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

In order to strengthen the overall planning and coordination of urban construction and ecological space in Baishan City, we should formulate a scientific land type planning of Production-Living-Ecological Space (PLES). In this paper, land use dynamic attitude model, land use transfer matrix, land use function center of gravity transfer model, eco-environmental quality index(EEQI) and geographic detector model are used to discuss the spatio-temporal evolution of PLES in Baishan City from 2000 to 2020. Spatio-temporal evolution of EEQI and its influencing factors. The results show that: (1) During the study period, Baishan City showed the characteristics that the production space first increased and then decreased, the ecological space decreased, and the living space continued to increase. Among them, the ecological space is the dominant space of Baishan City, covering an area of more than 80%. From the perspective of the transformation of PLES, from 2000 to 2020, Baishan City is mainly characterized by the transformation of ecological space into production space and living space. In the second type of space, green ecological space, forestry ecological space and other ecological space have decreased, while other types of space have increased in varying degrees. (2) During the study period, the overall EEQI of Baishan City remained in good condition, and the ecological environment quality(EEQ) of the three periods were 0.6571, 0.6412 and 0.6562 respectively. The higher EEQI is distributed in Changbai County and Linjiang City, while the areas with lower EEQI are concentrated in the north-central part of Hunjiang City, the middle part of Jiangyuan District and the northwest of Fusong County. (3) According to the analysis of the influencing factors of EEQ, the influence of the factors of spatial differentiation of EEQI in Baishan City changed significantly from 2000 to 2020, and the average annual rainfall was the core factor affecting the spatial differentiation of EEQ in Baishan City, the second is the urbanization rate and the distance from the county government, and the interaction between the average annual rainfall and the distance from the county government has a strong influence on the spatial differentiation of EEQ in Baishan City. This study reveals the evolution of spatial types and EEQ of PLES in Baishan City, and provides a scientific reference for the effective management and utilization of land resources in Baishan City. [ABSTRACT FROM AUTHOR]

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

This study aims to understand the connectivity characteristics of construction land during the spatial evolution of historic urban areas. The complex network analysis method is employed to examine the evolution of the spatial structure of construction land in the four periods of the Changsha historic urban area. The results indicate that the spatial network morphology of construction land follows a repeated development pattern resembling a cluster–mother–child relationship. Additionally, the vulnerability in the evolution of land use spatial relationships is very low, while the overall stability of the land use network structure decreases. The spatial equilibrium of land use shows a downwards trend, the accessibility of land where existing cultural relics and historic sites are located is generally moderate to low, and some plots require improvement. In the evolution of connectivity in the construction land of historic urban areas, the main development challenges are the uneven growth caused by imbalanced land value influenced by capital and the need to balance the preservation of land use patterns with improved accessibility to cultural relics and historic sites. Therefore, it is highly important to dynamically detect and optimize the network structure of construction land to protect and develop historic urban areas. [ABSTRACT FROM AUTHOR]

As a crucial industrial, agricultural, and energy base in China, the urban agglomerations in the Yellow River Basin (YRB) have faced increasingly significant pressure for carbon emission reduction since the implementation of the "Dual Carbon" strategy. This study focuses on 615 counties within the major urban agglomerations in the YRB, analyzing the spatiotemporal evolution of carbon budget in land use from 2000 to 2020. Methods such as the normalized revealed comparative advantage (NRCA) index and SOM-K-means model are employed to explore the carbon compensation zoning of counties in the urban agglomerations in the YRB from the perspective of main functional zones. The results show that: (1) From 2000 to 2020, there was a significant and continuous widening gap between carbon emissions and carbon absorption in the study area. The total carbon emissions increased significantly from 3.64 × 108 to 13.56 × 108 t, showing a "north high, south low" spatial distribution pattern. Meanwhile, the carbon absorption remained stable at around 6.47 × 107 t, with a spatial distribution pattern roughly characterized by "higher in the west, lower in the east, and highest in the central part". The carbon budget of various types of functional zones generally aligns well with their strategic positioning. (2) There is a significant regional difference in net carbon emissions among urban agglomerations, with the greatest internal variation observed in the Guanzhong Plain Urban Agglomeration (Gw = 0.471) and the most notable differences between the Lanzhou-Xining Urban Agglomeration and others (Gb = 0.554). (3) The study area consists of 273 payment zones, 257 balanced zones, and 85 compensated zones. Combining with the reconstruction of main functional areas, seven types of carbon compensation zones are identified. [ABSTRACT FROM AUTHOR]

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

Land use can affect energy consumption by changing the economic and social structure of cities. Thus, the optimization of land use patterns is key to promoting energy sustainability. In this study, we explored the spatiotemporal evolution of China's urban energy consumption and its driving factors from the role of land use, with the application of high-precision nighttime light images and land use data acquired from remote sensing satellites. A deep clustering network in deep learning was used for clustering analysis of urban energy consumption. The results indicated that the economic and structural effects of land use were the primary driving factors of the increasing urban energy consumption, whereas the decrease in the energy intensity (caused by technological progress) restrained the growth rate of energy consumption. With the exception of economically developed cities, generally, the contribution of the population size to the temporal increase in energy consumption was relatively small. The spatial difference in urban energy consumption was mainly due to the between-group differences among the diversified cluster groups, which were strongly influenced by land urbanization and population size. These conclusions can help the Chinese government formulate differentiated urban energy policies. [ABSTRACT FROM AUTHOR]

Freshwater ecosystems play a key role in the global carbon cycle by collecting, transporting, and processing a significant portion of global organic carbon. These processes can be disrupted in non-perennial rivers due to their changing hydrological patterns. We investigated how environmental factors influence organic matter dynamics in the Algars, a Mediterranean non-perennial river basin in the North-East Iberian Peninsula. We conducted seasonal sampling in 16 sites across the river network, collecting samples for (i) storage of benthic organic matter, (ii) transport of dissolved organic carbon and particulate organic matter, and (iii) organic matter processing via aerobic respiration in sediments (Raz–Rru method). We observed pronounced spatial and temporal fluctuations in organic matter processes, especially during distinct periods like summer and autumn. Consistent seasonal patterns of organic matter transport showed a remarkable longitudinal increase downstream, similar to observed aerobic respiration in sediments. Notably, high-flow events doubled observed seasonal transport (mean DOC load: 2344 ± 735 kg/day). Irregular spatial storage patterns between dry and wet channel sections were related to land use and flow intermittency. Notably, storage in dry channel sections was generally ten times higher than wet sections. Our study emphasizes the intricate influence of specific environmental variables on organic matter processes, within different organic matter fractions (for example, coarse and dissolved organic matter). Frequency of non-flow events, seasonal hydrological changes, and land use predominantly govern organic matter dynamics in the Algars basin. Understanding organic carbon dynamics in non-perennial systems will help estimate the impact of hydrological alterations associated with global change on river systems. [ABSTRACT FROM AUTHOR]

Tropical semiarid regions are naturally prone to environmental damage. Human activity can worsen this situation. To understand how human actions affect the ecosystem, plan land use effectively, and establish targeted management practices, assessing environmental vulnerability is crucial. This study focuses on a sub-basin receiving water transfers from the São Francisco River in Brazil's semiarid region. Here, we map and evaluate how land use and occupation alter natural vulnerability. We also propose zoning strategies to support water resource management and implement sustainable development policies in the region. To achieve this, we conducted an integrated analysis of physical factors (soil types, geology, climate, vegetation, and landforms) and spatial land-use data using geographic information systems (GIS) and map algebra techniques. Map algebra allowed us to combine these various datasets within the GIS environment, enabling the creation of maps that synthesize both natural and environmental vulnerability across the study area. Following analysis of these vulnerability maps, our findings reveal a high level of vulnerability. The areas with high to very high degrees of natural vulnerability coincide with the places that have high slopes, high altitudes, Lithic Neosols, and thick vegetation. Furthermore, the interaction between environmental factors and human activity exacerbates vulnerability. Based on the environmental vulnerability assessment, we defined four environmental management zones. These zones require distinct protection measures and management approaches. As a method to potentially improve the basin's vulnerability scenario, soil conservation measures are recommended. This approach is highly relevant for managing land in tropical semiarid regions and, with adaptations to specific regional factors, can be applied globally. [ABSTRACT FROM AUTHOR]

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

The characteristics of spatial and temporal distribution of carbon emissions from various land use types is one of the hot spots of researches. The carbon emission of Shanxi section of the Yellow River during 2000–2019 were analyzed based on the land use type data and fossil energy consumption data for the same period. Here, we explored the spatial and temporal characteristics of ecological stress using carbon emission, carbon footprint, carbon deficit, carbon ecological carrying capacity and carbon footprint pressure index models. The results indicated that the overall net carbon emissions had increased from 27959.67Gg CO2 equivalents in 2000–97355.36Gg CO2 equivalents in 2019 in the study area. During the study period, construction land was the main carbon sources, while forest land was the main carbon sinks. The carbon footprint of coal energy was the most dominant. The carbon footprint was greater than the ecological carrying capacity, which caused an ecological deficit during 2000–2019. The net carbon emissions and carbon footprint showed a continuous increasing trend, which became slower. The ecological pressure was spatially characterized as "less pressure in the northern and more pressure in the southern". The findings can provide scientific guidance for the land use sustainable development for coal cities. [ABSTRACT FROM AUTHOR]

One of the paramount challenges in natural resource management revolves around the delicate equilibrium between the demand for and the supply of diverse Ecosystem Services (ESs) within a cultural landscape. Recognizing the centrality of cultural landscapes to human well-being, the sustainability of these landscapes hinges upon the health and stability of ecosystems that can effectively provide the required ESs. Over the long term, the sustainable supply of ESs is constrained by the potential supply of ESs. Understanding the potential supply of ESs is crucial for averting compromises to the ecosystems within a landscape. This article introduces a novel perspective on evaluating the ESs of a landscape by means of efficiency analysis. Instead of presenting the potential supply of ESs in absolute terms, we offer a comparative analysis of ESs' relative supply to associated management costs. In principle, the efficiency of Landscape Units (LUs) is defined as the ratio of the potential supply of multiple ESs to the costs associated with land use and land cover management. The resultant efficiency maps serve as hot and cold spot maps, revealing efficient ecosystem compositions that yield multiple ESs. This composition reflects management efforts, incorporating various management costs. Forests emerge as pivotal ecosystems in landscapes, delivering the most ESs at the lowest costs. These efficiency maps offer valuable insights for regional planners, enabling them to enhance the supply of ES in inefficient LUs by studying the ecosystem structure and associated costs of the most efficient LUs. [ABSTRACT FROM AUTHOR]

Spatial variation in the landscape factors climate, geomorphology, and lithology cause significant differences in water quality issues even when land use pressures are similar. The Physiographic Environment Classification (PEC) classifies landscapes based on their susceptibility to the loss of water quality contaminants. The classification is informed by a conceptual model of the landscape factors that control the hydrochemical maturity of water discharged to streams. In New Zealand, a case study using climatic, topographic, and geological data classified the country into six, 36, and 320 classes at Levels 1 (Climate), 1–2 (Climate + Geomorphology), and 1–3 (Climate + Geomorphology + Lithology), respectively. Variance partitioning analysis applied to New Zealand's national surface water monitoring network (n = 810 stations) assessed the contributions of PEC classes and land use on the spatial variation of water quality contaminants. Compared to land use, PEC explained 0.6× the variation in Nitrate Nitrite Nitrogen (NNN), 1.0× in Total Kjeldahl Nitrogen (TKN), 1.8× in Dissolved Reactive Phosphorus (DRP), 2.3× in Particulate Phosphorus (PP), 2.6× in E. coli, and 4.3× in Turbidity (TURB). Land use explained more variation in riverine NNN, while landscape factors explained more variation in DRP, PP, E. coli, and TURB. Overall, PEC accounted for 2.1× more variation in riverine contaminant concentrations than land use. The differences in contaminant concentrations between PEC classes (p < 0.05), after adjusting for land use, were consistent with the conceptual model of hydrochemical maturation. PEC elucidates underlying causes of contaminant loss susceptibility and can inform targeted land management across multiple scales. [ABSTRACT FROM AUTHOR]

Global impervious surface area (ISA) has more than doubled over the last three decades, but the associated carbon emissions resulting from the depletion of pre-existing land carbon stores remain unknown. Here, we report that the carbon losses from biomass and top soil (0–30 cm) due to global ISA expansion reached 46–75 Tg C per year over 1993–2018, accounting for 3.7–6.0% of the concurrent human land-use change emissions. For the Annex I countries of UNFCCC, our estimated emissions are comparable to the carbon emissions arising from settlement expansion as reported by the national greenhouse gas inventories, providing independent validation of this kind. The contrast between growing emissions in non-Annex I countries and declining ones in Annex I countries over the study period can be explained by an observed emerging pattern of emissions evolution dependent on the economic development stage. Our study has implications for international carbon accounting and climate mitigation as it reveals previously ignored but substantial contributions of ISA expansion to anthropogenic carbon emissions through land-use effects. Terrestrial carbon emissions from global expansion of impervious surface area have been long overlooked in global carbon budget assessments. Qiu et al. report that these emissions account for up to 6.0–12.9% of those from human land use change. [ABSTRACT FROM AUTHOR]

In this article, we investigate situations involving firearm violence in Sweden. The spatiotemporal distribution of records is assessed in different contexts across the country and linked to land use, demographic, and socio-economic characteristics by area and by street segment. The findings emphasize the prevalence of evening shootings, particularly in economically disadvantaged areas where young people congregate in public places often in residential areas, in parks, in playgrounds, and in transit areas. Although two-thirds of shootings took place in larger urban municipalities, our study sheds light on the non-uniform distribution of gun violence along the rural–urban continuum. We conclude by offering suggestions for future research and practical interventions to address this pressing issue that negatively affects people and communities. [ABSTRACT FROM AUTHOR]

This study investigates the Dynamic Coupling between Land Use Economic Efficiency (LUEE) and Urban Ecological Resilience (UER) in the Yellow River Basin (YRB). This exploration is pivotal for elucidating the interaction mechanisms between economic growth and ecological governance. Furthermore, understanding this relationship is essential for fostering high-quality, sustainable urban development in the YRB. Utilizing panel data from 56 cities spanning 2003 to 2020, this study employed the coupling coordination degree (CCD) model, spatial correlation analysis, Kernel density estimation, convergence model, and Geodetector to systematically analyze the spatio-temporal distribution, dynamic trend, and determinants of the CCD between LUEE and UER in the YRB. The findings indicate that: (1) A general upward trend in both LUEE and UER, accompanied by a steady improvement in their CCD. (2) Significant spatial disparities in their CCD, with higher levels in the lower reaches. (3) Marked positive spatial autocorrelation, predominantly characterized by clusters where high (low) values are surrounded by high (low) values. (4) Regarding the impact of individual factors, government fiscal budget expenditure demonstrates the most robust explanatory power for the CCD within the YRB. Concerning the effects of two-factor interactions, the interplay between industrial structure upgrading and government fiscal budget expenditure emerges as the most significant determinant in influencing the CCD between LUEE and UER. This study enhances our comprehensive understanding of the interplay between economic and ecological systems. It offers scientific insights and strategic direction for harmonizing ecological governance with urban economic growth at both the regional and global scales. [ABSTRACT FROM AUTHOR]

Rapid urbanization will cause various land use changes and the vast occupation of green spaces, a critical factor in the deterioration of biodiversity in urbanized areas. Some species of wildlife are endangered due to habitat shrunk and fragmentation. However, Malaysia's current biodiversity protection range is still limited. The Ecological Network (EN) refers to a framework of ecological components, which can be obtained by geographical and technical approaches to support more ecological diversity ranges. Furthermore, little research has been found on EN in Malaysia and the impact of land use change on EN. Therefore, the Selangor region is selected as the study area. This paper quantifies land use change and measures the extent of land use change to obtain the EN's change. The result has shown that forestland has decreased, explored by people for housing and agriculture from 2000 to 2020. The EN has a trend of fragmentation. Overall, this study's results imply that the land use change led to EN's worsened performance from 2000 to 2020 in the study area. This paper hopes that this research could help supply information on conserving biodiversity in future development and urban sustainable planning in Malaysia. [ABSTRACT FROM AUTHOR]

As a new product of rapid urbanization, the sprawl of urban construction land can objectively reflect urban land use efficiency, which is of great significance to China's new urban construction. This study aimed to summarize the expansion patterns and utilization efficiency of urban construction land in China from the perspectives of the status, speed and trends of expansion, and to uncover the key factors that lead to the differential distribution of the expansion of construction land. It can also provide land management experience for other countries with rapid expansion of construction land. The results show the following. (1) The expansion of China's construction land presents a "point–line–plane" pattern of evolution, forming changing stages of point-like aggregation, linear series and planar spread. (2) China's construction land shows the characteristics of disorderly spread, a low utilization rate and low output efficiency. The speed of expansion presents clear characteristics of being high in the east and low in the west, mostly concentrated in the Yangtze River Delta, Pearl River Delta and the Beijing–Tianjin–Hebei urban agglomeration. Shanghai, Beijing, Shenzhen and Guangzhou have the highest intensity of construction land use. In Shandong Peninsula and eastern coastal areas, the intensity of the construction land use is generally high. In Xinjiang and Xizang, the intensity of construction land use is relatively low. (3) The urban economic level, population size, industrial structure, foreign investment and land policies have significant effects on the spatial distribution of the expansion of construction land. [ABSTRACT FROM AUTHOR]

Land use evaluation and capacity assessment for sustainable urban development in Ahvaz city has become crucial due to rapid urban expansion negatively impacting current and future development. Urban population growth and resource demands have led to a decline in environmental quality. Over the past decades, Ahvaz has identified potential areas for future expansion until 2030. This study assesses the city's physical expansion and spatial development patterns, aiming to identify optimistic future development scenarios. Emphasizing balanced and equitable development, the study focuses on suitable areas, environmental preservation, pollution reduction, and mitigating urban hazards. Various factors, including land use, soil type, vegetation density, landform, groundwater level, geology, and distances from railways, industries, roads, hinterland cities, water networks, faults, and the city itself, are considered. Three decision-making methods—logic, Analytical Hierarchy Process (AHP), and integrated Fuzzy-AHP—are applied to map urban physical development. Fuzzy logic, AHP models, Expert Choice, Arc GIS software, and a land fit zoning map are used for data analysis. The AHP assesses the factors' relative importance, followed by standardization based on fuzzy set theory. Criteria factors are then combined using AHP and fuzzy logic to generate a land suitability map. The study identifies five zones for Ahvaz's future development—very high, high, medium, low, and very low—based on their physical expansion potential. The third scenario, supported by maps, indicates favorable conditions for future development in the northwest and southwest zones, considering environmental characteristics, infrastructure, and land continuity. [ABSTRACT FROM AUTHOR]

The soil environment plays an important role in urban ecosystems. To study the heavy metal contamination of soil in Beilun District, Ningbo, we collected soil samples from 60 points in urban and peri-urban areas of Beilun District and analyzed the spatiotemporal variation and sources of heavy metal pollution in various land-use types. The results shown that the heavy metal contents in 2015 and 2022 were higher than the background soil values of Ningbo city, and there was an accumulation of heavy metals over these 7 years. The contents of heavy metals in green belts and woodland in 2022 were higher than those in 2015, while there was no significant change in agricultural land. The heavy metal contents in both years were mainly in the order green belts > agricultural land > woodland. The spatiotemporal distribution of heavy metal content showed that heavy metal pollution in Beilun District was concentrated in five industrial areas, and there was a trend toward the disappearance of highly polluted points. But the single-factor pollution index, pollution load index (PLI), and geoaccumulation index (Igeo) indicated that there was no significant heavy metal pollution in Beilun District, and individual elements at specific points showed slight pollution. The source analysis results showed that the main source of Hg is chemical, As is mainly derived from agricultural, Cr, Ni and Cu are mainly derived from natural, the main sources of Zn and Cd are electroplating and machinery activities, and the main source of Pb is traffic. These results specify a reference for future investigation on urban soil heavy metals, and the source apportionment results provide a scientific foundation for subsequent soil heavy metal pollution treatment. [ABSTRACT FROM AUTHOR]

This article examines Ghana's land development and planning laws from a historical perspective. The methodology involves a documentary study of historical records to explore the evolution of these laws, spanning from the colonial period to the present. Through this approach it sheds light on significant milestones such as the 1988 local government changes, the land use decree's implementation by military juntas, and the identified deficiencies in the decree pertaining to physical planning. The expositions from this study show that Ghana has over the years adopted many land development and planning laws, that there is a visible demand for land among people and a desire by developers to use their land. The topic of planning is thus a significant area for stakeholders in Ghana. The study, therefore, recommends that, given the fact that land development and planning laws are here to stay, and that people are the primary drivers of social change, these laws should respect the culture and norms of the people, and should as well always incorporate active public engagement for stakeholders to feel a sense of ownership and belongingness in the entire process. [ABSTRACT FROM AUTHOR]

The conversion of large-scale agricultural land into urban areas poses a significant challenge to achieving national and global food security targets, as outlined in Sustainable Development Goal number 2, which aims to eradicate hunger. Indonesia has experienced a significant decline in rice field areas, with a reduction of approximately 650 thousand hectares within a year (2017–2018), the largest being in Java. Hence, this study aims to examine the impact of urban expansion on agricultural land in the north coast region of West Java Province from 2013 to 2020 and develop a predictive model for 2030 to support sustainable land use planning. The primary methods employed were random forest (RF) analysis using Google Earth Engine, intensity analysis, multilayer perceptron-neural network (MLP-NN), Markov chains-cellular automata (Markov-CA), and stakeholder interviews. The model also evaluated the influence of "distance to tollgates" as a previously unexplored driving factor in existing land use modeling studies. Landsat image classification results using the RF algorithm showed 87–88% accuracy. Cropland has historically been and is projected to remain the primary target for the expansion of built-up areas. Spatial planning irregularities were found in the growth of these areas that adversely affected farmers' socioeconomic and environmental conditions. Evaluation of land use models using MLP-NN and Markov-CA demonstrated an accuracy rate of 86.29–86.23%. The distance to tollgates factor significantly impacts the models, albeit less than population density. The 2030 intervention scenario, which implements a firm policy for sustainable agricultural land use, offers the potential to maintain the predicted cropland loss compared to business as usual. [ABSTRACT FROM AUTHOR]

The phenomenon of urban heat island (UHI) is characterized by industrial, economic development, unplanned and unregulated land use as well as a rapid increase in urban population, resulting a warmer inner core in contrast to the surrounding natural environment, thus requiring immediate attention for a sustainable urban environment. This study examined the land use/land cover (LULC) change, pattern of spectral indices (Normalized Difference Vegetation Index, NDVI; Normalized Difference Water Index, NDWI; Normalized Difference Built-up Index, NDBI and Normalized Difference Bareness Index, NDBaI), retrieval of land surface temperature (LST) and Urban Thermal Field Variance Index (UTFVI) as well as identification of UHI from 2000 to 2022. The relationship among LST and LULC spectral indices was estimated using Pearson's correlation coefficient. The Landsat-5 (TM) and Landsat-8 (OLI/TIRS) satellite data have been used, and all tasks were completed through various geospatial tools like ArcGIS 10.8, Google Earth Engine (GEE), Erdas Imagine 2014 and R-Programming. The result of this study depicts over the period that built-up area and water bodies increased by 119.78 and 35.70%, respectively. On the contrary, fallow and barren decreased by 55.33 and 32.31% respectively over the period. The mean and maximum LST increased by 3.61 °C and 2.62 °C, and the study reveals that a high concentration of UTFVI and UHI in industrial areas, coal mining sites and their surroundings, but the core urban area has observed low LST and intensity of UHI than the peripheral areas due to maintained vegetation cover and water bodies. An inverse relationship has been found among LST, NDVI and NDWI, while adverse relationships were observed among LST, NDBI and NDBaI throughout the period. Sustainable environment planning is needful for the urban area, as well as the periphery region and plantation is one of the controlling measures of LST and UHI increment. This work provides the scientific base for the study of the thermal environment which can be one of the variables for planning of Asansol City and likewise other cities of the country as well as the world. [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]

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

Settlement expansion and commercial agriculture affect landscape sustainability and ecosystem service provision. Integrated landscape approaches are promoted to negotiate trade-offs between competing land uses and their reconciliation. Incorporating local perceptions of landscape dynamics as basis for such negotiations is particularly relevant for sub-Saharan Africa, where most people depend on natural ecosystems for livelihoods and well-being. This study applied participatory scenario building and spatially explicit simulation to unravel perceptions of the potential impact of rubber and settlement expansion on the provision of selected ecosystem services in southwestern Ghana under a business-as-usual scenario. We collected data in workshops and expert surveys on locally relevant ecosystem services, their indicator values, and the probable land-use transitions. The data was translated into an assessment matrix and integrated into a spatially explicit modeling platform, allowing visualization and comparison of the impact on ecosystem service provision of land-use scenarios under rubber plantation and settlement expansion. The results show the capacity of current (2020) and future land-use patterns to provide locally relevant ecosystem services, indicating a decline in capacity of ecosystem service provisioning in the future compared to the 2020 land-use patterns, a threat to the benefits humans derive from ecosystems. This highlights urgent need for policies and measures to control the drivers of land-use/land-cover change. Furthermore, the results emphasize the importance of diversifying land-use/land-cover types for sustainable landscape development. The paper contributes new insights into how spatially explicit and semi-quantitative methods can make stakeholder perceptions of landscape dynamics explicit as a basis for implementing integrated landscape approaches. [ABSTRACT FROM AUTHOR]

Landscapes are conceptually fuzzy and rich, and subject to plural framings. They are places of inquiry and intervention for scientists and practitioners, but also concepts bound to peoples' dynamic identities, knowledge systems, inspiration, and well-being. These varying interpretations change the way landscapes function and evolve. Developed in the 1930s, Q-methodology is increasingly recognized for being useful in documenting and interrogating environmental discourses. Yet its application in the context of how integrated landscape approaches better navigate land-use dilemmas is still in its infancy. Based on our experience and emerging literature, such as the papers in this special collection, this article discusses the value of Q-methodology in addressing landscape sustainability issues. Q-methodology helps unravel and communicate common and contradicting landscape imaginaries and narratives in translational and boundary-spanning ways, thus bridging actors' different understandings of problems and solutions and revealing common or differentiated entry points for negotiating trade-offs between competing land uses. The methodology can be empowering for marginalized people by uncovering their views and aspirational values to decision-makers and policymakers. We argue that this potential can be further strengthened by using Q to identify counter-hegemonic discourses and alliances that combat injustices regarding whose knowledge and visions count. In this way, applying Q-methodology in integrated landscape approaches can become a key tool for transitioning toward just, inclusive, and sustainable landscapes. [ABSTRACT FROM AUTHOR]

Studying the carbon effect of land use in watersheds is important for mitigating global warming, promoting coordinated emission reduction in different regions within the watersheds, and realizing high-quality development of the watersheds. Although a number of scholars have carried out relevant studies in the past, they mainly focused on carbon emissions, rarely involved the carbon balance formed by carbon sources and sinks, and lacked relevant studies on the development of low-carbon economy sub-region. Based on this, this study takes the Yellow River Basin as an example, explores the spatial and temporal evolution of carbon emissions from land use in counties in the Yellow River Basin from 1980 to 2020, and predicts the spatial pattern of carbon income and expenditure from land use under natural conditions in 2030 and 2060 using the PLUS model; and then superimposes on the main functional area planning, divides 735 counties in the Yellow River Basin into six low-carbon economic development subregions, and analyzes their economic development The model of their economic development is analyzed. The results show that: (1) the spatial and temporal differentiation of land use carbon balance in the Yellow River Basin has changed greatly over the past 40 years, (2) the spatial distribution pattern of land use carbon balance in the natural context in 2030 and 2060 is more similar to that in 1990, (3) the carbon emission reduction potentials and pattern optimization of the different low-carbon economic development subregions differ greatly, and they have different low-carbon economic development patterns. The results of this study provide a theoretical basis for scientifically and rationally formulating economic policies for low-carbon development in the counties of the Yellow River Basin, and also provide an important reference for related studies in other similar basins or regions in the world. [ABSTRACT FROM AUTHOR]

Different mechanisms act to structure metacommunities. Among them, the most studied are those related to environmental and spatial drivers. Also, most studies on metacommunities are based on data obtained in snapshot surveys, hindering the test of other potential mechanisms, including historical effects. Here, we quantified the relative importance of different sets of variables that could potentially structure zooplankton communities in 39 small reservoirs in Midwest Brazil. In addition to local environmental, spatial, and land use variables, as a proxy for potential priority effects, we tested the effect of earlier zooplankton composition on (later) zooplankton metacommunity structure. The relative importance of these sets of explanatory variables varied over time. However, earlier zooplankton community data were, in general, the most consistent predictors (especially for microcrustaceans) of zooplankton community structure. This result may indicate the role of historical effects on later zooplankton metacommunity structure. Our results highlight the importance of considering multiple surveys and past community data in spatiotemporal studies aiming to unveil the determinants of zooplankton metacommunity structure. [ABSTRACT FROM AUTHOR]

Background: Biodiversity is declining worldwide as ecosystems are increasingly threatened by multiple stressors associated with anthropogenic global change. Stressors frequently co-occur across scales spatially and temporally, resulting in joint effects that are additive or non-additive, i.e., antagonism or synergism. Forecasting and counteracting threats from intensifying stressors requires improved mechanistic understanding of joint effects, which is currently relatively low. To date, research on multiple stressors has been biased toward simplified scenarios, emphasized classification of interactions over realized joint effects, and mostly ignored adaptation (i.e., phenotypic plasticity or evolving life-history traits) of organisms. To investigate if more a realistic scenarios design incorporating complex spatiotemporal stressor profiles and adaption change joint effects and interactions of multiple stressors compared to simplified scenarios, we modified a spatially explicit meta-population model for a generic freshwater insect. We used the model to simulate different, hypothetical spatiotemporal profiles of a continuous and a discrete stressor and evaluated their joint effects and interactions. Agricultural land use represented the continuous stressor impacting meta-population patch quality and network connectivity and related scenarios implied different trajectories. Climatic events represented the discrete stressor impacting all patches simultaneously by temporary mortality events, with related scenarios implying different event severity. Adaptation mitigated the effects of climatic events based on previous events. Results: Excluding adaptation, we found that at higher levels of the discrete stressor (i.e., strong and frequent climatic events) it strongly dominates the joint effects, while at a low level (i.e., weak and infrequent climatic events) of the discrete stressor, the continuous stressor (i.e., land use) dominates. Yet, the continuous stressor always defined the interaction type, with decreasing land use stress leading to antagonism, and increasing land use stress leading to synergism. Adaptation reduced joint effects under decreasing land use stress, yet had little compensatory influence under increasing land use stress. Moreover, adaptation changed interaction sizes inconsistently across the different land use and climate scenarios, with change depending on the climate scenario. Here, interactions decreased in the moderate scenario but increased in the severe and intense scenarios. Conclusions: We highlight that realistic stressor scenarios accounting for potential adaptation are critical for a mechanistic understanding of how species respond to global change. To our knowledge, this is the first modeling study to show that stressor interactions depend on complex spatiotemporal stressor profiles and adaptation, following general principles. [ABSTRACT FROM AUTHOR]

Most species-rich communities consist of a limited number of common species and many rare species. This widespread ecological pattern can arise due to the predominance of either niche-based or neutral-based processes in community structuring. We tested two hypotheses related to this pattern for an anuran metacommunity composed of 35 species distributed in 187 ponds in the South Brazilian grasslands: (1) for common species, niche processes should prevail over neutral ones because common species evolved under a wider range of ecological filters than rare species; (2) for rare species, neutral processes should prevail because rare species are more prone to ecological drift and dispersion limitation. We found that the densities of both common (i.e., the 25% higher density ones) and rare (i.e., the 75% lower density ones) species depended mostly on pond structure and land use, indicating a similar response to niche-based processes in both groups of species. The importance of pond descriptors and type of land use, however, differed between the two groups, confirming that common and rare species represent ecologically distinct groups within the metacommunity. In addition, neutral processes—related to dispersion limitation or not—influenced both species groups, but the spatial filters selected explained a smaller portion of the variation in species densities compared to environmental descriptors. Our findings showcase the prevalence of niche processes in structuring communities of common and rare species of anurans in subtropical grasslands and indicate the variables of pond structure and land use that are relevant to guide conservation practices for the metacommunity of anurans in the South Brazilian grasslands. [ABSTRACT FROM AUTHOR]

Land use changes significantly impact the structure and functioning of ecosystems. The current research focus lies in how to utilize economic and policy instruments to regulate conflicts among stakeholders effectively. The objective is to facilitate rational planning and sustainable development of land utilization resources. The PLUS model integrates a rule-based mining method for land expansion analysis and a CA model based on multi-type stochastic seeding mechanism, which can be used to mine the driving factors of land expansion and predict the patch-level evolution of land use landscapes. Using the PLUS model, a simulation was conducted to study the future land use distribution in the research area over the next 30 years. Based on land use data from Guizhou Province in 2000, 2010, and 2020, a total of 16 driving factors were selected from three aspects: geographical environment, transportation network, and socio-economic conditions. Four scenarios, namely natural development, urban development, ecological conservation, and farmland rotection, were established. Comparative analysis of the simulated differences among the various scenarios was performed. (1) The overall accuracy of the land use simulation using the PLUS model in the study area was 0.983, with a Kappa coefficient of 0.972 and a FoM coefficient of 0.509. The research accuracy meets the simulation requirements. (2) Through the simulation of four different scenarios, the study investigated the land use changes in Guizhou Province over the next 30 years. Each scenario exhibited distinct impacts on land utilization. Comprehensive comparison of the different simulation results revealed that the farmland protection scenario aligns with the sustainable development goals of the research area. Currently, there is a relative scarcity of research on land use simulation, particularly in model application, for Guizhou Province. This study aims to provide a reference for the rational planning of land resources and high-quality urban construction in Guizhou, promoting the high-quality economic development in tandem with advanced ecological and environmental protection. [ABSTRACT FROM AUTHOR]

This study analyzed the occurrence of cold days, very cold days and cold spells in Poznań in the years 2008/09-2022/23. A cold day was defined as a day with Tmax < 0.0 °C and ≥-10.0 °C, whereas a very cold day was defined as a day with Tmax <-10.0 °C. In the next step, cold spells were determined, which are sequences of at least 5 days with Tmax < 0.0 °C. Circulation conditions were determined based on daily values of sea-level pressure, the height of the 500 hPa isobaric surface, as well as their anomalies and air temperature anomalies at the 850 hPa isobaric level. The conducted research showed a decrease in the number of cold and very cold days in Poznań in the years 2008/09-2022/23. A variation in the number of cold and very cold days and cold spells was noted across the city area, which is a consequence of the form of land use. The occurrence of cold spells in Poznań was associated with higher than average sea-level pressure. [ABSTRACT FROM AUTHOR]

Background: Mangrove ecosystems exhibit significant carbon storage and sequestration. Its capacity to store and sequester significant amounts of carbon makes this ecosystem very important for climate change mitigation. Indonesia, owing to the largest mangrove cover in the world, has approximately 3.14 PgC stored in the mangroves, or about 33% of all carbon stored in coastal ecosystems globally. Unfortunately, our comprehensive understanding of carbon flux is hampered by the incomplete repertoire of field measurement data, especially from mangrove ecosystem-rich regions such as Indonesia and Asia Pacific. This study fills the gap in greenhouse gases (GHGs) flux studies in mangrove ecosystems in Indonesia by quantifying the soil CO2 and CH4 fluxes for different land use types in mangrove ecosystems, i.e., secondary mangrove (SM), restored mangrove (RM), pond embankment (PE) and active aquaculture pond (AP). Environmental parameters such as soil pore salinity, soil pore water pH, soil temperature, air temperature, air humidity and rainfall are also measured. Results: GHG fluxes characteristics varied between land use types and ecological conditions. Secondary mangrove and exposed pond embankment are potential GHG flux sources (68.9 ± 7.0 and 58.5 ± 6.2 MgCO2e ha− 1 yr− 1, respectively). Aquaculture pond exhibits the lowest GHG fluxes among other land use types due to constant inundation that serve as a barrier for the release of GHG fluxes to the atmosphere. We found weak relationships between soil CO2 and CH4 fluxes and environmental parameters. Conclusions: The data and information on GHG fluxes from different land use types in the mangrove ecosystem will be of importance to accurately assess the potential of the mangrove ecosystem to sequester and emit GHGs. This will support the GHG emission reduction target and strategy that had been set up by the Indonesian Government in its Nationally Determined Contributions (NDC) and Indonesia's 2030 Forest and Other Land Use (FOLU) Net Sink. [ABSTRACT FROM AUTHOR]

Variation in personality among conspecific animals can be found both among and within populations. Spatially varying ecological factors can lead to differences in average behaviors among populations, but can also influence the amount of variation that occurs among individuals within populations. Few studies have evaluated how the amount of variation among individuals differs among populations, and studies of interpopulation variation in personality in general often use only a low number of study populations. We tested for variation in the mean and variance in boldness across ten widely dispersed populations of Eastern Box Turtles (Terrapene carolina carolina), a declining species with high repeatability for boldness. Using standardized assays conducted in the field, we found that mean boldness did not vary significantly across populations. At the individual level, temperature, sex, and potentially the amount of human-developed land near turtles influenced mean boldness. In contrast, after correcting for covariates that influence individual boldness levels, we found that populations differed significantly in the amount of variation in boldness among individuals. These findings add to our understanding of spatial variation in personality in general and suggest future directions to evaluate the importance of this diversity within T. c. carolina and similar model systems. Significance: Animal personality is a rapidly growing focus of behavioral ecology and increasingly is found to play a significant role in ecological processes. This study builds upon a growing body of literature on the declining reptile species Terrapene carolina carolina (the eastern box turtle) that shows highly consistent personality traits with probable fitness effects. We used this productive study system to show that spatial variation in ecological interactions has not led to divergence among populations in their average boldness, but that some populations have higher levels of variability among individuals. Furthermore, we highlight sex differences in boldness and a potential role for anthropogenic land use change in shaping boldness of T. c. carolina populations. These findings add to the growing body of literature on animal personality and explore the less commonly discussed phenomena of the absence of among-population variation in personality and the potential importance of levels of behavioral diversity within populations. [ABSTRACT FROM AUTHOR]

Globally, land cover change poses a serious threat to biodiversity and climate change. Anthropogenic actions have been the dominant force among all natural and human forces shaping the Land use and Land cover, causing dramatic changes to the global Landuse and Land cover. The Rajparian Wildlife Sanctuary in the Kashmir Himalayas has undergone substantial changes over the past few decades, but lacks extensive assessment. A multispectral remotely sensed dataset was used to assess changes in land use and land cover of the Sanctuary over almost 27 years (1993–2021). A maximum likelihood algorithm was used to classify Landsat images for the years 1993, 2006, and 2021 and geometric correction were rectified in Earth Resources Data Analysis System (2014) software. The results of the present study indicate that the study area has undergone major changes between 1993 and 2020. Forest and snow-covered areas have shrunk, while non-forested areas like grassland, barren, scrub, built-up areas, and river beds have shown positive growth in the sanctuary. Within the studied time period, a total loss of 5.9 km2 was recorded for the forest areas and 1.5 km2 for the snow covered areas. The main threats driving the Land use and Land cover change in the Sanctuary were grazing pressure, natural resource exploitation, poaching, and development projects. The results of this study may potentially provide information to planners, and decision-makers for the sustainable management of natural resources and implementing important management decisions in order to preserve the rich biodiversity of the Sanctuary. [ABSTRACT FROM AUTHOR]

Since 1990, global forest area has been reducing; tropical forests have suffered from different anthropogenic and natural factors that account for forest loss. Tropical deforestation is the second driver of anthropogenic emissions; increasing demand and investments in tropical forests drive these emissions. These forests attract significant foreign direct investments, but the effects of these investments on carbon emissions from land use, land-use change, and forestry are not well enshrined in the literature for the countries under study. This paper seeks to analyse the impact of foreign direct investments on carbon emissions from land use, land-use change, and forestry amongst 30 tropical forest countries from 1996 to 2019. The sampled countries were disaggregated by tropical blocs: Amazon, Congo basin, Australasia, and Southeast Asia, and by income levels; low-income, lower-middle, upper-middle, and high-income. The findings reveal a U-shape structure of the impact of FDI on carbon emissions from LULUCF within Congo basin and Amazon countries; at higher levels of FDI, emissions from LULUCF will increase while Australasia and Southeast Asian countries show an inverted U-shape impact, thus at higher levels of FDI, there will be a negative and significant impact on carbon emissions from LULUCF. The income levels reveal an inverted U-shape for low-income and high-income countries and a U-shape for upper-middle-income countries; the impact for lower-middle-income countries is not significant. Overall, for the whole sample, the impact depicts a U-shape. This paper proposes high-level development of environmental conditions for FDI for different sectors that align with country and regional green growth plans. Enhancing national and regional governance systems to enforce decisions and fight corruption effectively can significantly promote green FDI for green growth. [ABSTRACT FROM AUTHOR]

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

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

Cities worldwide have initiated the installation of urban climate sensors to monitor air quality in real time and take proactive measures against the growing threat of climate change. This study focuses on the city of Chicago and utilizes Microsoft's recently launched Project Eclipse sensors to evaluate air quality status. We extracted surrounding land use features near the installed sensors, integrating street view images from Google Street View (GSV) with conventional land use extraction toolkits. Principal component analysis (PCA) was conducted to decompose spatial information and evaluate the unique characteristics of using street view imagery. Additionally, we integrated XGBoost machine learning regression analysis and SHapley Additive exPlanations (SHAP) value calculation to investigate the impact of determinants on air quality. Analysis results indicated that the measured air pollution exposure from the sensors was consistent with the city's predefined values, except for the Northern intersection of West Belmont Avenue, which reported critical air quality issues. The regression analysis and SHAP calculation revealed significant differences in the impact of land use on air quality between the intersection of West Belmont Avenue and random observations. The city and local government agencies should address the existing built environment and land use conditions in the North to mitigate potential harm. [ABSTRACT FROM AUTHOR]

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

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

Desertification is a specific land-degrading process, reducing soil productivity and potentially threatening global food security. Therefore, spatially and temporally identifying and mapping desertification-sensitive areas is essential for better management. The current study aimed to (1) assess spatial areas sensitive to desertification and (2) examine the changing tendency of the desertification-sensitive areas over the past 25 years in the provincial Ninh Thuan. The desertification sensitivity index (DSI) was computed based on the Medalus model using 10 quantitative parameters, grouped into the soil, climate, and vegetation quality indexes, computed for the years 1996, 2005, 2010, and 2016. GIS was used to map desertification-sensitive areas associated with five DSI classes. Results showed that classes II and III had the highest area percentage, followed by classes IV and V, and class I. The classes most sensitive to desertification (classes IV and V) covered around 13 to 17%, and classes II and III were 25 to 32% of the total study area, respectively. The coastal areas located in the southeastern parts were more sensitive to desertification than the other parts. Over the four examined periods, the areas of classes IV and V increased while those of classes II and I decreased. These indicated that the study province tended to increase in its desertification sensitivity with a severe increase in the coastal areas over the past 25 years. The key factors involved in these changes could be related the human activities and climate variation, which could be more serious in southeastern areas than in the other areas. [ABSTRACT FROM AUTHOR]

Revealing the spatiotemporal evolution characteristics and key driving processes behind the habitat quality is of great significance for the scientific management of production, living, and ecological spaces in resource-based cities, as well as for the efficient allocation of resources. Focusing on the largest coal-mining subsidence area in Jiangsu Province of China, this study examines the spatiotemporal evolution of land use intensity, morphology, and functionality across different time periods. It evaluates the habitat quality characteristics of the Pan'an Lake area by utilizing the InVEST model, spatial autocorrelation, and hotspot analysis techniques. Subsequently, by employing the GTWR model, it quantifies the influence of key factors, unveiling the spatially varying characteristics of their impact on habitat quality. The findings reveal a notable surge in construction activity within the Pan'an Lake area, indicative of pronounced human intervention. Concurrently, habitat degradation intensifies, alongside an expanding spatial heterogeneity in degradation levels. The worst habitat quality occurs during the periods of coal mining and large-scale urban construction. The escalation in land use intensity emerges as the primary catalyst for habitat quality decline in the Pan'an Lake area, with other factors exhibiting spatial variability in their effects and intensities across different stages. [ABSTRACT FROM AUTHOR]

Bangladesh, a third-world country with the seventh highest population density in the world, has always struggled to ensure its residents' basic needs. But in recent years, the country is going through a serious humanitarian and financial crisis that has been imposed by the neighboring country Myanmar which has forced the government to shelter almost a million Rohingya refugees in less than 3 years (2017–2020). The government had no other option but to acquire almost 24.1 km2 of forest areas only to construct refugee camps for the Rohingyas which has led to catastrophic environmental outcomes. This study has analyzed the land use and land surface temperature pattern change of the Rohingya camp area for the course of 1997 to 2022 with a 5-year interval rate. Future prediction of the land use and temperature of Teknaf and Ukhiya was also done in this process using a machine learning algorithm for the years 2028 and 2034. The analysis says that in the camp area, from 1997 to 2017, percentage of settlements increased from 5.28 to 11.91% but in 2022, it reached 70.09%. The same drastically changing trend has also been observed in the land surface temperature analysis. In the month of January, the average temperature increased from 18.86 to 21.31 °C between 1997 and 2017. But in 2022. it was found that the average temperature had increased up to 25.94 °C in only a blink of an eye. The future prediction of land use also does not have anything pleasing in store. [ABSTRACT FROM AUTHOR]

Flood disasters are frequent natural disasters that occur annually during the monsoon season and significantly impact urban areas. This area is characterized by impermeable concrete surfaces, which increase runoff and are particularly susceptible to flooding. Therefore, this study aims to adopt Bi-variate statistical methods such as frequency ratio (FR) and weight of evidence (WOE) to map flood susceptibility in an urbanized watershed. The study area encompasses an urbanized watershed surrounding the Chennai Metropolitan area in southern India. The essential parameters considered for flood susceptibility zonation include geomorphology, soil, land use/land cover (LU/LC), rainfall, drainage, slope, aspect, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The flood susceptibility map was derived using 70% of randomly selected flood areas from the flood inventory database, and the other 30% was used for validation using the area under curve (AUC) method. The AUC method produced a frequency ratio of 0.806 and a weight of evidence value of 0.865 contributing to the zonation of the three classes. The study further investigates the impact of urbanization on flood susceptibility and is further classified into high, moderate, and low flood risk zones. With the abrupt change in climatic scenarios, there is an increase in the risk of flash floods. The results of this study can be used by policymakers and planners in developing a preparedness system to mitigate economic, human, and property losses due to floods in any urbanized watershed. [ABSTRACT FROM AUTHOR]

Rapid urbanization is profoundly impacting the ecological environment and landscape patterns, leading to a decline in ecosystem services (ES) and posing threats to both ecological security and human well-being. This study aimed to identify the spatial and temporal patterns of ecosystem service bundles (ESB) in the Beibu Gulf urban agglomeration from 2000 to 2030, analyze the trajectory of ESB evolution, and elucidate the drivers behind ESB formation and evolution. We utilized the Patch-generating Land Use Simulation (PLUS) model to establish baseline (BLS), carbon sequestration priority (CPS), and urbanization priority (UPS) scenarios for simulating land use patterns in 2030. Following the assessment of ecosystem service values (ESV) through the equivalent factor method, we identified the spatiotemporal distribution patterns of ESB using the K-means clustering algorithm. By employing stability mapping and landscape indices, we identified and analyzed various types of ESB evolutionary trajectories. Redundancy analysis (RDA) was employed to pinpoint the drivers of ESB formation and evolution. The results revealed that from 2000 to 2030, land use changes were primarily observed in cropland, forestland, and construction land. Between 2000 and 2020, 92.88% of the region did not experience shifts in ESB types. In UPS, the ESB pattern in the study area underwent significant changes, with only 76.68% of the region exhibiting stabilized trajectories, while the other two scenarios recorded percentages higher than 80%. Key drivers of ESB-type shifts included initial food provision services, elevation, slope, changes in the proportion of construction land, and population change. This multi-scenario simulation of ESB evolution due to land use changes aids in comprehending potential future development directions from diverse perspectives and serves as a valuable reference for formulating and changing ecological management policies and strategies. [ABSTRACT FROM AUTHOR]

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

We conducted a hospital-based case–control study to explore the association between proximity to various land use types and childhood leukemia and lymphoma. This research involved 428 cases of childhood leukemia and lymphoma (2016–2021), along with a control group of 428 children aged 1–15 in Tehran. We analyzed the risk of childhood cancer associated with land use by employing logistic regression adjusted for confounding factors such as parental smoking and family history. The odds ratio (OR) for children with leukemia and lymphoma residing within 100 m of the nearest highway was 1.87 (95% CI = 1.00–3.49) and 1.71 (95% CI = 1.00–2.93), respectively, in comparison to those living at a distance of 1000 m or more from a highway. The OR for leukemia with exposure to petrol stations within 100 m was 2.15 (95% CI = 1.00–4.63), and for lymphoma it was 1.09 (95% CI = 0.47–2.50). A significant association was observed near power lines (OR = 3.05; 95% CI = 0.97–9.55) within < 100 m for leukemia. However, no significant association was observed between power lines and the incidence of childhood lymphoma. There was no association between bus stations, major road class 2, and the incidence of childhood leukemia and lymphoma. In conclusion, our results suggest a possible association between the incidence of childhood leukemia and proximity to different urban land uses (i.e., highways and petrol stations). This study is the first step in understanding how urban land use affects childhood leukemia and lymphoma in Tehran. However, comprehensive studies considering individual-level data and specific pollutants are essential for a more nuanced understanding of these associations. [ABSTRACT FROM AUTHOR]

Shared Autonomous Vehicles (SAVs) are expected to have a transformative role in future transportation systems, by reducing vehicle ownership, helping in alleviating congestion, improving accessibility and traffic safety, and changing travel behavior and urban infrastructure. The potential introduction of SAVs in transportation systems has triggered the need of exploiting suitable tools for designing and planning SAV operations and services and assessing their impacts. An explicit category of such tools are agent-based models (ABMs), whose advantage in efficiently representing transportation systems with a fine level of detail, has allowed them to gain importance in modeling SAVs. This paper systematically reviews and organizes the current state-of-the-art on ABMs dealing with SAVs. The review is two-fold: first, the methodological aspects of exploiting ABMs in the context of SAV services and operations are analyzed and second, ABM-based findings on the anticipated impacts of SAVs to traffic, travel behavior, land uses, the environment and so on, are presented and discussed. The paper concludes with recommendations for future research on SAVs and other, potential ABM applications for that purpose. [ABSTRACT FROM AUTHOR]