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Over the past two and a half decades, rapid urbanization has led to significant land use and land cover (LULC) changes in Kabul province, Afghanistan. To assess the impact of LULC changes on land surface temperature (LST), Kabul province was divided into four LULC classes applying the Support Vector Machine (SVM) algorithm using the Landsat satellite images from 1998 to 2022. The LST was assessed using Landsat data from the thermal band. The Cellular Automata-Logistic Regression (CA-LR) model was applied to predict the future patterns of LULC and LST for 2034 and 2046. Results showed significant changes in LULC classes, as the built-up areas increased about 9.37%, while the bare soil and vegetation cover decreased 7.20% and 2.35%, respectively, from 1998 to 2022. The analysis of annual LST revealed that built-up areas showed the highest mean LST, followed by bare soil and vegetation. The future simulation results indicate an expected increase in built-up areas to 17.08% and 23.10% by 2034 and 2046, respectively, compared to 11.23% in 2022. Similarly, the simulation results for LST indicated that the area experiencing the highest LST class (≥ 32 °C) is expected to increase to 27.01% and 43.05% by 2034 and 2046, respectively, compared to 11.21% in 2022. The results indicate that LST increases considerably as built-up areas increase and vegetation cover decreases, revealing a direct link between urbanization and rising temperatures. [ABSTRACT FROM AUTHOR]

This study explores the relationship between landscape features and avian diversity in South Korea, examining both taxonomic and functional diversity. The Korean Peninsula serves as a pivotal habitat for resident bird species and a migratory pathway in the East Asia-Pacific flyway. Using a national dataset with block sizes ranging from 3.5 to 4.5 kilometers per side, we found that less urbanized open plains exhibit higher taxonomic diversity, while coastal regions with diverse water bird populations show higher functional diversity. These findings underscore the significance of conserving the existing land types and qualities in specific regions to substantially impact bird distribution and regional biodiversity. Remarkably, closed forests display diversity patterns akin to urban/built-up areas, despite their disparate land use characteristics. The stability of bird diversity indices across different land use types enables us to predict bird diversity indices based on the particular land use and land cover configurations. This study emphasizes the complementary nature of functional biodiversity indices in comprehending bird distribution patterns alongside taxonomic diversity indices. [ABSTRACT FROM AUTHOR]

The response of regional climate models (RCMs) to different input land cover information is complex and uncertain. Several studies by the regional climate modeling community have investigated the potential of land cover data to help understand land-atmosphere interactions at regional and local scales. This study investigates the regional climate response to introducing European Space Agency (ESA) land cover (LC) data into WRF-Hydro. In addition, this study assesses the potential impacts of afforestation and deforestation strategies on regional water and energy fluxes. An extended version of WRF-Hydro that accounts for a two-way river-land water flow to reduce unrealistic peaks in simulated discharge was employed. The two-way river-land flow setup yielded a better NSE and KGE of 0.47 and 0.69, respectively, over the Kulpawn basin compared to the default setup values of -0.34 and 0.2. Two land use/land cover change effects were deduced from synthetic numerical sensitivity experiments mimicking afforestation by closed shrubland expansion and deforestation by cropland expansion. The afforestation experiment yielded approximately 6% more precipitation, 3% more evapotranspiration, 27% more surface runoff, and 16% more underground runoff, while the deforestation by cropland expansion yielded -5% less precipitation, -3% less evapotranspiration, -3% less surface runoff, and - 9% less underground runoff over the Sissili-Kulpawn Basin (SKB). This result suggests that afforestation (deforestation) could increase the flood (drought) risk. Our synthetic numerical experiment mimics the regional water and energy budgets well and can help climate services and decision-makers by quantifying regional climate response to potential land cover changes. [ABSTRACT FROM AUTHOR]

Water plays a significant role in every sector of the ecosystem. The fluctuation of the water levels in lakes is influenced by natural and man-made factors within the water catchment. Lake Baringo, which has no visible outlet, has been rising drastically, causing panic among native communities and businesses on the shores of the lake. Using GIS and Remote Sensing, this study intends to analyze the changes in water lakes using the Automatic water extraction index (AWEI), determine the causes of the fluctuation using Land use Land cover, land surface temperature, soil erosion, and siltation in the lake basin and the lake respectively, precipitation, and later predict of the water level for the year 2030 using the MOLUSCE tool. The tool utilizes an artificial neural network and cellular automata to analyze land use and land cover conveniently. It was found that the lake's water level has been increasing drastically over the years, and the leading causes of the fluctuations were increased rainfall and human activities within the water basin. There are visible increased human activities within the water basin, such as agriculture, deforestation, settlement, and urbanization. It was also found that there will be a further increase in water level in 2030. With all the above results, it is recommended that better policies be made to conserve the water basin effectively, and a plan should be drawn to re-delineate the new riparian buffer. [ABSTRACT FROM AUTHOR]

The German government aims to convert its energy system to renewable energy by 2045. This requires a comprehensive understanding of land eligibility for renewables, particularly land-intensive ground-mounted photovoltaics (GM-PV). Federal states must set aside at least 2% of their land for renewable energy. This target value was derived using a top-down energy demand approach. Georeferenced land use data can be used to make bottom-up estimates. This study investigates how the choice of data source influences the bottom-up evaluation of land eligibility for GM-PV installations in Germany. This study evaluates the quality of data sources and their applicability for GM-PV scenario assessment by comparing the official data source Basis-DLM as the reference with the open-access data sources OpenStreetMap (OSM), Corine Land Cover (CLC), and Copernicus Emergency Management Service (CEMS). The intersection over union (IoU) and Matthews correlation coefficient (MCC) methods were used to analyse the differences in land use and eligibility due to the quality of the data sources and to compare their accuracy. The study's results show the crucial role of data source selection in estimating the potential for GM-PV in Germany. The results indicate that open-access data overestimate land eligibility by 4.0% to 4.5% compared to the official Basis-DLM data. Spatial similarities and discrepancies between the OSM, CEMS CLC, and Basis-DLM land uses were identified. The CLC data exhibit higher consistency with Basis-DLM. These findings emphasise the importance of selecting the appropriate data source depending on the purpose and the use of official data sources for accurate and spatially differentiated decision-making and project planning at different scales. Open-access data sources can be applied for initial orientation and large-scale rough assessment as they balance data accuracy and accessibility. [ABSTRACT FROM AUTHOR]

The validation of remotely sensed evapotranspiration (ET) products is important for the development of ET estimation models and the accuracy of the scientific application of the products. In this study, different ET products such as HiTLL, MOD16A2, ETMonitor, and SoGAE were compared using multi-source remote sensing data and ground-based data to evaluate their applicability in the Heihe River Basin (HRB) during 2010–2019. The results of the comparison with the site observations show that ETMonitor provides a more stable and reliable estimation of ET than the other three products. The ET exhibited significant variations over the decade, characterized by a general increase in rates across the HRB. These changes were markedly influenced by variations in land use and topographical features. Specifically, the analysis showed that farmland and forested areas had higher ET rates due to greater vegetation cover and moisture availability, while grasslands and water bodies demonstrated lower ET rates, reflecting their respective land cover characteristics. This study further explored the influence of various factors on ET, including land use changes, NDVI, temperature, and precipitation. It was found that changes in land use, such as increases in agricultural areas or reforestation efforts, directly influenced ET rates. Moreover, meteorological conditions such as temperature and precipitation patterns also played crucial roles, with warmer temperatures and higher precipitation correlating with increased ET. This study highlights the significant impact of land use and climatic factors on spatiotemporal variations in ET within the HRB, underscoring its importance for optimizing water resource management and land use planning in arid regions. [ABSTRACT FROM AUTHOR]

Kolonnawa marsh (KM) is an important wetland ecosystem in Colombo district, Sri Lanka that provides essential ecosystem services, and has undergone significant changes over recent decades due to continuous exploitation and reclamation. The values of wetlands are disregarded by decision-makers, despite the fact that they are crucial for improving the quality of water and offer chances for relaxation and amusement in metropolitan areas. Underestimation of the value of wetlands contributes to their continuing deterioration and inevitable loss. Investigating the changes in wetlands can provide crucial information for decision-making. This study aimed to monitor the spatiotemporal land-cover dynamics of KM with the prospect prediction as reduced total extent of KM gradually with time and marsh area being transformed into terrestrial vegetation with time. The collective images from Google Earth (2000 to 2021) and drone data (2022) were analyzed with the GIS application. Subsequently, 50-m2 grid squares with unique cell IDs are designed to link among land cover maps for spatiotemporal land-cover change analysis. Then, we calculate land cover category: surface water, marsh, and terrestrial vegetation proportions for each map in 50-m2 grid cells. Statistical comparison of the land cover changes in grid square cells shows that each land cover category has significant change with the time. The results showed that the reduction of KM marsh resulting in land cover changes has a positive implication on wetland degradation. Thus, interventions should be made for the restoration and sustainable management of KM. [ABSTRACT FROM AUTHOR]

Introduction: Anthropogenic disturbances resulting from extensive mining activities in tropical regions pose significant threats to native land use, leading to deforestation, biodiversity loss, climate change impacts, environmental degradation, health risks, landscape fragmentation, compromised ecological security, and societal wellbeing. Monitoring Land Use and Land Cover Change (LULCC) becomes imperative for evaluating the extent and nature of land degradation in mined areas. Methods: This study examined and compared land cover change patterns across three coalmined sites: Sohagpur (Site-I), Jamuna & Kotma (Site-II), Bishrampur (Site-III) in Central India over 3 decades using Landsat satellite imagery from 1994, 2007, and 2022. The Maximum Likelihood (ML) algorithm, within a supervised classification framework, was applied to discern mining impacts on decadal land use shifts. Results and discussion: The analysis revealed that 7.32%-17.61% of forest cover, 5.0%-10% of water bodies, and 3%-5% of agricultural lands were lost due to mining activities, with Site three and Site two experiencing greater losses compared to Site 1. Overall, native land cover diminished by 35% between 1994 and 2022. Indices including Soil Index, Climate Index, Terrain Index, Land Utilization Index, and Vegetation Index were derived to assess land degradation patterns. These indices were integrated using a weighted index model in ArcGIS to generate the Land Degradation Vulnerability Index (LDVI). Vulnerability notably escalated with mining expansion, particularly pronounced at Site 3 (Bishrampur) and lower at Site 1 (Sohagpur). The "extremely vulnerable" class encompassed a substantial area (25%-40%), while the "low vulnerable" class was less than 5% across all sites. This study's comprehensive analysis aids policymakers, planners, and managers in prioritizing targeted interventions and implementing sustainable land management 175 practices for ecorestoration, aligning with the goal of Zero Net Land Degradation 176 (ZNLD) in coal-mined landscapes. [ABSTRACT FROM AUTHOR]

Rapid population growth-induced urbanization, industrialization, deforestation, settlement expansion and habitat destruction are significant drivers of LU/LC change at global scale. The uncontrolled LU/LC change, specifically in developing countries like Ethiopia, poses significant environmental threats to water quality, biodiversity, and ecosystem services. This study, therefore, examined LU/LC trends in the Lake Tana catchment, Ethiopia from 1993 to 2022, analyzed the impact of changes on water quality and identified major driving factors for policy input. ArcGIS software and Landsat imagery were employed toproduce maps for 10 years. Water quality data were obtained from primary and secondary sources from 2002 to 2022. The relationship between LU/LC changes and water quality was examined using Pearson's correlation and principal component analysis (PCA). Results revealed significant (p < 0.05) spatio-temporal variations in water quality parameters, with almost all parameters showing a substantial increase from 2002 to 2012 and some declined in 2022. LU/LC changes were categorized into six classes with an overall accuracy and Kappa coefficient of 92.2% and 0.86%, respectively. Various LU/LC classes were highly correlated with a particular water quality parameter. Settlement and crop land expansion were positively correlated with nutrients such as (NH3 - + NH4 +-N), NO3-N, SRP, and TP. Forestation was positively correlated with good water quality such as DO and pH, while they were negatively correlated with temperature, EC, SRP, and NO3-N demonstrating a strong linkage between LU/LC and water quality. These findings therefore, validate the significance of prioritizing LU/LC changes for sustainable water quality and ecosystem service improvement in the Lake Tana catchment. [ABSTRACT FROM AUTHOR]

This study employs the robust capabilities of Google Earth Engine (GEE) to analyze and forecast land cover and land use changes in the Talas District, situated within the Zhambyl region of Kazakhstan, for a period spanning from 2000 to 2030. The methodology involves thorough image selection, data filtering, and classification using a Random Forest algorithm based on Landsat imagery. This study identifies significant shifts in land cover classes such as herbaceous wetlands, bare vegetation, shrublands, solonchak, water bodies, and grasslands. A detailed accuracy assessment validates the classification model. The forecast for 2030 reveals dynamic trends, including the decline of herbaceous wetlands, a reversal in bare vegetation, and concerns over water bodies. The 2030 forecast shows dynamic trends, including a projected 334.023 km2 of herbaceous wetlands, 2271.41 km2 of bare vegetation, and a notable reduction in water bodies to 24.0129 km2. In quantifying overall trends, this study observes a decline in herbaceous wetlands, bare vegetation, and approximately 67% fewer water bodies from 2000 to 2030, alongside a rise in grassland areas, highlighting dynamic land cover changes. This research underscores the need for continuous monitoring and research to guide sustainable land use planning and conservation in the Talas District and similar areas. [ABSTRACT FROM AUTHOR]

The Bale Ecoregion (BER) is known for its global importance in biodiversity and as a water tower for East African drylands. Land use and land covers (LULC) have been changing for decades, affecting forest ecosystem service values (ESVs), but available information is limited. The present study addresses these gaps by using contrasting watersheds representing the highland, midland, and lowland agroecologies in the BER. LULC classifications were performed using GIS and remote sensing tools. Multisite imagery data (using Landsat image resolution 30 m × 30 m) were generated for four observation periods: 1992, 2002, 2012, and 2022. A recently updated global ESV coefficient and the value transfer valuation method were applied to estimate the changes in ESVs related to LULC changes. The result demonstrates that between 1992 and 2022, forest land has decreased by 3%, 63%, and 22% in the highland, midland, and lowland areas, respectively. Different degrees of loss of ESVs were observed across the study periods and LULC. Of the 21 specific ESVs investigated, the highest annual losses were recorded for water ecosystem services both in the midland and lowland landscape positions. Increased ESVs for cultivated land could not offset losses in food ESVs at the scale of agroecology. Significant impacts of LULC changes on specific ecosystem services, such as water, and changes in natural connectivity between the highland and lowland areas were observed. The result suggests that stakeholders need to co-plan and manage the BER. This evidence provides a scientific underpinning for understanding the connection between LULC change and ESVs and supports informed policy decisions. [ABSTRACT FROM AUTHOR]

Introduction: The carbon storage service of ecosystems in ecologically fragile areas is highly sensitive to regional land use/land cover (LULC) changes. Predicting changes in regional carbon storage under different LULC scenarios is crucial for land use management decisions and exploring carbon sink potential. This study focuses on the Luan River Basin, a typical ecologically fragile area, to analyze the impact of LULC changes on carbon storage. Methods: The PLUS-InVEST model was employed to simulate LULC patterns for the year 2030 under three scenarios: natural development, cropland protection and urban development, and ecological protection. The model projected the future carbon sink potential of the basin under these scenarios. Results: From 2000 to 2020, carbon storage showed a trend of decrease followed by an increase. By 2030, compared to 2020, carbon storage is projected to increase by 16.97% under the ecological protection scenario and decrease by 22.14% under the cropland protection and urban development scenario. The increase in carbon storage was primarily due to the conversion of cropland and grassland to forestland, while the decrease was mainly associated with the conversion of forestland to grassland and cropland, and the transformation of grassland to cropland and construction land. In the potential LULC scenarios of 2030, certain regions within the basin exhibited unstable carbon sink potential, strongly influenced by LULC changes. These areas were predominantly characterized by artificially cultivated forests, shrubs, and agricultural land. Implementing appropriate forest management measures and optimizing agricultural land management practices are essential to enhance carbon sink potential in these regions. Population density, annual average temperature, and DEM (Digital Elevation Model) were the dominant factors driving the spatial variation of carbon sink potential in the Luan River Basin. Discussion: The research results provide a theoretical basis for rational planning of land use and the enhancement of carbon sink potential in ecologically fragile regions. [ABSTRACT FROM AUTHOR]

Social-ecological systems (SESs) possess a great diversity of land use and land cover (LULC) types with unique assemblages of biodiversity and ecosystem services. However, LULC changes due to landscape fragmentation are emerging as major threats to the system productivity of SESs around the world. This study examined changes to LULC extent and landscape patterns in the Village Tank Cascade Systems (VTCSs) of Sri Lanka using satellite imagery and GIS techniques between 1994 and 2021. Multispectral Landsat images (5 TM and 8 OLI/TIRS) obtained from Google Earth Engine were classified using machine learning algorithms. Overall accuracies obtained were 85.9% (1994) and 88.6% (2021). The LULC change matrix and spatial pattern metrics were used to examine LULC and landscape pattern change dynamics over the VTCS landscapes. LULC change matrix results revealed that forest, which is the dominant LULC class covering 73.7% of the total land area was reduced by 206,725 ha due to transformation into agricultural (70.43%) and scrub (24.33%) lands between 1994 and 2021. Over this time landscape pattern of the VTCS has gradually changed from forest to agricultural land-dominated landscape, with forest and agricultural land types showing a significant negative correlation (p < 0.001; R2 > 0.868), particularly in the southeastern region. Landscape patterns were analysed based on eight spatial metrics calculated at both the landscape and class levels using FRAGSTATS spatial pattern analysis software. At the landscape level, the structure became more dispersed and complex in shape. Heterogeneity was noted to have gradually increased with weakening connectivity, whereas the fragmentation process had gradually accelerated. At the class level, the dominance of forest patches decreased, fragmentation and isolation increased, and connectivity and shape complexity reduced leading to the loss of fragmented forest habitats. The number of patches within the agricultural class increased and became more aggregated and complex in shape. Landscape performance indicators show that VTCSs have experienced a gradual loss of environmental sustainability. Assessment of LULC along with fragmentation can help to monitor the spatial pattern impacts that determine ecological integrity. Thus, the study provides scientific guidance for ecological restoration in degraded VTCSs to effectively improve ecological productivity. [ABSTRACT FROM AUTHOR]

Optical and Synthetic Aperture Radar (SAR) imagery offers a wealth of complementary information on a given target, attributable to the distinct imaging modalities of each component image type. Thus, multimodal remote sensing data have been widely used to improve land cover classification. However, fully integrating optical and SAR image data is not straightforward due to the distinct distributions of their features. To this end, we propose a land cover classification network based on multimodal feature fusion, i.e., MFFnet. We adopt a dual-stream network to extract features from SAR and optical images, where a ResNet network is utilized to extract deep features from optical images and PidiNet is employed to extract edge features from SAR. Simultaneously, the iAFF feature fusion module is used to facilitate data interactions between multimodal data for both low- and high-level features. Additionally, to enhance global feature dependency, the ASPP module is employed to handle the interactions between high-level features. The processed high-level features extracted from the dual-stream encoder are fused with low-level features and inputted into the decoder to restore the dimensional feature maps, generating predicted images. Comprehensive evaluations demonstrate that MFFnet achieves excellent performance in both qualitative and quantitative assessments on the WHU-OPT-SAR dataset. Compared to the suboptimal results, our method improves the OA and Kappa metrics by 7.7% and 11.26% on the WHU-OPT-SAR dataset, respectively. [ABSTRACT FROM AUTHOR]

Large-Scale land cover mapping (LLCM) based on deep learning models necessitates a substantial number of high-precision sample datasets. However, the limited availability of such datasets poses challenges in regularly updating land cover products. A commonly referenced method involves utilizing prior products (PPs) as labels to achieve up-to-date land cover mapping. Nonetheless, the accuracy of PPs at the regional level remains uncertain, and the Remote Sensing Image (RSI) corresponding to the product is not publicly accessible. Consequently, the sample dataset constructed through geographic location matching may lack precision. Errors in such datasets are not only due to inherent product discrepancies, and can also arise from temporal and scale disparities between the RSI and PPs. In order to solve the above problems, this paper proposes an LLCM framework for generating labels for use with PPs. The framework consists of three main parts. First, initial generation of labels, in which the collected PPs are integrated based on D-S evidence theory and initial labels are obtained using the generated trust map. Second, for dynamic label correction, a two-stage training method based on initial labels is adopted. The correction model is pretrained in the first stage, then the confidence probability (CP) correction module of the dynamic threshold value and NDVI correction module are introduced in the second stage. The initial labels are iteratively corrected while the model is trained using the joint correction loss, with the corrected labels obtained after training. Finally, the classification model is trained using the corrected labels. Using the proposed land cover mapping framework, this study used PPs to produce a 10 m spatial resolution land cover map of Cambodia in 2020. The overall accuracy of the land cover map was 91.68% and the Kappa value was 0.8808. Based on these results, the proposed mapping framework can effectively use PPs to update medium-resolution large-scale land cover datasets, and provides a powerful solution for label acquisition in LLCM projects. [ABSTRACT FROM AUTHOR]

This study assessed the impacts of the land use/cover (LULC) and climate changes on the runoff and sediment flows in the Megech watershed. The Geospatial Water Erosion Prediction Project (GeoWEPP) was used to assess LULC and climate changes' impact on runoff, soil loss, and sediment yield. The QGIS 2.16.3 plugin module for land use change evaluation (MOLUSCE) tool with the cellular automata artificial neural network (CA-ANN) was used for LULC prediction based on historical data and exploratory maps. Two commonly used representative concentration pathways (RCPs)—4.5 and 8.5—were used for climate projection in the 2030s, 2050s, and 2070s. The LULC prediction analysis showed an expansion of cropland and settlement areas, with the reduction in the forest and rangelands. The climate projections indicated an increase in maximum temperatures and altered precipitation patterns, particularly with increased wet months and reduced dry periods. The average annual soil loss and sediment yield rates were estimated to increase under both the RCP4.5 and RCP8.5 climate scenarios, with a more noticeable increase under RCP8.5. By integrating DEM, soil, land use, and climate data, we evaluated runoff, soil loss, and sediment yield changes on only land use/cover, only climate, and the combined impacts in the watershed. The results revealed that, under all combined scenarios, the sediment yield in the Megech Reservoir was projected to substantially increase by 23.28–41.01%, showing a potential loss of reservoir capacity. This study recommends strong climate adaptation and mitigation measures to alleviate the impact on land and water resources. It is possible to lessen the combined impacts of climate and LULC change through implementing best-management practices and adaptation strategies for the identified scenarios. [ABSTRACT FROM AUTHOR]

The past decade has seen remarkable advancements in Earth observation satellite technologies, leading to an unprecedented level of detail in satellite imagery, with ground resolutions nearing an impressive 30 cm. This progress has significantly broadened the scope of satellite imagery utilization across various domains that were traditionally reliant on aerial data. Our ultimate goal is to leverage this high-resolution satellite imagery to classify land use types and derive soil permeability maps by attributing permeability values to the different types of classified soil. Specifically, we aim to develop an object-based classification algorithm using fuzzy logic techniques to describe the different classes relevant to soil permeability by analyzing different test areas, and once a complete method has been developed, apply it to the entire image of Pavia. In this study area, a logical scheme was developed to classify the field classes, cultivated and uncultivated, and distinguish them from large industrial buildings, which, due to their radiometric similarity, can be classified incorrectly, especially with uncultivated fields. Validation of the classification results against ground truth data, produced by an operator manually classifying part of the image, yielded an impressive overall accuracy of 95.32%. [ABSTRACT FROM AUTHOR]

• LULC changes of three decades from 1991 to 2021 were examined and predicted. • Multi-spectral remote sensing data and RFT were utilized to generate LULC maps. • ACR were considered to analyze the categorical land use change detection. • Impervious land has expanded 10 times within 30 years and will increase in the future. The integration of multi-spectral remote sensing and GIS-based analysis is significant for studying land cover changes, providing valuable insights for informed land management and sustainable development. The present study aims to examine land use land cover (LULC) changes of three decades from 1991 to 2021 and predict the future LULC change in Binh Duong province, Vietnam to explore a future research direction on land use change and associated challenges in the study region. Multi-spectral remote sensing data and random forest tree (RFT) were utilized to generate LULC maps. Areal statistics and annual change rate were considered to analyze the categorical land use change detection. Statistical measures such as user's accuracy, producer's accuracy, kappa coefficient, and confusion matrix were employed to assess the accuracy of LULC classification. To predict future LULC and simulate the spatio-temporal change, we considered previous year LULC maps, independent spatial variables and a combined artificial neural network (ANN) multi-layer perceptron approach. The analysis revealed that there was a huge transition from agricultural lands to residential land with industry and commerce which resulting an expansion of impervious lands and a rapid decline of agricultural land as well as of scrub land and barren lands, and a changeability of forest and plantation, croplands, and waterbodies. Our study revealed that the impervious land has expanded 10 times within 30 years and will increase in the future. [ABSTRACT FROM AUTHOR]

Increased urbanization has reduced the amount of green space, resulting in a reduced carbon sink potential across urban landscapes. Through the use of biogeochemical modeling, different land use scenarios have been developed and run for the future (2020–2099) to compare and quantify the potential for change in carbon and water dynamics by having more tree cover and reducing impervious surfaces or turf lawns in Lehigh Valley, PA. These results show that the effect of deforestation is larger than the effect of reforestation. Due to young-stand age trees having a lower capacity for carbon storage than mature trees, the loss of the mature trees has a more immediate impact. The conversion of lawns or impervious surfaces to forests has somewhat similar effects, although the higher nutrients of lawns allow the forest to grow better. However, replacing impervious surfaces with trees reduces runoff more. This study shows that within the city of Bethlehem, the most socially vulnerable area benefits the most from increasing the number of trees. When converting 25% of the impervious area to forest, South Bethlehem significantly increased its vegetation carbon, productivity, and carbon storage, reduced its runoff, and generally created a safer and cleaner environment for residents. [ABSTRACT FROM AUTHOR]

Human activities exert a profound influence on land use and land cover, and these changes directly influence habitat quality and ecosystem functioning. In the Gansu–Qinghai contiguous region of the upper Yellow River, habitat quality has undergone substantial transformations in recent years due to the synergistic impacts of natural processes and human intervention. Therefore, evaluating the effects of land use changes on habitat quality is crucial for advancing regional sustainable development and improving the worth of ecosystem services. In response to these challenges, we devised a two-pronged approach: a land use simulation (FLUS) model and an integrated valuation of ecosystem services and trade-offs (InVEST) model, leveraging remote sensing data. This integrated methodology establishes a research framework for the evaluation and simulation of spatial and temporal variations in habitat quality. The results of the study show that, firstly, from 1980 to 2020, the habitat quality index in the Gansu–Qinghai contiguous region of the upper Yellow River decreased from 0.8528 to 0.8434. Secondly, our predictions anticipate a decrease in habitat quality, although the decline is not pronounced across all scenarios. The highest habitat quality values were projected under the EP (Ecology Priority) scenario, followed by the CLP (Cultivated Land Priority) scenario, while the BAU (Business as Usual) scenario consistently yielded the lowest values in all three scenarios. Finally, the ecological land, including forest land and grassland, consistently occupied areas characterized by high habitat quality. In contrast, Construction land consistently appeared in regions associated with low habitat quality. The implementation of conservation measures emerges as a crucial strategy, effectively limiting the expansion of construction land and promoting the augmentation of forest land and grassland cover. This approach serves to enhance overall habitat quality. These outcomes furnish a scientific foundation for the judicious formulation of future land-use policies and ecological protection measures. [ABSTRACT FROM AUTHOR]

A third of the world's ecosystems are considered degraded, and there is an urgent need for protection and restoration to make the planet healthier. The Sustainable Development Goals (SDGs) target 15.3 aims at protecting and restoring the terrestrial ecosystem to achieve a land degradation-neutral world by 2030. Land restoration through inclusive and productive growth is indispensable to promote sustainable development by fostering climate change-resistant, poverty-alleviating, and environmentally protective economic growth. The SDG Indicator 15.3.1 is used to measure progress towards a land degradation-neutral world. Earth observation datasets are the primary data sources for deriving the three sub-indicators of indicator 15.3.1. It requires selecting, querying, and processing a substantial historical archive of data. To reduce the complexities, make the calculation user-friendly, and adapt it to in-country applications, a module on the FAO's SEPAL platform has been developed in compliance with the UNCCD Good Practice Guidance (GPG v2) to derive the necessary statistics and maps for monitoring and reporting land degradation. The module uses satellite data from Landsat, Sentinel 2, and MODIS sensors for primary productivity assessment, along with other datasets enabling high-resolution to large-scale assessment of land degradation. The use of an in-country land cover transition matrix along with in-country land cover data enables a more accurate assessment of land cover changes over time. Four different case studies from Bangladesh, Nigeria, Uruguay, and Angola are presented to highlight the prospect and challenges of monitoring land degradation using various datasets, including LCML-based national land cover legend and land cover data. [ABSTRACT FROM AUTHOR]

Air pollution, particularly fine particulate matter (PM2.5), poses a significant health risk, especially in high-density urban areas. Urban green space (UGS) can effectively mitigate this pollution. Despite their potential, strategies for effectively leveraging Land Use/Land Cover (LULC) optimization to combat PM2.5 remain largely unexplored. Ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) were employed to investigate the spatial heterogeneity relationship between UGS conversion and PM2.5 fluctuations across various scales and evolutionary stages, developing a multiscale practical framework for LULC synergy in combating air pollution. The areas of UGSs to/from other LULCs, PM2.5 concentrations and corresponding variation zones exhibited significant spatial clustering. These UGS conversions explained more than 65% of the PM2.5 changes in the study area, peaking at 76.4% explanatory power in the fourth stage. Compared to global spatial analysis (OLS: 0–0.48), local spatial regression analysis significantly improved the R2 value (GWR: 0.32–0.75, MGWR: 0.48–0.90), but the fitting quality of local spatial regression analysis decreased with increasing scale, highlighting the importance of scale diagnosis. A 2 km scale was identified as optimal for assessing the spatial heterogeneity impact of UGS and other LULC conversions on PM2.5 changes. Conversion areas from water bodies and bare land to UGSs maintain stable local spatial properties at this scale (bandwidths: 44–99). Our research provides new insights into LULC management and planning, offering a coordinated approach to mitigating urban air pollution. Additionally, a practical framework was established for addressing spatially continuous variables such as PM2.5, revealing effective approaches for addressing urban environmental issues. [ABSTRACT FROM AUTHOR]

This study provides a detailed analysis of land use and land cover (LULC) changes at the district level within the Delhi–Mumbai Industrial Corridor (DMIC) from 2001 to 2021. Using the Indian Meteorological Department's sub-divisional framework and MODIS data across seven primary LULC classes, the analysis is instrumental in informing infrastructure planning for existing and future smart cities and industrial clusters within the DMIC. The key findings reveal a yearly increase of 3031.40 sq. km. per year in agricultural land, with decreases in shrubland, grassland, and bareland of −1774.72 sq. km. per year, −1119.62 sq. km. per year, and −203.76 sq. km. per year, respectively. On the other hand, forests grew by a modest 148.14 sq. km. per year, while waterbodies and built-up lands saw minor increases of 55.73 sq. km. and 21.48 sq. km. per year. Ecologically Sensitive Areas (ESAs) were evaluated for LULC changes. The smart cities of Pune and Thane serve as excellent examples of balanced urban development and natural growth management. However, the study also highlights the need for further research to investigate LULC impacts on climatic variables, advocating for a regional planning approach in the DMIC. [ABSTRACT FROM AUTHOR]

Analysis of land use and land cover (LULC) change and its drivers and impacts in the biodiversity hotspot of Bale Mountain's socio-ecological system is crucial for formulating plausible policies and strategies that can enhance sustainable development. The study aimed to analyze spatio-temporal LULC changes and their trends, extents, drives, and impacts over the last 48 years in the Bale Mountain social-ecological system. Landsat imagery data from the years 1973, 1986, 1996, 2014, and 2021 together with qualitative data were used. LULC classification scheme employed a supervised classification method with the application of the maximum likelihood algorithm technique. In the period between 1973 and 2021, agriculture, bare land, and settlement showed areal increment by 153.13%, 295.57%, and 49.03% with the corresponding increased annual rate of 1.93%, 2.86%, and 0.83%, respectively. On the contrary, forest, wood land, bushland, grass land, and water body decreased by 29.97%, 1.36%, 28.16%, 8.63%, and 84.36% during the study period, respectively. During the period, major LULC change dynamics were also observed; the majority of woodland was converted to agriculture (757.8 km2) and grassland (531.3 km2); and forests were converted to other LULC classes, namely woodland (766.5 km2), agriculture (706.1 km2), grassland (34.6 km2), bushland (31.9 km2), settlement (20.5 km2), and bare land (14.3 km2). LULC changes were caused by the expansion of agriculture, settlement, overgrazing, infrastructure development, and fire that were driven by population growth and climate change, and supplemented by inadequate policy and institutional factors. Social and environmental importance and values of land uses and land covers in the study area necessitate further assessment of potential natural resources' user groups and valuation of ecosystem services in the study area. Hence, we suggest the identification of potential natural resource–based user groups, and assessment of the influence of LULC changes on ecosystem services in Bale Mountains Eco Region (BMER) for the sustainable use and managements of land resources. [ABSTRACT FROM AUTHOR]

Water quality assessment stands as a fundamental step in water resource management. In addition to substances and organisms present in the water, land use and land cover are two factors also affecting water quality. This study analyzed the correlation and influence between water quality and land use and land cover in the Miranda River Basin (MRB) and the natural wetlands in the Pantanal Biome. Using a watershed-wide approach, results suggested that water quality management policies can be considered in terms of sub-basins (smaller planning units). The Water Quality Index (WQI) was considered GOOD or EXCELLENT throughout the MRB. It was observed that the natural wetlands have the capacity to contribute to improving the WQI, with reductions in thermotolerant coliforms and turbidity; however, high values were found for thermotolerant coliforms, a temporal increasing trend of biochemical oxygen demand (BOD), and a reduction in dissolved oxygen and total phosphorus. Conversely, in these flooded areas, trends of a decrease in dissolved oxygen and an increase in BOD levels were found. Natural wetlands play important hydrological and ecological roles in water quality, storing, removing, and cycling nutrients. They are highly relevant areas for defining watershed management and conservation strategies, environmental protection, and providing ecosystem services. [ABSTRACT FROM AUTHOR]

Land use/land cover (LULC) conversion and droughts impact river water quality. Since the river water quality is significant, it was attempted to investigate the effect of hydrological drought (HD) and LULC changes on the water quality parameters of the Helle river in Iran. All the time series (1969–2020) were analyzed using dynamic analysis applications. The results indicated that due to the alteration of LULC and HD patterns, water hardness and salinity were increased by 13.7% and 16.1%, respectively. Moreover, the water quality parameters concentration was intensified. However, the river water potential of hydrogen (pH) decreased by 0.4%. The number of random elements in the time series of the pH, bicarbonate, sulfate, magnesium, and sodium was increased noticeably, and their predictability and long-term memory were reduced. The total dissolved solids (TDS), electrical conductivity (EC), chloride, potassium, and calcium's time series became more chaotic, and their predictability and long-term memory were improved. All the time series faced an increase in the number of noises. The alteration of HD patterns induced a reduction of correlation between HD and sulfate, sodium, magnesium, bicarbonate, pH, and TDS concentration and an augmentation of correlation between HD and chloride, calcium, potassium, and salinity concentration. The Helle river water quality deteriorated due to the alteration of HD patterns and LULC. Future studies can address the strategies to improve the water quality of the Helle river. [ABSTRACT FROM AUTHOR]

To formulate an effective growth management plan, it is imperative to comprehend the dynamic changes that transpire. This study focused on identifying such shifts spanning four decades, from 1990 to 2020, and utilized a GIS-integrated approach, employing cellular automata Markov chain model within TerrSet software for the MRBC area, to predict land use and land cover (LULC) for 2030. The accuracy evaluation of the classification method yielded overall accuracy percentages of 94.11%, 94.11%, 90.19%, and 94.12% for 1990, 2000, 2010, and 2020, respectively, accompanied by Kappa values of 0.921, 0.921, 0.895 and 0.922. The LULC map for 2020 was forecasted and compared to the actual map for validation, revealing a discrepancy of less than 5% in class distribution. The study findings indicated a 12.32% reduction in agricultural land (151.7 km2) compared to the 1990 LULC map in the projected 2030 map. In this future scenario, the converted region is allocated to urban and barren land classes. Consequently, decision-makers are urged to take necessary measures to preserve agricultural land from conversion, ensuring the enduring sustainability of agriculture. [ABSTRACT FROM AUTHOR]

Background and objectives: Land use and land cover change (LULCC) directly affects the human living environment and economic society by influencing ecosystem services. This study explores the complex relationship between LULCC and ecosystem services in the Dongting Lake region of China. Methods: The analysis incorporates the transfer matrix analysis of LULCC, ecosystem service value (ESV) accounting, Mann-Kendall mutation test, gray correlation, and path analysis. This study explored the spatial and temporal characteristics of LULCC and ecosystem services and revealed the comprehensive impacts of LULCC on ecosystem services from the perspective of factor interactions. Findings: The findings reveal that open water (OW) area and farmable land are the dominant land use types transferring in and out of the study area, with significant interconversion observed among open water area, wetland (WL), arable land (AL), and forest land (FL). Water conservation, waste disposal, climate regulation, and biodiversity conservation emerge as the most crucial ecosystem services in the study area. The study identifies consistent patterns of abrupt changes in LULCC and ESV across six analyzed time periods. Forest land exhibits the most substantial direct influence on climate regulation, gas exchange, soil formation and protection, conservation of biodiversity, rawmaterials, and recreation and leisure. Additionally, forest land, wetland, arable land, and openwater area contribute significantly to the total ESV, with forest land being the dominant factor influencing the spatial differentiation of ecosystem services in the Dongting Lake region. Conclusion: The research reveals the consistency of ecosystem services and LULCC trends in the Dongting Lake area. LULCC is a complex process influenced by multiple factors, in which the conversion between ecological and nonecological land use has a significant impact, and the complex coupling and coordination among LULCC drivers together influence ecosystem services. Furthermore, LULCC alters the structure and scale of ecosystems, resulting in alterations in the ESV. [ABSTRACT FROM AUTHOR]

Introduction: Land use land cover (LULC) change is an important factor driving global change, influenced by the interaction between human activities and natural ecosystems. The upper watershed of the Qingshui River is adversely affected due to anthropogenic activities. Therefore, analyzing the driving factors of land use changes in this area is crucial for ecological protection and sustainable development. Methods: Based on the long-term Landsat image data from 1990-2020, the spatiotemporal change characteristics of the LULC rate and its driving factors in the upper watershed of the Qingshui River basin were analyzed using the land use transfer matrix and dynamic degree of land use processes. The redundancy analysis was performed to investigate the links between LULC changes, socio-economic and climatic variables. Results: From 1990-2020, the area under waters and woodland decreased by -2.94 km² and -451.44 km², respectively. Meanwhile, grassland, arable land, construction land, and unused land area increased by 278.71 km², 115.72 km², 46.48 km² and 13.49 km². In terms of the proportion area to the total land (2334.10 km²), woodland accounted for 63.43-44.09% of the total land and was mainly distributed in the east; arable land was 17.00-21.96% and was largely distributed on both sides of the middle and lower parts of the basin; construction land comprised 1.03-3.02% and was generally found in flat areas near the downstream and water area was only 0.1-0.01% which primarily covered the Qingshui River and its tributaries from 1990-2020. Construction and unused lands showed the fastest rate of change, followed by water area and grasslands. Accelerated urbanization, rapid socio-economic development, and the farmland-to-forest policy were the main driving forces behind the change in LULC in the upper reaches of the Qingshui River. Discussion: The findings of this study can assist in planning strong management strategies for ecological protection and socio-economic sustainable development in the study area. [ABSTRACT FROM AUTHOR]

Generating high-resolution land cover maps using relatively lower-resolution remote sensing images is of great importance for subtle analysis. However, the domain gap between real lower-resolution and synthetic images has not been permanently resolved. Furthermore, super-resolution information is not fully exploited in semantic segmentation models. By solving the aforementioned issues, a deeply fused super resolution guided semantic segmentation network using 30 m Landsat images is proposed. A large-scale dataset comprising 10 m Sentinel-2, 30 m Landsat-8 images, and 10 m European Space Agency (ESA) Land Cover Product is introduced, facilitating model training and evaluation across diverse real-world scenarios. The proposed Deeply Fused Super Resolution Guided Semantic Segmentation Network (DFSRSSN) combines a Super Resolution Module (SRResNet) and a Semantic Segmentation Module (CRFFNet). SRResNet enhances spatial resolution, while CRFFNet leverages super-resolution information for finer-grained land cover classification. Experimental results demonstrate the superior performance of the proposed method in five different testing datasets, achieving 68.17–83.29% and 39.55–75.92% for overall accuracy and kappa, respectively. When compared to ResUnet with up-sampling block, increases of 2.16–34.27% and 8.32–43.97% were observed for overall accuracy and kappa, respectively. Moreover, we proposed a relative drop rate of accuracy metrics to evaluate the transferability. The model exhibits improved spatial transferability, demonstrating its effectiveness in generating accurate land cover maps for different cities. Multi-temporal analysis reveals the potential of the proposed method for studying land cover and land use changes over time. In addition, a comparison of the state-of-the-art full semantic segmentation models indicates that spatial details are fully exploited and presented in semantic segmentation results by the proposed method. [ABSTRACT FROM AUTHOR]

Most studies in the field of land use and land cover (LULC) classification in remote sensing rely on supervised classification, which requires a substantial amount of accurate label data. However, reliable data are often not immediately available, and are obtained through time-consuming manual labor. One potential solution to this problem is the use of already available classification maps, which may not be the true ground truth and may contain noise from multiple possible sources. This is also true for the classification maps of the MapBiomas project, which provides land use and land cover (LULC) maps on a yearly basis, classifying the Amazon basin into more than 24 classes based on the Landsat data. In this study, we utilize the Sentinel-2 data with a higher spatial resolution in conjunction with the MapBiomas maps to evaluate a proposed noise removal method and to improve classification results. We introduce a novel noise detection method that relies on identifying anchor points in feature space through clustering with self-organizing maps (SOM). The pixel label is relabeled using nearest neighbor rules, or can be removed if it is unknown. A challenge in this approach is the quantification of noise in such a real-world dataset. To overcome this problem, highly reliable validation sets were manually created for quantitative performance assessment. The results demonstrate a significant increase in overall accuracy compared to MapBiomas labels, from 79.85% to 89.65%. Additionally, we trained the L2HNet using both MapBiomas labels and the filtered labels from our approach. The overall accuracy for this model reached 93.75% with the filtered labels, compared to the baseline of 74.31%. This highlights the significance of noise detection and filtering in remote sensing, and emphasizes the need for further research in this area. [ABSTRACT FROM AUTHOR]

This study examined land use land cover change and its determinants in Tigray, Ethiopia and its livelihood zones. We used socioeconomic panel, and satellite data, and applied a mixed-effects model to analyse the factors influencing land allocation among different uses, and transition matrix to analyse land cover dynamics. The results revealed that; land use choices were influenced by plot level factors (such as plot elevation, distance, soil type and quality, and land tenure), household characteristics (such as education, dependency ratio, plot size and number owned, income, livestock and asset, perception of climate change, and access to market and main road), and community level factors (precipitation, product price, population density and livelihood zone variations). Transition matrix analysis showed that between 1986 and 2016, 12.8% of forest was converted to bare land, 6.26% bare land was converted to pasture, and 5.84% of cropland was converted to forest. However, net deforestation occurred in most of the livelihood zones. Therefore, local communities faced environmental and socio-economic challenges from capital constraints induced land fallowing, land fragmentation, and unmanaged land cover change. The study recommended sustainable land use planning and management, market linkages, improved access to roads, forestry subsidies, land tenure security, and land consolidation programs. [ABSTRACT FROM AUTHOR]

Effective management of groundwater resources is vital in arid and semi-arid regions like the Yalamlam Basin. Identifying appropriate places for groundwater artificial recharge is key to addressing water shortages and guaranteeing the long-term viability of aquifers. By combining the Analytical Hierarchy Process (AHP) approach with GIS technology, this research offers a thorough framework for evaluating the potential for groundwater recharge. Data collection for the study includes lineament density, slope, drainage density, rainfall, Land Use/Land Cover (LU/LC), soil, elevation, and TWI, among other crucial variables. In this study, lineament density, slope, drainage density, rainfall, LULC, soil, elevation and TWI are the most sensitive characteristics with relative weights of 30, 23, 16, 11, 8, 5, 4 and 3%, respectively. According to the study's findings, there are many zones in the Yalamlam region with varying capacities for groundwater recharging. Based on the locations that may be used for groundwater recharge, the study's findings were categorized into five groups: very high (12.7%), high (23.3%), moderate (29.6%), poor (24.4%), and very poor (10%). The validation of the groundwater protection zones was conducted by using eight wells in the basin. As part of the validation process, the Total Dissolved Solids (TDS) in the boreholes and the groundwater protection zones (GWPZs) were compared. A strong positive correlation was found between them (0.61). The results of this research can be used for the management of groundwater resources and provide valuable information for the creation of policies that focus on specific actions to boost groundwater recharge. These findings may considerably enhance the effectiveness of efforts aimed at implementing customized methods to encourage groundwater recharge. The results suggest that there are substantial prospects for using artificial recharge in groundwater management and planning to tackle the issue of groundwater availability in the study region. [ABSTRACT FROM AUTHOR]

There are various types of land use in the agricultural and pastoral areas of northern China, including natural grassland and artificial grassland, scrub land, forest land and farmland, may change the soil microbial community However, the soil microbial communities in these different land use types remain poorly understood. In this study, we compared soil microbial communities in these five land use types within the agro-pastoral ecotone of northern China. Our results showed that land use has had a considerable impact on soil bacterial and fungal community structures. Bacterial diversity was highest in shrubland and lowest in natural grassland; fungal diversity was highest in woodland. Microbial network structural complexity also differed significantly among land use types. The lower complexity of artificial grassland and farmland may be a result of the high intensity of anthropogenic activities in these two land-use types, while the higher structural complexity of the shrubland and woodland networks characterised by low-intensity management may be a result of low anthropogenic disturbance. Correlation analysis of soil properties (e.g., soil physicochemical properties, soil nutrients, and microbiomass carbon and nitrogen levels) and soil microbial communities demonstrated that although microbial taxa were correlated to some extent with soil environmental factors, these factors did not sufficiently explain the microbial community differences among land use types. Understanding variability among soil microbial communities within agropastoral areas of northern China is critical for determining the most effective land management strategies and conserving microbial diversity at the regional level. [ABSTRACT FROM AUTHOR]

Rapid urbanization currently poses a threat to cropland areas. Therefore, exploring the pattern of change in cropland areas and its driving mechanism is of great significance in safeguarding regional economic development and food security. In this study, data regarding cropland in the Changsha-Zhuzhou-Xiangtan city group (CZTCG) from 2000 to 2020 were collected, and dynamic changes in cropland in the past 20 years were analyzed. Based on this, the mechanisms that drive cropland change were explored in the context of multiple dimensions. Furthermore, an Inertial Development Scenario and a Cropland Priority Scenario were designed to simulate land use/land cover (LULC) changes in the CZTCG in 2025 and 2035, and, in particular, to analyze the characteristics of future spatiotemporal changes of cropland. The results demonstrated that the cropland area in the CZTCG in the past 20 years has shown a two-stage trend of increasing (2000-2015, increase of 8.25%) and decreasing (2015-2020, decrease of 2.91%), and the main reasons for cropland loss in the whole process of change were forest and building, with serious loss of cropland area in Changsha County and Wangcheng District. Between 2000 and 2020, socio-economic factors (GDP, total agricultural output), topography (elevation, slope), and climate change (average annual temperature, annual precipitation) were highly correlated with changes in cropland area, exhibiting significant temporal and regional variability. The simulation results indicate that until 2035, the Cropland Priority Scenario shows a significant increase in the area of cropland in the study area compared to the Inertial Development Scenario. Consequently, the implementation of a strategy that prioritizes cropland can effectively protect it, while helping to promote ecological development and regional socio-economic development. [ABSTRACT FROM AUTHOR]

Human activities, including agricultural expansion, urbanization, and industrial advancement, have led to land use/cover change (LULCC). These changes have had negative consequences, such as the loss of species and the degradation of forest areas. The Yayo Coffee Forest Biosphere Reserve (YCFBR) is undergoing changes due to forest fires and encroachment from coffee plantations, which are predicted to increase in the future. The objective of this study was to simulate the dynamics of LULCC and its impact on habitat quality in the area over the next three decades. The study used classified land cover (LC) maps of 1992, 2022, and 2052 (predicted), as well as factor and constraint maps of the area. In addition, household surveys, key informant interviews (KII), and focus group discussions (FGD) were conducted to understand the factors influencing changes in habitat quality. Habitat quality was simulated under three different future scenarios (Business as Usual; S1), conservation scenario (S2), and development scenario (S3) using CA-Markov, Future Land Use Simulation (FLUS), and Integrated Valuation of Environmental Services Tradeoffs (InVEST) models. The result showed a 28% increase in open forest and a 35% increase in agricultural area under S1, with a minimal increase in built-up area. The mediumlevel habitat quality increased from 11.3% in 1992 to 27.8% in 2052 S3. The highest average haitat quality value was 0.62 in S2, while the lowest was 0.51 in S3. If S3 is the case, this value is expected to fall below 40%, indicating the lowest possible level of habitat quality in the future. The model also predicts a 7% increase in high forest areas in S2, indicating the possibility of an alternative path to save high forest loss in the area. The main causes of habitat quality deterioration include population growth, agricultural land expansion, a lack of diverse incomes, land tenure issues, and settlement expansion. Based on the findings, S2 appears to be the best future scenario for maintaining habitat quality. This study provides useful information that will help planners and decision-makers effectively prepare future conservation strategies. [ABSTRACT FROM AUTHOR]

We analyzed the two main drivers (climate and land uses) shaping the composition of small mammal communities at 16 localities situated in the confluence of the Mediterranean and Eurosiberian regions (Barcelona, Spain). The study area represents a land use and land cover gradient showing urbanization and crop intensification in the lowlands and forest encroachment in mountain areas. We identified 2458 small mammal individuals of 12 different species from barn owl (Tyto alba) pellets. Three open-land species (Microtus duodecimcostatus, Crocidura russula, and Mus spretus) and one forest/generalist species (Apodemus sylvaticus) were dominant in the diet, accounting for 93% of prey. In order to disentangle the effects of both main drivers on the small mammal community, we used partial constrained ordination techniques, which allowed us to determine the pure effects (and shared effects) of the environmental factors. Land use predictors explained 33.4% of the variance (mostly crops), followed by 23.4% of the variance explained by the geo-climatic variables (mostly rainfall), and an additional 24.8% of the variance was shared by both groups of predictors, totaling 81.6% of environmental variance. The remaining 18.4% of variance was unexplained by environmental matrices. This pattern was consistent with expected associations of species and biotic influences at small spatial scales and highlighted that the number of species increased from the crops in the lowlands towards the highlands covered by deciduous and coniferous forests. [ABSTRACT FROM AUTHOR]

Ethiopia, marked by diverse geography and rapid land use/land cover (LULC) changes, grapples with formidable water resource management challenges. Understanding the hydrological response to these changes is paramount for sustainability. This review paper provides an overview of studies that investigated the hydrological impacts of LULC changes in Ethiopian basins. The review discusses methodologies, key findings, implications for sustainable land and water resource management, research gaps, and future directions. Emphasizing results from diverse studies that utilized hydrological and geospatial modelling, LULC significantly affects surface runoff, streamflow patterns, and groundwater recharge. The observed consequences are increased surface runoff, modified streamflow timing, and reduced groundwater recharge. The review underscores the urgency for integrated land and water management strategies to alleviate adverse effects. Future research priorities include basin-specific studies, long-term monitoring, integrated modelling, consideration of socio-economic factors, and incorporating climate change scenarios to enhance understanding and guide sustainable water resource management in Ethiopian basins. [ABSTRACT FROM AUTHOR]

Makkah Al-Mukarramah has undergone significant urban transformation in recent decades, transitioning from non-urban to urban landscapes driven by fast economic growth. This study aims to analyze the increase in population, urbanization, topography, and land use of Makkah City over the past 20 years, from 2000 to 2020. Makkah holds special significance for the Saudi government due to its religious and regional prominence, resulting in remarkable developmental strides within short timeframes. This has led to a surge in population and spatial expansion towards the city's outskirts, bringing about both quantitative and qualitative changes in the city. The growth rate in Makkah was 2.453% in 2020 and 2% in 2010, indicating a trajectory conducive to future land use/land cover planning. The population has shown remarkable growth, rising from 1,294,000 in 2000 to 1,578,722 in 2010 (22% increase) and further reaching 2,017,793 in 2020 (27.81% increase), nearly doubling over the two-decade span. The city's area expanded to 465 Km² in 2020, compared to 388 Km² in 2010 and 366 Km² in 2000, attributed to a notable increase in the number of districts from 60 in 2010 to 101 in 2020, marking a substantial 68.3% rise. This study used a map scale of 1:300,000 to classify features, such as mountains, urban areas, deserts, and roads. The results indicate a decrease in mountains and deserts, while urban areas and roads have increased, aligning with the population growth observed over the two decades. [ABSTRACT FROM AUTHOR]

Groundwater plays an important part in protecting the ecological values of the earth's systems. Assessment of groundwater (GW) recharge due to change in land use/land cover (LULC) and climate variables is essential for integrated water management and adaptation. This study is an attempt to check the combined influence of change in LULC and climate variables on GW recharge in Kallada river basin, Kerala, India. The LULC map is predicted for the year 2030 based on LULC maps during the years 1985, 1995, 2005 and 2015 using Markov chain and multilayer perceptron model. The major LULC category in the study area is plantation with an area of 797.8 km2 (49.6% in 2015) and dense forest with an area of 366.5 km2 (22.8% in 2015). Considering the period 1985–2005, a decrease of 7.43% dense forest and an increase of 2.55% built area were observed. The predicted LULC shows that there is a reduction in plantation (3.77%) and dense forest (1.28%) and increase in built-up land (4.12%) by 2030 as compared to the year 2015. The CMIP5 General Circulation Model is used for future projections of climate variables, namely daily precipitation, maximum temperature and minimum temperature under RCP 4.5 and RCP 8.5 scenarios. Multilayer perceptron neural network model was used for statistical downscaling of GCM projections. The SWAT model was used to estimate the groundwater recharge for the year 2030 using the predicted LULC map and hydro-meteorological variables. The results illustrate a significant drop in GW recharge in Kallada river basin by the year 2030. Average GW recharge in the Vettikavala and kottarakkara, which is in the western region of the basin, is reduced to 4.6% and 9.3% under RCP 4.5 and RCP 8.5 scenarios, compared to the average GW recharge in 2005, which was 24.8% of rainfall. The decline in the groundwater recharge in the western regions may be due the increase in built-up area and decrease in plantation. The proposed model results provide a reliable insight for water resources planners in conducting future research on groundwater resources. [ABSTRACT FROM AUTHOR]

The current paper aims to assess the effects of landscape change in a mountain river basin in the north of Iran through quantifying, mapping, and assessing carbon storage. The analyses were performed based on previous alterations in land use and land cover (LULC) (1988–2018) and on expected changes determined by three LULC alteration setups for 2048. The Landsat imagery from 2018, 2008, 1998, and 1988 was used for evaluating and predicting the spatiotemporal distributions of LULC changes. The future LULC image prediction has been generated using Land Change Modeler (LCM) module of TerrSet software for the years 2028, 2038, and 2048. Validation was carried out by overlaying the actual and projected to 2018 map. We integrated the Markov Chain (MC) and InVEST Carbon Storage and Sequestration (InVEST-CSS) models for simulating the ecosystem carbon storage and the long-term monetary valuation. In this process, we considered social costs/economic value because of the area's loss and gain of stored carbon. The results show that forests and rangelands with good and poor conditions decreased by 631.2, 10,374, and 10,254 ha, respectively, from 1988 to 2018. Overall, modeling and mapping LULC changes showed a descending trend in forests (0.66%), agriculture (0.1%), and rangelands (4.1%) in 2048. In addition, carbon storage has already been lost by 9.9 million tons (76.98 ha−1) from 1988 to 8.8 million tons (68.86 ha−1) in 2018 and is expected to have an 8.4 million tons (65.25 ha−1) loss by 2048. Monitoring the economic value of carbon storage from 1988 to 2018 shows a loss of $US 15684338 (121.8 ha−1) and estimates a loss of $US6972622 (54.18 ha−1) by 2048. Therefore, spatiotemporal design of InVEST model by estimating the carbon value over time focuses on continuous monitoring actions for both the carbon pools dynamic and LULC pattern. This consideration causes reduction the uncertainty of estimated models and also increases the continuous cost of those changes. This will help government and decision makers for long-term and accurate carbon sequestration strategies for ecosystem. [ABSTRACT FROM AUTHOR]

Many tropical landscapes have experienced the loss of traditional cultivation practices as they have transitioned to other land use systems.The Tanintharyi Region of southern Myanmar is a landscape experiencing a rapid land use regime shift from traditional subsistence farming to permanent cash crop agriculture. Despite previous research in this region on the expansion of large‐scale agribusiness, such as oil palm and rubber plantations, little is known about how the small‐scale shifting cultivation system practiced by the local Karen ethnic people in Tanintharyi has changed over recent decades or the underlying reasons for this transition.Our study explores this transition process and its drivers from a social and land system change perspective in four villages of Bokpyin Township in Tanintharyi. We investigated the drivers of land cover and land use change by collecting information through focus groups and individual interviews with community members who previously practised shifting cultivation. We also quantified the overall change in land cover and land use through remote sensing analysis of Landsat 8 (2020) and declassified KH‐9 satellite imagery (1976).Prior to a period of civil war in the 1980s, cultivators used a traditional 'slash‐and‐burn' agricultural system for subsistence use. The present land use system is dominated by permanent betel nut cultivation, which represents the primary income source for farming households. The reported drivers of the transition to cash crop farming are greater income potential and livelihood security, risk of losing fallow land due to the needs for new cultivation areas for in‐migration and new centralized land use policies and administration after the national ceasefire agreement in 2012. The documented change in land use systems has been paired with a concurrent net loss of forest that, given the conflicted governance within the landscape, seems likely to continue without additional land use planning efforts.Our study contributes to an increased understanding of changes in shifting cultivation systems that are being documented globally. Furthermore, our example of combining qualitative interviews with analysis of historical satellite imagery to detect and explain land use regime shifts can serve as a model for future work in different landscapes. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Anthropogenic activities have drastically transformed natural landscapes, profoundly impacting land use and land cover (LULC) and, consequently, the provision and functionality of ecosystem service values (ESVs). Evaluating the changes in LULC and their influence on ESVs is imperative to protect ecologically fragile ecosystems from degradation. This study focuses on a highly sensitive Upper Ganga riverine wetland in India, covering Hapur, Amroha, Bulandshahr, and Sambhal districts, which is well-known for its significant endemic flora and fauna. The study analyzes the subtle variability in ecosystem services offered by the various LULC biomes, including riverine wetland, built-up, cropland, forest, sandbar, and unused land. LULC classification is carried out using Landsat satellite imagery 5 and 8 for the years 2000, 2010, and 2020, using the random forest method. The spatiotemporal changing pattern of ESVs is assessed utilizing the value transfer method with two distinct value coefficients: global value coefficients (C14) for a worldwide perspective and modified local value coefficients X08 for a more specific local context. The results show a significant increase in built-up and unused land, with a corresponding decrease in wetlands and forests from 2000 to 2020. The combined ESVs for all the districts are worth US $5072 million (C14) and US $2139 million (X08) in the year 2000, which declined to US $4510 million (C14) and US $1770 million (X08) in the year 2020. The sensitivity analysis reveals that the coefficient of sensitivity (CS) is below one for all biomes, suggesting the robustness of the employed value coefficients in estimating ESVs. Moreover, the analysis identifies cropland, followed by forests and wetlands, as the LULC biomes most responsive to changes. This research provides crucial insights to stakeholders and policymakers for developing sustainable land management practices aimed at enhancing the ecological worth of the Upper Ganga Riverine Wetland. [ABSTRACT FROM AUTHOR]

In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especially pivotal for intricate image classification tasks. This study addresses the scarcity of such benchmark datasets across diverse settings, with a particular focus on the distinctive landscape of India. The study entails the creation of patch-based datasets, consisting of 4000 labelled images spanning four distinct LULC classes derived from Sentinel-2 satellite imagery. For the subsequent classification task, three traditional machine learning (ML) models and three convolutional neural networks (CNNs) were employed. Despite facing several challenges throughout the process of dataset generation and subsequent classification, the CNN models consistently attained an overall accuracy of 90% or more. Notably, one of the ML models stood out with 96% accuracy, surpassing CNNs in this specific context. The study also conducts a comparative analysis of ML models on existing benchmark datasets, revealing higher prediction accuracy when dealing with fewer LULC classes. Thus, the selection of an appropriate model hinges on the given task, available resources, and the necessary trade-offs between performance and efficiency, particularly crucial in resource-constrained settings. The standardized benchmark dataset contributes valuable insights into the relative performance of deep CNN and ML models in LULC classification, providing a comprehensive understanding of their strengths and weaknesses. [ABSTRACT FROM AUTHOR]

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF)), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC(2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape. [ABSTRACT FROM AUTHOR]

Urban tree canopy (UTC) provides urban residents with numerous benefits, including positive mental and physical health, the mitigation and prevention of urban heat islands, and a sense of place. Numerous studies have shown that as the wealth of a community decreases, so does the amount of UTC found in the community; thus, wealthier communities are more likely to enjoy the benefits that urban forests provide. Emerging technologies in remote sensing and GIS are allowing for new opportunities to study and understand the relationships between urban neighborhoods and UTC. In this study, land cover data for Chattanooga, Tennessee were derived from high-resolution (50 cm) multispectral imagery to assess the previously unknown extent and distribution of UTC and to measure the extent of UTC by neighborhood and census block group level. Using exploratory regression analysis, variables representing income, population density, race, educational attainment, and urban heat islands were analyzed to investigate the influence of UTC on neighborhood characteristics. This study found that UTC represented half of the total land cover composition, the tree equity was not as profound as shown in other cities, and the lack of UTC likely influences the prevalence of urban heat islands. This study also shows the importance and utility of using high-resolution imagery and land cover to assess and understand the impact and distribution of UTC in urban environments. [ABSTRACT FROM AUTHOR]

Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment. LULC mappings has been recently revolutionized by the use of high-resolution imagery from unmanned aerial vehicles (UAVs). The present study proposes an innovative approach for obtaining LULC maps using consumer-grade UAV imagery combined with two machine learning classification techniques, namely RF and SVM. The methodology presented herein is tested at a Mediterranean agricultural site located in Greece. The emphasis has been placed on the use of a commercially available, low-cost RGB camera which is a typical consumer's option available today almost worldwide. The results evidenced the capability of the SVM when combined with low-cost UAV data in obtaining LULC maps at very high spatial resolution. Such information can be of practical value to both farmers and decision-makers in reaching the most appropriate decisions in this regard. [ABSTRACT FROM AUTHOR]

The interplay between climate and land use/cover significantly shapes streamflow characteristics within watersheds, with dominance varying based on geography and watershed attributes. This study quantifies the relative and combined impacts of land use/cover change (LULCC) and climate change (CC) on streamflow variability in the Baro River Basin (BRB) using the Soil and Water Assessment Tool Plus (SWAT+). The model was calibrated and validated with observed streamflow data from 1985 to 2014 and projected the future streamflow from 2041 to 2070 under two Shared Socio-Economic Pathway (i.e., SSP2-4.5 and SSP5-8.5) scenarios, based on the ensemble of four Coupled Model Intercomparison Project (CMIP6) models. The LULCC was analyzed through Google Earth Engine (GEE) and predicted for the future using the Land Change Modeler (LCM), revealing reductions in forest and wetlands, and increases in agriculture, grassland, and shrubland. Simulations show that the decrease in streamflow is attributed to LULCC, whereas an increase in flow is attributed to the impact of CC. The combined impact of LULCC and CC results in a net increase in streamflow by 9.6% and 19.9% under SSP2-4.5 and SSP5-8.5 scenarios, respectively, compared to the baseline period. Our findings indicate that climate change outweighs the impact of land use/cover (LULC) in the basin, emphasizing the importance of incorporating comprehensive water resources management and adaptation approaches to address the changing hydrological conditions. [ABSTRACT FROM AUTHOR]

Land use land cover (LULC) changes resulting from copper exploration in Kitwe District, Copperbelt Province has adversely impacted the environment. To understand LULC change dynamics associated with mining activities, this study mapped LULC changes using the Google Earth Engine (GEE) from 1990 to 2020. In addition, the Zambian legal framework for mine closure was assessed in terms of adequacy and comprehensiveness. A remote sensing analysis using Landsat TM (1990, 2000, and 2010) and OLI (2020) images was performed and the GEE Random Forest classifier algorithm was employed to detect LULC changes. Then, transition matrices and overall changes were calculated for each LULC class. The LULC classification had an overall accuracy and kappa coefficient of 82.47% and 0.78, respectively. In total, 45.2% of the district area (360.92 km2) experienced LULC changes from 1990 to 2020. The overall change indicates that the areas of built-up area, bare land, and grassland/pasture/agricultural land gained 35.84, 14.67, and 43.53 km2, respectively, while forest lost 95.30 km2, with the major driver being the privatization of mining companies. Several concerns regarding the mine closure process practiced in Zambia have principally been raised to the government. Although the legislation generally conformed to international best practices, a gap involving various pieces of legislation, overlapping requirements, and different interpretations of the laws by different governmental departments makes the system complex and unmanageable. An area of concern is the government's capability and competence to implement legislation. Ineffective law enforcement, that is, the inadequacy of the legislation, is to blame for LULC changes in mining areas, resulting in mining corporations not paying attention to the changes made, particularly regarding mine closures. This study provides decision-makers and land use planners with baseline knowledge on LULC changes that can be valuable for future mining legislation and how these legislations can be effectively executed to ensure sustainable mine closure. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate how different methods of determining design rainfall levels and land usage affect flood hydrographs in an urban catchment; specifically, the catchment in southern Poland. The data included daily precipitation records from 1981 to 2020 and land cover information from Corine Land Cover and Urban Atlas databases for 2006 and 2018. The analysis involved examining precipitation data, determining design rainfall levels, analyzing land usage databases, exploring the influence of design rainfall levels on hydrograph characteristics, and investigating the database's impact on these characteristics. No discernible trend in precipitation was found. The highest design rainfall values followed the GEV distribution, while the lowest followed the Gumbel distribution. Both land usage databases indicated an increasing human influence from 2006 to 2018. This study conclusively showed that the method used for estimating design rainfall and the choice of the land usage database significantly affected hydrograph characteristics. Multivariate analyses are recommended for design rainfall assessments, while the Urban Atlas database is preferred for urban catchment land usage determinations due to its detailed information. [ABSTRACT FROM AUTHOR]