Remote Sensing Applications-Society and Environment最新文献

Land use and land cover (LULC) change is a dynamic process which is significantly influenced by anthropogenic activities. Analysing historical LULC trends and predicting future dynamics is critical to provide insights for decision-makers and planners aiming for sustainable land management and development. This study focuses on the Bernam River Basin (BRB). It employs an integrated approach that combines the Multi-Layer Perceptron (MLP), the Cellular Automata (CA)-Markov algorithm, remote sensing, and Geographical Information System (GIS) techniques. Using multi-temporal 10m resolution Sentinel-2 Landsat imagery from 2010, 2020, and 2022, the study classified LULC into seven categories: water, forest, wetlands, agriculture, urban, barren, and rangeland areas. Change analysis from 2010 to 2020 was conducted, with 2022 validating predicted LULC transitions. The MLP model, trained on land change driver variables, facilitated the generation of transition potentials for simulating future LULC changes. A spatially explicit CA-Markov model implemented LULC change projections for 2022, 2025, 2050, and 2075, based on the transition potentials. The analysis reveals an annual increase of 0.24% in water, 0.61% in forest, and 2.11% in urban areas, while wetlands (2.69%), agriculture (2.47%), barren (3.51%), and rangeland (4.58%) experienced declines. The CA-Markov approach accurately predicted LULC transitions for 2022, validated through an error matrix with an overall accuracy of 91.56% based on 450 sampling points. Predictions for 2025–2075 indicate rising trends in water (1.76%), wetlands (29.18%), agriculture (60.08%), urban (96.53%), barren (0.59%), and rangeland areas (3.57%). Forests are expected to decrease by 12% (261.52 km²). The study identified agriculture and urban expansion as the primary drivers of LULC changes in the river basin. These findings provide critical information for regional authorities to formulate evidence-based policies and management strategies, ensuring the environmental sustainability of BRB. Furthermore, these predicted LULC patterns can be integrated into complementary models, such as the Soil and Water Assessment Tool, to assess the impacts of LULC changes on water resources.

The livestock sector in rural Colombia is critical for employment and food security but is heavily affected by climate and its change. There is a need for solutions to address key challenges arising from vulnerabilities that impact the productivity and sustainability of forages and the livestock sector. Increasing the yields of forage crops can improve the availability and affordability of livestock products while also easing the pressure on land resources. This study aims to develop remote sensing-based approaches for forage monitoring and biomass prediction in Colombia to support decision-making towards increased productivity, competitiveness and reduction of environmental impacts. Ten locations were sampled between 2018 and 2021 across climatically distinct areas in Colombia, comprising five farms in Patía in Cauca department, four farms in Antioquia department, and one research farm at Palmira in Valle de Cauca department. Ash content (Ash), crude protein (CP %), dry matter content (DM g/m²) and in-vitro digestibility (IVD %) were measured from Kikuyu and Brachiaria grasses during the field sampling campaigns. Multispectral bands from coincident Planetscope acquisitions along with various derived vegetation indices (VIs) were used as predictors in the model development. For each site and forage parameter, the importance of specific predictors varied, with the NIR band and Red-Green ratio generally performing best. To determine the optimum models, the effects of using a 1) averaging kernel, 2) feature selection approaches, 3) various regression algorithms and 4) meta learners (simple ensembling and stacks) were explored. Algorithms belonging to classes of commonly used models; Decision Trees, Support Vector Machines, Neural Networks, distance-based methods, and linear approaches were tested. The performance evaluation based on unseen test data revealed that CP and DM prediction performed moderately well for all three sites (R² 0.52–0.75, RMSE 1.7–2 % and R² 0.47–0.65, RMSE 182–112 g/m² respectively). The best performing models varied by site and response variable, with Regularized Random Forest, Partial Least Squares, Random Forests, Bagged Multivariate Adaptive Regression and Bayesian Regularized Neural Networks being the top performing algorithms and Random Forest Stack being the best performing meta learner. The workflow and thorough analysis of performance affecting factors presented in this study can benefit timely grassland monitoring and biomass prediction at the local level and help contribute to the sustainable management of tropical grasslands in Colombia.

All glaciers respond to climatic changes by fluctuating their mass. Investigations of glacier dynamics are necessary for glacier monitoring. Himalayan glaciers make ongoing glacier observations challenging due to their location in a severe topographic environment and inhospitable terrain. Glacier area contraction or extension, together with a corresponding snout shift, can be linked to oscillations in glacier mass. Sentinel-1 dual-polarized datasets were used in this investigation to retrieve glacier surface velocity. Estimates of ice thickness were enhanced by segmenting the glacier into 100-m height intervals. Also, ice thickness variations between 2017 and 2022 have been used to compute glacier mass balance, and the results for several glacier zones have been briefly analyzed. The study revealed that the maximum surface velocity above Gangotri Glacier was approximately 0.33 m/day, with an estimated average of 0.09 m/day. Surface velocities of the central trunk have been seen to range from 0.12 m/day to 0.23 m/day. Additionally, between 2017 and 2022, the surface velocity was spotted between 0.19 m/day to 0.35 m/day. For the glacier, an average ice thickness of 189 ± 17.01 m was calculated. In the central parts, where the drag was least noticeable, thicknesses up to 587 ± 52.83 m were estimated. In the lower accumulation zone and middle reaches, the thickness was found to be decreasing between 2017 and 2022, which can be attributed to increased melting and glacier slowdown. Due to the increased glacier movement throughout time, the lower accumulation reaches over the main glacier body, and its tributaries have experienced mass balancing rates ranging from −1.3 m.w.e./year to −0.5 m.w.e./year (thickness change between −3 m/year and −0.6 m/year). With the help of previous research and existing data, the results were compared and validated. The suggested algorithm and findings can serve as inputs for satellite-based ice thickness measurements and as fundamental research for the forthcoming NISAR mission (expected by mid-2024) which will carry L- and S-band antennas.

The Increasing frequency and severity of forest fires worldwide highlights the need for more effective Burnt area mapping. Finding the effects of fire on vegetation and putting mitigation methods in place, depends on post-fire evaluation. In this study, the location of the burned regions and the severity of the fire were determined using high-resolution multi-spectral images from Sentinel 2 on Google Earth Engine (GEE) platform. Three widely used fire severity indices—differenced Normalized Burn Ratio (dNBR), Relativized Burn Ratio (RBR), and Relativized dNBR (RdNBR)—based on pre-fire Normalized Burn Ratio (NBR) and post-fire NBR—were computed and compared based on their accuracy using very high-resolution planet imagery fire points and equal number of random non fire points. Maps also validated with active fires, ground based photos and crowdsourced images. The accuracy (AUC) of the RdNBR map was 85%, RBR - 84% and dNBR −82%. The RdNBR index demonstrated highest level of accuracy. Then the loss to vegetation using pre-fire and post-fire NDVI was analysed. The analysis of pre-fire and post-fire NDVI provided insights into the extent of vegetation loss. The analysis of vegetation loss offered valuable information regarding the impact of fire on the affected areas. Google building dataset was used to monitor the percent of buildings under threat due to these fires. Around 8.77% of buildings were found in high severity region. Accurate mapping aids post-fire evaluation, guided mitigation strategies, and enhanced forest management and ecological restoration.

Surface coal mining in the Appalachian region has led to a significant forest disturbance over time. Evaluating the effectiveness of current reclamation practices in promoting vegetation growth on reclaimed mine sites is a key to understanding how much vegetation has changed in those sites since reclamation. This study employed statewide airborne lidar data to assess changes in lidar vegetation structural metrics on reclaimed mine lands in the Lower Levisa Watershed of Eastern Kentucky between 2011 and 2019 and compare vegetation growth at various reclaimed sites reclaimed in different decades. Eighteen inactive surface mines were selected for the study and categorized into four groups based on the release of their reclamation bonds in different decades. Lidar point cloud data were processed in ArcGIS Pro using filtering and segmentation algorithms to calculate various vegetation attributes from the point clouds, including maximum vegetation height (H_max), mean height (H_mean), standard deviation of height (H_SD), canopy cover (CC), and height percentiles (10, 50 and 75), which were represented as lidar metrics. The process of generating the lidar metrics involved creating Digital Elevation Models (DEMs) and Digital Surface Models (DSMs), calculating Canopy Height Models (CHMs), creating LAS height metrics and generating point statistics rasters to derive these metrics. Change maps for each metric were visually assessed over time, and circular plots with a radius of 12 m were established within each site for further statistical analysis. Significant changes in lidar vegetation metrics were observed between 2011 and 2019 with significant differences among sites reclaimed at different time periods. There was an overall increase in H_mean from 2011 to 2019, with values ranging from 2.4 to 3.8 m. Sites reclaimed in the 1980s experienced an average decrease in canopy cover of −0.5%, while those from the 1990s, 2000s, and 2010s demonstrated increases of 4.9%, 10.1%, and 18.1%, respectively, suggesting that canopy growth rates are higher in younger sites compared to older ones. Vertical variability of the vegetation also increased over time, as indicated by increasing H_SD values. Utilizing statewide airborne lidar data allowed for a comprehensive and detailed assessment of vegetation dynamics on reclaimed mine lands. The findings of this study serve as a foundation for future research endeavors focused on vegetation recovery assessment and success in reclaimed mine lands using lidar data.

Ireland’s coastline, a critical and dynamic resource, is facing challenges such as erosion, sedimentation, and human activities. Monitoring these changes is a complex task we approach using a combination of satellite imagery and deep learning methods. However, limited research exists in this area, particularly for Ireland. This paper presents the Landsat Irish Coastal Segmentation (LICS) dataset, which aims to facilitate the development of deep learning methods for coastal water body segmentation while addressing modelling challenges specific to Irish meteorology and coastal types. The dataset is used to evaluate various automated approaches for segmentation, with U-NET achieving the highest accuracy of 95.0% among deep learning methods. Nevertheless, the Normalised Difference Water Index (NDWI) benchmark outperformed U-NET with an average accuracy of 97.2%. The study suggests that deep learning approaches can be further improved with more accurate training data and by considering alternative measurements of erosion. The LICS dataset and code are freely available to support reproducible research and further advancements in coastal monitoring efforts.

Aquatic invasive plant, water hyacinth poses serious environmental and socioeconomic challenges. Understanding and predicting the spatiotemporal distribution of this species is important for reducing its environmental impact. Therefore, the present study aimed to model the distribution of water hyacinths in an important ecological region (Lake Tana) of Ethiopia using four machine learning models. We used 11 variables obtained from Sentinel-1 SAR bands, Sentinel-2A bands and indices, and bioclimate data sources. The models use 458 presence and 458 randomly generated pseudoabsence data as response variables and employ a tenfold bootstrap sampling method. The area under the curve (AUC), receiver operator curve (ROC), true skill statistics (TSS), coefficient of rank correlation (COR), sensitivity, specificity, and kappa coefficient were used to evaluate the models. The findings demonstrate that the random forest model outperforms the other models, with AUC values of 0.93 and 0.95, TSS values of 0.77 and 0.82, and kappa values of 0.76 and 0.82 in the wet and dry seasons, respectively. B12 (16% and 20%), NDWI (15% and 12%), mean annual temperature (13% and 14%), and B5 (11% and 12%) were found to be the most relevant variables during the wet and dry seasons, respectively. Water hyacinths have greater spatial coverage during the wet season than during the dry season because of high rainfall, high water levels and nutrient runoff. We can conclude that to detect and predict the spatiotemporal conditions of water hyacinth accurately, integrating Sentinel image indices and bands with bioclimatic variables and using machine learning models are crucial.

Although remote sensing approaches offer unprecedented opportunities to understand urban land cover dynamics including informal settlements areal extent, challenges such as spectral confusions still persist, particularly when segregating land cover types like informal settlements from planned formal settlements. The improvements in Earth Observation (EO) data analytic tools such as introduction of Google Earth Engine (GEE) cloud computing platform, provide prospects to improve separability of these settlements from other urban land cover classes, via their advanced data processing and filtering algorithm, which allows for the synergic use of multisource and multi-temporal data, thus improving detection and monitoring of these settlements. This study harnessed the advance data analytic powers of GEE cloud computing platform coupled with higher resolution Sentinel-2 data to map the geographical extent of informal settlement in the Cape Town Metropolitan Area. The classification yielded six land cover classes: formal settlements, informal settlements, water, bare or built-up areas, vegetated lands, and croplands. Built-up formal settlement was the most dominant class, accounting for 70% of the total Cape Town surface area, while open water was the least dominant, accounting for 2%. Informal settlements accounted for approximately 7% of all settlements. Although overall accuracy was within acceptable limits (68%), some classes, such as vegetated lands and formal settlements, reported low class accuracies due to spectral similarities with other classes. The findings highlight the importance of the GEE platform, as well as the interaction of contextual and spectral characteristics, as well as various sentinel-2 derived water, built up, and vegetation indices in mapping informal settlements. These findings are critical for the facilitation of improved urban planning, provision of services and assisting in alleviating social as well as environmental issues within the Cape Town Metropolitan area.

The change in climate and the hot temperature environment increased the risk of drought around the workplace. Predicting and forecasting the drought occurrence is essential for managing water resources and agricultural plans. Therefore, in this study, a novel Chimp-based Wide ResNet Prediction Framework (CWRPF) is designed to predict the drought. The key motive of the presented research is to predict the drought and no drought conditions derived from the satellite images. The satellite images are collected from the Bhuvan site. Initially, the satellite images are noise-filtered. The filtered images are then injected into the feature analysis phase to compute the drought indices of a specific area by the fitness function activated in the framework. After estimating the drought indices, the drought condition was categorized. Finally, the designed system is tested in the MATLAB platform and has gained more significant results by providing a 97.68% accuracy rate, R2 as 0.998, and lower RMSE and MAE values of 0.223 and 0.193. The accumulated results are compared with existing techniques to validate the improvement score. The accuracy of the CWRPF is more remarkable than that of other prediction models. Therefore, the system is efficient for drought prediction in satellite images.

This research presents a random forest classification approach to map the response of the obligate-seeder Eucalyptus species, Mountain Ash (Eucalyptus regnans) and Alpine Ash (E. delegatensis), to disturbance from timber harvesting in the Victorian Central Highlands in south-eastern Australia. A Sentinel-2 MultiSpectral Instrument (MSI) composite image was classified and analysed using a random forest algorithm trained using field data collected within fifty-three sites. Training and validation datasets were produced by randomly sub setting using a 70:30 split. Validation was performed by producing a confusion matrix using the points which were excluded from model training. The random forest model demonstrated strong performance at distinguishing Eucalyptus regrowth from the dominant understory species, Silver Wattle (Acacia dealbata), achieving an F1-score of 97.3% and true skill statistic of 96.4%.

This study showcases the operational insights that satellite remote sensing data and machine learning can provide for regional-scale monitoring and management of E. regnans and E. delegatensis dominant ecosystems following disturbance. Due to the high conservation value of these communities, and their sensitivity to frequent high intensity disturbance and low precipitation during regeneration, this research seeks to provide a means to assess the condition of regenerating forest and in doing so enhance our understanding of these ecologically significant ecosystems in response to changing environmental conditions.