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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]

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]

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]

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]

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]

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]

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]

Land cover classification, or semantic segmentation, has been one of the most critical research areas in remote sensing (RS) and remains crucial for many downstream applications. Although deep learning (DL) based models have recently dominated this field, models trained using the dataset of one region generally cannot predict reliable classification results in other regions. Despite the abundance of high-resolution (HR) satellite images around the globe, not enough HR labels are available for building a one-for-all-purpose model. This study utilizes widely available low-resolution (LR) labels with weakly supervised methods to obtain land cover maps worldwide. Previous methods designed new learning components, such as loss functions, to fully use LR labels or trained DL models with LR labels to generate intermediate labels for further training and processing. Since this information is directly used or extracted from the original LR labels, methods without additional robustness are sensitive to noise in the labels, which causes error propagation in the results. On this basis, a novel label refinement approach is proposed that transforms noisy original LR labels into refined HR labels using two steps of noise filtering. First, based on spectral indices from the HR images, we select relatively confident labels from the LR labels through a Markov Random Field optimization framework. Second, a shallow classifier such as random forest (RF) is trained using the selected pixels to supplement previously unselected labels and refine low-confidence labels with new and high-confidence labels. The results showed that in the experiment on the Data Fusion Contest 2020 dataset, the semantic segmentation models trained using our refined HR labels had a 2–14% higher average accuracy than those trained using the original LR labels. They also outperformed other weakly supervised methods directly using original LR labels and had a 7.5% higher average accuracy than the winning method. [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]

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]

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]

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]

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]

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 expansion of agricultural frontiers in the Amazon region poses a significant threat to forest conservation and biodiversity persistence. This study focuses on Pará state, Brazil, aiming to identify and characterize the predominant production systems by combining remote sensing data and landscape structure metrics. A rule-based classification tree algorithm is applied to classify hexagonal cells based on land cover, deforestation patterns, and distance from dairy facilities. The results reveal three dominant production systems: Natural Region, Non-Intensive Beef, and Initial Front, with livestock production being prominent. The analysis indicates that there is a correlation between the productive area and deforestation, emphasizing the role of agriculture as a driver of forest loss. Moreover, road networks significantly influence production system spatial distribution, highlighting the importance of infrastructure in land use dynamics. The Shannon diversity index reveals that areas with production systems exhibit greater diversity in land use and land cover classes, reflecting a wider range of modifications. In contrast, natural vegetation areas show lower Shannon values, suggesting that these areas are more intact and are less affected by human activities. These findings underscore the urgent need for sustainable development policies that will mitigate threats to forest resilience and biodiversity in Pará state. [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]

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]

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]

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]

Evapotranspiration (ET) is critical to surface water dynamics. Effective water resource management necessitates an accurate ET estimation. In the Yellow River Basin China, a study area, cutting-edge technologies are needed to improve large-scale ET estimates. This study estimates ET using GSEBAL, an advanced ET estimation algorithm. Google Earth Engine integrates the surface energy balance model-based GSEBAL. The technique includes the collection, preparation, and calculation of ET using Landsat imagery and ERA5-Land meteorological data from 1990 to 2020. The study examined satellite LST, albedo, and NDVI data. The GSEBAL model calculates soil heat flow, net radiation, and sensible heat flux. The study tested the GSEBAL model utilizing essential ET datasets such as ECOSTRESS, MOD16, and SSEBop. The study showed that the model effectively predicted daily and seasonal ET variations in different climates. Root mean squared error, bias, and Pearson's correlation coefficient verified the model's reliability. The study also analyzed land use and land cover (LULC) over 30 years using Random Forest classifiers. In the 1990–2020 YRBC ET, land use changes affect ET rates annually and seasonally. The study area experiences changes in LST, NDVI, and LULC. Maximum ET values rose from 214.217 mm in 1990 to 234.891 mm in 2000. The pattern flipped in 2020, decreasing to 221.456 mm. In 2010, Summer had the highest ET, 484.455 mm. 2020 spring ET is 314.727 mm. Low ET decreased from 24.652 mm in 1990 to 18.2 mm in 2020, reducing water loss. Fall ET peaks at 24.9 mm in 2020; winter ET is 18.75 mm. [ABSTRACT FROM AUTHOR]

Rational utilization of natural resources is crucial in arid and semi-arid areas due to their vulnerable ecosystems and low resource resilience. Achieving a balance between grassland production and livestock grazing, known as the pasture-livestock balance, is essential for the sustainable development of grassland resources on the Mongolian Plateau (MP). This study focuses on the grassland regions of 8 provinces in eastern Mongolia (MNG) and 7 leagues in Inner Mongolia (IMNG), China, during the period from 2018 to 2022. Machine learning methods were employed for land cover classification and above-ground biomass (AGB) estimation. The grassland carrying capacity was assessed using the grassland carrying capacity index (GCC). The results indicate that: (1) The grassland classification accuracy on the MP exceeds 95%, with grassland area accounting for approximately 47% of the total.(2)The AGB of the grasslands exhibits a clear spatial heterogeneity, increasing from southwest to northeast. Additionally, nearly 80% of the grassland productivity is of high quality, reaching up to 250 g/m2.(3) Between 2018 and 2022, the MP exhibited a relatively high grassland carrying capacity, with an average of 1.8 SU/ha. However, the overall grassland carrying condition has gradually deteriorated, primarily due to factors such as grassland fires and an increase in livestock numbers. Based on the varying degrees of grassland degradation, different policy recommendations have been proposed. This study approach, findings and policy suggestions are significant for the development of livestock farming and grassland management on the MP. [ABSTRACT FROM AUTHOR]

This study performed landslide susceptibility mapping in Awabel Woreda, situated in the east Gojjam zone of the Amhara region in Ethiopia. The occurrence of landslides and slope instability is widespread in Awabel Woreda, leading to the devastation of agricultural fields, crops, and residences, the demise of animal life, and the forced relocation of local inhabitants from their dwellings. The present investigation utilized remote sensing and GIS techniques in conjunction with the Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models to map landslide risk zones. For the landslide susceptibility mapping of this study, nine causative factors, such as "elevation, slope, aspect, drainage density, lineament density, land use and land cover (LULC), soil texture, rainfall, and lithology", were considered. A total of 130 past landslide events were identified via field survey and Google Earth image, of which 70% (91) of them were used as training datasets and 30% (39) were used as validation datasets of the proposed models (i.e., FR and AHP). The nine contributing variables and their classes were evaluated, and factor weights were computed using the IDRISI Selva 17.0 expert programme. The landslide susceptibility indexes (LSI) of the FR and AHP models were calculated and categorized into five relative zones using ArcGIS 10.7. The landslide susceptibility map (LSM) produced by the FR model shows that 93.2 km2 (14.52%) of the study area is classified as very low susceptibility, 167.51 km2 (26.09%) as low susceptibility, 174.10 km2 (27.12%) as moderate susceptibility, 137.03 km2 (21.34%) as high susceptibility, and 70.30 km2 (10.93%) as very high susceptibility to landslides. Based on the AHP model's LSM, different landslide susceptibility zones were identified in the study area. Specifically, 140.46 km2 (21.88%) of the region falls into the very low susceptibility zone, 116.78 km2 (18.59%) falls into the low susceptibility zone, 147.94 km2 (23.04%) falls into the moderate susceptibility zone, 154.04 km2 (23.99%) falls into the high susceptibility zone, and 82.78 km2 (12.89%) falls into the very high susceptibility zone. The validation investigation demonstrated that the FR and AHP models had accuracy rates of 89.73 and 87.18%, respectively. The FR model exhibited marginally more accurate results than AHP, primarily because of the direct correlation between previous and current occurrences of landslides. Nevertheless, the AHP model's effectiveness relies on the individual's expertise and the characteristics of the components that cause the outcome. The landslide susceptibility maps generated through these models provide valuable insights for land management and disaster mitigation efforts, with delineated zones indicating very low to very high susceptibility areas. Highlights: The study combines Frequency Ratio and Analytical Hierarchy Process models with GIS to improve landslide risk assessment in Ethiopia's highlands. Slope, rainfall, and rock types are key factors increasing landslide risks in the study area. The models demonstrate strong predictive accuracy, offering valuable insights for disaster management and land-use policy formation. [ABSTRACT FROM AUTHOR]

The Qilian Mountain Area (QMA) serves as a crucial ecological barrier and strategic water conservation zone in China. Recent years have seen heightened social attention to environmental issues within the QMA, underscoring the need for accurate and continuous land cover maps to support ecological monitoring, analysis, and forecasting. This paper presents the QMA_LC30 dataset, which includes 9 land cover categories and spans the period from 1990 to 2020, with updates every 5 years. The dataset primarily utilizes 30 m Landsat series data and features: 1) High precision, achieved through a geographical division and hierarchical classification decision tree approach, complemented by visual interpretation. 2) Robust consistency, ensured by a change detection method based on a benchmark map. The QMA_LC30 dataset undergoes rigorous accuracy validation, achieving an overall accuracy of over 0.92 for all 7 periods of land cover maps. Compared to GlobeLand30, ESA WorldCover, ESRI 2020 Land Cover, FROM_GLC30, and GLC_FCS30, QMA_LC30 demonstrates the highest consistency with remote sensing images. [ABSTRACT FROM AUTHOR]

Large-scale, high-resolution land cover classification is a prerequisite for constructing Earth system models and addressing ecological and resource issues. Advancements in satellite sensor technology have led to improvements in spatial resolution and wider coverage areas. Nevertheless, the lack of high-resolution labelled data is still a challenge, hindering the large-scale application of land cover classification methods. In this study, a Transformer-based weakly supervised method for cross-resolution land cover classification using outdated data is proposed. First, to capture long-range dependencies without overlooking the fine-grained details of objects, a U-Net-like Transformer based on a reverse difference mechanism (RDM) using dynamic sparse attention is designed. Second, an anti-noise loss calculation module based on optimal transport (OT) is proposed. The anti-noise loss calculation identifies confident areas and vague areas based on the OT matrix, which relieves the effect of noises on outdated land cover products. By introducing a weakly supervised loss with weights and using an unsupervised loss, the RDM-based U-Net-like Transformer was trained. Remote sensing images with 1 m resolutions and the corresponding ground truths of six states in the United States were used to validate the performance of the proposed method. The experiments used outdated land cover products with 30 m resolutions from 2013 as training labels and produced land cover maps with 1 m resolutions from 2017. The results showed the superiority of the proposed method over state-of-the-art methods. The code is available at . [ABSTRACT FROM AUTHOR]

Understanding land use and land cover (LULC) classification is critical for addressing environmental and human needs, particularly in developing countries. Nigeria is a developing country experiencing rapid population growth and economic development leading to increased LULC changes. While many studies have been done on LULC changes, there is a need for a comprehensive review of existing knowledge and limitations of LULC analyses in Nigeria. Hence, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method, this review paper presents a systematic review of LULC analyses in Nigeria by examining the adopted remote sensing data, pre-classified global and regional LULC maps, and classification and validation methods. This paper draws attention to the significant growth in LULC studies and highlights a need for awareness and access to existing and readily available LULC data. This review provides a broad overview of LULC data, classification methods, focus, scale, and constraints associated with LULC analysis in Nigeria. Also, it provides probable solutions to the challenges and GEE-based LULC classification scripts. There is a need to create and prioritize a national LULC data repository to ensure sustainable land monitoring and management in Nigeria. This will facilitate the spatial and temporal assessment of LULC at different scales and regions. High-resolution imagery and advanced classification methods such as deep learning need to be adopted to ensure accurate land cover analysis at different scales. Also, increased awareness programs, collaboration, and capacity-building initiatives will be beneficial to addressing current and emerging challenges related to LULC studies in Nigeria. [ABSTRACT FROM AUTHOR]

• The objective of the work is to develop CNN-based DL model for LULC classification. • An assessment of LULC is carried out for the classified maps of 2011 and 2021. • The change detection analysis of Chamarajanagar district is performed for one decade. • The proposed method is outperformed, with an accuracy of 95.27 % and 94.57 %. The change detection analysis of land use land cover (LULC) is an important task in several fields and applications such as environmental monitoring, urban planning, disaster management, and climate change studies. This study focuses on the use of remote sensing (RS) and geographic information systems (GIS) to identify the changes in Chamarajanagar district, which is located in Karnataka state, South India. This paper mainly focuses on the classification and change detection analysis of LULC in 2011 and 2021 using linear imaging self-scanning sensor-III (LISS-III) satellite images. Traditional methods for LULC classification involve manual interpretation of satellite images, which provides lower accuracy. Therefore, this paper proposed the Convolutional Neural Network (CNN)-based deep learning classification method for LULC classification. The main objective of the research work is to perform an accurate change detection of the Chamarajanagar district using the classified maps of the years 2011 and 2021. The proposed classification method is outperformed, with a classification accuracy of 95.27 % and 94.57 % for LISS-III satellite imagery of the years 2011 and 2021 respectively. Further, change detection analysis has been carried out using classified maps and results show a decline of 3.23 sq. km, 22.7 sq. km, and 3.83 sq. km in the areas covered by vegetation, agricultural land, and forest area, respectively. In other classes, such as built-up, water bodies, and barren land, an increase in land cover was observed by 5.59 sq. km, 1.99 sq. km, and 20.92 sq. km, respectively. [ABSTRACT FROM AUTHOR]

Over 30 million people rely on Lake Victoria for survival in Northeast African countries, including Ethiopia, Eritrea, Somalia, and Djibout. The lake faces significant challenges due to changes in land use and climate. This study used multi-source remote sensing data in the Google Earth Engine (GEE) platform to create Land Use and Land Cover (LULC), land surface temperature (LST), and Normalized Difference Water Index (NDWI) layers in the period 2000–2023 to understand the impact of LULC and climate change on Lake Victoria Basin. The land use/land cover trends before 2020 indicated an increase in the urban areas from 0.13% in 2000 to 0.16% in 2020. Croplands increased from 6.51% in 2000 to 7.88% in 2020. The water surface area averaged 61,559 square km, which has increased since 2000 with an average rate of 1.3%. The "Permanent Wetland" size change from 2000 to 2020 varied from 1.70% to 1.83%. Cropland/Natural Vegetation Mosaics rose from 12.77% to 15.01%, through 2000 to 2020. However, more than 29,000 residents were displaced in mid-2020 as the water increased by 1.21 m from the fall of 2019 to the middle of 2020. Furthermore, land-surface temperature averaged 23.98 degrees in 2000 and 23.49 in 2024. [ABSTRACT FROM AUTHOR]

Remote sensing imagery (RSI) segmentation plays a crucial role in environmental monitoring and geospatial analysis. However, in real-world practical applications, the domain shift problem between the source domain and target domain often leads to severe degradation of model performance. Most existing unsupervised domain adaptation methods focus on aligning global-local domain features or category features, neglecting the variations of ground object categories within local scenes. To capture these variations, we propose the scene covariance alignment (SCA) approach to guide the learning of scene-level features in the domain. Specifically, we propose a scene covariance alignment model to address the domain adaptation challenge in RSI segmentation. Unlike traditional global feature alignment methods, SCA incorporates a scene feature pooling (SFP) module and a covariance regularization (CR) mechanism to extract and align scene-level features effectively and focuses on aligning local regions with different scene characteristics between source and target domains. Experiments on both the LoveDA and Yanqing land cover datasets demonstrate that SCA exhibits excellent performance in cross-domain RSI segmentation tasks, particularly outperforming state-of-the-art baselines across various scenarios, including different noise levels, spatial resolutions, and environmental conditions. [ABSTRACT FROM AUTHOR]

This study investigates land use and land cover changes in the Teiči Strict Nature Reserve, Latvia, from 1982 to 2023 and models potential future changes based on four legislative scenarios extending to 2064. The research aims to assess the effectiveness of existing conservation zoning in relation to historical and projected changes in land use and land cover, addressing the ongoing debate between fixed and adaptable conservation strategies. The study employs remote sensing imagery, geographical information system data, and land use and land cover modeling methods to analyze historical changes and predict future trends. The results suggest substantial land use and land cover changes over the past four decades, including intensified urbanization, agricultural expansion, and a decline in peat bogs and forested areas. Scenario projections indicate that continued land use and land cover changes could further challenge the efficiency of current conservation strategies, with varying outcomes depending on legislative measures and climate change impacts. The study concludes that adaptive management and variable conservation zoning are necessary to address these dynamic changes and preserve the reserve's ecological integrity. The results emphasize the importance of integrating predictive modeling into conservation planning to improve flexibility and sustainability in protected areas. [ABSTRACT FROM AUTHOR]

ABSTRACT Accurate information on cropland area is essential for agricultural development and planning. Here, we evaluated the performance of six land cover products (CLCD, MCD12Q1, CCI‐LC, GLC‐FCS, CNLUCC, and CACD) in identifying cropland extent in a large irrigation district of China from 1995 to 2020 based on visual interpretation samples. The results indicate that CACD performs best with an average kappa coefficient (KC) of 0.89, followed by CLCD (KC = 0.87), GLC‐FCS (KC = 0.75), CNLUCC (KC = 0.65), MCD12Q1 (KC = 0.49), and CCI‐LC (KC = 0.29). Additionally, the cropland area provided by statistical yearbooks is significantly lower than that identified by CACD, with an average underestimation of −34%. We also find that these land cover products exhibit poor consistency in identifying cropland. The average percentage of grids labeled as “completely consistent”—where all six products identify those grids as cropland—is only 16.0% across the entire irrigation district, highlighting the uncertainty of existing land cover products in identifying cropland areas. [ABSTRACT FROM AUTHOR]

The Awash River Basin is essential in combating climate change by absorbing atmospheric carbon. The goal of this study was to determine carbon stocks using the linear correlation between field inventory and remote sensing data in the basin. The regression model was created using each plot studied from field inventory and the corresponding vegetation indices (VI) values obtained from ArcGIS software in the basin's study area. The linear correlation values for estimated carbon stock with Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were R2 = 0.36, R = 0.60, and R2 = 0.52, R = 0.72, respectively. In contrast, the values for the Soil-Adjusted Vegetation Index (SAVI) were R2 = 0.36 and R = 0.60. The results show that the relationship between carbon stock and EVI is stronger for estimating carbon stock than other indices (NDVI and SAVI). The linear regression model or equation developed for NDVI, EVI, SAVI, and field-level carbon stock was Y = 16.25x–1.093, Y = 8.935x–1.1254, and Y = 12.988x–1.0895, respectively. The estimated carbon stock from the EVI linear regression model was 15,904,158.24 tons, with an average carbon stock value of 111.96 tons per hectare. The findings concluded that EVI has a stronger correlation with carbon stock stand estimation than NDVI and SAVI. This study could serve as a baseline for the estimation of carbon stock using vegetation indices in different and large-scale land use land cover types. High-resolution multispectral imagery and cloud-based geospatial analysis platforms could provide more accurate results. [ABSTRACT FROM AUTHOR]

The change in land cover eventually occurs as the population increases, resulting in high human activities. Monitoring this change can be beneficial for territorial planning and ecosystem monitoring. Therefore, this study aimed to evaluate the spatial-temporal patterns and rates of land cover change in Nampula City, Mozambique, over the past three decades (1989-2020). For this purpose, data from the Landsat-5 TM and Landsat-8 OLI / TIRS satellites were applied as input to two classification systems: (1) thresholding-NDVI and MNDWI and (2) supervised classification. The results showed that the supervised classification method performed better than the thresholding system, with an overall accuracy of 92.4% and a kappa coefficient of 0.89. Estimates pointed to a reduction of 0.04% in the water area and 20.3% in cultivated land. In contrast, barren rock and urban areas experienced an increase of 18.2%, while shrubs and grasslands showed a growth of 2.1% of their area. The results showed a considerable change over the study period and that the spatial dynamics of crop and barren rock and urban areas resulting from human interventions require special consideration. This study provides an opportunity for further studies on the spatial dynamics of land cover change in Nampula City, facilitating effective land management and sustainable development strategies in the region. [ABSTRACT FROM AUTHOR]

Land use change is related to a series of core issues of global environmental change, such as environmental quality improvement, sustainable utilization of resources, energy reuse and climate change. In this study, Google Earth Engine (GEE), a remote sensing natural environment monitoring and analysis platform, was used to realize the combination of Landsat TM/OLI data images with spectral features and topographic features, and the random forest machine learning classification method was used to supervise and classify the low-cloud composite image data of Ordos City. The results show that: (1) GEE has a powerful computing function, which can realize efficient and high-precision in-depth analysis of long-term multi-temporal remote sensing images and monitoring of land use change, and the accuracy of acquisition can reach 87%. Compared with other data sets in the same period, the overall and local classification results are more distinct than ESRI (Environmental Systems Research Institute) and GlobeLand 30 data products. Slightly lower than the Institute of Aerospace Information Innovation of the Chinese Academy of Sciences to obtain global 30 m of land cover fine classification products. (2) The overall accuracy of the land cover data of Ordos City from 2003 to 2023 is between 79–87%, and the Kappa coefficient is between 0.79–0.84. (3) Climate, terrain, population and other interactive factors combined with socio-economic population data and national and local policies are the main factors affecting land use change between 2003 and 2023. [ABSTRACT FROM AUTHOR]

The Madurai local planning authority, encompassing a land area of 726.34 sq. km, has encountered challenges of droughts and flash floods during the north–east monsoon season. These issues have arisen as a result of notable alterations in land use and the swift pace of urbanization. This comprehensive study aims to assess the effects of land use changes on direct runoff within the study area over a span of 40 years, from 1980 to 2020. This study attempted to understand the evolution of land use and land cover change in the Madurai LPA region over the past 2 decades and its corresponding impact on runoff generation. The study also predicted the trend of LULC in 2040. LANDSAT images from different years were acquired to create land use and land cover maps using ERDAS IMAGINE 9.1 and Arc GIS Version 10.1. Four hydrological soil groups were determined using data from the Madurai Atlas, and the surface runoff was calculated using the soil conservation service–curve number. The accuracy of the land use and land cover maps was evaluated using an error matrix and kappa index. LULC predictions for 2040 were made using the cellular automata and artificial neural network model. The analysis showed that agricultural land increased by 5.9% between 1980 and 2020, while forest cover decreased by 0.2% and urban settlements grew by 7.4% in the D hydrological soil group. The predicted land use for 2040 indicates that agricultural land will account for 54.1%, followed by 1% forest cover and 15.8% urban areas. The accuracy of the predicted land use map was validated using the 2020 map, with a 91% accuracy and a kappa coefficient of 0.8. The Madurai region has experienced a notable surge in urbanization, highlighting the urgency for effective flood management and the implementation of urban development strategies that prioritize the creation of green spaces and efficient storm water drainage systems. [ABSTRACT FROM AUTHOR]

This study investigates the impacts of Land Use/Land Cover (LULC) changes and climate change on surface runoff in Gdańsk, Poland, which is crucial for local LULC planning and urban flood risk management. The analysis employs two primary methodologies: remote sensing and hydrological modeling. Remote sensing was conducted using Google Earth Engine and Land Change Modeler in IDRISI Terrset software to analyze historical (1985–2022) and future (2050–2100) LULC. Hydrological modeling was performed using the Natural Resources Conservation Service curve number method to assess the overall impact of LULC changes on Gdańsk's hydrology at the local scale. The Orunia basin, a critical area due to intensive LULC development, was selected for detailed hydrological analysis using the Hydrologic Modeling System (HEC-HMS). The analysis encompassed three scenarios: LULC changes, climate change, and combined LULC and climate change effects. The LULC analysis revealed a marked increase in urban area, a shift in forest and vegetation cover, and a reduction in agricultural land. HEC-HMS simulations showed an increase in the runoff coefficient across selected decades, which was attributed to the combined effect of LULC and climate change. The projected increases under the Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 scenarios for 2050 and 2100 are projected to surpass those observed during the baseline period. The findings highlight that the synergistic effects of LULC and climate change have a more significant impact on Gdańsk's hydrology at both local and basin scales than their separate effects. These insights into LULC shifts and urban hydrological responses hold implications for sustainable urban planning and effective flood risk management in Gdańsk and similar urban settings. [ABSTRACT FROM AUTHOR]

Monitoring and providing accurate land use and land cover (LULC) change information is vital for sustainable environmental planning. This study used Landsat imagery from 2002 to 2022 to create updated LULC change maps for the eThekwini Municipality. Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost) were used to conduct these LULC classifications, with XGBoost achieving the highest accuracy (80.57%). The generated maps revealed a significant decrease in cropland and an increase in impervious surfaces. As such, this research established a framework for continuous LULC mapping and highlighted Landsat 9's potential in LULC classifications. [ABSTRACT FROM AUTHOR]

The commune of Po in Burkina Faso is an area with high agroecological potential and favorable climatic context. These assets make the commune one of the preferred destinations for agropastoral population, in search of better living conditions. This situation has an impact on the dynamics of land use and land cover. The aim of this study is to assess the nature of land cover as well as the intensity of their use over the last two decades (2003-2023). Changes were detected using remote sensing and geographic information systems (GIS). The methodology is based on supervised classification by maximum likelihood, which has made it possible to highlight land use and land cover classes. The transition matrix, and the conversion and expansion rates derived from it, indicate that the various land-use units in Po have changed between 2003 and 2023. Vegetation formations (wooded savannah and gallery forest) have declined overall, to the benefit of farms, which have increased by 28.64%. This knowledge of the dynamics of land cover and land use in Po is helping to provide local authorities and technical services with a better understanding of the mechanisms and strategies to be implemented for effective management of the commune's natural resources. [ABSTRACT FROM AUTHOR]

Rapid urbanization can change local climate by increasing land surface temperature (LST), particularly in metropolitan regions. This study uses two decades of remote sensing data to investigate how urbanization-induced changes in land use/land cover (LULC) affect LST in the Beijing Region, China. By focusing on the key issue of LST and its contributing variables through buffer zones, we determined how variables influence LST across buffer zones—core, transit, and suburban areas. This approach is crucial for identifying and prioritizing key variables in each zone, enabling targeted, zone-specific measures that can more effectively mitigate LST rise. The main driving variables for the Beijing Region were determined, and the spatial-temporal relationship between LST and driving variables was investigated using a geographically weighted regression (GWR) model. The results demonstrate that the Beijing Region's LST climbed from 2002 to 2022, with increases of 0.904, 0.768, and 0.248 °C in core, transit, and suburban areas, respectively. The study found that human-induced variables contributed significantly to the increase in LST across core and transit areas. Meanwhile, natural variables in suburban areas predominated and contributed to stabilizing local climates and cooling. Over two decades and in all buffer zones, GWR models slightly outperformed ordinary least squares (OLS) models, suggesting that the LST is highly influenced by its local geographical location, incorporating natural and human-induced variables. The results of this study have substantial implications for designing methods to mitigate LST across the three buffer zones in the Beijing Region. [ABSTRACT FROM AUTHOR]

The Great Green Wall (GGW) initiative, which started in 2007 and is still in development as of 2024, aims to combat desertification and enhance sustainability over 8000 km across Africa's Sahel-Sahara region, encompassing 11 key countries and 7 countries associated with the initiative. Because of limited ground measurements for the GGW project, the progress and impacts of the GGW initiative have been a challenging problem to monitor and assess. This study aims to utilize satellite remote sensing data to analyze changes in the key factors related to the sustainability of the GGW region, including land cover type, vegetation index, precipitation rate, land surface temperature (LST), surface soil moisture, etc. Results from temporal analysis of these factors indicate that the deserts along the GGW are retreating and the regional mean of the Normalized Difference Vegetation Index (NDVI) has an increasing trend, although the precipitation has a slightly decreasing trend, over the past two decades. Further analysis shows spatial heterogeneity of vegetation, precipitation, and soil moisture changes. Desertification is still a challenging issue in some GGW countries. These results are helpful in understanding climate change in the GGW regions and the impacts of the Great Green Wall initiative. [ABSTRACT FROM AUTHOR]