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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]

Accurate and quantitative assessment of Land Use and Land Cover (LULC) changes is crucial for understanding the spatial dynamics and environmental impacts within specific regions. In hilly terrains like the Nilgiris district in Tamil Nadu, India, these assessments are particularly challenging due to the complex topography and when classified using sparse ground truth labels. With numerous data mining algorithms being validated for several earth observation applications, demands are also increasing in selecting the best classifier algorithm for LULC mapping. Popularly implemented pixel-based data mining classifiers such as Random Forest (RF), Support Vector Machine (SVM), C5.0 Decision trees (C50), Naive Bayes (NB), Multinomial Logistic Regression (MLR), AdaBoost, Bagged CART, Nearest Shrunken Centroids (NSC), Genetic Algorithm based CART (Evetree), Neural Networks with PCA (NNPCA), k-Nearest Neighbours (k-NN), Multi-Layer Perceptron (MLP), and 1 Dimensional – Convoluted Neural Networks (1DCNN) were studied by integrating different auxiliary variables with sparse ground truth labels (391 Nos.). The accuracy of the predictions was then validated using Overall Accuracy (OA), Kappa, and disagreement measures based on the validation datasets. The most influential auxiliary variables contributing to the classification determined through PFI (Permutation Feature Importance) analysis, resulted with Digital Elevation Model (DEM) being the most influential auxiliary variable, among others. From the validation measures and the visual assessment facilitated for each algorithm, the effective performance in classification was depicted by Support Vector Machine - Linear Kernel (SVM - L) and followed by Random Forest (RF) algorithms with OA of 88%; 85% and Kappa of 0.84; 0.82, respectively. The algorithms also yielded the least disagreement measures for both algorithms. The findings of the research described the effective performance of the SVM and RF algorithms for classifying LULC at 10 m resolution through multisource data integration and under limited sampling and parameterization conditions. The statistical insights derived indicated a 4.3% decrease in the forest area with 7.2% increase in agricultural area in the last 2 years and 6.6% increase in the tea plantation area in the last 5 years. [ABSTRACT FROM AUTHOR]

Urban populations are increasingly exposed to excessive heat. Heat distribution in the urban environment can be affected by several factors, including the spatial arrangement of land use/land cover (LULC) that is specific to a given city. This study applies a climate model with urban canopy parameterisation to downscale future climate projections and simulate the spatio‐temporal pattern of heat in the urban environment to better understand the effect of LULC structure on its distribution. Heat conditions are characterised by climate indices that are well representative in two mid‐sized Central European cities of Brno and Ostrava (Czech Republic). Our results show that the annual number of hot days (HOT), summer days (SUD), tropical nights (TRN) and warm nights (WAN) will increase significantly (p < 0.01) in the 21st century in both cities. The model also simulates a more intensive increase and a higher spatio‐temporal variability in all indices in Brno compared to Ostrava. In Brno, the annual number of HOT and TRN is projected to be more than 500% of the 1981–2010 reference period's value by the end of the 21st century under the RCP 8.5 scenario. To determine the causes of the differences in heat distribution, we applied LULC configuration metrics and correlation analysis using various geographical factors. The higher risk of urban heat in Brno compared to Ostrava can be attributed to a more homogenised and less fragmented LULC structure and to the more substantial role of altitude in the complex terrain of Brno. Other factors, such as the presence of impervious surfaces and vegetation, have a similar effect on the variability of the studied indices in both cities. Urban planners should consider the role of the LULC structure and the changes that can be made in a city when designing adaptation measures to mitigate the effects of urban heat. [ABSTRACT FROM AUTHOR]

The study of the impact of urbanization on river ecosystems is an important part of constructing sustainable cities. How to quantitatively study the impact of urbanization on river ecosystems is the difficulty of urban ecological research. This study quantitatively investigated the effects of LULC on water quality and diatom assemblages in urban streams by correlation analysis, multivariate analysis, and path analysis. The results showed that (1) the percentage of LULC type in buffer 600 m of reference stream sites is significantly different from that of urban stream sites. In reference sites, the average percentage of green was 94.2%, barren 3.3%, and impervious surface 2.5%. In urban upstream sites, the average percentage of impervious surface was 63.1%, green 32.8%, water 3.3%, and barren 0.8%. In urban downstream, the average percentage of impervious surface was 59.0%, green 36.5%, water 2.7%, and barren 1.8%. (2) One-way analysis results showed that water quality variables were significantly differences among the sites. The correlation analysis results indicated that LULC had a significant influence on water quality. Green had a significant negative correlation with high concentrations of NO3-N, NH4-N, and Cond. but positively correlated with MSUBST. RDA results showed that the selected water quality variables, MSUBST, and LULC types have a significant impact on the spatial patterns of the diatom assemblages. (3) Path analysis results showed that both LULC types and water quality variables exerted significant effects on diatom assemblages. This study first clarifies the quantitative relationships among LULC types, water quality, and diatom assemblages in the Beijing area. And green land was positively correlated with water quality and river ecosystems. We believed that increasing green space in urban core areas is an effective measure for improving water quality and restoring river ecosystems in the urban area. [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]

Land use refers to anthropogenic phenomena in the natural environment; humans utilize land resources for their developmental activities. On the other hand, the ecosystems of land use and land cover alter the natural world—the artificial infrastructure leads toward a busted concrete jungle instead of a green footprint. The global green footprint is continually shrinking owing to overutilization of natural resources. The present research examines the land use pattern that changes from 1990 to 2021 and projected projections for 2041 and 2061 in the Ramgarh District. The study also focuses on how artificial modifications alter the concentration level of pollutants in the atmosphere. The Landsat data utilized for 1990, 2000, 2011, and 2021 were incorporated into the LULC map using supervised classification and for analysis of future predictions for 2041 and 2061 using an ANN‐based on MLPNNs (multi‐layer perceptron neural networks) for Ramgarh District. It also focuses on the trend and patterns of atmospheric pollutants from data using NASA‐GIOVANNI MERRA‐2. The current study reveals that in 1990, water bodies, coal mining, vegetation, built‐up, agriculture, and barren land were 3.01%, 2.24%, 54.07%, 3.64%, 36.85%, and 0.18 %. However, in 2021, water bodies decreased to 1.61%, vegetation to 45.47%, barren land to 0.65%, and an increasing tendency was observed in built‐up areas to 6.65%, coal mining to 2.43%, and farmland to 43.19%. A significant trend in atmospheric pollutants, such as CO2, SO2, SO4, NO2, and dust, is observed in the Ramgarh district. The importance of this study is to attain the maximum level of environmental sustainability; it would also encourage the local level planning fitted during the extraction of natural resources. [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]

The present study explores the association between urban growth, land use modifications, and changes in land surface temperatures in Navi Mumbai over three decades (1991-2021). The study offers an in-depth perspective on Navi Mumbai's changing urban terrain by adopting advanced GIS and Remote Sensing applications combined with the Landsat series satellite data. The hybrid image classification process, supported by Google Earth Engine, ArcGIS, and ERDAS, reveals the pattern of urbanization and categorizes land use. Preliminary results indicate discernible shifts in land use classes over the study period, reflecting the profound impact of urbanization on Navi Mumbai's spatial configuration. The spatiotemporal analysis of LST patterns uncovers notable variations in thermal signatures across different urban land cover types. The study's findings shed light on the urbanization dynamics within Navi Mumbai and underscore the importance of incorporating geospatial analyses in urban planning and environmental management strategies. [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]

This study employs Landsat images from 1995, 2009, and 2022, utilizing Google Earth Engine and Random Forest algorithm in R software for land use and land cover change analysis in Dar es Salaam Metropolitan City. Results show a substantial shift, notably in bushland and forest, with a 14.57% and 2.9% decline, respectively. Drivers of change include urban (14.87%) and agricultural (4.47%) growth. Overall, 64.3% of land cover changed, primarily transitioning from bushland to agriculture (25.7%) and forest to agriculture (9.2%). Qualitative insights underscore unregulated urban expansion, informal settlements, migration, human activities, and inadequate planning as significant contributors, aiding sustainable urban governance. [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 objective of this study was to assess the dynamics of carbon and nitrogen in soil, forest floor, and aboveground biomass in 9.5 years-old planted stands of three Khaya spp. (K. grandifoliola, K. ivorensis, and K. senegalensis). The study was conducted at the Reserva Natural Vale (RNV), Brazil. The stands were planted at 5 × 5 m spacing, distributed over rectangular plots of 1250 m2. Soil bulk density at the evaluated depths, as well nitrogen contents, were similar among the species. However, K. ivorensis exhibited higher carbon concentration in the soil. In general, there were no differences in carbon and nitrogen content in soil between the three species; however, the values obtained are comparable to those of the reference area–Native Forest. The carbon stocks in the aboveground biomass for K. grandifoliola, K. ivorensis, and K. senegalensis averaged 37.97, 33.66 and 33.86 Mg ha−1, respectively (p ≤ 0.05). These values collectively represent about 28% of the total carbon stocks across the observed compartments. Notably, the nitrogen content within the aboveground biomass did not differ among these species. Therefore, African mahogany possesses a robust potential to store both carbon and nitrogen. [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.

Urban expansion in Ethiopia, particularly informal construction, has led to complex and dynamic changes in livelihoods and wetlands. This study examined the effects of urban expansion on the Cheleleka Wetland and the livelihood of the peripheral community in Hawassa. A mixed research design was used to quantitatively analyze Landsat imagery (1991, 2001, 2011, 2021) and socioeconomic data. Key informant interviews and focus group discussion data were narrated qualitatively. The built-up area substantially increased from 718.11 ha in 1991 to 4820.71 ha in 2021. However, agricultural and wetland areas decreased from 8807.58 ha and 8177.04 ha in 1991 to 6382.44 ha and 7030.26 ha in 2021, respectively. Water and forest areas fluctuated. Built-up areas had a significant advantage over other land use and cover classes, with agricultural, wetland, and forest areas being converted into built-up areas throughout the study period. As a result, the area of Lake Hawassa expanded. Currently, the Cheleleka Wetland has been negatively impaired by overgrazing, cutting of grass, animal dung dumps, eucalyptus plantations, agricultural expansion, and urban expansion. Urban expansion has exacerbated social and economic divisions, resulting in the concentration of poor-quality neighborhoods in the peripheral community. To protect the wetlands, alternative livelihoods and participatory land-use planning are crucial. [ABSTRACT FROM AUTHOR]

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]

Defining the most suitable intervention to mitigate coastal hazards in a specific area, under an integrated coastal management strategy, is complex for several reasons including the lack of stakeholders’ consensus. In this respect, Cost-Benefit Analyses (CBAs) can help decision-makers to better understand the environmental, social and economic implications of a planned intervention. Nevertheless, CBAs are not yet largely applied in coastal management studies, as could be expected. Hence, this work aims to consolidate the use of a CBA to support coastal management decisions, considering its application to a real case study. The case study consists in evaluating the long-term impacts, benefits and costs of a detached breakwater for the protection of a coastal village located on the Portuguese northwest coast against flooding and erosion. This assessment was made based on the estimation of the costs of the structure and the benefits associated with the protection of the coastal community and natural areas, considering morphodynamics’ forecasts determined through numerical modelling. Out of several configurations defined based on length (L; in meters) and distance to the shoreline (D; in meters), four detached breakwater scenarios were selected. Results demonstrate that scenario L200D200 can be a feasible solution with overall low costs and low benefits; L300D400 can be an alternative solution with higher costs but higher benefits too. The consolidation of the CBA described is a step forward to improving the expedition of future analysis and proof of its potential in what concerns analysis at the local scale. [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]

Developing models for land use and land cover (LULC) and monitoring changes through predictive scenarios is crucial for supporting urban development strategies and improving our understanding of urban dynamics. Analysis of urban growth patterns based on LULC data from remote sensing using Geographic Information System (GIS) and Remote Sensing (RS) provides valuable insights into LULC changes. The CA-Markov model was used to predict LULC changes based on maps for 2012 and 2023, derived from satellite imagery using the maximum likelihood method, with an accuracy of 93% and 94% for each map. Analysis of urban growth patterns in Sabang City from 2013 to 2021 shows that the expansion of the built-up area is mainly driven by the conversion of bareland around the city center, with a 67% expansion pattern, 1% infilling pattern, and 16% outlying pattern. In Scenario 1, the growth of the built-up area in the city center is not significant, while in Scenario 2, the built-up area is projected to increase by 32 hectares to 742.6428 hectares by 2032. The urban growth pattern aligns better with Scenario 2, which emphasizes land conservation for forests and water bodies to preserve the highest carbon reserves from LULC changes. [ABSTRACT FROM AUTHOR]