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Rapid urbanization in African metropolises like the Greater Asmara Area, Eritrea, poses numerous environmental challenges, including soil sealing, loss of vegetation cover, threats to protected natural areas, and climate change, among others. Mapping and assessing ecosystem services, particularly analyzing their spatial and temporal distribution is crucial for sustainable spatial planning. This study aims at mapping and analyzing ecosystem services hotspots and coldspots dynamics in the Greater Asmara Area to identify recent trends and opportunities for enhancing ecosystem services supply. Utilizing remote sensing images, we produced land cover maps for 2009 and 2020 and mapped six ecosystem services through a lookup table approach. The study includes provisioning, regulating and maintenance, and cultural ecosystem services. We analyzed their spatio-temporal variations, identifying ecosystem services hotspots and coldspots and their changes over time. Results show that overall ecosystem services potential in the Greater Asmara Area remains low but stable, with some improvements. By 2020, areas with no ecosystem services potential decreased in southern regions like Gala Nefhi and Berik, and new hotspots and coldspots emerged in central Gala Nefhi. This pilot study demonstrates the feasibility and key challenges of the ecosystem services hotspots and coldspots approach for sustainable spatial planning in rapidly urbanizing African metropolitan regions. Despite limitations, the study offers valuable insights into ecosystem services potentials, and related hotspots and coldspots dynamics, raising awareness and paving the way for further research and application. Highlights: Ecosystem services (ES) hotspot/cold spot analysis aids sustainable planning in rapidly urbanizing African cities. We mapped ES hotspot/cold spot dynamics in Greater Asmara Area, Eritrea, from 2009 to 2020. GAA's ES potential is low but stable, showing some improvements over time. It is crucial to interpret observed ES cold/hotspot dynamics in GAA with caution. Our approach is replicable in other resource-scarce, rapidly urbanizing African cities. [ABSTRACT FROM AUTHOR]

Automatic glacier extraction from remote sensing images is the most important approach for large scale glacier monitoring. Commonly used band calculation indices to enhance glacier information are not effective in identifying shadowed glaciers and debris-covered glaciers. In this study, we used the Kolmogorov–Smirnov test as the theoretical basis and determined the most suitable band calculation indices to distinguish different land cover classes by comparing inter-sample separability and reasonable threshold range ratios of different indices. We then constructed a glacier classification decision tree. This approach resulted in the development of a method to automatically extract glacier areas at given spatial and temporal scales. In comparison with the commonly used indices, this method demonstrates an improvement in Cohen's kappa coefficient by more than 3.8%. Notably, the accuracy for shadowed glaciers and debris-covered glaciers, which are prone to misclassification, is substantially enhanced by 108.0% and 6.3%, respectively. By testing the method in the Qilian Mountains, the positive prediction value of glacier extraction was calculated to be 91.8%, the true positive rate was 94.0%, and Cohen's kappa coefficient was 0.924, making it well suited for glacier extraction. This method can be used for monitoring glacier changes in global mountainous regions, and provide support for climate change research, water resource management, and disaster early warning systems. [ABSTRACT FROM AUTHOR]

Information on green space coverage patterns and influencing factors in built-up areas is essential for urban planners, as it is related to the well-being of residents. In this study, WorldCover land cover product was used to extract green space maps and analyze landscape patterns of urban green space coverage (UGSC) of built-up areas in China's Yangtze River Delta region under different scales (i.e., built-up area, district/county, and prefecture-level city) in 2020. Additionally, cold and hot spot analyses were performed to represent the regional aggregation of high and low green space coverage. The random forest model was used to calculate the relative importance and marginal effect of the factors influencing UGSC. The results show that, in general, the UGSC gradually decreases from the southern to the northern regions. The pattern of UGSC is basically the same as that of tree/shrub coverage. Precipitation, the size of built-up area, and the area of urban paved roads are the main influencing factors of UGSC. Nonlinear relationships are observed between the size of built-up areas and UGSC, as well as between the area of urban paved roads and UGSC. The detailed mapping of the UGSC pattern and identifying key influencing factors offer valuable insights for urban planning and sustainable development. [ABSTRACT FROM AUTHOR]

Vapor pressure deficit (VPD) is a critical variable in assessing drought conditions and evaluating plant water stress. Gridded products of global and regional VPD are not freely available from satellite remote sensing, model reanalysis, or ground observation datasets. We present two versions of the first gridded VPD product for the Continental US and parts of Northern Mexico and Southern Canada (CONUS+) at a 1 km spatial resolution and daily time step. We derived VPD from Daymet maximum daily temperature and average daily vapor pressure and scale the estimates based on (1) climate determined by the Köppen-Geiger classifications and (2) land cover determined by the International Geosphere-Biosphere Programme. Ground-based VPD data from 253 AmeriFlux sites representing different climate and land cover classifications were used to improve the Daymet-derived VPD estimates for every pixel in the CONUS+ grid to produce the final datasets. We evaluated the Daymet-derived VPD against independent observations and reanalysis data. The CONUS+ VPD datasets will aid in investigating disturbances including drought and wildfire, and informing land management strategies. [ABSTRACT FROM AUTHOR]

Applying unsupervised classification techniques on remote sensing images enables rapid land cover classification. Using remote sensing imagery from the ZY1-02D satellite's VNIC and AHSI cameras as the basis, multi-source feature information encompassing spectral, edge shape, and texture features was extracted as the data source. The Lanczos algorithm, which determines the largest eigenpairs of a high-order matrix, was integrated with the spectral clustering algorithm to solve for eigenvalues and eigenvectors. The results indicate that this method can quickly and effectively classify land cover. The classification accuracy was significantly improved by incorporating multi-source feature information, with a kappa coefficient reaching 0.846. Compared to traditional classification methods, the improved spectral clustering algorithm demonstrated better adaptability to data distribution and superior clustering performance. This suggests that the method has strong recognition capabilities for pixels with complex spatial shapes, making it a high-performance, unsupervised classification approach. [ABSTRACT FROM AUTHOR]

Shrimp farming has expanded over coastal areas in Mexico, particularly in the protected regions of Sonora and Sinaloa. Over the past 30 years, the economic activity associated with these farms has grown so much that the amount of shrimp produced in these ponds now exceeds that harvested from traditional shrimp fisheries. Establishing shrimp ponds has led to significant land changes. The construction of these ponds has fragmented local ecosystems, resulting in permanent alterations to areas such as floodplains, mangrove forests, and dunes, many of which are protected zones. This study aimed to investigate the long-term growth of shrimp farms from 1993 to 2022 and their impact on land-use changes in surrounding ecosystems, focusing on protected areas in the Sinaloa and Sonora coastal regions. We analyzed Landsat images using the Google Earth Engine platform. Our findings indicate that shrimp farm development over the past three decades has been extensive, with protected areas experiencing fragmentation and changes. Remote sensing and platforms like Google Earth Engine enable the effective monitoring of these spatiotemporal changes and their impacts, helping to identify the most affected areas. [ABSTRACT FROM AUTHOR]

The Aburrá Valley, located in the northwestern region of Colombia, has undergone significant land cover changes and urban expansion in recent decades, driven by rapid population growth and infrastructure development. This region, known for its steep topography and dense urbanization, faces considerable environmental challenges. Monitoring these transformations is essential for informed territorial planning and sustainable development. This study leverages Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission, covering 2017–2024, to propose a methodology for the multitemporal analysis of land cover dynamics and urban expansion in the valley. The novel proposed methodology comprises several steps: first, monthly SAR images were acquired for every year under study from 2017 to 2024, ensuring the capture of surface changes. These images were properly calibrated, rescaled, and co-registered. Then, various multitemporal fusions using statistics operations were proposed to detect and find different phenomena related to land cover and urban expansion. The methodology also involved statistical fusion techniques—median, mean, and standard deviation—to capture urbanization dynamics. The kurtosis calculations highlighted areas where infrequent but significant changes occurred, such as large-scale construction projects or sudden shifts in land use, providing a statistical measure of surface variability throughout the study period. An advanced clustering technique segmented images into distinctive classes, utilizing fuzzy logic and a kernel-based method, enhancing the analysis of changes. Additionally, Pearson correlation coefficients were calculated to explore the relationships between identified land cover change classes and their spatial distribution across nine distinct geographic zones in the Aburrá Valley. The results highlight a marked increase in urbanization, particularly along the valley's periphery, where previously vegetated areas have been replaced by built environments. Additionally, the visual inspection analysis revealed areas of high variability near river courses and industrial zones, indicating ongoing infrastructure and construction projects. These findings emphasize the rapid and often unplanned nature of urban growth in the region, posing challenges to both natural resource management and environmental conservation efforts. The study underscores the need for the continuous monitoring of land cover changes using advanced remote sensing techniques like SAR, which can overcome the limitations posed by cloud cover and rugged terrain. The conclusions drawn suggest that SAR-based multitemporal analysis is a robust tool for detecting and understanding urbanization's spatial and temporal dynamics in regions like the Aburrá Valley, providing vital data for policymakers and planners to promote sustainable urban development and mitigate environmental degradation. [ABSTRACT FROM AUTHOR]

Spectral reconstruction (SR) from multispectral images (MSIs) is a crucial task in remote sensing image processing, aiming to enhance the spectral resolution of MSIs to produce hyperspectral images (HSIs). However, most existing deep learning-based SR methods primarily focus on deeper network architectures, often overlooking the importance of extracting multiscale spatial and spectral features in the MSIs. To bridge this gap, this paper proposes a multiscale spatial–spectral dense residual attention fusion network (MS2Net) for SR. Specifically, considering the multiscale nature of the land-cover types in the MSIs, a three-dimensional multiscale hierarchical residual module is designed and embedded in the head of the proposed MS2Net to extract spatial and spectral multiscale features. Subsequently, we employ a two-pathway architecture to extract deep spatial and spectral features. Both pathways are constructed with a single-shot dense residual module for efficient feature learning and a residual composite soft attention module to enhance salient spatial and spectral features. Finally, the spatial and spectral features extracted from the different pathways are integrated using an adaptive weighted feature fusion module to reconstruct HSIs. Extensive experiments on both simulated and real-world datasets demonstrate that the proposed MS2Net achieves superior performance compared to state-of-the-art SR methods. Moreover, classification experiments on the reconstructed HSIs show that the proposed MS2Net-reconstructed HSIs achieve classification accuracy that is comparable to that of real HSIs. [ABSTRACT FROM AUTHOR]

Grassland ecosystems provide a range of services in semi-arid and arid regions. However, they have significantly declined due to overgrazing and desertification. In the current study, we employed Landsat time series data (TM, OLI, OLI-2) spanning from 1990 to 2024, combined with vegetation indices such as NDVI and SAVI, along with NDWI and digital elevation models (DEMs), to analyze land cover dynamics in the Ugii Lake watershed area, Mongolia. By integrating multisource remote sensing data into the advanced XGBoost (extreme gradient boosting) machine learning algorithm, we achieved high classification accuracy, with overall accuracies exceeding 94% and Kappa coefficients greater than 0.92. The results revealed a decline in montane grasslands (−6.2%) and an increase in other grassland types, suggesting ecosystem redistribution influenced by climatic and anthropogenic factors. Cropland exhibited resilience, recovering from a significant decline in the 1990s to moderate growth by 2024. Our findings highlight the stability of barren land and underscore pressures from ecological degradation and human activities. This study provides up-to-date statistical data to support decision-making in the conservation and sustainable management of grassland ecosystems in Mongolia under changing climatic conditions. [ABSTRACT FROM AUTHOR]

Land use/land cover (LULC) nomenclature is commonly organized as a tree-like hierarchy, contributing to hierarchical LULC mapping. The hierarchical structure is typically defined by considering natural characteristics or human activities, which may not optimally align with the discriminative features and class relationships present in remotely sensed imagery. This paper explores a novel cluster-based class hierarchy modeling framework that generates data-driven hierarchical structures for LULC semantic segmentation. First, we perform spectral clustering on confusion matrices generated by a flat model, and then we introduce a hierarchical cluster validity index to obtain the optimal number of clusters to generate initial class hierarchies. We further employ ensemble clustering techniques to yield a refined final class hierarchy. Finally, we conduct comparative experiments on three benchmark datasets. Results demonstrating that the proposed method outperforms predefined hierarchies in both hierarchical LULC segmentation and classification. [ABSTRACT FROM AUTHOR]

Why should we confine land cover classes to rigid and arbitrary definitions? Land cover mapping is a central task in remote sensing image processing, but the rigorous class definitions can sometimes restrict the transferability of annotations between datasets. Open vocabulary recognition, i.e. using natural language to define a specific object or pattern in an image, breaks free from predefined nomenclature and offers flexible recognition of diverse categories with a more general image understanding across datasets and labels. The open vocabulary framework opens doors to search for concepts of interest, beyond individual class boundaries. In this work, we propose to use Text As supervision for COntrastive Semantic Segmentation (TACOSS), and we design an open vocabulary semantic segmentation model that extends its capacities beyond that of a traditional model for land cover mapping: In addition to visual pattern recognition, TACOSS leverages the common sense knowledge captured by language models and is capable of interpreting the image at the pixel level, attributing semantics to each pixel and removing the constraints of a fixed set of land cover labels. By learning to match visual representations with text embeddings, TACOSS can transition smoothly from one set of labels to another and enables the interaction with remote sensing images in natural language. Our approach combines a pretrained text encoder with a visual encoder and adopts supervised contrastive learning to align the visual and textual modalities. We explore several text encoders and label representation methods and compare their abilities to encode transferable land cover semantics. The model's capacity to predict a set of different land cover labels on an unseen dataset is also explored to illustrate the generalization capacities across domains of our approach. Overall, TACOSS is a general method and permits adapting between different sets of land cover labels with minimal computational overhead. Code is publicly available online 1 1 https://github.com/eceo-epfl/RS-OVSS.. [ABSTRACT FROM AUTHOR]

Detecting building changes with multi-temporal remote sensing (RS) imagery at a very high resolution can help us understand urbanization and human activities, making informed decisions in urban planning, resource allocation, and infrastructure development. However, existing methods for building change detection (BCD) generally overlook critical uncertainty phenomena presented in RS imagery. Specifically, these uncertainties arise from two main sources: First, current building change detection datasets are designed primarily to detect changes in buildings, while changes in other land-cover classes are often classified as an unchanged background. Because of the manual labeling process, background elements that resemble buildings, such as roads and bridges, are at significant risk of being misclassified as building changes, introducing aleatoric uncertainty at the data level. Second, changes in parts of buildings that affect appearance, texture, or style without altering their semantic meaning, known as pseudo-changes, along with the imbalance between changed and unchanged samples, together lead to epistemic uncertainty at the model level. To address these challenges, we present an Uncertainty-Aware BCD (UA-BCD) framework. In detail, we employ a Siamese pyramid vision transformer to extract multi-level features from bi-temporal images, which are then decoded via a general decoder to obtain a coarse change map with inherent uncertainty. Subsequently, we introduce the aleatoric uncertainty estimation module to estimate the aleatoric uncertainty and embed it into the feature space. Then, a knowledge-guided feature enhancement module is developed to leverage the knowledge encoded in the coarse map to enhance the multi-level features and generate a refined change map. Finally, we propose an epistemic uncertainty estimator that takes the bi-temporal images and the refined change map as input and outputs an estimate of epistemic uncertainty. This estimation is supervised by the entropy calculated from the refined map, ensuring that the UA-BCD framework can produce a change map with lower epistemic uncertainty. To comprehensively validate the efficacy of the UA-BCD framework, we adopt a dual-perspective verification approach. Extensive experiments on five public building change datasets demonstrate the significant advantages of the proposed method over current state-of-the-art methods. Additionally, an application in Dongxihu District, Wuhan, China, confirms the outstanding performance of the proposed method in large-scale BCD. The source code of the project is available at https://github.com/Henryjiepanli/UA-BCD. [ABSTRACT FROM AUTHOR]

Over the last two decades, an increased number of studies have been conducted to assess land use and land cover changes (LULCC) across the world using various techniques of remote sensing and GIS. Here, we critically reviewed the available literature assessing the spatiotemporal dynamics of LULC in Morocco to emphasize the advances and identify the gaps. Sixty-six peer-reviewed papers, published until August 2023, were selected and analysed. The most interesting findings are: (i) a growing tendency in the number of publications that are mostly concentrated in the northern part of Morocco (i.e., Occidental Meseta and Rif) and less frequent in the Saharan part and Oriental Meseta, (ii) remote sensing (mainly Landsat imagery) and ArcGIS are the top data sources and software used in image processing, respectively, (iii) agricultural land, built-up, and bare soil are the main studied LULC classes that are showing an increasing trend at the expense of forest and vegetated areas that are showing an inverse trend, (iv) population growth and related activities (mainly agriculture expansion and urbanization), conjugated to the warming climate trend, are the main drivers of LULCC. Future studies should extend beyond a simple description towards a deeper understanding of LULCC impacts on biodiversity, ecosystem services, and population well-being under global changes, considering socioeconomic, ecological, biophysical, and cultural characteristics in interpreting LULCC. Our pioneer synthesis would have a double importance: helps decision-makers in defining priorities for management and environmental policy change, and sheds light on the gaps that still remain an open research topic. [ABSTRACT FROM AUTHOR]

Green space reduction substantially affects coastal land surface temperature (LST). Using geospatial techniques, this study investigates the influence of green space on reducing LST in three coastal cities of Bangladesh. Using Landsat data from 1990 to 2020, the study analyzed the normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and statistical methods to validate the LST relationship with land use land cover classes. Results showed green space decreased for the growth of built-up areas, which greatly influenced the LST. Obtained results indicate that built-up areas had the highest LST compared to any other land use class. LST rises with the increase of built-up areas but decreases with green space areas. The LST and NDVI relationship found a negative correlation, indicating that green space/vegetation influences LST. Conversely, the LST positively correlated with NDBI, confirming that built-up areas strongly influence increasing LST. The thermal environment of the coastal area is deteriorating, and the results highlight the importance of green space in temperature reduction. The rising land surface temperatures can be mitigated by increasing tree and vegetation cover, implementing green rooftops, promoting cool pavements and adopting smart growth practices. These findings can assist policymakers and management in understanding how green space is directly involved in the welfare of city dwellers, especially in vulnerable coastal regions. [ABSTRACT FROM AUTHOR]

Accurate spatio-temporal land cover information in agricultural irrigation districts is crucial for effective agricultural management and crop production. Therefore, a spectral-phenological-based land cover classification (SPLC) method combined with a fusion model (flexible spatiotemporal data fusion, FSDAF) (abbreviated as SPLC-F) was proposed to map multi-year land cover and crop type (LC-CT) distribution in agricultural irrigated areas with complex landscapes and cropping system, using time series optical images (Landsat and MODIS). The SPLC-F method was well validated and applied in a super-large irrigated area (Hetao) of the upper Yellow River Basin (YRB). Results showed that the SPLC-F method had a satisfactory performance in producing long-term LC-CT maps in Hetao, without the requirement of field sampling. Then, the spatio-temporal variation and the driving factors of the cropping systems were further analyzed with the aid of detailed household surveys and statistics. We clarified that irrigation and salinity conditions were the main factors that had impacts on crop spatial distribution in the upper YRB. Investment costs, market demand, and crop price are the main driving factors in determining the temporal variations in cropping distribution. Overall, this study provided essential multi-year LC-CT maps for sustainable management of agriculture, eco-environments, and food security in the upper YRB. [ABSTRACT FROM AUTHOR]

This study addresses the significant issue of rapid land use and land cover (LULC) changes in Lahore District, which is critical for supporting ecological management and sustainable land-use planning. Understanding these changes is crucial for mitigating adverse environmental impacts and promoting sustainable development. The main goal is to evaluate historical LULC changes from 1994 to 2024 and forecast future trends for 2034 and 2044 utilizing the CA-Markov hybrid model combined with GIS methodologies. Landsat images from various sensors (TM, OLI) were employed for supervised classification, attaining high accuracy (> 90%). Historical LULC changes from 1994 to 2024 were analyzed, revealing significant transformations in Lahore. The build-up area expanded by 359.8 km², indicating rapid urbanization, while vegetation cover decreased by 198.7 km² and barren lands by 158.5 km². Water bodies remained relatively stable during this period. Future LULC trends were projected for 2034 and 2044 using the CA-Markov hybrid model (CA-MHM), which achieved a high prediction accuracy with a kappa coefficient of 0.92. The research indicated significant urban growth at the expense of vegetation and barren land. Future forecasts suggest ongoing urbanization, underscoring the necessity for sustainable land management techniques. This research is a significant framework for urban planners, providing insights that combine development with ecological conservation. The results highlight the necessity of incorporating predictive models into urban policy to promote sustainable development and environmental preservation in quickly changing areas such as Lahore. [ABSTRACT FROM AUTHOR]

Urbanisation and changes in land use and land cover (LULC) significantly impact Urban Heat Islands (UHI). Despite extensive research on UHI, regional variations demand localised studies. This research assesses the UHI effect in Damascus, Syria, where rapid urbanisation threatens environmental balance. Leveraging Google Earth Engine, Landsat 8, and MODIS data, LULC changes, temperature patterns, NDVI, and NDBI, have been analysed with a focus on the summers of 2013, 2017, and 2022. Results revealed a substantial 12 % increase in urban areas between 2013–2022, accompanied by an 11 % decrease in green spaces. Land surface temperatures (LST) rose from 47.1 °C in 2013 to 48 °C in 2022. NDBI analysis confirmed built-up area expansion, while NDVI indicated dwindling vegetation cover. The pronounced UHI effect (48.7 °C) in industrial and densely populated areas during summer were observed. A strong positive correlation existed between LST and NDBI 90 %, and a moderately negative correlation between LST and NDVI. These findings highlight the adverse impact of urbanisation on Damascus' climate. They underscore the need for urban planning strategies prioritising green space preservation and sustainable development to mitigate the UHI effect and ensure a healthier urban environment. [ABSTRACT FROM AUTHOR]

The clumping index (CI) is a key structural parameter that quantifies the nonrandomness of the spatial distribution of vegetation canopy leaves. Investigating seasonal variations in the CI is crucial, especially for estimating the leaf area index (LAI) and studying global carbon and water cycles. However, accurate estimations of the seasonal CI have substantial challenges, e.g., from the need for accurate hot spot measurements, i.e., the typical feature of the bidirectional reflectance distribution function (BRDF) shape in the current CI algorithm framework. Therefore, deriving a phenologically simplified stable CI product from a high-frequency CI product (e.g., 8 days) to reduce the uncertainty of CI seasonality and simplify CI applications remains important. In this study, we applied the discrete Fourier transform and an improved dynamic threshold method to estimate the start of season (SOS) and end of season (EOS) from the CI time series and indicated that the CI exhibits significant seasonal variation characteristics that are generally consistent with the MODIS land surface phenology (LSP) product (MCD12Q2), although seasonal differences between them probably exist. Second, we divided the vegetation cycle into two phenological stages based on the MODIS LSP product, ignoring the differences mentioned above, i.e., the leaf-on season (LOS, from greenup to dormancy) and the leaf-off season (LFS, after dormancy and before greenup of the next vegetation cycle), and developed the phenologically simplified two-stage CI product for the years 2001–2020 using the MODIS 8-day CI product suite. Finally, we assessed the accuracy of this CI product (RMSE = 0.06, bias = 0.01) via 95 datasets from 14 field-measured sites globally. This study revealed that the CI exhibited an approximately inverse trend in terms of phenological variation compared with the NDVI. Globally, based on the phenologically simplified two-stage CI product, the CILOS is smaller than the CILFS across all land cover types. Compared with the LFS stage, the quality for this CI product is better in the LOS stage, where the QA is basically identified as 0 and 1, accounting for more than ~90% of the total quality flag, which is significantly higher than that in the LFS stage (~60%). This study provides relatively reliable CI datasets that capture the general trend of seasonal CI variations and simplify potential applications in modeling ecological, meteorological, and other surface processes at both global and regional scales. Therefore, this study provides both new perspectives and datasets for future research in relation to CI and other biophysical parameters, e.g., the LAI. [ABSTRACT FROM AUTHOR]

The remote sensing community benefits from new sensors and easier access to Earth Observation data to frequently released new land-cover maps. The propagation of such independent and heterogeneous products offers promising perspectives for various scientific domains and for the implementation and monitoring of land-use policies. Yet, it may also confuse the end-users when it comes to identifying the most appropriate product to address their requirements. Data fusion methods can help to combine competing and/or complementary maps in order to capitalize on their strengths while overcoming their limitations. We assessed the potential of the Dempster–Shafer Theory (DST) to enhance oil palm mapping in Sumatra (Indonesia) by combining four land-cover maps, hereafter named DESCALS, IIASA, XU, and MAPBIOMAS, according to the first author's name or the research group that published it. The application of DST relied on four steps: (1) a discernment framework, (2) the assignment of mass functions, (3) the DST fusion rule, and (4) the DST decision rule. Our results showed that the DST decision map achieved significantly higher accuracy (Kappa = 0.78) than the most accurate input product (Kappa = 0.724). The best result was reached by considering the probabilities of pixels to belong to the OP class associated with DESCALS map. In addition, the belief (i.e., confidence) and conflict (i.e., uncertainty) maps produced by DST evidenced that industrial plantations were detected with higher confidence than smallholder plantations. Consequently, Kappa values computed locally were lower in areas dominated by smallholder plantations. Combining land-use products with DST contributes to producing state-of-the-art maps and continuous information for enhanced land-cover analysis. [ABSTRACT FROM AUTHOR]

Buildings are fundamental elements of human environments, and detecting changes in them is crucial for land cover studies, urban expansion monitoring, and the detection of illegal construction activities. Existing methods primarily focus on pixel-level differences in bi-temporal remote sensing imagery. However, pseudo-changes, such as variations in non-building areas caused by differences in illumination, seasonal changes, and other factors, pose significant challenges for reliable building change detection. To address these issues, we propose a novel object-level contrastive-learning-based multi-branch network (OCL-Net) for detecting building changes by integrating bi-temporal remote sensing images. First, we design a multi-head decoder to separately extract more distinguishable building change features and auxiliary semantic features from bi-temporal images, effectively leveraging building-specific priors. Second, an object-level contrastive learning loss is designed and jointly optimized with a pixel-level similarity loss to ensure the global consistency of buildings. Finally, an attention-based discriminative feature generation and fusion block is designed to enhance the representation of multi-scale change features. We validate the effectiveness of the proposed method through comparative experiments on the publicly available WHU-CD and S2Looking datasets. Our approach achieves IoU values of 88.54% and 51.94%, respectively, surpassing state-of-the-art methods for building change detection. [ABSTRACT FROM AUTHOR]

Land cover mapping is essential to understanding global land-use patterns and studying biodiversity composition and the functioning of eco-systems. The introduction of remote sensing technologies and artificial intelligence models made it possible to base land cover mapping on satellite imagery in order to monitor changes, assess ecosystem health, support conservation efforts, and reduce monitoring time. However, significant challenges remain in managing large, complex satellite imagery datasets, acquiring specialized datasets due to high costs and labor intensity, including a lack of comparative studies for the selection of optimal deep learning models. No less important is the scarcity of aerial datasets specifically tailored for agricultural areas. This study addresses these gaps by presenting a methodology for semantic segmentation of land covers in agricultural areas using satellite images and deep learning models with pre-trained backbones. We introduce an efficient methodology for preparing semantic segmentation datasets and contribute the "Land Cover Aerial Imagery" (LICAI) dataset for semantic segmentation. The study focuses on the Franciacorta area, Lombardy Region, leveraging the rich diversity of the dataset to effectively train and evaluate the models. We conducted a comparative study, using cutting-edge deep-learning-based segmentation models (U-Net, SegNet, DeepLabV3) with various pre-trained backbones (ResNet, Inception, DenseNet, EfficientNet) on our dataset acquired from Google Earth Pro. Through meticulous data acquisition, preprocessing, model selection, and evaluation, we demonstrate the effectiveness of these techniques in accurately identifying land cover classes. Integrating pre-trained feature extraction networks significantly improves performance across various metrics. Additionally, addressing challenges such as data availability, computational resources, and model interpretability is essential for advancing the field of remote sensing, in support of biodiversity conservation and the provision of ecosystem services and sustainable agriculture. [ABSTRACT FROM AUTHOR]

Rapid urban growth is a subject of worldwide interest due to environmental problems. Population growth, especially migration from rural to urban areas, leads to land use and land cover (LULCC) changes in urban centres. Therefore, LULCC and urban growth analyses are among the studies that will help decision-makers achieve better sustainable management and planning. The objective of this study was to ascertain the impact of urbanization, which resulted from migration, on the alterations in LULCC, with a particular focus on the changes in forest areas surrounding the Bartın city centre between 2000 and 2020. Spatial databases for two periods were used to determine changes in urban growth. The spatial and temporal LULCC patterns of land use were quantified by interpreting spatial data. Remote sensing (RS) and geographical information systems (GIS) have been used for data collection, analysis, and presentation. The LULCC was assessed under nine classes using optical remote sensing methods on stand-type maps created from aerial photos. To determine how urban growth affects LULCC, land use status and transition matrices were created for each of the five sprawl zones around Bartın city. The annual change in forest areas is determined by the "annual forest rate". The results indicate that the urbanization of Bartın city from 2000 to 2020 increased by approximately 19% (2510645.82 m2). However, this did not harm the forests; cover increased by 10.32% (174729.65 m²) over the same period. The process of urbanization was particularly evident in open areas and agricultural zones. During this period, there was a 37% reduction in agricultural areas (2943229.85 m²) and a 59% reduction in open areas (1265457.76 m²). The sprawl of Bartın city can be attributed to changes in its demographic structure, which mainly includes the migration of the rural population to urban areas and the emergence of new job opportunities. Factors such as challenging urban living conditions, insecure environments because of the increase in temporary foreign asylum seekers, and retirees returning to their hometowns are believed to have contributed to this population growth. [ABSTRACT FROM AUTHOR]

The study assessed land cover changes in rural communities of Zimbabwe pre and post land reform era (1980 - 2020). A mixed methods research design was adopted with the use of both qualitative and quantitative research methods. Remote sensing, questionnaires, interviews and observations were used for data collection. Microsoft Excel and ArcMap 10.5 software were adopted for data analysis. Results indicated that clearance of land for agriculture especially crop production, wood abstraction for domestic use and selling, establishment of settlements were the major drivers of change in Shurugwi South. Between 2000 and 2020, results showed that water bodies, shrub land and dense woodlands declined whilst grassland and cultivated/bare land increased. Findings indicated that the rates of increase in cultivated/bare lands and grassland were faster between 2000 and 2020. More so, generally water bodies and shrub land decreased during the 2000–2020 period. These changes indicated that land reform program and associated land reallocation in 2000 influenced the rates and pattern of land cover changes. The changes in land ownership and use rights following the land reforms affected land use patterns between 2000 and 2020. Naturally vegetated land is depleting hence the need for land resource conservation. [ABSTRACT FROM AUTHOR]

Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover features in Cuenca de la Laguna Merín, Uruguay, while comparing the performance of Random Forests, Support Vector Machines, and Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission imagery, Google Earth Engine, training and validation datasets and quoted classifiers. The methods involve creating a multisource database, conducting feature importance analysis, developing models, supervised classification and performing accuracy assessments. Results indicate a low significance of microwave inputs relative to optical features. Short-wave infrared bands and transformations such as the Normalised Vegetation Index, Land Surface Water Index and Enhanced Vegetation Index demonstrate the highest importance. Accuracy assessments indicate that performance in mapping various classes is optimal, particularly for rice paddies, which play a vital role in the country's economy and highlight significant environmental concerns. However, challenges persist in reducing confusion between classes, particularly regarding natural vegetation features versus seasonally flooded vegetation, as well as post-agricultural fields/bare land and herbaceous areas. Random Forests and Gradient-Boosting Trees exhibited superior performance compared to Support Vector Machines. Future research should explore approaches such as Deep Learning and pixel-based and object-based classification integration to address the identified challenges. These initiatives should consider various data combinations, including additional indices and texture metrics derived from the Grey-Level Co-Occurrence Matrix. [ABSTRACT FROM AUTHOR]

Soil erosion has been a significant threat to agricultural productivity, dam sustainability, and ecosystem services in the highlands of Ethiopia. Despite some watershed-specific research on this problem, the spatiotemporal distribution of soil erosion risks is still rarely assessed in South Wollo. This study aims to monitor land cover changes, analyze landscape mosaics, and estimate soil loss in South Wollo from 1990 to 2020. The Revised Universal Soil Loss Equation (RUSLE) was utilized to estimate soil loss. Key informant interviews and field observations were conducted to gain further insights into land cover change and soil erosion. The results showed that agricultural land use was dominant, covering 50.75 % of the study area in 1990 and it continued to expand at an annual rate of 1.73 %, primarily on shrubland and grassland. The landscape structure was more heterogeneous in the north compared to the central and southern areas. In 1990, the mean soil loss was 21.13 t ha-1yr−1, totaling 41.53 million tons. By 2020, These figures had increased to 28.97 t ha-1yr−1 and 49.86 million tons, respectively. Areas experiencing very severe soil erosion (>50 t ha-1yr−1) expanded from 10.81 % (1990) to 13.57 % (2020). The magnitude of soil loss also differed among land cover types, with the highest rates observed in bare land (>85 t ha-1yr−1), followed by agricultural land (>30 t ha-1yr−1). Soil loss factors and landscape metrics exhibited a significant correlation with soil loss, though the strength and direction of these interactions varied. Land cover change and its associated soil erosion were primarily exacerbated by policy factors, including tenure insecurity and disregard for farmers' indigenous conservation knowledge. This study offers valuable insights into the trends of land cover transitions, landscape structure, and soil loss. It will help guide sustainable land management, aimed at increasing green legacy and reducing soil loss. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) change, driven by environmental and human activities, significantly impacts ecosystems, climate, biodiversity, and socio-economic systems. This study focuses on Khyber Pakhtunkhwa (KPK), Pakistan, a region with sensitive ecosystems and diverse landscapes, to analyze LULC dynamics and their environmental consequences. Based on Landsat imagery from 2000, 2010, and 2020, we used the Random Forest algorithm on Google Earth Engine (GEE) to classify LULC, and the CA-ANN model to project future scenarios for 2030, 2050, and 2100. Additional simulations were conducted using the MOLUSCE Plugin in QGIS. The results revealed a 138.02% (4071.98 km2) increase in urban areas from 2000 to 2020, marking urbanization as a major driver of LULC change. Urban expansion strongly correlated with land surface temperature (LST) (R2 = 0.89), amplifying the urban heat island effect. Rising LST showed negative correlations with the key environmental indices NDVI (−0.88), MNDWI (−0.49), and NDMI (−0.62), signaling declining vegetation cover, water resources, and soil moisture, respectively. Projections for 2100 predict LST rising to 55.3 °C, with NDVI, MNDWI, and NDMI dropping to 0.36, 0.17, and 0.21, respectively. Vegetation health, as indicated by the Leaf Area Index (LAI), also declined, with maximum and minimum values falling from 4.66 and −5.75 in 2000 to 2.16 and −2.55 in 2020, reflecting increased barren land and reduced greenness. The spatial analysis highlights significant transitions from vegetated to barren or urban land, leading to declining moisture levels, water stress, soil erosion, and biodiversity. Projections show continued reductions in forests, vegetation, and agricultural lands, replaced by barren and built-up areas. Declines in key indices such as NDVI, MNDWI, and NDMI indicate deteriorating vegetation, water resources, and soil moisture levels. These findings emphasize the need for sustainable urban planning and environmental management. Expanding urban green spaces, using reflective materials, and preserving vegetation and water resources are vital to mitigating heat island effects and maintaining ecological balance. Anticipated declines in LST, NDVI, MNDWI, NDMI, and LAI stress the urgency for climate adaptation strategies to protect human health, ecosystem services, and economic stability in KPK. [ABSTRACT FROM AUTHOR]

Shallots are one of the horticultural commodities that have fluctuating prices. Market integration occurs horizontally but not vertically due to poor information systems at the producer and consumer levels. This study aimed to estimate the area of shallot land quickly using the rapid classification of croplands method. The research was conducted in Merek District, Karo Regency, North Sumatra. Primary data obtained from survey activities were processed using the Google Earth Engine platform. Classification and regression trees (CART) and random forest (RF) algorithms were used to classify land cover as onion and non-onion classes. The shallot land area based on this method was 74.4 hectares, with an area accuracy of 95% (RF) and 24% (CART) and a location accuracy of 92% (CART and RF). The rapid classification of croplands method can estimate land area quickly. It helps stakeholders who need information on shallot production projections and can be developed to improve the vertical market integration information system (market integration between producers and consumers). Some areas for improvement of this method are limited access and resolution, inability to describe up to the level of garden bunds, and the condition of the area covered by clouds, which will affect the accuracy of the results. [ABSTRACT FROM AUTHOR]

The purpose of this work is to determine land-use and land-cover (LULC) patterns, land surface temperature (LST), and normalized difference vegetation index (NDVI) changes in Thatta district using Landsat data from 1991 to 2021 and evaluate the relationship between LST and NDVI. The research process employed the selection of the study area, data acquisition, preprocessing, and classification of remotely sensed images for the estimation of the land use land cover change (LULC), vegetation index (NDVI), and evaluation of LST using thermal bands in the Landsat dataset. The study revealed the area under built-up structures has increased from 1991 to 2021. Although the vegetation cover showed an increase, the bare soil showed a decreasing pattern, indicating a constant change in the LULC patterns in the region. The confusion matrix method for accuracy valuation of LULC data of 2021 revealed an overall accuracy of 88.24%, with a Kappa coefficient of 84.22%, while the Artificial Neural Network Multilayer Perceptron (ANN-MLP) model had a Kappa validation of 0.95 for 2021. The highest maximum temperature is observed for 2021, indicating a positive relationship between LST and built-up structures, while regression analysis found a negative correlation between LST and NDVI. This study provides a valuable monitoring framework to help resource managers develop strategies to manage land resources. [ABSTRACT FROM AUTHOR]

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Land use and land cover (LULC) changes are significantly impacting the natural environment. Human activities and population growth are negatively impacting the natural environment. This negative impact directly relates to climate change, sustainable agriculture, inflation, and food security at local and global levels. Remote sensing and GIS tools can provide valuable information about change detection. This study examines the correlation between population growth rate and LULC dynamics in three districts of South Punjab, Pakistan—Multan, Bahawalpur, and Dera Ghazi Khan—over a 30-year period from 2003 to 2033. Landsat 7, Landsat 8, and Sentinel-2 satellite imagery within the Google Earth Engine (GEE) cloud platform was utilized to create 2003, 2013, and 2023 LULC maps via supervised classification with a random forest (RF) classifier, which is a subset of artificial intelligence (AI). This study achieved over 90% overall accuracy and a kappa value of 0.9 for the classified LULC maps. LULC was classified into built-up, vegetation, water, and barren classes in Multan and Bahawalpur, with an additional "rock" class included for Dera Ghazi Khan due to its unique topography. LULC maps (2003, 2013, and 2023) were prepared and validated using Google Earth Engine. Future predictions for 2033 were generated using the MOLUSCE model in QGIS. The results for Multan indicated substantial urban expansion as built-up areas increased from 8.36% in 2003 to 25.56% in 2033, with vegetation and barren areas displaying decreasing trends from 82.96% to 70% and 7.95% to 3.5%, respectively. Moreover, areas containing water fluctuated and ultimately changed from 0.73% in 2003 to 0.9% in 2033. In Bahawalpur, built-up areas grew from 1.33% in 2003 to 5.80% in 2033, while barren areas decreased from 79.13% to 74.31%. Dera Ghazi Khan expressed significant increases in built-up and vegetation areas from 2003 to 2033 as 2.29% to 12.21% and 22.53% to 44.72%, respectively, alongside reductions in barren and rock areas from 32.82% to 10.83% and 41.23% to 31.2%, respectively. Population projections using a compound growth model for each district emphasize the demographic impact on LULC changes. These results and findings focus on the need for policies to manage unplanned urban sprawl and focus on environmentally sustainable practices. This study provides critical awareness to policy makers and urban planners aiming to balance urban growth with environmental sustainability. [ABSTRACT FROM AUTHOR]

Anthropogenic activities, particularly land reclamation and industrialization, have severely damaged South Korea's intertidal zones, resulting in a decline in biodiversity. In our study, we assessed the macrobenthic community in the intertidal zone of Anmyeon Island, South Korea, and used remote sensing to evaluate the impact of anthropogenic activities on the adjacent land areas. Spearman and Principal Coordinate Analysis (PCoA) indicated that the remote-sensing ecological index (RSEI) is a viable indicator for assessing the dissimilarity of macrobenthic communities in these zones. Moreover, biota–environment matching (BIO–ENV) and distance-based redundancy analysis (dbRDA) demonstrated that land cover types significantly influence the macrobenthic communities in nearby intertidal zones. Our study suggested that urbanization and agricultural activities have affected the terrestrial ecological environment and the adjacent intertidal communities. Consequently, the protection of these zones should extend beyond their borders to include the management of anthropogenic activities on adjacent lands. Our research contributes valuable insights to help inform conservation strategies and the policy-making necessary to safeguard South Korea's intertidal zones. [ABSTRACT FROM AUTHOR]

Due to the various types of land cover and large spectral differences in remote sensing images, high-quality semantic segmentation of these images still faces challenges such as fuzzy object boundary extraction and difficulty in identifying small targets. To address these challenges, this study proposes a new improved model based on the TransDeepLab segmentation method. The model introduces a GAM attention mechanism in the coding stage, and incorporates a multi-level linear up-sampling strategy in the decoding stage. These enhancements allow the model to fully utilize multi-level semantic information and small target details in high-resolution remote sensing images, thereby effectively improving the segmentation accuracy of target objects. Using the open-source LoveDA large remote sensing image datasets for the validation experiment, the results show that compared to the original model, the improved model's MIOU increased by 2.68%, aACC by 3.41%, and mACC by 4.65%. Compared to other mainstream models, the model also achieved superior segmentation performance. [ABSTRACT FROM AUTHOR]

The Land Use Land Cover (LULC) classification from the Remote Sensing (RS) images is significant in various land use researchers. However, numerous methods have been developed for LULC classification which failed to capture the multiple deep temporal features. To mitigate this limitation, the Long Short-Term Memory (LSTM) with Temporal Pyramid Pooling (TPP) layer technique is proposed for LULC classification. The TPP layer is incorporated into the LSTM network to capture multiple temporal features and enhance its ability to differentiate the various classes of LULC. The ResNet-50 based feature extraction technique is developed to extract the deep meaningful features which help to differentiate the LULC classes. The performance of LSTM with the TPP layer technique is evaluated on EuroSAT, SIRI-WHU and UCM datasets. The proposed LSTM with TPP layer technique obtained 98.37% accuracy on the EuroSAT dataset, 98.77% accuracy on SIRI-WHU dataset, 99.75% accuracy on the UCM dataset and 99.35% accuracy on NWPU dataset when compared with existing algorithms like optimized self-attention fused Convolutional Neural Networks. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) dynamics have a substantial impact on human–environment interactions. Nowadays, remote sensing imagery has emerged as a useful tool for mapping and tracking LULC changes. Spectral optical indices derived from remote sensing data can provide insightful information about vegetation health, urban expansion, water bodies, deforestation patterns, and many other applications. The present study examines the use of popular optical spectral indices: vegetation index (NDVI), water indices (NDWI and MNDWI), urban indices (UI and NDBI), and bare land index (MNDBI) in threshold-based classification for LULC mapping using Algiers (Algeria) as a case study, and assesses the potential impacts of their spatiotemporal (at a seasonal and annual temporal scales) variations associated with natural seasonal changes and/or the evolution of the city's fabric. Here, a geo-statistical analysis of the values of the selected spectral indices at the level of each LU-class is conducted, threshold values (that account for seasonal variations) are proposed, and a classification approach (making use of best performing indices) is proposed and tested. Although fast and easy to implement, the proposed threshold-based LULC classification approach was successfully used for mapping LULC for the study zone with a high accuracy (an overall accuracy of 90.20 and a kappa of 0.84 for the demonstration year of 2017). The outcomes of the study heighten the potential and the limitations of the use of spectral indices for LULC mapping practices and consequent applications in environmental and urban studies. [ABSTRACT FROM AUTHOR]

Military operations have long been recognized to cause significant environmental consequences. However, research on the environmental impacts of military operations remains fragmented despite the rise of modern technologies, including remote sensing (RS) and geographic information system (GIS). Hence, this study sought to review the literature on using RS and GIS approaches to assess military operations' environmental impacts. The PRISMA 2009 systematic review process was employed in this research. The eligibility screening of literature involves searching using selected keywords, Boolean operators, and a wild card operator in Google Scholar, Scopus, and Web of Science. This study identified a growing trend in the publication from 2000 to 2024, with an average yearly cumulative percentage of 4.17%. The articles are not limited to RS and GIS journals (22.22%), showing a broader interest across an array of publication domains (33.33%). Further, this study indicates a global interest in the field, with 24 countries contributing to the body of knowledge. Methodologically, assessing the military impact through change detection on land use and land cover (LULC) (55.56%) is the dominant approach, with researchers favoring the combined use of RS and GIS software (79%). Optical sensors (79.49%) with moderate spatial resolution (61.90%) are the preferred imagery types. The impact on the terrestrial environment is widespread (36.84%), often involving the entire armed forces (55.26%). Overall, this review offers information into the role of RS and GIS in assessing the environmental impacts of military operations and understanding the complex nature of the military and the environment. [ABSTRACT FROM AUTHOR]

Introduction: Monitoring LULC changes is crucial for developing strategies for natural resource management, assessing the current potential of a region, and addressing global environmental issues. In this context, this study examines land use and land cover (LULC) changes in forest and non-forest areas of Anamur district, located in the Mediterranean Region of Türkiye, between 2000 and 2020. Methods: Using the intensity analysis method, which offers a detailed and efficient approach to understanding LULC changes, the study analyzes transitions at interval, category, and transition levels. LULC maps were generated through supervised classification of Landsat satellite images, focusing on seven classes: Coniferous, Broad-Leaved, Mixed, Treeless Gap, Settlement, Agriculture, and Water. The analysis evaluated changes within and between these categories, interpreting the results through graphical outputs. The driving forces behind these changes were also explored, and their underlying causes were discussed. Results and Discussion: Results at the interval level revealed that the most significant changes occurred during the 2000-2010 period. At the category level, the Coniferous category exhibited the highest degree of change in both intervals. During 2000-2010, Coniferous gains predominantly replaced non-forest areas (Agriculture, Settlement, and Water), while this pattern was less evident in 2010-2020. In contrast, Treeless Gap gains primarily replaced Coniferous areas during 2010-2020, while no significant losses in Treeless Gap were targeted by other categories. Broad-Leaved species were found to heavily target Water losses, likely due to their higher water demands compared to Coniferous species, as supported by prior studies. This research highlights the advantages of intensity analysis in LULC studies, offering insights into spatial changes and their intensity across categories. It aims to promote its adoption and underscores the importance of targeted conservation and land management strategies to mitigate the impacts of forest loss, land use changes, and water resource pressures. [ABSTRACT FROM AUTHOR]

Featured Application: This research underscores the feasibility of utilizing the Copernicus LUCAS 2018 database, including in its original form, to classify basic land cover and land use types across Europe, with overall accuracy ca. 80%. The precise use of classification accuracy metrics enables reliable accuracy analyses across various ML models. The "overall accuracy OA" metric should be reported instead of, or alongside, "average accuracy ACC" to ensure clarity and comparability. Classification of remote sensing images using machine learning models requires a large amount of training data. Collecting this data is both labor-intensive and time-consuming. In this study, the effectiveness of using pre-existing reference data on land cover gathered as part of the Land Use–Land Cover Area Frame Survey (LUCAS) database of the Copernicus program was analyzed. The classification was carried out in Google Earth Engine (GEE) using Sentinel-2 images that were specially prepared to account for the phenological development of plants. Classification was performed using SVM, RF, and CART algorithms in GEE, with an in-depth accuracy analysis conducted using a custom tool. Attention was given to the reliability of different accuracy metrics, with a particular focus on the widely used machine learning (ML) metric of "accuracy", which should not be compared with the commonly used remote sensing metric of "overall accuracy", due to the potential for significant artificial inflation of accuracy. The accuracy of LUCAS 2018 at Level-1 detail was estimated at 86%. Using the updated LUCAS dataset, the best classification result was achieved with the RF method, with an accuracy of 83%. An accuracy overestimation of approximately 10% was observed when reporting the average accuracy ACC metric used in ML instead of the overall accuracy OA metric. [ABSTRACT FROM AUTHOR]