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Within major city confines, floods are a major cause of worry and are mainly due to excessive urbanization encroaching on natural landscapes which would otherwise have served as areas of infiltration. The study combines the growth of urban landscape to estimate the maximum surface runoff and aimed to quantify this runoff generated and peak discharge for better urban management practices. These past five decades, the area experienced erratic expansion along with various changes in its land classification, resulting in several flood events in various parts. Runoff estimation was made using Curve Number method for the watershed. Annual rainfall deviation from mean saw an increase by 16% on an average in the past decade, with more than a 100% deviation from mean in 2017. Topographical maps generated to study the changes contributing to flood situations show a 90% increase in concretization over the past two decades and more than 50% reduction in the amount of natural vegetative cover in that same time period. Statistical analysis shows a good fit of the selected model for runoff estimation and well correlated variables. The model satisfactorily predicted runoff from the simulated data analysis with evaluation criteria NSE = 0.9945, MAE = 5.4121, r = 0.9975, R2 = 0.9949, RMSE = 6.8117 and PBias = 1.1436. The results revealed a steady increase in yearly runoff, due to topographical changes and increase in precipitation intensity over time. The study suggests intervention efforts be targeted spatially to ensure suitable flood-control structures and systems. [ABSTRACT FROM AUTHOR]

Deep convolutional networks often encounter information bottlenecks when extracting land object features, resulting in critical geometric information loss, which impedes semantic segmentation capabilities in complex geospatial backgrounds. We developed LULC-SegNet, a semantic segmentation network for land use and land cover (LULC), which integrates features from the denoising diffusion probabilistic model (DDPM). This network enhances the clarity of the edge segmentation, detail resolution, and the visualization and accuracy of the contours by delving into the spatial details of the remote sensing images. The LULC-SegNet incorporates DDPM decoder features into the LULC segmentation task, utilizing machine learning clustering algorithms and spatial attention to extract continuous DDPM semantic features. The network addresses the potential loss of spatial details during feature extraction in convolutional neural network (CNN), and the integration of the DDPM features with the CNN feature extraction network improves the accuracy of the segmentation boundaries of the geographical features. Ablation and comparison experiments conducted on the Circum-Tarim Basin Region LULC Dataset demonstrate that the LULC-SegNet improved the LULC semantic segmentation. The LULC-SegNet excels in multiple key performance indicators compared to existing advanced semantic segmentation methods. Specifically, the network achieved remarkable scores of 80.25% in the mean intersection over union (MIOU) and 93.92% in the F1 score, surpassing current technologies. The LULC-SegNet demonstrated an IOU score of 73.67%, particularly in segmenting the small-sample river class. Our method adapts to the complex geophysical characteristics of remote sensing datasets, enhancing the performance of automatic semantic segmentation tasks for land use and land cover changes and making critical advancements. [ABSTRACT FROM AUTHOR]

It is widely accepted that land use and land cover (LULC) is an important conditioning factor for landslide occurrence, especially when considering the role of tree roots in stabilizing slopes and consolidating the soil. However, it is still difficult to assess the impacts of a specific LULC type on landslide distribution. The objective of the present study is to reveal the relationship between bamboo and landslide distribution at the regional scale. We aim to answer the following question: do the areas covered by bamboo have a higher susceptibility to landslides? Wenzhou City in SE China was taken as the study area, and a landslide inventory containing 1725 shallow landslides was constructed. The generalized additive model (GAM) was employed to assess the significance of LULC and nine additional factors, all of which were generated using the GIS platform. The frequency ratio (FR) method was used to analyze and compare the landslide density in each LULC category. Machine learning models were applied to perform landslide susceptibility mapping of the region. The results show that in the Wenzhou region, LULC is the second most important factor for landslide occurrences after the slope factor, whereas bamboo has a relatively higher FR value than most other LULC categories. The accuracies of the landslide susceptibility maps obtained from the random forest and XGBoost models were 79.6% and 85.3%, respectively. Moreover, 23.8% and 25.5% of the bamboos were distributed in very-high- and high-susceptibility-level areas. The incidents and density of landslides in bamboo areas were significantly higher than those with debris flow and rock collapses, indicating a promotional effect of bamboo on slope failure in the study area. This work will improve our understanding regarding the role of geological and ecological conditions that affect slope stability, which may provide useful guidance for land use planning and landslide risk assessment and mitigation at the regional scale. [ABSTRACT FROM AUTHOR]

The amalgamation of Himalayan and Indo-Burmese biodiversity has made the state of Manipur, India, a unique ecosystem. In addition, the region is a strategic place for the country to establish an economic corridor to Southeast Asia. In recent times, the region has witnessed tremendous infrastructural/road development. Subsequently, forest fragmentation related to urbanization and road expansion has emerged in the Himalayan foothills. The development of roads brought rapid changes in land use and land cover (LULC) and thus subsequent environmental degradation. The current study attempts to understand how the development of road networks has impacted the natural cover in Manipur, India. A spatio-temporal analysis was performed to assess the relationship between the development of road networks and LULC changes using the Landsat satellite images over a decade (2012–2022). The results showed significant changes in the area coverage of LULC categories such as agricultural land, built-up areas, forest, and water bodies with the increase in road density. To have a holistic view, the study area was segregated into three functional zones based on their urban land use pattern, i.e., urban center, peri-urban, and urban peripheral fringes. Urban sprawl in the urban center has led to the rapid conversion of forested lands into built-up areas and agricultural lands in the peri-urban and urban peripheral fringes, respectively. The decline of forest areas to urbanization in peri-urban and urban peripheral fringes calls for conservation and restoration initiatives. The study also emphasizes how different stakeholders can be involved and empowered to strengthen public–private partnerships for conservation and restoration in such sensitive ecosystems. Urban planners and developers should be critical in making informed decisions through understanding ecological concerns in tandem with infrastructural development. [ABSTRACT FROM AUTHOR]

Abstract Within major city confines, floods are a major cause of worry and are mainly due to excessive urbanization encroaching on natural landscapes which would otherwise have served as areas of infiltration. The study combines the growth of urban landscape to estimate the maximum surface runoff and aimed to quantify this runoff generated and peak discharge for better urban management practices. These past five decades, the area experienced erratic expansion along with various changes in its land classification, resulting in several flood events in various parts. Runoff estimation was made using Curve Number method for the watershed. Annual rainfall deviation from mean saw an increase by 16% on an average in the past decade, with more than a 100% deviation from mean in 2017. Topographical maps generated to study the changes contributing to flood situations show a 90% increase in concretization over the past two decades and more than 50% reduction in the amount of natural vegetative cover in that same time period. Statistical analysis shows a good fit of the selected model for runoff estimation and well correlated variables. The model satisfactorily predicted runoff from the simulated data analysis with evaluation criteria NSE = 0.9945, MAE = 5.4121, r = 0.9975, R2 = 0.9949, RMSE = 6.8117 and PBias = 1.1436. The results revealed a steady increase in yearly runoff, due to topographical changes and increase in precipitation intensity over time. The study suggests intervention efforts be targeted spatially to ensure suitable flood-control structures and systems.

This study investigates the dynamic trajectories of land use and land cover (LULC) changes in Lombok Island, West Nusa Tenggara, Indonesia, from 2013 to 2022. Utilizing Landsat satellite imagery and a combination of land cover classes from the Indonesian Ministry of Environment and Forestry (MoEF) with the machine learning-based Random Forest algorithm, we aimed to improve classification accuracy and model land cover transitions over time. Intensity analysis was used to measure the impact of population-related land use changes, while trajectory analysis quantified the directional shifts in land cover was employed to quantify and characterize these changes. The analysis highlights substantial transitions from primary and secondary forests to agricultural lands and urban areas, driven by urbanization, population growth, and infrastructure development. Specifically, the period saw a significant forest loss of 28,095 hectares, accounting for 24% of the total forest area, alongside a modest forest gain of 2,453 hectares, indicating ongoing environmental pressures. Despite conservation efforts, rapid economic growth continues to threaten Lombok's forest ecosystems. These findings underscore the urgent need for sustainable land management policies to balance development and ecological preservation while mitigating future forest losses.

Growing human demands are placing significant pressure on groundwater resources, causing declines in many regions. Identifying areas where groundwater levels are declining due to human activities is essential for effective resource management. This study investigates the influence of land use and land cover, crop types, and precipitation patterns on groundwater level trends across the Mississippi River Watershed (MRW), USA. Groundwater storage changes from 2003 to 2015 were estimated using data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission. A spatiotemporal analysis was conducted at four scales: the entire MRW, groundwater regimes based on groundwater level change rates, 31 states within the MRW, and six USGS hydrologic unit code (HUC)-2 watersheds. The results indicate that the Lower Mississippi region experienced the fastest groundwater decline, with a Sen's slope of −0.07 cm/year for the mean equivalent water thickness, which was attributed to intensive groundwater-based soybean farming. By comparing groundwater levels with changes in land use, crop types, and precipitation, trends driven by human activities were identified. This work underscores the ongoing relevance of GRACE data and the GRACE Follow-On mission, launched in 2018, which continues to provide vital data for monitoring groundwater storage. These insights are critical for managing groundwater resources and mitigating human impacts on the environment. [ABSTRACT FROM AUTHOR]

Wildfire represents a significant threat to ecosystems and communities in the Department of Amazonas, Peru, causing losses in biodiversity and land degradation and affecting socioeconomic security. The objective of this study was to analyze the spatial and temporal distribution of burned areas (BAs) from 1986 to 2023 to identify recurrence patterns and their impact on different types of land use and land cover (LULC). Landsat 5, 7, and 8 satellite images, processed by Google Earth Engine (GEE) using a decision tree approach, were used to map and quantify the affected areas. The results showed that the BAs were mainly concentrated in the provinces of Utcubamba, Luya, and Rodríguez de Mendoza, with a total of 1208.85 km2 burned in 38 years. The most affected land covers were pasture/grassland (38.25%), natural cover (forest, dry forest, and shrubland) (29.55%) and agricultural areas (14.74%). Fires were most frequent between June and November, with the highest peaks in September and August. This study provides crucial evidence for the implementation of sustainable management strategies, fire prevention, and restoration of degraded areas, contributing to the protection and resilience of Amazonian ecosystems against future wildfire threats. [ABSTRACT FROM AUTHOR]

The Teesta basin is shared by Bangladesh and India, holds significant importance in the bilateral relationship, and sustains the livelihoods of over 30 million people in Bangladesh. Employing a cellular-automata model (CA), we accurately estimate LULC for the 2020s and projected for the 2050s and 2080s. A semi-distributed hydrological model, Soil Water Assessment Tool (SWAT), is used to generate flow for the base period (1995–2014), the near future (2035–2064), and the far future (2071–2100). SWAT model is forced by eight general circulation models (GCMs) under two socioeconomic pathways (SSP245 and SSP585). The CA-Markov prediction indicates LULC changes, especially increased agriculture and settlements by 76 and 42%, and decreased forest and water by 13 and 36%, respectively, which are expected by 2050s and will influence discharge patterns. This results in additional discharge increases of 4% (–8 to 5%) for SSP245 and 5% (–8 to 10%) for SSP585 scenarios during wet seasons. In the far future, monsoon flow will increase by 13% (0.4 to 23%) for SSP245 and 52% (–29 to 151%) under SSP245 and SSP585. A marginal change in winter flow was shown by –6% (–16 to 4%) reduction under SSP245 and –13% (–64 to 63%) under SSP585 reduction in the 2080s. [ABSTRACT FROM AUTHOR]

The cultivated land under gram (Cicer arietinum) crop fields and bare land of Khushab are transforming into Eucalyptus (Eucalyptus camaldulensis) forest land cover because of the rise in the groundwater table due to the construction of Greater Thal Canal. The research objective was to analyze land use and land cover (LULC) changes that have altered the vegetation paradigm of Khushab from 2000 to 2020, with future predictions for 2030. The study utilizes multi-temporal Landsat imageries, including Landsat 5, Landsat 7, and Landsat 8, spanning 20 years, obtained from the Google Earth Engine database. Five LULC classes: water body, urban, vegetation, bare land, and forest were used and the MOLUSE plugin in QGIS 10.18.24 version, and ANN–CA model for simulation were executed for the future prediction. There was a significant transformation of cropland into forest from 2000 to 2020. The overall classification accuracies of the years 2000, 2010, and 2020 were 76%, 86%, and 77%, respectively. A Kappa value of 0.93 indicates a high level of accuracy between the observed and predicted LULC maps for 2030. The future prediction indicates a significant increase in the Eucalyptus forest area expected around 14.02 km2 by 2030 claiming carbon credits. Human activities are driving the conversion of croplands, barren lands, and various other land categories into forest cover in Kushab. This LULC transformation into forests is driving a negative carbon footprint contributing carbon credits to a global green economy. This study recommends the Pakistani government to adopt policies that further enhance the LULC into low energy-intensive forest production, stimulate green agriculture. [ABSTRACT FROM AUTHOR]

The expanding cropland profoundly affects stream water quality. However, the relationships between landscape patterns and stream water quality in different cropland composition classes remain unclear. We observed total nitrogen (TN), total phosphorus (TP) concentrations, and landscape patterns changed in 78 sub-watersheds of the Taihu Lake Basin's Jiangsu segment from 2005 to 2020. The results showed that cropland area was positively correlated with TN and TP concentrations. The 21.10% reduction in cropland area, coupled with a 41.00% increase in building land, has led to an escalation in cropland fragmentation. Meanwhile, TN and TP concentrations declined by 26.67% and 28.57%, respectively. Partial least squares suggested that forest interspersion and juxtaposition metrics and forest area percentage were dominant factors influencing water quality in high- and medium-density cropland zones, respectively. The Cellular Automata–Markov Model shows reasonable distribution of forests. Scenarios with enhanced forest interspersion and juxtaposition metrics (75.28 to 91.12) showed reductions in TP (26.92% to 34.61%) and TN (18.45% to 25.89%) concentrations by 2025 compared to a natural economic development scenario. Landscape configuration optimization could assist managers in improving water quality. [ABSTRACT FROM AUTHOR]

Land use and land cover changes significantly affect the function and value of ecosystem services (ES). Exploring the spatial correspondence between changes in land cover and ES is conducive to optimizing the land use structure and increasing regional coordinated development. Thus, this study aimed to examine changes in land use and land cover (30 × 30 m) in Laos between 2000 and 2020 and their effects on ecosystem services value (ESV) using the Global Surface Cover Database land use data for 2000 to 2020, ArcGIS technology, and the table of Costanza's value coefficients. The study results indicated that forest (79.5%), cultivated land (10.6%), and grassland (8.3%) were the dominant land use types in Laos over the past two decades. The forest area decreased significantly, while there were increases in other land types, and the forest was transformed into cultivated land and grassland. ES in Laos was valued at about USD 140–150 billion, with forest contributing the most, followed by cultivated land and grassland. ESV over the last two decades in Laos has increased by USD 3.94 million. Large values were assigned to regulating services (40%) and supporting services (14%). The ESV of food production, soil formation, and water supply increased, and the ESV of climate regulation, genetic resources, and erosion control decreased. In addition, the elasticity value of artificial surfaces was more prominent, with a more evident impact on ESV. For future development, Laos should rationally plan land resources, develop sustainable industries, maintain the dynamic balance of second-category ESV, and achieve sustainable economic and ecological development. This study provides a scientific basis for revealing changes in ESV in Laos over the past two decades, maintaining the stability and sustainable development of the environment in Laos, and realizing the sustainable use and efficient management of the local environmental resources. [ABSTRACT FROM AUTHOR]

Elevated temperatures in urban centers have become a common problem in cities around the world. However, the climate problems in suburban areas are equally severe; there is an urgent need to find zero-carbon ways to mitigate this problem. Recent studies have revealed the thermal performance of vegetation, buildings, and water surfaces. They functioned differently regarding the climate at different periods of the day. Accordingly, this study synthesizes remote sensing technology and meteorology station observation data to deeply explore the differences in the role of each climate-influencing factor in the suburban areas of Chengdu. The land surface temperature (LST) and air temperature (Ta) were used as thermal environmental indicators, while the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), normalized difference built-up index (NDBI), and altitude were used as environmental factors. The results showed that the relevant influences of the environmental factors on the climate in the sample areas were significantly affected by the time of the day. The NDVI (R2 = 0.5884), NDBI (R2 = 0.3012), and altitude (R2 = 0.5638) all showed strong correlations with Ta during the night (20:00–7:00), which gradually weakened after sunrise, yet the NDWI showed a poorer cooling effect during the night, which gradually strengthened after sunrise, reaching a maximum at 15:00 (R2 = 0.5012). One reason for this phenomenon was the daily weather changes. These findings facilitate the advancement of the understanding of the climate in suburban areas and provide clear directions for further thermal services targeted towards people in different urban areas. [ABSTRACT FROM AUTHOR]

The Guanabara Bay hydrographic region (GBHR) has served as a central hub for human settlement and resource utilization throughout Brazil's history. However, the region's high population density and intense industrial activity have come at a cost, leading to a significant decline in water quality. This work aimed to identify homogeneous regions in GBHR according to water quality parameters in dry and rainy periods. The following water quality monitoring variables were monitored at 49 gauge stations: total phosphorus (TP), nitrate (NO3−), dissolved oxygen (DO), hydrogenionic potential (pH), turbidity (Turb), thermotolerant coliforms (TCol), total dissolved solids (TDS), biochemical oxygen demand (BOD), water temperature (Tw), and air temperature (Ta). The statistical analysis consisted of determining principal components, cluster analysis, seasonal differences, and Spearman's correlation. The water quality parameter correlations were not expressively influenced by seasonality, but there are differences in the concentrations of these parameters in the dry and rainy periods. In the dry period, urban pressure on water quality is mainly due to fecal coliforms. The resulting clusters delimited areas under urban, agricultural, and forestry influence. Clusters located in areas with high demographic density showed high concentrations of TCol and TP, while clusters influenced by forestry and agriculture had better water quality. In the rainy season, clusters with urban influence showed problems with TCol and TP, in addition to some characteristics in each group, such as high TDS, NO3−, and BOD. Forested areas showed high DO, and clusters under agricultural influence had higher concentrations of TCol, BOD, and NO3− concerning forested regions. The troubling state of sanitation in GBHR occurs in metropolitan regions due to lack of a formal sanitation system. [ABSTRACT FROM AUTHOR]

The Bangkok Metropolitan Region (BMR), located in the Chao Phraya River basin delta, is particularly vulnerable to floods, with susceptibility heightened by geographical aspects and rapid urbanization. This study aimed to assess spatiotemporal flood exposure and allow proper flood‐risk recognition among all stakeholders through a three‐phase flood exposure assessment. First, land use and land cover (LULC) changes were identified based on a 30‐year Landsat time series. Second, built‐up areas that overlapped with past flood inundation maps were designated as flood exposure areas. Third, a rainfall‐runoff inundation (RRI) model simulated the 2011 Thailand Flood, the largest on record, by analyzing inundation depth implications across three decades. The findings revealed a dramatic increase in the use of built‐up areas and the associated flood exposure. In 1992, built‐up areas accounted for approximately 20% of the total area, sharply increasing to nearly 45% by 2022, according to the LULC classification. The flood exposure increased from 648.83 km2 in 1992 to 1681.26 km2 by 2022, demonstrating a linear trend. Notably, the catastrophic 2011 flood did not inhibit urbanization in flood‐prone areas, highlighting the need for robust policies, such as the segmentation of flood‐risk zones, to mitigate future exposure in the region. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the spatiotemporal concentration of air pollutants in the tropospheric layer of Jordan, in the Middle East, for 2019–2022. The study utilized remotely sensed data from two satellite systems, Sentinel-5P and Landsat-9, to retrieve information about the concentration of nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) and land use types, respectively. The Google Earth Engine (GEE) platform and JavaScript were used to produce monthly short-term average concentration maps and time series for the three pollutants. Pearson correlation analysis was performed to evaluate the performance of Sentinel-5P data against ground-based monitoring stations in estimating NO2, SO2, and CO concentration at a regional scale. Results revealed a moderate correlation, with r-values of 0.42, 0.43, and 0.40, for NO2, SO2, and CO, respectively. The spatiotemporal analysis showed a higher concentration of SO2 and NO2 in the northern and middle regions of the country, coinciding with the spatial distribution of built-up areas and the main urban centers. On a temporal scale, the highest concentration of the three pollutants was observed in the winter months for all governorates of Jordan. For instance, it was found that the highest value of NO2 was in Balqa Governorate in December 2022, 1.57 * 10^4 mol/m2. The highest average monthly SO2 values were observed in Jerash Governorate in December 2019, 7.36 * 10^4 mol/m2. CO concentrations were mainly concentrated in the western parts of the Jordan rift valley.

Abstract A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability.

Coastal land transformation has been identified as a topic of research in many countries around the world. Several studies have been conducted to determine the causes and impacts of land transformation. However, much less is understood about coupling change detection, factors, impacts, and adaptation strategies for coastal land transformation at a global scale. This review aims to present a systematic review of global coastal land transformation and its leading research areas. From 1,741 documents of Scopus and Web of Science, 60 studies have been selected using the PRISMA-2020 guideline. Results revealed that existing literature included four leading focus areas regarding coastal land transformation: change detection, driving factors, impacts, and adaptation measures. These focus areas were further analyzed, and it was found that more than 80% of studies used Landsat imagery to detect land transformation. Population growth and urbanization were among the major driving factors identified. This review further identified that about 37% of studies included impact analysis. These studies identified impacts on ecosystems, land surface temperature, migration, water quality, and occupational effects as significant impacts. However, only four studies included adaptation strategies. This review explored the scope of comprehensive research in coastal land transformation, addressing change detection, factor and impact analysis, and mitigation-adaptation strategies. The research also proposes a conceptual framework for comprehensive coastal land transformation analysis. The framework can provide potential decision-making guidance for future studies in coastal land transformation.

The land use and land cover (LULC) of a watershed play an important role in controlling its hydrological processes. With the help of applying the Soil and Water Assessment Tool (SWAT), this study aims to quantify the impact of changing LULC on hydrological responses and water quality in the Big Creek Lake watershed in Mobile County, South Alabama. A number of data sources were input into the SWAT model as part of its calibration and validation, including land use and land cover (LULC), weather variables, digital elevation models (DEMs), soil properties, and measured streamflows. The total monthly streamflow increased by about 62 m3/s and the average nitrogen and phosphorus are estimated to have increased by about 3,172 kg/Ha and 892 kg/Ha per year respectively over the thirty years because of the increasing agricultural land (11,406 acres), urban development (3,350 acres), and decreasing forested areas (11,482 acres). This research could be helpful for water resource managers and planners by incorporating the results in the monitoring and planning for the future.

A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability. [ABSTRACT FROM AUTHOR]

Monitoring areas susceptible to desertification contributes to the strategic development of regions located in environments of extreme hydric and social vulnerability. Therefore, the objective of this study is to evaluate the process of soil degradation in the Desertification Nucleus of Cabrobó (DNC) over the past three decades using remote sensing techniques. This study used primary climatic data from TerraClimate, geospatial data of land use and land cover (LULC), and vegetation indices (SAVI and LAI) via Google Earth Engine (GEE) from Landsat 5/TM and 8/OLI satellites, and established the aridity index (AI) from 1992 to 2022. The results indicated 10 predominant LULC classes with native vegetation suppression, particularly in agriculture and urbanization. SAVI ranged from −0.84 to 0.90, with high values influenced by La Niña episodes and increased rainfall; conversely, El Niño episodes worsened the rainfall regime in the DNC region. Based on the Standardized Precipitation Index (SPI), it was possible to correlate normal and severe drought events in the DNC with years under the influence of El Niño and La Niña phases. In summary, the AI images indicated that the DNC remained semi-arid and that the transition to an arid region is a cyclical and low-frequency phenomenon, occurring in specific periods and directly influenced by El Niño and La Niña phenomena. The Mann–Kendall analysis showed no increasing trend in AI, with a Tau of −0.01 and a p-value of 0.97. During the analyzed period, there was an increase in Non-Vegetated Areas, which showed a growing trend with a Tau of 0.42 in the Mann–Kendall analysis, representing exposed soil areas. Annual meteorological conditions remained within the climatic pattern of the region, with annual averages of precipitation and actual evapotranspiration (ETa) close to 450 mm and an average temperature of 24 °C, showing changes only during El Niño and La Niña events, and did not show significant increasing or decreasing trends in the Mann–Kendall analysis. [ABSTRACT FROM AUTHOR]

The Tonle Sap Lake (TSL) landscape is a region of vast natural resources and biological diversity in the heart of Southeast Asia. In addition to serving as the foundation for a highly productive fisheries system, this landscape is home to numerous globally threatened species. Despite decades of recognition by several government and international agencies and the fact that nine protected areas have been established within this region, natural land cover such as grasslands have experienced considerable decline since the turn of the century. This project used local expert knowledge to train and validate a random forest supervised classification of Landsat satellite imagery using Google Earth Engine. The time series of thematic maps were then used to quantify the conversion of grasslands to croplands between 2004 and 2023. The classification encompassed a 10 km buffer surrounding the landscape, an area of nearly 3 million hectares. The average overall accuracy for these thematic maps was 82.5% (78.5–87.9%), with grasslands averaging 76.1% user's accuracy. The change detection indicated that over 207,281 ha of grasslands were lost over this period (>59.5% of the 2004 area), with approx. 89.3% of this loss being attributed to cropland expansion. The results of this project will inform conservation efforts focused on local-scale planning and the management of commercial agriculture. [ABSTRACT FROM AUTHOR]

The prediction of land use and land cover (LULC) patterns is essential for land use planning, natural resources management, and climate change mitigation. In this study, the artificial neural network (ANN) technique and QGIS software have been proposed to project 5- and 10-year future LULC for the Mahanadi River basin in Chhattisgarh, India. The results reveal that slope, aspect, and hillshade maps have significant effects on LULC. The projection of the future LULC for the years 2027 and 2032 reveals that the grassland and cropland areas would significantly decline and increase, respectively, in the study region. The results from trend analysis show an increasing trend for forests, permanent wetlands, cropland, urban built-up land, waterbodies, and cropland/natural vegetation, while decreasing trends for the remaining classes. The overall finding from this study suggests that remote sensing enabled with the ANN technique has the potential to project the future LULC with high accuracy. [ABSTRACT FROM AUTHOR]

This paper examines the effects of climatic factors, specifically temperature and precipitation, on land use and land cover (LULC) on the Kerch Peninsula using structural equation modeling (SEM). The Normalized Difference Vegetation Index (NDVI) was used as a mediator in the model to accurately assess the impact of climate change on vegetation and subsequent LULC dynamics. The results indicate that temperature exerts a significant negative influence on LULC in the early periods, inducing stress on vegetation and leading to land degradation. However, this influence diminishes over time, possibly due to ecosystem adaptation and the implementation of resilient land management practices. In contrast, the impact of precipitation on LULC, which is initially minimal, increases significantly, highlighting the need for improved water resource management and adaptation measures to mitigate the negative effects of excessive moisture. The NDVI plays a crucial mediating role, reflecting the health and density of vegetation in response to climatic variables. An analysis of lagged effects shows that both precipitation and temperature exert delayed effects on LULC, underscoring the complexity of water dynamics and ecosystem responses to climatic conditions. These results have important practical implications for land resource management and climate adaptation strategies. Understanding the nuanced interactions between climatic factors and LULC can inform the development of resilient agricultural systems, optimized water management practices, and effective land use planning. Future research should focus on refining models to incorporate nonlinear interactions, improving data accuracy, and expanding the geographic scope to generalize findings. This study highlights the importance of continuous monitoring and adaptive management to develop sustainable land management practices that can withstand the challenges of climate change. [ABSTRACT FROM AUTHOR]

Characterizing the probable site of infection of cutaneous leishmaniasis (CL) in face of anthropic changes in the Amazon makes it possible to understand the distribution of this disease in order to adopt control measures. The objective of this study is to identify landscape typologies resulting from changes in land use and land cover in the Metropolitan Region of Santarém regarding the occurrence of CL cases in 2012 and 2014. Landscape typologies were developed from TerraClass project data using regular 1 km² cells. Landscape presence and dominance metrics were used to generate cells with a single class and cells with more than one class, called mosaics. For cases of leishmaniasis, the metric was the presence of the disease. Association analyses were extracted from a 2x2 contingency table. The primary forest typology (PP04) had the highest number of cells in both years analyzed. However, changes in land use and land cover were evidenced by the growth in the number of cells with mosaics of agriculture (PP11 and PP12), urbanized areas (PP03 and PP10), and pastures (PP13). The presence of at least one case of CL in each year occurred in ten typologies, particularly in compositions with urbanized areas, pastures, and secondary vegetation. Typologies with the agriculture class, although the number of cells increased, did not follow the same growth logic regarding the presence of the disease. This study makes it possible to identify and characterize the places where CL occurs and provides further information for health surveillance agencies. [ABSTRACT FROM AUTHOR]

Chattanooga, Tennessee is one of many cities in the Southeastern United States that is experiencing rapid urban growth. As these metropolitan areas continue to grow larger, more and more of Earth's unique temperate forest, an ecosystem of enormous cultural, ecological, and recreational significance in the Southeastern United States, is destroyed to make way for new urban development. This research takes advantage of the extensive temporal archive of multispectral satellite imagery provided by the Landsat program to conduct a 37-year analysis of urban forest canopy cover across the City of Chattanooga. A time series of seven Landsat 5 scenes and three Landsat 8 scenes were acquired between 1984 and 2021 at an interval of five years or less. Each multispectral image was processed digitally and classified into a four-class thematic raster using a supervised hybrid classification scheme with a support vector machine (SVM) algorithm. The obtained results showed a loss of up to 43% of urban forest canopy and a gain of up to 134% urban land area in the city. Analyzing the multidecade spatiotemporal forest canopy in a rapidly expanding metropolitan center, such as Chattanooga, could help direct sustainable development efforts towards areas urbanizing at an above-average rate. [ABSTRACT FROM AUTHOR]

The fluviomarine plains in a situation of hypersalinity are ecosystems characteristic of tropical zones, condition which is made possible by the high evaporation and scarce rainfall, giving rise to the crystallization of seawater salts on the soil surface. In addition, they face constant occupation even in unfavorable circumstances. This research aims to identify and monitor the potential for economic activities in the fluviomarine plain of the Apodi-Mossoró river through the DPSIR structure (Demand-Pression-State-Impact-Response), which can provide an analysis of the relationships systemic between anthropic and environmental actions. For this, bibliographic surveys, and Control Listing (Checklist) were carried out in the field to identify the main indicators that characterize the problem. The results showed that the demands for food, energy, and real estate space cause various pressures on the natural state, so that the most significant impacts are the loss of biodiversity and changes in the natural composition of the soil, caused mainly by shrimp farming, solar salt flats, onshore oil activity and population growth. To mitigate this damage and ensure sustainability, the main responses were inspection based on environmental standards and the implementation of environmental sanitation. [ABSTRACT FROM AUTHOR]

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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]

In recent years, satellite data have become available for free to the remote sensing community. Land use and land cover (LULC) changes are identified in remote sensing applications using Landsat satellite data. However, there is a lack of studies that utilize these data to assess the performance of satellite data on LULC classification and monitoring changes in complex landscapes. This study aims at evaluating LULC changes for the years 2013, 2018, and 2023 in the Sangha area using Landsat-8 OLI images. The Support Vector Machine (SVM) algorithm was implemented for detecting changes in the Sangha area. The results revealed that wetland forest and water bodies drastically declined, with a net change of −33.78 and-19.22%, respectively, while open forest, urban area, and bare soils with +77.91, +52.81, and +40.52% correspondingly substantially increased between 2013 and 2023. The overall accuracy and Kappa statistics achieved were above 91% and 0.85, respectively. [ABSTRACT FROM AUTHOR]

In the face of rapid urbanization in Saudi Arabia, understanding the impact of landscape changes on land surface temperature (LST) is crucial for sustainable urban planning. This study assesses the influence of landscape morphology on LST and predicts future LST changes. A multi-temporal land use and land cover (LULC) analysis using random forest (RF) quantified urban expansion and its ecological impacts. Accuracy assessment using the kappa coefficient illustrated the precision of the classification techniques. Morphological Spatial Pattern Analysis (MSPA), developed in Python, analyzed the structural evolution of urban areas, vegetation and exposed rocks. Polynomial regression models established the relationship between landscape morphology and LST and predicted future temperature trends. Results for Asir show a significant improvement in accuracy of the LULC models over three decades, with overall accuracy increasing from 89.99% in 1990 to 91.72% in 2020. The bootstrapping trend analysis showed an urban expansion with a positive slope of 9.27 and a decline in water bodies with a negative slope of −0.03. The MSPA analysis reflected a significant urban expansion, with the core area growing from 45.19 km2 in 2001 to 230.33 km2 in 2021. The vegetated areas showed resilience and dynamic connectivity despite slight reduction and fragmentation. The polynomial regression predicted an increase in future average LST by 2030, with urban core areas reaching 58.47 °C, vegetated cores 42.19 °C and exposed rock cores 55.79 °C. These results highlight the link between urban expansion and LST rise and make the case for integrating green infrastructure and cooling strategies into urban development. [ABSTRACT FROM AUTHOR]

The land use and land cover (LULC) of a watershed play an important role in controlling its hydrological processes. With the help of applying the Soil and Water Assessment Tool (SWAT), this study aims to quantify the impact of changing LULC on hydrological responses and water quality in the Big Creek Lake watershed in Mobile County, South Alabama. A number of data sources were input into the SWAT model as part of its calibration and validation, including land use and land cover (LULC), weather variables, digital elevation models (DEMs), soil properties, and measured streamflows. The total monthly streamflow increased by about 62 m3/s and the average nitrogen and phosphorus are estimated to have increased by about 3,172 kg/Ha and 892 kg/Ha per year respectively over the thirty years because of the increasing agricultural land (11,406 acres), urban development (3,350 acres), and decreasing forested areas (11,482 acres). This research could be helpful for water resource managers and planners by incorporating the results in the monitoring and planning for the future. [ABSTRACT FROM AUTHOR]

The increasing demand for gold, notably in the jewellery industry and other sectors such as finance, electronics, and aerospace, has exerted pressure on gold exploration and exploitation worldwide. Recently, Batouri has witnessed several exploration and exploitation efforts, predominantly by small scale mining companies. These activities have impacted the quality of the environment within Batouri gold district. This research assesses the impact of artisanal and small-scale gold mining on the environment in the Batouri gold district, notably in the Kambele and Pater mining sites, where limited scientific studies on the environmental impacts of gold mining activities have been carried out. Eighteen surface water samples were collected from the Kambele and Pater mining sites during the dry season. Four trace elements (Mercury, Lead, Cyanide, and Arsenic) were analyzed to determine the quality of water in the study area using a Buck Scientific Atomic Absorption Spectrometer 205. The concentration of Mercury (102.64—5550.38 μg/L) and Lead (336.7–2072 μg/L) was found to be far greater than the European directives and the WHO pollution guidelines while the concentration of Cyanide (1.45–10.35 μg/L) and Arsenic (0.12–0.42 μg/L) were below both the European directives and WHO pollution guidelines. The order of abundance was as follows: Hg > Pb > CN > As. Spatial interpolation was used to understand the spatial and concentration distributions of the pollutants over the study area. A timeseries analysis was conducted to determine the changes in the environment as a result of mining activities in Batouri over 20 years (2002–2022). The results of the change fallowing, or farming areas, and bare ground areas, while mature and young savannah forests together with water resources showed a decrease as a result of mining activities. Deforestation, abandoned pits, mine collapse, rockfall, air pollution, soil and subsoil degradation, water pollution, and destruction of the natural environment are the main environmental problems observed in the field. These environmental problems can be averted by encouraging reforestation, filling mine pits with waste rock, and gold recovery using gravity-based methods such as jigs and shaking tables, which are more environmentally friendly and enforce environmental protection policies. [ABSTRACT FROM AUTHOR]

This research examines the significance of restoring efficient water management systems in India's semiarid environment, with special emphasis on the role of traditional irrigation structures, such as tanks, in collecting and storing limited water resources. Assessing the benefits of any restoration program, especially when socioeconomic and environmental benefits are involved, is challenging. In the context of tank rehabilitation, a cost-benefit analysis will be conducted regarding economic and ecological returns in the post-desiltation phase. Since the restoration process requires a significant investment, assessing the project's viability during the planning stage is better. The present study proposes a novel method to indirectly analyse the cost-benefit of the tank restoration process by correlating run-off and storage capacity of tanks before the planning phase. The Ambuliyar sub-basin, which covers an area of 930 square kilometres in Tamil Nadu, India, comprising 181 tanks (water bodies) of varying sizes and shapes, was taken for this study. This study employed the Soil Conservation Service Curve Number (SCS-CN) method, incorporating factors such as soil type, land cover, land use practices, and advanced remote sensing and Geographic Information System (GIS) tools to simulate surface run-off. Run-off volume and tank capacity were compared for all seasons at the micro-watershed level. The results demonstrated that the run-off volume in each micro-watershed significantly exceeded the tank capacity across all seasons. Even during the summer, the run-off volumes in the micro-watershed were considerably higher than the tank capacity. The findings suggest tank restoration can effectively store run-off and significantly fulfil agricultural and other essential needs throughout the year, thereby improving the local rural economy. This study also highlights the need for periodic maintenance and rehabilitation of these tank systems to retain their functionality. [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]

Featured Application: Results provide essential information for the initial design and adaptation potential of Nature-Based Solutions. It may serve as guidance to practitioners and data providers. Flooding events, like in Germany in 2021, highlight the need for re-naturalising banks of rivers and streams to naturally mitigate future flooding. To identify potential areas for Nature-Based Solutions (NBS), the NBS Toolkit—a decision-support tool for Europe—was developed within the H2020 OPERANDUM project. The tool builds on suitability mapping, which is progressively adopted for pre-assessing areas for Nature-Based Solutions. The NBS Toolkit operates with European open-source data, which is available at different spatial resolutions. In this study, we performed a GIS-based analysis to examine the impact of different resolution data on the resulting suitability maps. The results suggest that for large-scale measures such as riparian forest buffers, coarser resolutions are sufficient and may save processing time and capacities. However, fine resolution datasets can bring added value to urban suitability mapping and are of greater importance for small-scale, local Nature-Based Solutions. [ABSTRACT FROM AUTHOR]

Land use and land cover (LULC) changes alter the structure and functioning of natural ecosystems, impacting the potential and flow of ecosystem services. Ecological restoration projects aiming to enhance native vegetation have proven effective in mitigating the impacts of LULC changes on ecosystem services. A key element in implementing these projects has been identifying priority areas for restoration, considering that resources allocated to such projects are often limited. This study proposes a novel methodological framework to identify priority areas for restoration and guide LULC planning to increase the provision of water ecosystem services (WESs) in a watershed in southeastern Brazil. To do so, we combined biophysical models and multicriteria analysis to identify priority areas for ecological restoration, propose environmental zoning for the study area, and quantify the effects of LULC changes and of a planned LULC scenario (implemented environmental zoning) on WES indicators. Previous LULC changes, from 1985 to 2019, have resulted in a nearly 20% increase in annual surface runoff, a 50% increase in sediment export, a 22% increase in total nitrogen (TN) export, and a 53% increase in total phosphorus (TP) export. Simultaneously, they reduced the provision of WESs (baseflow −27%, TN retention −10%, and TP retention −16%), except for sediment retention, which increased by 35% during the analyzed period. The planned LULC scenario successfully increased the provision of WESs while reducing surface runoff and nutrient and sediment exports. The methodology employed in this study proved to be effective in guiding LULC planning for improving WES. The obtained results provide a scientific foundation for guiding the implementation of WES conservation policies in the studied watershed. This method is perceived to be applicable to other watersheds. [ABSTRACT FROM AUTHOR]

Abstract The Bangkok Metropolitan Region (BMR), located in the Chao Phraya River basin delta, is particularly vulnerable to floods, with susceptibility heightened by geographical aspects and rapid urbanization. This study aimed to assess spatiotemporal flood exposure and allow proper flood‐risk recognition among all stakeholders through a three‐phase flood exposure assessment. First, land use and land cover (LULC) changes were identified based on a 30‐year Landsat time series. Second, built‐up areas that overlapped with past flood inundation maps were designated as flood exposure areas. Third, a rainfall‐runoff inundation (RRI) model simulated the 2011 Thailand Flood, the largest on record, by analyzing inundation depth implications across three decades. The findings revealed a dramatic increase in the use of built‐up areas and the associated flood exposure. In 1992, built‐up areas accounted for approximately 20% of the total area, sharply increasing to nearly 45% by 2022, according to the LULC classification. The flood exposure increased from 648.83 km2 in 1992 to 1681.26 km2 by 2022, demonstrating a linear trend. Notably, the catastrophic 2011 flood did not inhibit urbanization in flood‐prone areas, highlighting the need for robust policies, such as the segmentation of flood‐risk zones, to mitigate future exposure in the region.

Migratory birds exhibit seasonal geographic range (hereafter, range) dynamics during the annual cycle. Few studies have examined how migratory birds select their habitats for range occupancy at the species level and space use at the individual level simultaneously. We hypothesized that environmental variables directly related to fitness components would affect the range occupancy probabilities of migrants, whereas environment variables related to movements and flights would affect the space use intensities of migrants. We built Bayesian integrated species distribution models (ISDMs) to evaluate the effects of climate conditions, wind conditions, and landcover compositions on the seasonal range dynamics of American white pelicans Pelecanus erythrorhynchos (hereafter, pelican) during summer and winter. The ISDMs estimated the summer range occupancy probabilities of pelicans with Breeding Bird Survey data, winter range occupancy probabilities with Christmas Bird Count data, and summer and winter space-use intensity rates with eBird data jointly. We evaluated the predictive performance of ISDMs using independent datasets of pelican GPS locations. Integrated species distribution models outperformed the occupancy-only models in the predictive performance of occupancy probabilities. Climate conditions had opposite effects on the range occupancy probabilities between the breeding and non-breeding grounds, whereas landcovers had relatively consistent effects on range occupancy probabilities between the seasons.

The semantic segmentation of land use and land cover (LULC) is a crucial and widely employed remote sensing task. Conventional convolutional neural networks and vision transformers have been extensively utilized for LULC segmentation. However, high-resolution remote sensing images contain a wealth of spatial and color texture information, which is not fully exploited by traditional deep learning approaches. The information bottleneck of CNNs and transformers results in the loss of a significant amount of texture detail information during the feature extraction process, which further limits the performance of LULC segmentation. We present DDPM-SegFormer, a new framework that merges a denoising diffusion probabilistic model (DDPM) and vision transformer for LULC segmentation. The aim is to address the difficulties arising from extraction in complex geographic landscapes and to alleviate information bottlenecks. The framework utilizes the ability of a DDPM to generate refined semantic features and that of vision transformer to model the global image context. Our framework introduces two main innovations. First, we use a DDPM for the first time in LULC segmentation to generate highly refined multiscale semantic features. This approach alleviates the information bottleneck caused by relying solely on a CNN or transformer architecture. Second, we develop an effective feature-level fusion strategy that utilizes multihead cross-attention between the DDPM and Transformer. This approach achieves the harmonious fusion of fine-scale semantic features, generating continuous and highly refined semantic features that enhance the segmentation accuracy. The results indicate that DDPM-SegFormer achieves an MIOU of 83.72% and an F1-score of 90.97% for the large-scale LoveDA dataset and an MIOU of 90.91% and an F1score of 93.30% for the Tarim Basin LULC dataset in a desert scenario. The research demonstrated that the refined and continuous semantic features produced by DDPM-SegFormer can significantly enhance LULC segmentation performance.

This research examines the significance of restoring efficient water management systems in India’s semiarid environment, with special emphasis on the role of traditional irrigation structures, such as tanks, in collecting and storing limited water resources. Assessing the benefits of any restoration program, especially when socioeconomic and environmental benefits are involved, is challenging. In the context of tank rehabilitation, a cost-benefit analysis will be conducted regarding economic and ecological returns in the post-desiltation phase. Since the restoration process requires a significant investment, assessing the project’s viability during the planning stage is better. The present study proposes a novel method to indirectly analyse the cost-benefit of the tank restoration process by correlating run-off and storage capacity of tanks before the planning phase. The Ambuliyar sub-basin, which covers an area of 930 square kilometres in Tamil Nadu, India, comprising 181 tanks (water bodies) of varying sizes and shapes, was taken for this study. This study employed the Soil Conservation Service Curve Number (SCS-CN) method, incorporating factors such as soil type, land cover, land use practices, and advanced remote sensing and Geographic Information System (GIS) tools to simulate surface run-off. Run-off volume and tank capacity were compared for all seasons at the micro-watershed level. The results demonstrated that the run-off volume in each micro-watershed significantly exceeded the tank capacity across all seasons. Even during the summer, the run-off volumes in the micro-watershed were considerably higher than the tank capacity. The findings suggest tank restoration can effectively store run-off and significantly fulfil agricultural and other essential needs throughout the year, thereby improving the local rural economy. This study also highlights the need for periodic maintenance and rehabilitation of these tank systems to retain their functionality.