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In tropical wetlands, in addition to flooding, fire also contributes to the structure of biodiversity across environmental gradients. In fire‐prone ecosystems, flower‐rich fire refuges can be maintained by a seasonal patch‐burning mosaic. Here, we evaluate how a set of floral traits that influence and are related to the pollination systems of tree and non‐tree species varies in response to spatial patterns of fire frequency in a landscape with floodable and flood‐free areas. We classified the sampled species according to flower size, color, shape, symmetry, floral resource, type of pollination units, flowering duration, and anthesis time and compared them between floodable and flood‐free areas with high, moderate, or low fire frequency. We analyzed the functional richness in response to landscape metrics related to spatial patterns of fire frequency. The composition of floral traits of non‐tree species was stable, while tree species differed among fire frequency classes, especially in floodable areas. Many floral traits of tree species are ecological indicators, especially in floodable areas with high fire frequency. In contrast, among non‐tree species, only floral tissues as resources for pollinator were related to flood‐free areas with moderate/high fire frequency. The floral functional richness of the tree species positively linked with the modified Simpson diversity index, and for both trees and non‐trees, it negatively associated with the homogenization of the fire frequency (e.g., connectance based on probability that two adjacent sites belong to the same fire frequency class was negatively related to the functional richness of floral traits). These results demonstrate that homogenization of fire frequency in the landscape decreases the functional richness. Patches with different combinations of fire frequency and flood regime can support a shared set of floral traits besides those specifically related to a particular regime. The spatial heterogeneity of fire and flood patterns in wetlands needs to be preserved to support a higher diversity of pollination systems mediated by functional complementarity between the flowers of the tree and non‐tree species. [ABSTRACT FROM AUTHOR]

Global ecosystems are facing challenges posed by warming and excessive carbon emissions. Urban areas significantly contribute to carbon emissions, highlighting the urgent need to improve their ability to sequester carbon. While prior studies have primarily examined the carbon sequestration benefits of single green or blue spaces, the combined impact of urban blue–green spaces (UBGSs) on carbon sequestration remains underexplored. Meanwhile, the rise of machine learning provides new possibilities for assessing this nonlinear relationship. We conducted a study in the Yangzhou urban area, collecting Landsat remote sensing data and net primary productivity (NPP) data at five-year intervals from 2001 to 2021. We applied the LightGBM-SHAP model to systematically analyze the correlation between UBGSs and NPP, extracting key landscape metrics. The results indicated that landscape metrics had varying impacts on NPP. At the patch and type level, the Percentage of Landscape was significantly positively correlated with NPP in green space, while the contiguity index and fractal dimension index favored carbon sequestration under certain conditions. The contribution of blue space was lower, with some indicators exhibiting negative correlations. At the landscape level, the contagion index and aggregation index of UBGS had positive effects on NPP, while the division index and landscape shape index were negatively correlated with NPP. The results enhance the understanding of the relationship between UBGS and carbon sequestration, and provide a reference for urban planning. [ABSTRACT FROM AUTHOR]

This research was done through spatial analysis to verify whether the use of public policies in the sugarcane sector contributes to the expansion of the functional connectivity of the landscape. In this context, our paper aimed to analyze the role of the Agro-environmental Protocol for the Sugar-Alcohol Sector in landscape connectivity based on two main questions: (1) Did the actions taken by the sugar-energy sector signatory towards the protocol cause an increase in forest patches in riparian Permanent Preservation Areas (PPA)? (2) After signing the protocol, was there an improvement in functional connectivity, and if so, what was the improvement in functional connectivity within all restored permanent preservation areas? As such, we compared the evolution of riparian PPA based on functional connectivity indices for the years 2006, 2020 and analyzed these indices in a hypothetical scenario with all PPA restored. Connectivity analyses have shown significant differences between the analyzed scenarios. However, the indices have shown extremely low values in all scenarios. We conclude that even though the actions proposed by the public policy may have resulted in an increase in the total forested area and have positively affected connectivity, they may be insufficient to affect the resilience of the natural landscape. [ABSTRACT FROM AUTHOR]

Natural river landscapes can be biodiversity hotspots but are one of the most human altered ecosystems with habitats significantly damaged around the world, and a third of fish populations threatened with extinction. While riparian ecosystems have been negatively altered by anthropogenic activities, effective planning and restoration strategies can reverse negative impacts by improving habitat quality. However, restoring rivers requires appropriate data on current riparian health while also considering priorities for different stakeholders. To address this, a Geographic Information System (GIS) was used to create a new and transferable restoration priority model based on a section of the river Linth in Switzerland as a case study. The restoration priority model is founded on connectivity, river condition, national priority species and species hotspots. Landscape change of the riparian zone was analyzed using aerial imagery and landscape metrics. Almost a quarter of rivers within the study area were considered high or very high restoration priority, with many aquatic species set to benefit from restoration. From 1946 to 2019, the riparian landscape became highly fragmented due to significant growth in impervious surfaces and a concomitant loss of agricultural land. The GIS model provides a tool by which environmental agencies can manage natural features over large scales, while also planning priorities and targeting conservation strategies to the areas of greatest need. [ABSTRACT FROM AUTHOR]

Wetland is a suitable habitat for water birds, and it enhances cultural ecosystem services. But the rapid transformation of such habitat, especially in floodplain environments, is an emerging crisis. Wetland reclamation and fragmentation are two major issues leading to poor habitat and landscape. The present paper aimed to explore the spatio-temporal changes in the suitability of wetland bird habitat, wetland landscape pattern, and the connection between them. Two wetlands, including a wetland of national importance, were taken as cases for this study. Time series Landsat and Sentinel images were taken for developing modeling parameters and Land Use Land Cover (LULC) for the years 2016 and 2020. The first transformation of wetland was accounted from the LULC maps of both years. Machine learning algorithm-based spatial models were developed for mapping the poor landscape condition of the existing wetland parts. Finally, semi-subjective analytic hierarchy approach (AHP)-based models were developed for assessing waterbird habitat suitability. Results demarcated more than 48% area belonging primarily to edges and tiny patches of wetlands under a poor state in 2020. Although the total wetland area was reduced between 2016 and 2020, the wetland area found to be highly suitable habitat increased from 25.5 to 59.44% of the total area during that period. The suitability of edge-preferring bird habitat showed a 10% increase. The increasing poverty of the landscape was caused by declining edge-preferring bird habitat suitability. From 1990 to 2020, 27% of wetlands were converted to single-cropped lands, and 5% were converted to multi-cropped agricultural land. Since the study spatially identified the potential suitable area and trend of wetland habitat transformation, this could help policymakers define suitable planning for the restoration and conservation of such promising bird habitat. [ABSTRACT FROM AUTHOR]

Protected areas are a primary instrument for biodiversity conservation, and area-based targets have become a hallmark of global efforts with the 2022 Kunming-Montreal Global Biological Framework recommending at least 30 percent of land and water be protected by 2030. In parallel, the United States has implemented “America the Beautiful,” a call for local, state, and regionally led efforts to conserve, connect, and restore 30 percent of U.S. lands and waters by 2030. Achieving these goals is complicated, however, by the multiple policy scales at which conservation decisions are made and governed and the limited guidance provided on how gains to protected and connected areas should be evaluated. We assess the connectedness of U.S. protected areas at multiple scales and find that less than 3 percent of the United States is protected and connected. Connectedness increases when the area under investigation is partitioned into smaller policy units (e.g., counties), a product of the modifiable areal unit problem. Similarly, connectedness values increase by an order of magnitude when assessed relative to the protected area network rather than considering all land area. Both findings support the need for standardized reporting frameworks and highlight the challenges in coordinating conservation goals across administrative units. [ABSTRACT FROM AUTHOR]

Background: Complexity in landscape structure is often assessed using individual metrics related to ecological processes. However, this rarely incorporates important relationships among metrics and may miss landscape structure effects. Multivariate statistics provide techniques for assessing overall landscape structure effects. We assessed how multivariate statistics could be used to connect landscape structure with an ecological process [bobcat (Lynx rufus) wildlife crossing structure (WCS) use]. We tested how landscape structure at WCS sites compared to the surrounding landscape and how structure affected detections at WCS sites. Our study was conducted in Cameron County, Texas, USA where WCSs are in various stages of construction and monitoring. We used a classified land use/land cover map and aerial LiDAR to calculate configuration and density metrics at WCS and random sites. We created indices for configuration and density using principal components analysis to assess landscape structure effects on camera trap detections at WCSs. Results: Landscape structure at WCSs did not differ from random locations. Wildlife crossing structure use increased with greater woody cover and decreased with increasing vegetation density. Our indices allowed identification of differences in how configuration and density impacted WCS use. Ordination methods helped identify individual contributions of landscape metrics to the overall landscape structure effect. Conclusions: Wildlife crossing structures are permanent fixtures on landscapes, so selecting appropriate locations using broad-scale landscape structure likely increases target species use. Using indices of landscape structure provides planners with a more holistic approach to WCS placement and provides a more comprehensive picture of landscape pattern and process relationships. [ABSTRACT FROM AUTHOR]

This study aims to investigate the impact of spatio-temporal dynamics of urban sprawl on habitats in Istanbul, using the CORINE 1990 and 2018 land cover datasets. In this context, by means of geographic information system (GIS) tools and techniques, land cover maps are created for land cover change analysis and visualization, the Shannon's entropy method is performed for measuring urban growth and sprawl, and the landscape metrics method is applied for assessing habitat loss and fragmentation. Results indicate that the rate of urban sprawl in the area increased by 0.1230 as per the Shannon's entropy index over the 28-year period. Similarly, the Shannon Diversity Index and the Shannon Evenness Index rose from 1.74 to 1.85 and from 0.68 to 0.70, respectively, indicating an increase in urbanization within the area. Moreover, an analysis of patch numbers reveals that habitat fragmentation increased in shrub and/or herbaceous vegetation associations (72.55%), heterogeneous agricultural areas (45.11%), arable lands (42.5%), forests (36.13%) and pastures (15.05%), due to urbanization. Habitat fragmentation has had a detrimental effect on the local biodiversity. While 15 flora species were identified as vulnerable, 13 as endangered and 9 as critically endangered, 19 fauna species were identified as vulnerable and 5 as endangered. This study highlights that the natural habitats and biodiversity of Istanbul will suffer further decline due to urbanization unless sustainable urban planning and management policies are put into practice. It is essential to have controlled urban development to preserve the ecosystem's carrying capacity, and urbanization decisions must consider this requirement. [ABSTRACT FROM AUTHOR]

Most cities in the world suffer from excessive population and unplanned urban growth. The objective of the present study was to investigate the spatiotemporal changes of built-up areas and their growth in the proposed Greater Silchar City (GSC) (Assam, India). The obtained LANDSAT satellite data from 1991 to 2021 for the GSC have divided into non-built-up and built-up land use categories, and recode tools in Erdas Imagine 2014 software have been used to improve the accuracy of the output. The study area has been classified into 8 spatial directions and 11 concentric circles with radial distances of 1 km and resulted from intersected areas consisting of 60 gradient zones. The study used Shannon entropy model for detailed urban sprawl study of every nook and corner. FRAGSTATS v4.2 tools have been used to analyse landscape metrics in respective spatial directions. The built-up area growth of the city was 15.18 km2 during the mentioned period. The landscape metrics and LU/LC results show the maximum built-up growth has taken place in South to South-West direction (3243.87 ha) and the area within a 2–3-km radius (221.85 ha) of the central business district, which is the periphery of the existing Silchar municipal limits. The entropy values indicated that the GSC has compacted infill growth form in the core area, while the dispersed urban sprawl trends followed with increasing distance from the city centre. Moreover, the research findings will provide a conceptual model for assessing urban growth trends for other cities as well. [ABSTRACT FROM AUTHOR]

Natural landscapes are scenic complexes accessible to the public within national parks. Exploring their aesthetic quality can promote an emotional connection between the public and the landscape, which has a positive significance in enhancing human well-being. In this study, Mount Wuyi National Park in China was considered as the study area, and key dimensions tailored to individual and combined natural landscapes for aesthetic quality evaluation were proposed. Seventeen landscape metrics were selected to comprehensively measure the aesthetic quality level of the natural landscapes within the study area. The results indicated that (1) the spatial distribution of 341 individual natural landscapes exhibited a pattern of denser concentration in the south and east, with a relatively sparse distribution in the north and west. (2) Based on the elements of land cover, vegetation, topography, and landforms, 41 types of combined natural landscapes were identified in Mount Wuyi National Park. (3) Southeastern natural landscapes possess significant advantages in aesthetic service provision, uniqueness, diversity, combination, and coordination, and are concentrated in natural landscapes of high aesthetic quality. Natural landscapes of moderate aesthetic quality were distributed across the northern, southwestern, and some parts of the southeastern areas within the study region. The findings provide a reference for aesthetic quality assessment in natural landscapes at the micro-scale and serve as a basis for the scientific management of natural landscapes in national parks. [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]

Urban forests are expected to mitigate the urban heat island (UHI) effect in megacities. The mechanism and factors influencing the cooling effect of urban forest have been extensively discussed; however, the spatial scale effect of cooling heterogeneity of the urban forest is still uncertain. Based on Landsat 8/9 OLI/TIRS imagery, the relationship between land surface temperature (LST) and the spatial patterns of forest vegetation in Beijing, Shanghai, and Tianjin was investigated at different spatial scales, including patch level, rural–urban gradient, and multiple spatial extents. The results indicated that the cooling effect of forest vegetation is stronger than that of grassland. The combination of the three indicators—Area, Normalized Difference Vegetation Index (NDVI), and the percentage of neighboring greenspace (NGP)—can largely explain the differences in cooling intensity between forest vegetation patches. The results suggest that the cooling effect of forest vegetation was affected by air humidity, and the cooling intensity of forest vegetation is stronger in coastal cities than in inland cities. In dry cities, the impact of the patch area on the cooling intensity of forest patches is greater than the NDVI, while the opposite is true in humid coastal cities. The LST variations in the urban–rural gradient can largely be explained by the landscape composition. This study proposes to apply larger spatial extents (e.g., 450 m × 450 m grid in this study) to investigate the relationship between landscape configuration metrics (e.g., Aggregation and Cohesion in this study) and the LST; and to use smaller spatial extents (e.g., 90 m × 90 m grid in this study) to reveal the relationship between area and shape related metrics. This study extends our scientific understanding of scaling effects to the relationship between landscape metrics and the LST. [ABSTRACT FROM AUTHOR]

Abstract Background Complexity in landscape structure is often assessed using individual metrics related to ecological processes. However, this rarely incorporates important relationships among metrics and may miss landscape structure effects. Multivariate statistics provide techniques for assessing overall landscape structure effects. We assessed how multivariate statistics could be used to connect landscape structure with an ecological process [bobcat (Lynx rufus) wildlife crossing structure (WCS) use]. We tested how landscape structure at WCS sites compared to the surrounding landscape and how structure affected detections at WCS sites. Our study was conducted in Cameron County, Texas, USA where WCSs are in various stages of construction and monitoring. We used a classified land use/land cover map and aerial LiDAR to calculate configuration and density metrics at WCS and random sites. We created indices for configuration and density using principal components analysis to assess landscape structure effects on camera trap detections at WCSs. Results Landscape structure at WCSs did not differ from random locations. Wildlife crossing structure use increased with greater woody cover and decreased with increasing vegetation density. Our indices allowed identification of differences in how configuration and density impacted WCS use. Ordination methods helped identify individual contributions of landscape metrics to the overall landscape structure effect. Conclusions Wildlife crossing structures are permanent fixtures on landscapes, so selecting appropriate locations using broad-scale landscape structure likely increases target species use. Using indices of landscape structure provides planners with a more holistic approach to WCS placement and provides a more comprehensive picture of landscape pattern and process relationships.

Abstract Background Monitoring forage in livestock operations is critical to sustainable rangeland management of soil and ecological processes that provide both livestock and wildlife habitat. Traditional ground-based sampling methods have been widely used and provide valuable information; however, they are time-consuming, labor-intensive, and limited in their ability to capture larger extents of the spatial and temporal dynamics of rangeland ecosystems. Drones provide a solution to collect data to larger extents than field-based methods and with higher-resolution than traditional remote sensing platforms. Our objectives were to (1) assess the accuracy of vegetation cover height in grasses using drones, (2) quantify the spatial distribution of vegetation cover height in grazed and non-grazed pastures during the dormant (fall–winter) and growing seasons (spring–summer), and (3) evaluate the spatial distribution of vegetation cover height as a proxy for northern bobwhite (Colinus virginianus) habitat in South Texas. We achieved this by very fine scale drone-derived imagery and using class level landscape metrics to assess vegetation cover height configuration. Results Estimated heights from drone imagery had a significant relationship with the field height measurements in September (r 2 = 0.83; growing season) and February (r 2 = 0.77; dormant season). Growing season pasture maintained residual landscape habitat configuration adequate for bobwhites throughout the fall and winter of 2022–2023 following grazing. Dormant season pasture had an increase in bare ground cover, and a shift from many large patches of tall herbaceous cover (40–120 cm) to few large patches of low herbaceous cover (5–30 cm) (p

Background: Monitoring forage in livestock operations is critical to sustainable rangeland management of soil and ecological processes that provide both livestock and wildlife habitat. Traditional ground-based sampling methods have been widely used and provide valuable information; however, they are time-consuming, labor-intensive, and limited in their ability to capture larger extents of the spatial and temporal dynamics of rangeland ecosystems. Drones provide a solution to collect data to larger extents than field-based methods and with higher-resolution than traditional remote sensing platforms. Our objectives were to (1) assess the accuracy of vegetation cover height in grasses using drones, (2) quantify the spatial distribution of vegetation cover height in grazed and non-grazed pastures during the dormant (fall–winter) and growing seasons (spring–summer), and (3) evaluate the spatial distribution of vegetation cover height as a proxy for northern bobwhite (Colinus virginianus) habitat in South Texas. We achieved this by very fine scale drone-derived imagery and using class level landscape metrics to assess vegetation cover height configuration. Results: Estimated heights from drone imagery had a significant relationship with the field height measurements in September (r2 = 0.83; growing season) and February (r2 = 0.77; dormant season). Growing season pasture maintained residual landscape habitat configuration adequate for bobwhites throughout the fall and winter of 2022–2023 following grazing. Dormant season pasture had an increase in bare ground cover, and a shift from many large patches of tall herbaceous cover (40–120 cm) to few large patches of low herbaceous cover (5–30 cm) (p < 0.05). Conclusions: Drones provided high-resolution imagery that allowed us to assess the spatial and temporal changes of vertical herbaceous vegetation structure in a semi-arid rangeland subject to grazing. This study shows how drone imagery can be beneficial for wildlife conservation and management by providing insights into changes in fine-scale vegetation spatial and temporal heterogeneity from livestock grazing. [ABSTRACT FROM AUTHOR]

Simple Summary: This study focuses on improving the understanding of how forest fragmentation impacts pollination by using a modified version of the Lonsdorf model, which better accounts for bee movement in agricultural landscapes. The researchers created simulated landscapes with varying proportions of forest and degrees of fragmentation. We found that increased forest fragmentation, characterized by smaller and more isolated patches, can enhance pollination services due to greater nesting suitability and nearby floral resources. The findings emphasize the importance of using different models for pollination mapping, as the modified model provides unique insights compared to the original Lonsdorf model. Pollination mapping and modeling have opened new avenues for comprehending the intricate interactions between pollinators, their habitats, and the plants they pollinate. While the Lonsdorf model has been extensively employed in pollination mapping within previous studies, its conceptualization of bee movement in agricultural landscapes presents notable limitations. Consequently, a gap exists in exploring the effects of forest fragmentation on pollination once these constraints are addressed. In this study, our objective is to model pollination dynamics in fragmented forest landscapes using a modified version of the Lonsdorf model, which operates as a distance-based model. Initially, we generated several simulated agricultural landscapes, incorporating forested and agricultural habitats with varying forest proportions ranging from 10% to 50%, along with a range of fragmentation degrees from low to high. Subsequently, employing the modified Lonsdorf model, we evaluated the nesting suitability and consequent pollination supply capacity across these diverse scenarios. We found that as the degree of forest fragmentation increases, resulting in smaller and more isolated patches with less aggregation, the pollination services within landscapes tend to become enhanced. In conclusion, our research suggests that landscapes exhibiting fragmented forest patch patterns generally display greater nesting suitability due to increased floral resources in their vicinity. These findings highlight the importance of employing varied models for pollination mapping, as modifications to the Lonsdorf model yield distinct outcomes compared to studies using the original version. [ABSTRACT FROM AUTHOR]

The objective of this research was to examine the influence of urban configuration on the mitigation of land surface temperature (LST) and the prediction of land use and land cover change through the utilization of convolutional neural network modeling. The results indicate that the formation of different urban heat island patterns is significantly influenced by both urban geometry and land use land cover (LULC) types. However, there is no significant correlation between these factors and LST across all configuration metrics. The associations between landscape configuration and land cover types exhibit variability contingent upon the particular forest cover categories under examination. Furthermore, the application of predictive LULC mapping reveals a divergent pattern, characterized by a rise in the overall extent of vegetation but a decline in the inner context of the Shiraz metropolitan area. The projected trajectory of built-up areas indicates a continued trend of urban expansion. The unique landscape patterns are a result of the distinct characteristics of each LULC. According to recommendations, to address the issue of mean LST, it is advisable for urban landscape planning to give priority to cohesion, density, and continuity while simultaneously minimizing fragmentation, variability, and complexity. Practical Applications: This research provides valuable insights into the following aspects for urban planners, policymakers, and practitioners to address the following. (1) Selecting appropriate landscape metrics: this study identifies suitable landscape metrics to represent and interpret different landscape structures and land use land cover changes (LULCCs) over time. (2) Understanding regional variations: this research highlights that different landscape metrics have distinct effectiveness in different regions. This knowledge helps urban planners and policymakers to tailor their strategies and interventions based on specific regional characteristics, ensuring more effective mitigation of the urban heat island effect. (3) Different correlations with configuration metrics: this study reveals that the correlations between landscape configuration and LST differ in land cover types. (4) Anticipating future changes: this research utilizes machine learning models to predict future LULCC and landscape metrics. This information is valuable for urban planners and policymakers in anticipating and preparing for future urban expansion and changes in vegetation areas. It enables them to proactively design and implement strategies to manage the surface urban heat island effect. Urban planners and policymakers can utilize these insights to develop comprehensive strategies that integrate land-use design, landscape configuration, and urban form to mitigate the negative impacts of urbanization. [ABSTRACT FROM AUTHOR]

Trees provide vital ecosystem services, yet anthropogenic activities have continued to reduce tree cover (TC) globally, leading to disparities in the distribution of ecosystem services at various scales. Similarly, empirical analysis of indicators that measure variations in TC at various temporal and spatial scales is limited in the semi-arid regions where global environmental challenges such as climate change are already affecting TC distribution. This paper quantified recent (2017 - 2019) TC changes in the semi-arid region of Ghana using Sentinel-2 times series data and the application of landscape metrics. The results showed that TC declined by 30% within two years. Urbanizing areas showed more rapid TC depletion rates, while some rural areas experienced a slight increase in TC. The findings suggest the need for local environmental governance by consolidating actions towards conserving semi-arid TC. Sustainable charcoal and fuelwood production are imperative for a long-term sustainable supply of ecosystem services. [ABSTRACT FROM AUTHOR]

Changing built-up area is the obvious reason for the fluctuation of land surface temperature phenomena that leads to a distressing urban environment. Urban vegetation has the potential to minimize the land surface temperature intensity. The present study investigated the relationship between land surface temperature (LST) and vegetation patterns in Asansol Municipal Corporation. Landsat 5 TM and Landsat 8 OLI satellite images for the years 1991 and 2021 were used for analysis of landscape metrics viz., class area, patch density, edge density, and mean shape index. Correlation techniques were applied to depict the association between the variables. The study concludes that vegetation configuration has no significant relationship with LST during the study period. However, the vegetation composition is slightly associated with LST which suggests that vegetation composition may play a crucial role in mitigating the LST phenomenon. However, the relationship is very complex and varies spatially and scale-wise. [ABSTRACT FROM AUTHOR]

Understanding past landscape changes is crucial to promote agroecological landscape transitions. This study analyzes past land cover changes (LCCs) alongside subsequent degradation and improvements in the study area. The input land cover (LC) data were taken from ESRI's ArcGIS Living Atlas of the World and then assessed for accuracy using ground truth data points randomly selected from high-resolution images on the Google Earth Engine. The LCC analyses were performed on QGIS 3.28.15 using the Semi-Automatic Classification Plugin (SCP) to generate LCC data. The degradation or improvement derived from the analyzed data was subsequently assessed using the UNCCD Good Practice Guidance to generate land cover degradation data. Using the Landscape Ecology Statistics (LecoS) plugin in QGIS, the input LC data were processed to provide landscape metrics. The data presented in this article show that the studied landscape is not static, even over a short-term time horizon (2017–2022). The transition from one LC class to another had an impact on the ecosystem and induced different states of degradation. For the three main LC classes (forest, crops, and rangeland) representing 98.9% of the total area in 2022, the landscape metrics, especially the number of patches, reflected a 105% increase in landscape fragmentation between 2017 and 2022. Dataset:  https://hdl.handle.net/20.500.11766.1/FK2/YUXPQY ; https://hdl.handle.net/20.500.11766.1/FK2/U4JHNU ; https://hdl.handle.net/20.500.11766.1/FK2/UN7DKQ. Dataset License: CC-BY-SA [ABSTRACT FROM AUTHOR]

Context: There is a gap of knowledge on the effects of fine resolution landcover patterns on the distribution of air temperatures within neighborhoods, as well as on how these effects may differ depending on temporal (i.e., summer and winter, diurnal and nocturnal), and spatial (i.e. extent) scales. Objectives: (1) Evaluate the effects of compositional and configurational fine-scale landcover patterns on the spatial distribution of air temperatures within neighborhoods. (2) Determine differences between winter and summer seasons and diurnal and nocturnal periods. (3) Evaluate if these effects relate to the spatial extent used for the analysis. Methods: Relationships between four landscape metrics and air temperature within four contrasting neighborhoods located in Santiago. Landcover was classified in six classes (built up, barren, grass, evergreen, deciduous, woody) from 1.5m resolution satellite images and temperature acquired from dataloggers located within neighborhoods. Linear mixed models were used for testing the relationships at six spatial extents. Results: Landcover composition and configuration influence temperatures within neighborhoods, but these effects can greatly differ depending on the season, time of the day and extent of analysis. Grass and evergreen trees show the highest effects on neighborhood´s temperatures among the six landcover classes. Grass reduces summer temperatures at smaller extents but may increase temperatures at larger extents. Evergreen trees play a major role during the winter season increasing coldest nocturnal temperatures at all the analyzed extents. These vegetation effects appear to be mostly associated with the average and largest size of their respective patches. Conclusions: Fine-scale landcover patterns play a role in regulating temperatures within neighborhoods, but these effects depend on the season, time of the day and spatial extent. Researchers and decision makers must be aware that results obtained at a given scale cannot be directly translated to another scales. [ABSTRACT FROM AUTHOR]

Context: Birds, as indicators of biodiversity, are experiencing habitat reduction and loss due to landscape changes. Evidence is mounting that the response of bird richness to landscape patterns remains controversial on a global scale. In this study, we conducted a quantitative global synthesis to gain a comprehensive understanding of this relationship. Our findings contribute to the development of bird conservation strategies that align with the objectives of SDG15. Objective: Through a quantitative review, this study investigated the effects of landscape patterns on bird richness and analyzed the sources of heterogeneity in the results. Methods: A random-effects model was utilized to merge the impacts of landscape metrics on bird richness, and a meta-regression analysis was performed to investigate the origins of heterogeneity. Results: The review encompassed 101 articles from 51 countries worldwide. Field sampling emerged as the primary method for acquiring bird data, with multiple linear regression and generalized linear models as the main analytical approaches. The meta-analysis results highlighted landscape area as a crucial factor influencing bird richness. Regarding landscape composition, the proportions of forests, shrublands, and water bodies positively impacted bird richness, while agricultural land and urban land had negative effects. The relationship between landscape complexity and bird richness is influenced by factors, including net primary productivity (NPP) and precipitation. Landscape heterogeneity was identified as a contributing factor to increased species richness. Conclusion: Compared to landscape complexity, indicators of landscape composition and heterogeneity are more suitable as reference tools for bird conservation. The results of landscape complexity exhibit variation. Moreover, our findings underscore the crucial role of preserving forested areas in supporting bird diversity, emphasizing the necessity to account for regional variations when establishing forest cover thresholds. [ABSTRACT FROM AUTHOR]

The article assesses the extent and pattern of urban area expansion in the Jorhat municipality in Assam, India using geographic information system and remote sensing techniques. The study quantifies urban sprawl through parameters including land cover changes. It analyzes the impact of urban sprawl on environment, such as habitat loss and biodiversity, and infrastructure and shares insights for informed decision-making in urban planning and management and sustainability efforts.

This study aimed to assess the relationship between landscape characteristics and water quality in two distinct basins. Through the utilization of multiple stepwise regression analyses and redundancy analysis, the quantitative association between landscape metrics at both the watershed and riparian buffer scales was examined. The findings revealed that metrics at the riparian buffer scale exhibited greater effectiveness in predicting water quality compared to those at the watershed scale. At the 100-m buffer scale, specific variables such as forest edge density, and effective mesh size of urban areas in the Khorramabad basin, as well as forest aggregation index, urban edge density, and agricultural patch splitting in the Chalus basin, were identified as significant factors influencing water quality. In contrast, at larger scales, only metrics related to agriculture and urban land uses demonstrated dominance, indicating that these land-use classes play a decisive role in driving changes in water quality within the study areas. Moreover, as the scale increased, the influence of dominant variables on water quality exhibited a decreasing trend. Despite the minimal human activities observed in the Chalus basin, their impact on the quality of the Chalus River was substantial. Urban and agricultural land uses were consistently identified as dominant factors across all scales, suggesting that even a substantial amount of forest cover cannot fully mitigate the effects of human activities in a basin. This research underscores the significance of considering both the composition and configuration of the landscape when assessing water quality. [ABSTRACT FROM AUTHOR]

The expansion of urban areas contributes to the growth of impervious surfaces, leading to increased pollution and altering the configuration, composition, and context of land covers. This study employed machine learning methods (partial least square regressor and the Shapley Additive exPlanations) to explore the intricate relationships between urban expansion, land cover changes, and water quality in a watershed with a park and lake. To address this, we first evaluated the spatio-temporal variation of some physicochemical and microbiological water quality variables, generated yearly land cover maps of the basin adopting several machine learning classifiers, and computed the most suitable landscape metrics that better represent the land cover. The main results highlighted the importance of spatial arrangement and the size of the contributing watershed on water quality. Compact urban forms appeared to mitigate the impact on pollutants. This research provides valuable insights into the intricate relationship between landscape characteristics and water quality dynamics, informing targeted watershed management strategies aimed at mitigating pollution and ensuring the health and resilience of aquatic ecosystems. [ABSTRACT FROM AUTHOR]

Often urban compactness indexes ignore the variegated intensity of urban development in their focus on the configuration. In this article, I propose an extension to the index of moment of inertia (IMI) as a compactness measure that can account for multiple ordinal classes representing development intensity. I demonstrate this index for developed areas for each county in the contiguous United States. These counties have a range of development configurations and intensity compositions. I show that for this data set, ignoring the multiple class composition and only focusing on the configuration overestimates the urban compactness in almost all instances. I demonstrate the impact of different analytical choices on the index for this data set. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) phenomenon is negatively impacted by rapid urbanization, which significantly affects people's everyday lives, socioeconomic activities, and the urban thermal environment. This study focuses on the impact of composition, configuration, and landscape patterns on land surface temperature (LST) in Lahore, Pakistan. The study uses Landsat 5-TM and Landsat 8-OLI/TIRS data acquired over the years 2000, 2010 and 2020 to derive detailed information on land use, normalized difference vegetation index, LST, urban cooling islands (UCI), green cooling islands (GCI) and landscape metrics at the class and landscape level such as percentage of the landscape (PLAND), patch density (PD), class area (CA), largest patch index (LPI), number of patches (NP), aggregation index (AI), Landscape Shape Index (LSI), patch richness (PR), and mean patch shape index (SHAPE_MN). The study's results show that from the years 2000 to 2020, the built-up area increased by 17.57%, whereas vacant land, vegetation, and water bodies declined by 03.79%, 13.32% and 0.4% respectively. Furthermore, landscape metrics at the class level (PLAND, LSI, LPI, PD, AI, and NP) show that the landscape of Lahore is becoming increasingly heterogeneous and fragmented over time. The mean LST in the study area exhibited an increasing trend i.e. 18.87°C in 2000, 20.93°C in 2010, and 22.54°C in 2020. The significant contribution of green spaces is vital for reducing the effects of UHI and is highlighted by the fact that the mean LST of impervious surfaces is, on average, roughly 3°C higher than that of urban green spaces. The findings also demonstrate that there is a strong correlation between mean LST and both the amount of green space (which is negative) and impermeable surface (which is positive). The increasing trend of fragmentation and shape complexity highlighted a positive correlation with LST, while all area-related matrices including PLAND, CA and LPI displayed a negative correlation with LST. The mean LST was significantly correlated with the size, complexity of the shape, and aggregation of the patches of impervious surface and green space, although aggregation demonstrated the most constant and robust correlation. The results indicate that to create healthier and more comfortable environments in cities, the configuration and composition of urban impermeable surfaces and green spaces should be important considerations during the landscape planning and urban design processes. [ABSTRACT FROM AUTHOR]

Urban heat islands (UHIs) are a significant environmental problem, exacerbating the urban climate and affecting human health in the Asir region of Saudi Arabia. The need to understand the spatio-temporal dynamics of UHI in the context of urban expansion is crucial for sustainable urban planning. The aim of this study was to quantify the changes in land use and land cover (LULC) and urbanization, assess the expansion process of UHI, and analyze its connectivity in order to develop strategies to mitigate UHI in an urban context over a 30-year period from 1990 to 2020. Using remote sensing data, LULC changes were analyzed with a random forest model. LULC change rate (LCCR), land cover intensity (LCI), and landscape expansion index (LEI) were calculated to quantify urbanization. The land surface temperature for the study period was calculated using the mono-window algorithm. The UHI effect was analyzed using an integrated radius and non-linear regression approach, fitting SUHI data to polynomial curves and identifying turning points based on the regression derivative for UHI intensity belts to quantify the expansion and intensification of UHI. Landscape metrics such as the aggregation index (AI), landscape shape index (LSI), and four other matrices were calculated to assess UHI morphology and connectivity of the UHI. In addition, the LEI was adopted to measure the extent of UHI growth patterns. From 1990 to 2020, the study area experienced significant urbanization, with the built-up area increasing from 69.40 to 338.74 km2, an increase of 1.923 to 9.385% of the total area. This expansion included growth in peripheral areas of 129.33 km2, peripheral expansion of 85.40 km2, and infilling of 3.80 km2. At the same time, the UHI effect intensified with an increase in mean LST from 40.55 to 46.73 °C. The spatial extent of the UHI increased, as shown by the increase in areas with an LST above 50 °C from 36.58 km2 in 1990 to 133.52 km2 in 2020. The connectivity of the UHI also increased, as shown by the increase in the AI from 38.91 to 41.30 and the LSI from 56.72 to 93.64, reflecting a more irregular and fragmented urban landscape. In parallel to these urban changes, the area classified as UHI increased significantly, with the peripheral areas expanding from 23.99 km2 in the period 1990–2000 to 80.86 km2 in the period 2000–2020. Peripheral areas also grew significantly from 36.42 to 96.27 km2, contributing to an overall more pronounced and interconnected UHI effect by 2020. This study provides a comprehensive analysis of urban expansion and its thermal impacts. It highlights the need for integrated urban planning that includes strategies to mitigate the UHI effect, such as improving green infrastructure, optimizing land use, and improving urban design to counteract the negative effects of urbanization. [ABSTRACT FROM AUTHOR]

Hillside urban expansion (HUE) force nature and semi-nature habitats (referred as nature habitats) to migrate to higher slope areas, potentially resulting in modifications to the landscapes of these habitats, and affect biodiversity and human well-being. To date, few studies have considered the implications of HUE on nature habitat landscapes across different scales. Thus, a spatial identification framework was developed for HUE through the utilization of multisource data. Landscape metrics and spatial econometric models were employed to quantify and assess the impacts of HUE on nature habitat landscapes in China at the city, county, and grid scales, respectively. Results reveal that HUE accounted for 43.25% of urban expansion in China from 2000 to 2020. At the city scale, low HUE was found to have the most significant impact on nature habitat landscape fragmentation. Medium HUE and heavy HUE had more impact of specific landscape metrics with smaller areas at detailed spatial resolutions, and medium HUE have the more considerable impact and surpassed that of low HUE and heavy HUE. HUE exhibited a more pronounced impact on nature habitat landscape fragmentation in regions with larger city sizes and population densities, as well as lower topographic relief and elevations.

Floods pose a significant threat, exceeding other natural disasters in economic and social impacts (Agrawal et al. 2020; Zuo et al. 2023; Kurata et al. 2023). Urbanization exacerbates this risk by altering hydrology and increasing flood exposure due to population growth in floodplains (Mustafa et al. 2018; Cao et al. 2022). Climate change further intensifies this, potentially raising the number of flood-prone people by 2030 (Nguyen et al. 2021; Tellman et al. 2021; Keenan-Jones 2023). Disaster mitigation, particularly spatial planning for risk reduction, is crucial for sustainable development in rapidly urbanizing areas. Current plans often rely solely on static land-use data, neglecting dynamic landscape changes. This study utilizes land-use data (Landsat 8 OLI, 2014–2022) and landscape metrics to simulate 2030 flood risk under urban expansion. We show increased urbanization leads to wider and deeper floods due to land-use changes and fragmentation. Spatial planning needs to consider these dynamics, not just elevation. Our model demonstrates that expanding residential and non-residential areas by 5.54% and 1.47% increases flood-prone areas by 6.5% and 36.9%, respectively. This information can inform policymakers in developing more robust spatial planning for flood mitigation.

Small water bodies, such as ponds, are widely distributed in agricultural watersheds. They not only serve agricultural irrigation and drainage but also play a crucial role in reducing nitrogen and phosphorus in agricultural non-point source pollution and improving river water quality. However, the linkage between river water quality and the spatial distribution pattern of small water bodies is still unclear. In this study, an agricultural watershed, consisting of nine sub-watersheds and over 1800 ponds was selected, to evaluate the impact of small water body distribution on river water quality. Despite ponds account for less than 0.6 % of the sub-watershed area, they largely determine water quality variations. Specifically, sub-watersheds with lower connectivity among ponds and shorter distances between ponds and farmland exhibited better water quality. These two indicators alone explain more than 87 % variations of nutrient concentrations among the sub-watersheds. This indicates that dispersed ponds that are located close to farmlands serve as important sinks for pollutants in agricultural watersheds. In this study, we emphasize the importance of location of small water bodies in maintaining river water quality, which can provide crucial information for future agricultural watershed land use planning and the development of optimized strategies for water quality improvement.

Understanding the spatial distribution and mechanisms driving soil organic carbon (SOC) is crucial for assessing soil carbon stocks and implementing effective carbon sequestration strategies in agricultural landscapes. The linear and nonlinear relationships between environmental variables and SOC have been extensively documented, but the threshold and interaction effects among multiple covariates on SOC remain underexplored. This study focused on farmland within the Qilu Lake watershed in Yunnan Province, China, which is characterized by complex surface conditions shaped by both climate change and anthropogenic activities. Utilizing 216 soil samples from the watershed, this research aimed to investigate the threshold and interaction effects of environmental variables on SOC. To achieve this, gradient boosted decision tree (GBDT) combined with partial dependence analysis were employed to elucidate the spatial distribution of SOC and the intricate relationships between environmental factors and SOC. In order to enhance the accuracy of SOC prediction, we employed the landscape metrics as environmental variables, thereby facilitating a more comprehensive description of the landscape. The results indicated that GBDT (R2 = 0.47) outperformed random forest (R2 = 0.38), achieving higher accuracy and lower uncertainty, indicated by a narrower 90% prediction interval. The SOC distribution was predominantly influenced by soil moisture, elevation, and the contagion index (CONTAG), with threshold effects observed at relatively high soil moisture levels (>50%), CONTAG levels (>85%), and relatively low elevations (

Extensive farmland loss and fragmentation due to urbanization pose a significant challenge to sustainable urban development and food security. However, existing research often separately examines the drivers of area or fragmentation-related metrics of built-up land and farmland, potentially undermining effectiveness of policy since the same factors influence the two land types differently. This study addresses the gap by exploring the spatiotemporal dynamics of and the combined effects of various factors on both farmland and built-up land. By utilizing the Multiscale Geographically Weighted Regression model (MGWR), this study investigates the dynamics and combined effects of two socioeconomic factors and three location factors on the landscape patterns of built-up land and farmland in Chengdu from 2000 to 2015. To examine the performance of MGWR, the results are compared with Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR). The results indicate that MGWR exhibits stronger explanatory power than OLS and GWR. All five factors showed influence at the county scale on the area of both farmland and built-up land, with only the distance to rural settlements and population density affecting landscape metrics at the city scale. The five selected factors showed opposite effects on the aggregation index and the area of built-up land and farmland. Specifically, gross domestic product and population density were similarly associated with the landscape patterns of both land types, but these relationships shifted to contradictions with urban development. Distance to urban and rural settlements inversely affected the size of built-up land and farmland yet had consistent effects on their fragmentation. This study presents an integrated perspective and novel methodology for analyzing landscape pattern evolution, providing policymakers with insights for balancing urban expansion and farmland protection by considering area and landscape patterns of two land types. The findings advance the application of ecological indicators in sustainable land-use strategies and provide a novel perspective for exploring sustainable urbanization-associated farmland evolution.

This research focuses on the urban expansion occurring in the Kamrup Metropolitan District—an area experiencing significant urbanization—with the aim of understanding its patterns and projecting future growth. The research covers the period from 2000 to 2022 and projects growth up to 2052, providing insights for sustainable urban planning. The study utilizes the maximum likelihood method for land use/land cover (LULC) delineation and the Shannon entropy technique for assessing urban sprawl. Additionally, it integrates the cellular automata (CA)-Markov model and the analytical hierarchy process (AHP) for future projections. The results indicate a considerable shift from non-built-up to built-up areas, with the proportion of built-up areas expected to reach 36.2% by 2032 and 40.54% by 2052. These findings emphasize the importance of strategic urban management and sustainable planning. The study recommends adaptive urban planning strategies and highlights the value of integrating the CA Markov model and AHP for policymakers and urban planners. This can contribute to the discourse on sustainable urban development and informed decision-making.

The industrial and economic development has initiated the rapid growth of small and medium-sized towns in India. Rewari City, a part of the National Capital Region of India, is undergoing rapid urban expansion. This study analyzes the process of urban expansion in Rewari city, its effect on land use & land cover dynamics and landscape spatial patterns. The methodology of the study is reliant on open-source Landsat satellite data, GIS-based unsupervised classification, and spatial metrics analysis. The city expansion has been analyzed for a period of 31 years, from 1989 to 2020, and population growth has been studied since 1901. Within the study period, built-up area increased by 704%, with an annual expansion rate of 12.8 %. The other land cover classes, such as agriculture land, vegetation, barren land, and water bodies shrank in size over the years. Between 1989 and 2020, 69.4 % of the increase in built up area came at the expense of vegetation and agricultural land. It was also found that per capita land consumption rate increased significantly from 0.0024 to 0.0084, hinting towards dispersed and low-density development. Built-up land had a growth rate nearly 5 times higher than population, indicating urban sprawl. An evaluation of different landscape metrics revealed that the landscape of Rewari has lost land use diversity. The findings of this study offer information about the present state of urban growth. It also serves as a valuable resource for formulating comprehensive planning and development policies, ensuring the promotion of sustainable urban development.

Abstract Temporal and spatial changes in land cover in wetland ecosystems indicate the severity of degradation. Understanding such processes in the past, present, and future might be necessary for managing any type of development plan. Therefore, this research has monitored and analyzed the Hoor Al-Azim International Wetland to determine the orientation of its changes in various future scenarios. Wetland status modeling was conducted using developed hybrid approaches and cellular automata along with evaluating the accuracy of the modeled maps. The dynamics of the landscape were simulated using a higher accuracy approach in three scenarios—Water Conservation, Water Decreasing, and Business-as-Usual- to get the level of degradation of the wetland. The results showed that the amount of water in the wetland has decreased in all three periods, and the salt lands and vegetation have undergone drastic changes. The water bodies experienced a reduction of 148,139 ha between 1985 and 2000, followed by a decrease of 9107 ha during the 2000–2015 period. However, based on the results, these developments are expressed better by the developed hybrid approach than the CA-MC approach and are more reliable for future simulation. The figure of merit index, which assesses the hybrid model's accuracy, yielded a value of 18.12%, while the CA-MC model's accuracy was estimated at 14.42%. The assessment of degradation in hexagonal units showed the least degradation in the water conservation scenario compared with the other two scenarios in 2030.

As a consequence of global climate change, sea level rise (SLR) presents notable risks to both urban and natural areas located near coastlines. For developing effective strategies to mitigate and adapt to these risks, it is essential to evaluate the potential impacts of SLR in coastal areas. While substantial research has been conducted on mapping the broad-scale impacts of SLR based on scenarios of Global Mean Sea Level (GMSL), consideration of regional scenarios, systematic classification, and distinct stages of SLR have been largely overlooked. This gap is significant because SLR impacts vary by region and by the level of SLR, so adaptations, planning, and decision-making must be adapted to local conditions. This paper aims to precisely identify the landscape and urban morphology changes caused by the impact of SLR for each foot of elevation increase based on remote sensing technologies, focusing on St. Johns County, Florida, and Chatham County, Georgia. These two counties are both situated along the southeastern coastline of the United States but with completely different urban forms due to distinct historical and cultural developments. Regional forecasting SLR scenarios covering the period from 2020 to 2100 were utilized to assess the landscape transformation and urban changes, incorporating selected landscape and urban metrics to calculate quantitative data for facilitating comparative analyses. This study investigated gradual alterations in urban morphology and green infrastructure both individually and in combination with the effect on wetlands due to SLR. The mapping outcomes of this research were generated by employing comprehensive remote sensing data. The findings of this research indicated that, when the sea level rose to 3 feet, the wetlands would experience notable alterations, and the level of fragmentation in urban built areas would progressively increase, causing most of the metric data to exhibit a pronounced decline or increase. [ABSTRACT FROM AUTHOR]

Changes in the environmental conditions of Land Use/Land Cover can also significantly influence surface water, mostly due to the dramatically increased biophysical characteristics of the land surfaces. Therefore, extracting multi-scale surface water bodies in urbanized areas is essential. Besides, the past relationships of surface water bodies mostly used low-resolution images. However, the water surface mapping and spatial analysis of multi-scale structure changes in urban areas with high-resolution satellite images have not yet been studied. The present study focused on multi-scale extraction and spatial analysis of growth pattern changes in urban water bodies using Sentinel-2 MSI imagery. Satellite data for the study were obtained from the location map of Chhattisgarh, in the central part of India. The available Sentinel-2 images covering the study area over the period 2012–2020. Initially, we applied image pre-processing steps to remove the shadow noise and distortions and convert the images into a suitable form for mapping. Then, the water surface mapping process combines multi-band water indices (Normalized Difference Vegetation Index and Normalized Difference Water Index (NDWI), Modified NDWI, Urban Difference WI, and Urban Difference Shadow Index and object-oriented methods to extract multi-scale water body information using high-resolution images. Study periods for change analysis were divided into sets. (a) Inter-annual variation is evaluated using the Water Area Frequency Index (WAFI) covering the entire study area over the time period 2012–2020. (b) The spatial variation of land use patterns (i.e., changes in the growth pattern of different scales) in the selected water areas is assessed using eight landscape metrics over three different time periods (2012, 2015, and 2020). These two variations in the WAFI layer are post-processed to determine three multi-scale water scenarios (rivers, streams, canals, and reservoirs): (1) Artificial Waterway (AW of canals, lakes, and reservoirs); (2) Natural Waterway (NW of rivers, streams); and (3) No Waterway (NWW). Two sets of qualitative evaluations are considered: inter-annual variation and spatial variation. The inter-annual variation of surface water areas is evaluated in terms of the WAFI metric percentage. Then the spatial variation of the landscape metrics (Shannon's Diversity Index, Largest Patch Index, Area and Edge metric Percentage, Landscape Division Index, Area-Weighted Mean Shape Index, Aggregation Index, Edge Density, and Patch Density) is analyzed in terms of changes in the selected water areas and changes in the growth pattern for three different time periods. Results and evaluation demonstrate that the accuracy assessment of water mapping methods achieves a high overall accuracy of 95 to 98% for three different time periods. In addition, the observed results of the spatial analysis (AW, NW, and NWW) cover the multi-scale water areas with the highest landscape pattern of fragmentation. Overall, the qualitative findings should therefore be useful for managing and protecting the urban environment. [ABSTRACT FROM AUTHOR]

Urban expansion in developing countries has proven to be a controversial topic among scholars and researchers. This study aimed to measure spatiotemporal patterns of urban expansion in three cities in AlQassim Province: Unaizah, Ar Rass, and Al Mithnab. The research employed various methods used in urban expansion analysis and measurement. Four satellite images from 2013 to 2023 were used to monitor changes in urban expansion using the support vector machine algorithm in geographic information systems, considered an essential and standard technique for understanding and measuring urban land use patterns. Land use classifications (urban land, non-urban land, and agricultural land) were extracted using this method. The study used three methodological approaches to help measure urban expansion: the urban expansion intensity index, the Shannon entropy index, and landscape metrics. The study found that all three measures can contribute to detecting different patterns of urban expansion. Therefore, the results are as follows: First, the three study cities experienced urban expansion based on the increase in the urban expansion intensity index, with their development rate ranging from moderate to high speed. Second, the study revealed that the Shannon entropy index for expansion exhibits a highly compressed development distribution. Third, the results of the landscape metrics measurements using the FRAGSTATS program indicate that urban expansion generally exhibits an irregular distribution. The study emphasizes the necessity of considering environmental, social, economic, and cultural factors, as this significantly improves the understanding of relationships between causes, characteristics, and changes resulting from urban expansion in cities. [ABSTRACT FROM AUTHOR]

The purpose of the presented study is to evaluate the comprehensive impact of different land cover types on the temperature development in the entire Shandong Province by a 20-year-long (MODIS) LST time series from 2003 to 2022. To find out the primary influencing factors, methods such as the Pearson correlation, stepwise analysis, and best-subset selection were applied. The results revealed that the average temperatures had been rising in summer during day- and night-time by 2.32 ℃ and 1.22 ℃, respectively and in winter during day- and night-time by 3.25 ℃ and 1.33 ℃, whereby a significant contribution can be attributed to the period 2014-2022. Substantial variations in LSTs emerge between built-up and vegetated areas and landlocked and coastal regions. Moreover, we could identify a contribution of 0.089 ℃, caused merely by the extension of built-up areas of 1.65% in the entire Shandong Province. Modeling the combined effects of further relevant variables/factors, the percentage of cropland area (crop-per) and the number of landscape patches (NPl) indicate considerable influence on the daytime temperature in the temporal domain. [ABSTRACT FROM AUTHOR]

The rapid urban growth and anthropogenic activities have posed a threat to the local environment and ecosystem around the world. This situation has become a hindrance to planners and policy makers for sustainable urban development. Therefore, this study mainly focuses on the assessment of urban growth patterns in relation to urban sprawl in Jalpaiguri urban agglomeration. Multi-temporal Landsat data have been used for land use change detection and urban sprawl quantification. The maximum likelihood classifier technique has been performed to create land use land cover maps for each study year (2001, 2011 and 2021). Urban expansion intensity index has been applied to determine the magnitude of urban expansion. Landscape metrics and Shannon's entropy have been employed to assess the urban sprawl to a spatial extent. Spatiotemporal land use changes reveal that the non-urban class (vegetation, agriculture, water bodies, and fallow) have been decreasing consistently with an increase in built-up areas over time. Built-up area has increased by almost seven times in the span of the last 20 years (2001-2021). In the first decade, the growth rate of urban areas was 145.42% with a medium speed of expansion and in the next decade, it was 180.83% with a very high speed. Landscape metrics show that the fragmentation of the entire urban landscape into small patches happened from 2001 to 2011 in a higher magnitude indicating the occurrence of sprawling characteristics. But in recent times, the entire landscape is aggregating into large single urban patches which indicate a clumpy situation and would affect the local ecological environment. Shannon's entropy model also verifies the compact urban sprawl in different directions and distances from the city centre. The understanding of urban growth dynamics and land use changes is essential for addressing the rapid urbanization within this urban region. There is an immediate need for an appropriate strategy for effective utilization of land use and monitoring of uncontrolled and haphazard urban growth. This research study would help the urban planner to take a specific scope of action for future urban growth and development. [ABSTRACT FROM AUTHOR]

The role of spatial factors in reducing carbon emissions has been receiving increasing attention from researchers; however, these impacts may involve spatial heterogeneity. In this study, 337 prefecture-level cities in China were taken as the research object. Based on national-level urban data, the global impact of urban spatial form on carbon emissions was then investigated using ordinary least squares regression, the spatial error model, and the spatial lag model. The local effects of urban spatial form on carbon emissions in different cities were then investigated using geographically weighted regression. The findings are as follows. Overall, the larger the urban built-up area and the more fragmented and decentralized the urban land use, the greater the carbon emissions. Conversely, the more centralized the urban center of a city, the lower its carbon emissions. Locally, for some Chinese cities, the total area, landscape shape index, and mean Euclidean nearest-neighbor distance were found to have significant positive effects on carbon emissions, while the largest-patch index had a significant negative impact. For all Chinese cities, the patch density was found to have no significant effect on carbon emissions. In 29% of the cities in which the landscape division index was found to significantly affect carbon emissions, this effect was positive, while it was negative in the remaining 71%. The policy implications emerging from this study lie in the need for decision-makers and urban planners to guide the shaping of low-carbon urban spatial forms. [ABSTRACT FROM AUTHOR]

This research focuses on the urban expansion occurring in the Kamrup Metropolitan District—an area experiencing significant urbanization—with the aim of understanding its patterns and projecting future growth. The research covers the period from 2000 to 2022 and projects growth up to 2052, providing insights for sustainable urban planning. The study utilizes the maximum likelihood method for land use/land cover (LULC) delineation and the Shannon entropy technique for assessing urban sprawl. Additionally, it integrates the cellular automata (CA)-Markov model and the analytical hierarchy process (AHP) for future projections. The results indicate a considerable shift from non-built-up to built-up areas, with the proportion of built-up areas expected to reach 36.2% by 2032 and 40.54% by 2052. These findings emphasize the importance of strategic urban management and sustainable planning. The study recommends adaptive urban planning strategies and highlights the value of integrating the CA Markov model and AHP for policymakers and urban planners. This can contribute to the discourse on sustainable urban development and informed decision-making. [ABSTRACT FROM AUTHOR]

Temporal and spatial changes in land cover in wetland ecosystems indicate the severity of degradation. Understanding such processes in the past, present, and future might be necessary for managing any type of development plan. Therefore, this research has monitored and analyzed the Hoor Al-Azim International Wetland to determine the orientation of its changes in various future scenarios. Wetland status modeling was conducted using developed hybrid approaches and cellular automata along with evaluating the accuracy of the modeled maps. The dynamics of the landscape were simulated using a higher accuracy approach in three scenarios—Water Conservation, Water Decreasing, and Business-as-Usual- to get the level of degradation of the wetland. The results showed that the amount of water in the wetland has decreased in all three periods, and the salt lands and vegetation have undergone drastic changes. The water bodies experienced a reduction of 148,139 ha between 1985 and 2000, followed by a decrease of 9107 ha during the 2000–2015 period. However, based on the results, these developments are expressed better by the developed hybrid approach than the CA-MC approach and are more reliable for future simulation. The figure of merit index, which assesses the hybrid model's accuracy, yielded a value of 18.12%, while the CA-MC model's accuracy was estimated at 14.42%. The assessment of degradation in hexagonal units showed the least degradation in the water conservation scenario compared with the other two scenarios in 2030. [ABSTRACT FROM AUTHOR]