown density are the most important predictors of microhabitats in a multi-layered forest.

Description: Solar irradiance derived from satellite imagery is useful for solar resource assessment, as well as climate change research without spatial limitation. The University of Arizona Solar Irradiance Based on Satellite–Korea Institute of Energy Research (UASIBS-KIER) model has been updated to version 2.0 in order to employ the satellite imagery produced by the new satellite platform, GK-2A, launched on 5 December 2018. The satellite-derived solar irradiance from UASIBS-KIER model version 2.0 is evaluated against the two ground observations in Korea at instantaneous, hourly, and daily time scales in comparison with the previous version of UASIBS-KIER model that was optimized for the COMS satellite. The root mean square error of the UASIBS-KIER model version 2.0, normalized for clear-sky solar irradiance, ranges from 4.8% to 5.3% at the instantaneous timescale when the sky is clear. For cloudy skies, the relative root mean square error values are 14.5% and 15.9% at the stations located in Korea and Japan, respectively. The model performance was improved when the UASIBS-KIER model version 2.0 was used for the derivation of solar irradiance due to the finer spatial resolution. The daily aggregates from the proposed model are proven to be the most reliable estimates, with 0.5 km resolution, compared with the solar irradiance derived by the other models. Therefore, the solar resource map built by major outputs from the UASIBS-KIER model is appropriate for solar resource assessment.

Description: The change in water storage driven by the Three Gorges Project directly affects the terrestrial water migration and redistribution in the Yangtze River Basin (YRB). As a result, a new water balance is established and regional evapotranspiration (ET) fluctuates in the process. In this paper, data from multiple-sources including from the Gravity Recovery and Climate Experiment (GRACE) satellite, land surface models (LSMs), remote sensing, and in-situ observations were used to monitor the temporal and spatial evolution of terrestrial water and estimate changes in ET in the Three Gorges Reservoir (TGR) from 2002 to 2016. Our results showed that GRACE data scaled using the scale factor method significantly improved the signal amplitude and highlighted its spatial differences in the TGR area. Combining GRACE with surface hydrological observations, ET in the TGR area was estimated to have overall change characteristics highly consistent with results from the MOD16 Moderate Resolution Imaging Spectroradiometer (MODIS), and the uncertainties of monthly ET are mainly from TWS changes derived by GRACE uncertainties such as measurement errors and leakage errors. During our study period, the cyclical ET was mainly driven by climate precipitation but short-term (monthly) ET in the TGR area was also directly affected by human-driven water storage. For example, rising water levels in the three water storage stages (2003, 2006, and 2008) caused an abnormal increase in regional ET (up to 22.4 cm/month, 19.2 cm/month and 29.5 cm/month, respectively). Usually, high precipitation will cause increase in ET but the high precipitation during the water release periods (spring and summer) did not have a significant impact on the increased ET due to the water level in the TGR having decreased 30 m in this stage. Our results also indicate that the short-term fluctuations in flooded area and storage capacity of the TGR, i.e., the man-made mass changes in the main branch and tributaries of the Yangtze River, were the main factors that influenced the ET. This further illustrated that a quantitative estimation of changes in the ET in the TGR allows for a deeper understanding of the water balance in the regional land water cycle process as driven by both climate and human factors.

Description: The distribution of hydrometeors in thunderstorms is still under investigation as well as the process of electrification in thunderclouds leading to lightning discharges. One indicator of cloud electrification might be high values of the Linear Depolarization Ratio (LDR) at higher vertical levels. This study focuses on LDR values derived from vertically pointing cloud radars and the distribution of five hydrometeor species during 38 days with thunderstorms which occurred in 2018 and 2019 in Central Europe, close to our radar site. The study shows improved algorithms for de-aliasing, the derivation of vertical air velocity and the classification of hydrometeors in clouds using radar data. The comparison of vertical profiles with observed lightning discharges in the vicinity of the radar site (≤1 km) suggested that cloud radar data can indirectly identify “lightning” areas by high LDR values observed at higher gates due to the alignment of ice crystals, likely because of an intensified electric field in thunderclouds. Simultaneously, the results indicated that at higher gates, there is a mixture of several hydrometeor species, which suggests a well-known electrification process by collisions of hydrometeors.

Description: The scarcity of resources, the limited land, and the overstressing of tourism, as well as the estrangements of movement, make the insular territories relevant case studies in terms of their regional management and governance and, consequently, sustainable development. Thereby, Transportation and Infrastructures’ Sustainability in these territories is not an exception. In this regard, the present study, through exploratory tools, expects to analyze, using accessibility and connectivity indicators, the impacts over the social-economic sphere that the local Transportation and Infrastructures may deliver to the populations of the Canary Islands Archipelago. The study enables us to identify the islands of La Palma, El Hierro, Fuerteventura, and La Gomera as those with better accessibility patterns.

Description: The general consensus on future climate projections poses new and increased concerns about climate change and its impacts. Droughts are primarily worrying, since they contribute to altering the composition, distribution, and abundance of species. Grasslands, for example, are the primary source for grazing mammals and modifications in climate determine variation in the available yields for cattle. To support the agriculture sector, international organizations such as the Food and Agriculture Organization (FAO) of the United Nations are promoting the development of dedicated monitoring initiatives, with particular attention for undeveloped and disadvantaged countries. The temporal scale is very important in this context, where long time series of data are required to compute consistent analyses. In this research, we discuss the results regarding long-term grass biomass estimation in an extended African region. The results are obtained by means of a procedure that is mostly automatic and replicable in other contexts. Zambia has been identified as a significant test area due to its vulnerability to the adverse impacts of climate change as a result of its geographic location, socioeconomic stresses, and low adaptive capacity. In fact, analysis and estimations were performed over a long time window (21 years) to identify correlations with climate variables, such as precipitation, to clarify sensitivity to climate change and possible effects already in place. From the analysis, decline in both grass quality and quantity was not currently evident in the study area. However, pastures in the considered area were found to be vulnerable to changing climate and, in particular, to the water shortages accompanying drought periods.

Description: Spectral similarity measures can be regarded as potential metrics for kernel functions, and can be used to generate spectral-similarity-based kernels. However, spectral-similarity-based kernels have not received significant attention from researchers. In this paper, we propose two novel spectral-similarity-based kernels based on spectral angle mapper (SAM) and spectral information divergence (SID) combined with the radial basis function (RBF) kernel: Power spectral angle mapper RBF (Power-SAM-RBF) and normalized spectral information divergence-based RBF (Normalized-SID-RBF) kernels. First, we prove these spectral-similarity-based kernels to be Mercer’s kernels. Second, we analyze their efficiency in terms of local and global kernels. Finally, we consider three hyperspectral datasets to analyze the effectiveness of the proposed spectral-similarity-based kernels. Experimental results demonstrate that the Power-SAM-RBF and SAM-RBF kernels can obtain an impressive performance, particularly the Power-SAM-RBF kernel. For example, when the ratio of the training set is 20 % , the kappa coefficient of Power-SAM-RBF kernel (0.8561) is 1.61 % , 1.32 % , and 1.23 % higher than that of the RBF kernel on the Indian Pines, University of Pavia, and Salinas Valley datasets, respectively. We present three conclusions. First, the superiority of the Power-SAM-RBF kernel compared to other kernels is evident. Second, the Power-SAM-RBF kernel can provide an outstanding performance when the similarity between spectral signatures in the same hyperspectral dataset is either extremely high or extremely low. Third, the Power-SAM-RBF kernel provides even greater benefits compared to other commonly used kernels when the sizes of the training sets increase. In future work, multiple kernels combining with the spectral-similarity-based kernel are expected to be provide better hyperspectral classification.

Description: We evaluated the response of vegetation’s photosynthetic activity to drought conditions from 1998 to 2014 over Romania and the Republic of Moldova. The connection between vegetation stress and drought events was assessed by means of a correlation analysis between the monthly Standardized Precipitation Evaporation Index (SPEI), at several time scales, and the Normalized Difference Vegetation Index (NDVI), as well as an assessment of the simultaneous occurrence of extremes in both indices. The analysis of the relationship between drought and vegetation was made for the growing season (from April to October of the entire period), and special attention was devoted to the severe drought event of 2000/2001, considered as the driest since 1961 for the study area. More than three quarters (77%) of the agricultural land exhibits a positive correlation between the two indices. The sensitivity of crop areas to drought is strong, as the impacts were detected from May to October, with a peak in July. On the other hand, forests were found to be less sensitive to drought, as the impacts were limited mostly to July and August. Moreover, vegetation of all land cover classes showed a dependence between the sign of the correlation and the elevation gradient. Roughly 60% (20%) of the study domain shows a concordance of anomalously low vegetation activity with dry conditions of at least 50% (80%) in August. By contrast, a lower value of concordance was observed over the Carpathian Mountains. During the severe drought event of 2000/2001, a decrease in vegetation activity was detected for most of the study area, showing a decrease lasting at least 4 months, between April and October, for more than two thirds (71%) of the study domain.