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The use of bi-temporal remote sensing images for detecting changes in land cover is an important means of obtaining surface change information, thus contributing to urban governance and ecological environment monitoring. In this article, we propose a deep learning model named Siam-UNet3+ for high-resolution remote sensing image change detection. This model integrates the full-scale skip connections and full-scale deep supervision of the network UNet3+, which can achieve the multi-scale feature fusion of remote sensing images, effectively avoiding the locality disadvantage of convolution operations. Different from UNet3+, Siam-UNet3+ has made major improvements, including the following: (1) incorporating a Siamese network in the encoder, which can process bi-temporal remote sensing images in parallel; (2) leveraging the residual module as the backbone, which can avoid gradient vanishing (or exploding) and model degradation problems; (3) adding a Triplet Attention module to the decoder, which can avoid information redundancy that may occur in full-scale skip connections and increase the ability to focus on changing patterns; and (4) designing a hybrid loss function consisting of focal loss and dice loss, which is more suitable for remote sensing image change detection tasks. In this study, we conducted change detection experiments using the publicly available LEVIR-CD dataset, as well as two local datasets in Beijing. Through comparative experiments with five other models and ablation experiments, the proposed model Siam-UNet3+ in this article demonstrated significant advantages and improvements in four evaluation metrics, namely, precision, recall, F1-score, and overall accuracy (OA), proving to have great potential in the application to highresolution remote sensing image change detection tasks. [ABSTRACT FROM AUTHOR]

In areas where urban land subsidence is frequent and significant, there are problems with heavy workload, high cost, long observation period, and difficult technical maintenance of the regional elevation reference frame in leveling measurement. In this paper, we propose a method of using continuous operation reference stations (CORSs) as nodes to perform dynamic maintenance of regional elevation reference frames. Using continuous years of Global Navigation Satellite System (GNSS) observation data and surface mass load data (land hydrology, atmospheric pressure, and sea level elevation), we calculate the normal height variation of CORSs, dynamically correct the normal height of CORSs, and verify the accuracy of the calculated CORS normal height variation results using multiperiod leveling observation data. The results showed that the average difference between the normal height variation determined using CORSs data and surface mass load data and the normal height variation of the station obtained by two-stage leveling measurement is 4.37 mm, and the calculated average accuracy of the normal height variation of CORSs is about 5.42 mm. CORSs demonstrating long-term stable continuous observation can be selected as nodes of the elevation control network to achieve the dynamic maintenance of the regional elevation reference framework using CORS data. The research results can provide exploratory experience for future elevation benchmark maintenance work in Beijing. [ABSTRACT FROM AUTHOR]

Land use/land cover (LULC) change information is crucial for monitoring purposes, formulating strategies, socioeconomic progress, and decision-making. The main objective of this study was to analyze and quantify the changes in land use as well as land cover patterns within the Maamora forest in Morocco, and to identify the key factors that influenced its trend from 1989 to 2022. In this study, multispectral remote sensing (RS) data were employed to detect land cover changes in the Maamora forest using Landsat images for the years 1989, 1999, 2009, 2019 and 2022. The maximum likelihood classification (MLC) method was applied to classify the Landsat images using ArcMap 10.4 software to analyze the current state of the study area. Seven LULC classes (cork oak, eucalyptus, pine, acacia, bare land, daya, and others) were successfully classified, achieving overall accuracies surpassing 86% and Kappa coefficients greater than 0.85 for all selected dates. The results of the land use/land cover change detection indicate a decrease in the cork oak area from 60.71% to 44.42%, along with an increase in the eucalyptus area from 18.11% to 39.31%. Moreover, the pine, acacia, bare land, daya, and other classes went from 17.22, 2.80, 0.95, 0.05, and 0.12% to 4.58, 0.02, 10.84, 0.34, and 0.48% respectively. Indeed, from 1989 to 2022, around 50.84% of the study area’s surface remained unchanged, whereas 49.16% underwent changes, transitioning to other land cover classes or endured degradation. This research underscored the anthropogenic transformation of the Maamora woodland, which has led to the degradation of its natural resources. Broadly, these findings can serve as foundational data for future research endeavors and offer valuable insights to concentrate on the key factors driving forest degradation in order to inform the development of interventions aimed at preserving the sustainability of natural species and the overall ecosystem. [ABSTRACT FROM AUTHOR]

Rapid urbanization and land use/land cover (LULC) changes have become global phenomena, significantly impacting ecosystems and climate, which are key concerns in eco-climatology. Focusing on Riyadh, the rapidly growing capital of Saudi Arabia, this study investigated the spatiotemporal dynamics of ecosystem service values (ESVs) in response to LULC changes using an eco-climatological modeling approach. Support vector machine algorithms were employed on Google Earth Engine to classify Landsat imagery and map LULC in Riyadh for 1993, 2003, 2013, and 2023. ESVs were quantified using global coefficients to assess the impact of LULC changes on the eco-climatological system. Results revealed substantial LULC changes during 1993–2023, with built-up areas expanded by 330.79% (777.76 km²), vegetation by 114.30% (32.14 km²), and waterbody areas by 888.89% (7.20 km²). Conversely, barren soil and cropland areas declined by 9.41% (727.90 km²) and 84.40% (89.14 km²), respectively. These LULC changes led to significant alterations in ESVs, with barren soil and cropland losses resulting in ESV reductions of $591.56 million and $555.91 million, respectively. However, increased vegetated areas contributed to $750.40 million ESV rise. Spatially, western Riyadh experienced the most pronounced ESV declines due to rapid urbanization. Overall, total ESV decreased by $206.37 million over the 30-year, with supporting and cultural service values declining by $174.04 million and $39.91 million, respectively. Provisioning and regulating services increased by $3.58 million and $4.02 million. The eco-climatological modeling approach effectively captured the complex interactions between LULC dynamics, ecosystems, and ESVs in this arid environment, highlighting the need for sustainable land management strategies that balance urban growth, ecosystem preservation, and climate change adaptation and mitigation. This study contributes to eco-climatology by demonstrating advanced modeling techniques for assessing spatiotemporal ESV dynamics in response to LULC changes, informing future research and policy in rapidly urbanizing regions. [ABSTRACT FROM AUTHOR]

Monthly night-time light (NTL) data can be very useful for studying intra-year socio-economic dynamics. The Earth Observation Group at Colorado School of Mines started providing monthly NTL imagery for free since 2021; based on Defence Meteorological Satellite Program Operational Linescan System (DMSP-OLS) data. In the current study, an attempt has been made to produce an improved monthly DMSP product from 1992 to 2013. The monthly DMSP product suffers from several drawbacks such as spatial inconsistency, random fluctuations in the data for consecutive years, pixel saturation in bright cores, and blooming effect around settlements. As a result, geometric errors, background noise, and radiometric errors persist in the monthly DMSP images. This research tries to address each of these errors by applying various techniques to produce a vigorously tested and improved monthly DMSP dataset consisting of 216 images (1992–2013). Automatic intensity-based registration has been used to register the monthly images to their corresponding annual composite stable light image. Thresholding and land cover data have been used to remove the background noise. Finally, ridgeline regression is implemented considering F14 December 2003 as the reference image. Each technique has been verified by qualitative analysis of the intermediate outputs. The quantitative assessments of the improved monthly DMSP product reveal that it is a good proxy measure when split from 1992 to 2002 and 2003–2013 with an R2 of 0.84 & 0.82 w.r.t GDP and 0.83 & 0.80 w.r.t population in contrast to the original DMSP data with R2 of 0.70 & 0.75 w.r.t GDP and 0.73 and 0.80 w.r.t population. Transect analysis across six urban cities verifies that the improved product shows reduced saturation in urban areas and increased contrast in sub-urban regions. Finally, a consistent monthly DMSP product is delivered to the research community which will be useful for studying time-series intra-year changes in urbanization, economic growth, and other socio-economic dynamics. [ABSTRACT FROM AUTHOR]

Rainfall intensity and land use changes and from natural vegetation to cultivated fields both impact on river streamflows and sediment dynamics. Assessing the magnitude of these flow and sediment changes is important for water management. However, many rivers are still under-studied/monitored and data sparse. Fully distributed rainfall–runoff models, such as the Jena Adaptable Modelling System (JAMS/J2000), allow for simulation of streamflow, its flow components and potential sediment yield given land use and hydrological changes. This study demonstrates the use of this method in the Bot River, a data-scarce catchment in South Africa. A hydrological model was implemented, and sediment yield was estimated under three land use and two hydrological flow component scenarios. The model reported Nash-Sutcliffe efficiencies (E2) of 0.64 and 0.32 (for streamflow) for model calibration and validation periods, respectively. Modelled mean annual sediment yield increased from 423 t/km2/year in 1990 to 490 t/km2/year in 2018, attributable to land use changes. [ABSTRACT FROM AUTHOR]

The present study aims to conduct the first known comprehensive global evaluation of Aerosol Optical Thickness (AOT) retrievals derived from the Sentinel 2 Sen2Cor algorithm between 2018 and 2022, using data from over 400 AERONET stations. The results indicate that Sentinel 2 tends to underestimate AOT, especially at higher aerosol loadings. Although there appears to be a good overall correlation (r = 0.57) with low RMSE (0.16) and relative mean bias (RMB = -2.46%) between AERONET and Sentinel 2 AOT datasets, regional analysis reveals significant variation in performance across regions, with areas like Europe and Americas exhibiting stronger correlations and lower RMSE than others like Indian subcontinent and Southern Africa. Temporal analysis also suggests an improvement in Sentinel 2 performance, particularly in capturing extreme AOT values in 2022. While elevation does not appear to have any noticeable impact on AOT estimation, slight variations could be observed over certain land cover types like sparse vegetation and permanent water bodies. Though improvements are certainly required over certain geographical regions like Indian subcontinent, Sentinel 2 AOT can, nevertheless, be reliably used for aerosol studies in urban areas or at a finer spatial resolution, especially in Europe, Mainland U.S.A. and East Asia. [ABSTRACT FROM AUTHOR]

The emergence of crowdsourced geographic information (CGI) has markedly accelerated the evolution of land-use and land-cover (LULC) mapping. This approach taps into the collective power of the public to share spatial information, providing a relevant data source for producing LULC maps. Through the analysis of 262 papers published from 2012 to 2023, this work provides a comprehensive overview of the field, including prominent researchers, key areas of study, major CGI data sources, mapping methods, and the scope of LULC research. Additionally, it evaluates the pros and cons of various data sources and mapping methods. The findings reveal that while applying CGI with LULC labels is a common way by using spatial analysis, it is limited by incomplete CGI coverage and other data quality issues. In contrast, extracting semantic features from CGI for LULC interpretation often requires integrating multiple CGI datasets and remote sensing imagery, alongside advanced methods such as ensemble and deep learning. The paper also delves into the challenges posed by the quality of CGI data in LULC mapping and explores the promising potential of introducing large language models to overcome these hurdles. [ABSTRACT FROM AUTHOR]

This study employs Landsat images from 1995, 2009, and 2022, utilizing Google Earth Engine and Random Forest algorithm in R software for land use and land cover change analysis in Dar es Salaam Metropolitan City. Results show a substantial shift, notably in bushland and forest, with a 14.57% and 2.9% decline, respectively. Drivers of change include urban (14.87%) and agricultural (4.47%) growth. Overall, 64.3% of land cover changed, primarily transitioning from bushland to agriculture (25.7%) and forest to agriculture (9.2%). Qualitative insights underscore unregulated urban expansion, informal settlements, migration, human activities, and inadequate planning as significant contributors, aiding sustainable urban governance. [ABSTRACT FROM AUTHOR]

To understand the potential consequences of arable land use changes for insect conservation and ecosystem functioning, it is fundamental to know how insect species with different functional traits respond to crop choice and production method.This study examined the effects of crop type and production method on functional traits of butterfly, bumblebee and carabid beetle communities using species abundance data from 78 fields in Southern Finland. Surrounding landscape composition was also accounted for. The studied traits were associated with dispersal capacity, habitat or diet specialization and phenology—the key determinants modifying species responses to agricultural disturbances and land use changes.Butterfly habitat breadth was narrowest and wingspan shortest in long‐term fallows. Fallows also supported the highest share of butterflies overwintering in early development stages and bumblebees with late‐emerging queens. The tongue length of bumblebees was longest in organic oat fields, probably due to flowering weeds with long corolla.For carabid beetles, the proportion of poor flyers and carnivores was highest in perennial crops and fallows. Carabid beetles overwintering as adults were relatively more abundant in organic than in conventional production, probably due to more intensive tillage in organic fields.In all insect groups, poor dispersers and/or specialists decreased with increasing arable land cover in the surrounding landscape.Increasing the area of long‐term fallows and perennial crops and enhancing within‐field plant diversity while maintaining landscape heterogeneity would promote insect species sensitive to agricultural disturbances and land use changes and their associated ecosystem services in boreal farmland. [ABSTRACT FROM AUTHOR]

This paper maps the supply, demand, and budget of five ecosystem services (ESs) based on land use/cover data classified for the Little Akaki River catchment in central Ethiopia, which include urban and peri-urban areas. For land use characterization, we used a recently introduced method of mapping urban morphology types (UMTs), followed by land cover analysis (within the identified UMTs) for 2006 and 2016 by distributing sample points randomly over the extent of each UMT. Additionally, ecosystem service supply and demand data were collected using expert assessment. The UMT maps revealed 14 primary and 38 sub-UMTs for 2006 and 14 primary and 41 sub-UMTs for 2016. From primary UMTs, agriculture UMT (37%) in 2006 and residential UMT (32%) in 2016 are dominant, revealing a recently increasing transformation of UMTs into residential UMTs. The land cover change analysis shows a decrease in evapotranspiring surfaces (from 46% in 2006 to 32% in 2016) and an increase in built surfaces (from 24% in 2006 to 32% in 2016), indicating rapid urbanization within the catchment that will possibly lead to degradation of ESs. The ESs capacity map shows that agriculture UMTs deliver very high relevant food and high relevant capacity for flood regulation. Vegetation UMT shows very high relevant capacity for temperature regulation, recreation UMT for recreation service, and waterbodies for water supply. Overall, this paper demonstrated that rapid urbanization at the expense of ecologically essential land covers most likely degrade ESs in the Little Akaki River catchment, contributing to the global ecosystem service degradation and climate change. Hence, we highly recommend the application of green infrastructure planning that protects evapotranspiring surfaces that supply multiple ESs in the catchment. This in addition contributes to the global effort to protect green spaces that supplies multiple ecosystem service. [ABSTRACT FROM AUTHOR]

Forest cover in Indonesia reached 95.97 million hectares or 51.2% of the total land area. Fluctuations in forest cover from 2014 to 2022 highlight the need for accurate land mapping to understand factors influencing landscape patterns. Decreases in forest cover increase vulnerability to natural disasters, particularly floods, causing significant economic and environmental losses. This study focuses on strategies for land cover mapping, especially in under-monitored regions, leveraging recent advancements in satellite and drone data for rapid and precise mapping. Deep learning methods like convolutional neural networks (CNN) have shown high accuracy in vegetation cover detection, which can be further improved with new activation functions. This study proposes the new modification activation function, with adding a linear component to enhance CNN accuracy and effectiveness. The proposed demonstrated a significant increase in accuracy, maintaining high values during testing (90.07%) on the Vegetation Density of Peatland Drone dataset. Statistical analysis showed high data distribution with significant performance variation. Despite slower computation times, the proposed excelled in convergence speed and effectiveness compared to other activation. ANOVA and Friedman analyses confirmed significant performance and computation time differences among activation methods. The proposed shows excellent potential in enhancing CNN model accuracy and stability for land cover classification. [ABSTRACT FROM AUTHOR]

The objective of this study was to assess the dynamics of carbon and nitrogen in soil, forest floor, and aboveground biomass in 9.5 years-old planted stands of three Khaya spp. (K. grandifoliola, K. ivorensis, and K. senegalensis). The study was conducted at the Reserva Natural Vale (RNV), Brazil. The stands were planted at 5 × 5 m spacing, distributed over rectangular plots of 1250 m2. Soil bulk density at the evaluated depths, as well nitrogen contents, were similar among the species. However, K. ivorensis exhibited higher carbon concentration in the soil. In general, there were no differences in carbon and nitrogen content in soil between the three species; however, the values obtained are comparable to those of the reference area–Native Forest. The carbon stocks in the aboveground biomass for K. grandifoliola, K. ivorensis, and K. senegalensis averaged 37.97, 33.66 and 33.86 Mg ha−1, respectively (p ≤ 0.05). These values collectively represent about 28% of the total carbon stocks across the observed compartments. Notably, the nitrogen content within the aboveground biomass did not differ among these species. Therefore, African mahogany possesses a robust potential to store both carbon and nitrogen. [ABSTRACT FROM AUTHOR]

Context: Decades of intensifying human activities have led to drastic changes in land use and land cover (LULC) in the Poyang Lake Basin (PLB), resulting in significant changes in landscape pattern and ecosystem service value (ESV), thereby affecting regional sustainability. Objectives: We focused on understanding the impact of LULC changes on the landscape pattern and ESV of the PLB and used the Patch-generating Land Use Simulation model (PLUS) to predict LULC changes in 2050. Methods: We evaluated landscape patterns using landscape metrics and calculated ESV using the ecosystem service equivalent factor method. The Pearson correlation coefficient was used to analyze the correlation between landscape patterns and ESV from 1990 to 2020. Then, we combined the PLUS model and the ecosystem service equivalent factor method to calculate the ESV under multiple scenarios from 2020 to 2050. Results: From 1990 to 2020, the LULC of the PLB changed to varying degrees. The PLB has undergone a rapid process of landscape fragmentation, and the total ESV of the PLB has decreased. The total ESV was positively correlated with the CONTAG index and negatively correlated with the SHDI index. Between 2020 and 2050, the ESV of the PLB is projected to decrease under the NDS (nature development scenario) and EDS (economic development scenario) and increase under the EPS (ecological protection scenario). Conclusions: ESV responded to changes in landscape pattern. We recommend that the PLB should increase patch connectivity. Additionally, future development in the PLB should prioritize ecological protection to prevent further declines in ESV. [ABSTRACT FROM AUTHOR]

Soil erosion is both the cause and effect of land degradation. Land use/land cover conversion that changes the inherent landscape structure of watersheds leads to soil loss increase. Pantabangan-Carranglan Watershed (PCW) as a major source of irrigation, electricity, biodiversity, livelihood, and other ecosystem services, thus, it is imperative to spatially and temporally estimate the soil erosion within its boundary to assist and guide decision-makers in planning conservation and management of the watershed. Using the Revised Universal Soil Loss Equation (RUSLE) model, remotely sensed data, soil analysis, and geographical information system, the soil erosion rate in PCW was estimated. Results showed that there is increasing soil erosion in PCW over time. In 2010 soil erosion rate was estimated to be 134 tons·ha-1·yr-1 which increased to 141 tons·ha-1·yr-1 and 154 tons·ha-1·yr-1 in 2015 and 2020, respectively. Considering the average soil erosion rate and land cover types in PCW, annual crop and open/barren land cover types have the highest average soil erosion rate through time with moderate and catastrophic erosion levels, respectively. [ABSTRACT FROM AUTHOR]

Introduction: Transportation corridors, as special economic corridors, have a greater impact on land cover and landscape pattern changes. Methods: Therefore, 10 buffer zones were established at 1 km intervals on both sides of the Longhai Railway as the centerline to trace the impact of the railroad corridor on the land use change and regional landscape pattern change of the cities along the line from 1985 to 2020. Result: The results show that: (1) The land cover changes along the railroad corridor during the 35 years are mainly characterized by the conversion between cropland, grassland, and construction land. Compared with 1985, in 2020, the construction land increased by 161.96%, the grassland area decreased by 11.83%, and the cropland area decreased by 15.83%. (2) The fragmentation of land patches and vegetation coverage is negatively and positively correlated with the buffer zone distance, respectively. In the same year, the comprehensive landuse dynamic degree is smaller as it is further away from the railway. The nighttime light index in the buffer zone is significantly correlated with the land aggregation index and average patch area, and the closer to the railroad, the higher the land aggregation index of construction land. (3) In terms of zoning, the intensity of land cover and landscape pattern changes in the eastern section is higher than that in the western section, with a higher degree of land fragmentation and more agglomeration of construction land, and the transportation corridor has a greater impact on the change of integrated land use motives in this region. The results of the study can provide a scientific basis for optimising the spatial pattern of land and improving the ecological environment in the construction of cross-regional transport corridors. [ABSTRACT FROM AUTHOR]

Vegetation is a major contributor of terrestrial evaporation and influences subsequent precipitation over land. Studies suggest that forests are crucial for moisture recycling, although the specific contribution of different vegetation to precipitation remains unclear. Using a moisture recycling approach, we investigate the contribution of transpiration from trees and non‐tree vegetation to precipitation over Africa. We use precipitation source regions from simulated atmospheric moisture trajectories, constrained by observation‐based evaporation and precipitation products, and fractional vegetation cover data. Our findings show that trees provide a higher flux to precipitation (∼777 mm year−1) than non‐tree vegetation (∼342 mm year−1). However, considering the smaller spatial extent of trees compared to non‐tree vegetation, precipitation in most watersheds effectively depends more on the latter. Overall, non‐tree vegetation appears equally important as trees in terms of volumetric contributions to precipitation, and deserves attention in further research, considering ongoing land use changes that affect the continental water cycle. Plain Language Summary: A large part of rainfall on Earth derives from evaporation from land. This process, referred to as terrestrial moisture recycling, is controlled for a large part by vegetation cover. Different classes of vegetation cover use water differently, but it is unclear how they contribute to moisture recycling (and thus precipitation) over land. In this study, we estimate the contribution of trees and other, non‐tree vegetation to precipitation over the African continent. We use major watersheds, and track back the source regions of rainfall. We find that overall, trees contribute relatively more to precipitation compared to non‐tree vegetation. However, due to the extensive coverage of other vegetation classes (such as grass‐ and shrublands), many regions depend on non‐tree vegetation for rainfall. Ongoing land use and land cover (LULC) changes may disturb terrestrial moisture recycling patterns. The findings of this study emphasize that the impacts of all LULC changes, including non‐tree vegetation, on the water cycle should be considered and further researched. Key Points: We provide a first estimate of the contribution of transpiration from trees and non‐tree vegetation to precipitation over major African watershedsOn average, trees contribute more to continental precipitation (777 mm year−1) compared to non‐tree vegetation (342 mm year−1)Considering the extent of non‐tree vegetation, most watersheds depend mostly on non‐tree transpiration for precipitation throughout the year [ABSTRACT FROM AUTHOR]

Hyperspectral Image Processing (HSIP) is an essential technique in remote sensing. Currently, extensive research is carried out in hyperspectral image processing, involving many applications, including land cover classification, anomaly detection, plant classification, etc., Hyperspectral image processing is a powerful tool that enables us to capture and analyze an object's spectral information with greater accuracy and precision. Hyperspectral images are made up of hundreds of spectral bands, capturing an immense amount of information about the earth's surface. Accurately classifying and predicting land cover in these images is critical to understanding our planet's ecosystem and the impact of human activities on it. With the advent of deep learning techniques, the process of analyzing hyperspectral images has become more efficient and accurate than ever before. These techniques enable us to categorize land cover and predict Land Use/Land Cover (LULC) with exceptional precision, providing valuable insights into the state of our planet's environment. Image classification is difficult in hyperspectral image processing because of the large number of data samples but with a limited label. By selecting the appropriate bands from the image, we can get the finest classification results and predicted values. To our knowledge, the previous review papers concentrated only on the classification method. Here, we have presented an extensive review of various components of hyperspectral image processing, hyperspectral image analysis, pre-processing of an image, feature extraction and feature selection methods to select the number of features (bands), classification methods, and prediction methods. In addition, we also elaborated on the datasets used for classification, evaluation metrics used, various issues, and challenges. Thus, this review article will benefit new researchers in the hyperspectral image classification domain. [ABSTRACT FROM AUTHOR]

ABSTRACT The socioeconomic prosperity of a region is intricately tied to its infrastructure development, particularly the connectivity provided by highways. Preserving the economic well‐being of an area demands meticulous planning and the execution of construction projects with minimal risk. Landslide hazard zonation (LHZ) is a crucial tool for assessing the likelihood of landslide occurrences in specific regions. This study delves into the LHZ of a 171.2 km2 area, focusing on critical causative factors such as slope inclination and aspect, slope mass material, Land Use/Land Cover (LULC), distance from fault lines, roads, and drainage systems. The classification resulted in four distinct zones along the National Highway (NH‐109) stretch from Bhowali to Almora. Notably, the moderate‐hazard zone encompasses 60.4% of the area, followed by the high‐hazard zone at 24.4%, the low‐hazard zone at 11.9%, and the very high‐hazard zone at 3.3%. To enhance the reliability of the study, 63 previously identified landslide locations were marked based on diverse sources, including field studies and landslide inventory data from the Geological Survey of India (GSI). In the validation process, 12 past landslide occurrences were situated within the very high‐hazard zone, 32 in the high‐hazard zone, 18 in the moderate‐hazard zone, and only one in the low‐hazard zone. The receiver operating characteristic (ROC) curve yielded a commendable quality with a % area under the curve (AUC) of 71.2%. In summary, this research underscores the importance of LHZ in assessing and mitigating landslide risks along a stretch of the NH‐109 corridor. The findings provide valuable insights for informed decision‐making in infrastructure development and risk management, contributing to the sustainable growth and protection of the region's livelihood. [ABSTRACT FROM AUTHOR]

With the advent of modern Earth observation (EO) systems, the opportunity of collecting satellite image time series (SITS) provides valuable insights to monitor spatiotemporal dynamics. Within this context, accurate land use/land cover (LULC) mapping plays a pivotal role in supporting territorial management and facilitating informed decision-making processes. However, traditional pixel-based and object-based classification methods often face challenges to effectively exploit spectral and spatial information. In this study, we propose Orthrus, a novel approach that fuses multi-scale information for enhanced LULC mapping. The proposed approach exploits several 2D encoding techniques to encode times series information into imagery. The resulting image is leveraged as input to a standard convolutional neural network (CNN) image classifier to cope with the downstream classification task. The evaluations on two real-world benchmarks, namely Dordogne and Reunion-Island, demonstrated the quality of Orthrus over state-of-the-art techniques from the field of land cover mapping based on SITS data. More precisely, Orthrus exhibits an enhancement of more than 3.5 accuracy points compared to the best competing approach on the Dordogne benchmark, and surpasses the best competing approach on the Reunion-Island dataset by over 3 accuracy points. [ABSTRACT FROM AUTHOR]

Improvements in high-resolution satellite remote sensing and computational advancements have sped up the development of global datasets that delineate urban land, crucial for understanding climate risks in our increasingly urbanizing world. Here, we analyze urban land cover patterns across spatiotemporal scales from several such current-generation products. While all the datasets show a rapidly urbanizing world, with global urban land nearly tripling between 1985 and 2015, there are substantial discrepancies in urban land area estimates among the products influenced by scale, differing urban definitions, and methodologies. We discuss the implications of these discrepancies for several use cases, including for monitoring urban climate hazards and for modeling urbanization-induced impacts on weather and climate from regional to global scales. Our results demonstrate the importance of choosing fit-for-purpose datasets for examining specific aspects of historical, present, and future urbanization with implications for sustainable development, resource allocation, and quantification of climate impacts. There has been a surge in global datasets of urban land recently. This paper shows large discrepancies in urban area across scales among multiple such datasets, which can influence the magnitude and direction of estimated urban climate impacts. [ABSTRACT FROM AUTHOR]

This study explores the main elements causing flooding in Pakistan's Swabi area and finds that elevation, slope, precipitation, and vicinity of rivers all play a major role in flooding occurrences. Low‐lying areas, steeper slopes, intense monsoon rainfall, and proximity to rivers increase vulnerability to floods. Additional factors such as curvature, normalized difference vegetation index (NDVI), topographic wetness index (TWI), land use and land cover (LULC), and soil type exhibit comparatively less impact on flooding. The evaluation of flood risk incorporates nine factors through the AHP procedure, assigning weights that emphasize the importance of rainfall, slope, elevation, and distance to rivers using GIS software. The resulting flood hazard map categorizes the region into high‐, moderate‐, low‐, and very low‐risk zones, with approximately 49.42% identified as high flood risk areas. Evacuation planning designates secure zones, moderate‐risk areas, and high‐risk zones, emphasizing the need for flexible and adaptable routes in response to evolving flood scenarios. The study's comprehensive approach, integrating GIS and AHP, provides valuable insights for effective flood management in the Swabi district, despite limitations related to data quality. The findings contribute to resolving flooding issues and offer a foundation for coordinated actions by authorities and communities in flood‐prone areas. [ABSTRACT FROM AUTHOR]

In recent years, Ghana, particularly the inhabitants of the Upper East Region, has experienced profound impact of flooding, largely attributable to the complex interplay of climatic factors. This research embarks on a comprehensive assessment of flood risk zones nestled within the White Volta Basin, situated in the Upper East Region. The study employs advanced cartographic methodologies and uses Geographic Information Systems (GIS) in conjunction with the Analytical Hierarchy Process (AHP) to systematically categorize areas susceptible to inundation. Leveraging geospatial datasets acquired from satellites such as Landsat and Sentinel. Topographic, slope, and Land Use/Land Cover (LULC) maps have been constructed. The empirical findings underscore the susceptibility of specific regions, including the Talensi District, territories within Bawku West, and some segments of the Bolgatanga Municipal area, to escalated flood risk. Additionally, the research underscores the high vulnerability of communities such as Nunku, Tolla, Zaare, Pwalugu, Balungu, Winkongo, Biung, and Tongo to the negative impact of inundation. Significantly, the study unveils a pivotal factor in the perpetuation of flood devastation—namely, the role of water discharge. This intrinsic linkage between discharge rates and flood occurrences underscores the pressing need to address this critical component in mitigation strategies to reduce adverse impacts on the basin's resident communities. The insights derived from the study offer some level of hope for residents, providing essential knowledge concerning flood-prone areas and optimal timing for agricultural activities to safeguard their cherished livelihoods. [ABSTRACT FROM AUTHOR]

Restoration involves the recovery and repair of environments because environmental damage is not always irreversible, and communities are not infinitely resilient to such harm. When restoration projects are applied to nature, either directly or indirectly these may take the form of ecological, forestry or hydrological restoration, for example. In the current scenario of global climate change and increasing intensity of disturbances the importance of restoration in all types of ecosystems in order to adapt to the new conditions (so called prestoration) is evident. Whatever the objective of the restoration initiative, there is a lack of consensus as regards common indicators to evaluate the success or failure of the different initiatives implemented. In this study, we have carried out an extensive meta-analysis review of scientific papers aiming to evaluate the outcomes of restoration projects. We have done a review and selected 95 studies implemented in Europe. We explored the main pre-restoration land cover in which restoration initiatives have been implemented, the main causes of degradation, the objective of the restoration action and the indicators selected to analyze the success or failure of the action. We identified a total of 84 indicators in the analyzed papers and compared with the ones proposed for forest in the recent Nature Restoration Law. The analysis revealed five indicators commonly used for the evaluation of restoration initiatives (abundance, coverage, density, Ellenberg indicator, and richness), even where the initial objective has not yet been achieved. Our findings underscore both the benefits and challenges associated with a specific set of harmonized indicators for evaluating the success or failure of restoration initiatives. [ABSTRACT FROM AUTHOR]

Wetlands play a vital role in regulating the global carbon cycle, providing biodiversity, and reducing flood risks. These functions maintain ecological balance and ensure human well-being. Timely, accurate monitoring of wetlands is essential, not only for conservation efforts, but also for achieving Sustainable Development Goals (SDGs). In this study, we combined Sentinel-1/2 images, terrain data, and field observation data collected in 2020 to better understand wetland distribution. A total of 22 feature variables were extracted from multi-source data, including spectral bands, spectral indices (especially red edge indices), terrain features, and radar features. To avoid high correlations between variables and reduce data redundancy, we selected a subset of features based on recursive feature elimination (RFE) and Pearson correlation analysis methods. We adopted the random forest (RF) method to construct six wetland delineation schemes and incorporated multiple types of characteristic variables. These variables were based on remote sensing image pixels and objects. Combining red-edge features, terrain data, and radar data significantly improved the accuracy of land cover information extracted in low-mountain and hilly areas. Moreover, the accuracy of object-oriented schemes surpassed that of pixel-level methods when applied to wetland classification. Among the three pixel-based schemes, the addition of terrain and radar data increased the overall classification accuracy by 7.26%. In the object-based schemes, the inclusion of radar and terrain data improved classification accuracy by 4.34%. The object-based classification method achieved the best results for swamps, water bodies, and built-up land, with relative accuracies of 96.00%, 90.91%, and 96.67%, respectively. Even higher accuracies were observed in the pixel-based schemes for marshes, forests, and bare land, with relative accuracies of 98.67%, 97.53%, and 80.00%, respectively. This study's methodology can provide valuable reference information for wetland data extraction research and can be applied to a wide range of future research studies. [ABSTRACT FROM AUTHOR]

The digital number of a given pixel in a satellite image does not only measure the radiance originating from the area of the Earth’s surface represented by this pixel, but also measures radiance originating from surrounding areas represented by adjacent pixels. This adjacency effect, also known as image blurring, is a fundamental limitation of satellite images, and introduces errors in all image processing techniques that quantify the properties of the Earth’s surface on a per-pixel basis. Image deblurring via deconvolution, is a traditional technique to reduce the adjacency effect, which has proven effective in a variety of remote sensing applications. For example, it has provided an almost fourfold reduction of the adjacency error, when using support vector machine classifiers to estimate land cover proportion at a subpixel level. The remote sensing literature usually assumes that satellite images should be deconvolved with a kernel defined by the sensor’s Point Spread Function PSF(x, y), overlooking the results of three seminal empirical studies which systematically demonstrated that the optimum deconvolution kernel is a shrunk PSF of the form PSF(x/β, y/β), where β < 1 is the optimum shrink factor. The current empirical procedure to find the optimum shrink factor is laborious and restrictive since it requires suitable satellite images of much higher resolution, such that their Ground Sampling Distance (GSD) is equal to the GSD of the sensor of interest divided by an integer number much greater than one. A new theoretical procedure that uses synthetic edge images to find the optimum shrink factor is proposed and applied to the same cases considered by the current empirical procedure, showing that the same results are obtained. The new procedure is much simpler to apply and more accurate and versatile, opening a variety of research paths on satellite image deblurring. [ABSTRACT FROM AUTHOR]

Rice cropping has been the great contribution to the water consumer and greenhouse gas emitter. It is of great significance to accurately monitor the water resources and rice cropping area for the food security under climate change. Among them, Yingjiang County is located in the central area of Yunnan Province, indicating the better representation of typical agriculture. However, the optical remote sensing cannot fully meet the accurate identification of some crops, due to the pixel heterogeneity, mixed pixels, and spectral similarity. Moreover, it is still lacking on the optical images for crop monitoring under the cloudy and rainy climates during the paddy rice growing period in tropical regions. Some difficulties are also remained to extract the rice using multi-spectral remote sensing. Current techniques are focused mainly on the optics and radar image fusion for mapping paddy rice. The applicability has been limited in the absence of optical images. The purpose of this study is to monitor the rice planting areas with frequent clouds and rain in the potential application of Sentinel-1 SAR data. Several features were extracted from the temporal curve of backscattering coefficient using SAR data in the entire growth period of rice. Multiple features were also extracted from the temporal changes of backscattering coefficient in the SAR images. The large-scale water rice maps were obtained to combine the new remote sensing index, named NDRI (Normalized Difference Vegetation Index). The Sentinel-1 satellite was provided all-weather synthetic aperture radar imaging in the C-band. The Sentinel-1 SAR GRD dataset was accessed in the GEE cloud computing platform. Firstly, the SG filtering was used to smooth the temporal curves of backscattering coefficients in the different land covers. Then, the dynamic time warping distances among different temporal curves were calculated using rice samples as a reference. Meanwhile, two feature values were calculated to quantify the different characteristics of rice growth, including the difference between the minimum temporal curves and water bodies, and the difference between the maximum temporal curves and vegetation. Their multiplication was taken as the remote sensing index for the classification of rice threshold. Finally, the spatial distribution of rice was further corrected to generate the slope data from NASADEM images using overlay analysis. The effectiveness of the improved model was validated, taking Yingjiang County in Yunnan Province as the study area. The optical images were almost unusable during the period from June to September in the whole growing season of paddy rice, due to the extensive cloud cover. The results showed that: 1) The temporal SAR features of rice were accurately extracted with an overall accuracy of 89.42% and a Kappa coefficient of 0.82; 2) The planting area of rice in Yingjiang County in 2023 was 199.83km2, where the planting area along the Dayingjiang River was accounted for 90% of the total county. There was the significantly spatial aggregation of rice planting. This finding can provide the strong reference to accurately extract the spatial distribution of rice in the cloudy and rainy areas for the decision-making on agricultural policies. [ABSTRACT FROM AUTHOR]

ABSTRACT Diurnal roosts are vital to bats and growing evidence suggests that bats increasingly exploit synanthropic structures, such as buildings. The use of synanthropic roosts (SRs) has been explained as a consequence of the decreased availability of natural roosts imposed by habitat destruction, although growing evidence suggests that the use of SRs may be beneficial to bats in many ways, including enabling the expansion of distributional ranges. Based on data from Europe and North America, our study aimed to (1) analyse the proportion of use of synanthropic vs. natural roosts, (2) test the hypothesis that bats are forced to use SRs in response to the destruction of natural habitats, (3) analyse latitudinal variation of the proportion of use of SRs and (4) address the highly contrasting pattern in the proportion of use of SRs between the two continents in the light of historical aspects of coexistence between bats and humans. We compiled published data on day roosts obtained by means of radio‐telemetry, providing unbiased evidence of the proportion of synanthropic and natural roosts. We compared the obtained patterns between the two continents, analysed the relationship between the proportion of use of SRs, land cover and latitude and analysed historical reconstruction of anthropogenic land use. In Europe (n = 3385 roosts), SRs were used by twice more (26) bat species and were used 17× more frequently than in North America (n = 6795). We found no support for the hypothesis that bats use SRs in response to habitat destruction. The use of SRs increases with latitude in Europe, but not in North America, despite decreasing areas of human‐altered habitats. Historical processes related to the length of coexistence between bats and humans on both continents, rather than the current state of nature, may underlie the contrasting patterns observed. [ABSTRACT FROM AUTHOR]

There is a need to assess whether ecological resources are being protected on large, federal lands. The aim of this study was to present a methodology which consistently and transparently determines whether two large Department of Energy (U.S. DOE) facilities have protected valuable ecological lands on their sites compared to the surrounding region. The National Land Cover Database (2019) was used to examine the % shrub-scrub (shrub-steppe) and other habitats on the DOE’s Hanford Site (HS, Washington) and on the Idaho National Laboratory (INL), compared to a 10-km and 30-km diameter band of land surrounding each site. On both sites, over 95% is in shrub-scrub or grassland, compared to the surrounding region. Approximately 70% of 10 km and 30-km bands around INL, and less than 50% of land surrounding HS is located in these two habitat types. INL has preserved a significantly higher % shrub/scrub habitat than HS, but INL allows grazing on 60% of its land. HS has preserved a significantly higher % grassland than INL but no grazing on site is present. The methodology presented may be used to compare key ecological habitat types such as grasslands, forest, and desert among sites in different parts of the country. This methodology enables managers, resource trustees, and the public to (1) make remediation decisions that protect resources, (2) assess whether landowners and managers have adequately characterized and protected environmental resources on their sites, and (3) whether landowners and managers have protected the integrity of that land as well as its climax vegetation. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Location Methods Results Main Conclusions Primary productivity is an important mechanism influencing the species richness of animal taxa such as mammals. However, the factors that mediate the relationship between carnivore species richness, for example, and productivity are not well understood. We examined whether the relationship between carnivore species richness and primary productivity followed a hump‐shaped relationship and explored potential pathways by which productivity influences carnivore communities.Western Great Basin, USA.We deployed a camera‐trap grid across a strong precipitation—and the corresponding productivity—gradient and estimated carnivore species richness using a novel application of a multisession community occupancy model. To quantify the relationship between carnivore species richness and productivity, we included the linear and quadratic effects of average annual precipitation as covariates on carnivore species richness. We fit additional community models linking carnivore species richness to vegetation and land cover variables to identify factors that mediate the relationship between carnivore richness and productivity.The linear effect of precipitation on carnivore richness was positive and the quadratic effect was negative, supporting the hump‐shaped relationship where richness declines at the highest levels of productivity. Similarly, carnivore species richness followed a quadratic relationship with vegetation height along the productivity gradient. The relationship between carnivore species richness and proportion tree cover was positive, with limited evidence that open vegetation types in the surrounding landscape increased site‐level richness.In addition to supporting the notion that primary productivity has a strong and nonlinear influence on local‐scale carnivore species richness, our results suggest that vegetation structure may be an important mediator of this relationship. Although future changes in precipitation and productivity due to climate change are uncertain, our results indicate that targeting areas of intermediate productivity and vegetation height, such as woodlands, may be key for conserving species‐rich carnivore communities along productivity gradients. [ABSTRACT FROM AUTHOR]

Considering the characteristics of remote sensing land cover images with a wide variety of types and complex object edges as well as the limited receptive field of local convolutions in existing segmentation networks resulting in inadequate utilization of contextual information, leading to issues such as blurred object edges and low segmentation accuracy, this paper proposes a remote sensing land cover segmentation algorithm based on the UNet3+ network architecture. Firstly, during the decoding process, a similarity-aware point affiliation operator is introduced as an upsampling method. This operator aggregates multiple proposals from the feature pyramid to enhance the segmentation capability for object boundary details. Secondly, in the encoding process, a selective kernel module is introduced to optimize the downsampling approach. This module enables neurons to achieve an adaptive receptive field size, facilitating the acquisition of multi-scale information from target features and precise capture of valuable detailed semantic information. Finally, in the skip-connection phase, a dual multi-scale attention module is added to perform weighted fusion of features from different scales, enabling the model to better focus on both local details and global contextual information. Experimental results on the WHDLD and ISPRS Potsdam datasets demonstrate that the proposed algorithm achieves mean intersection over union (MIoU) improvements of 64.4% and 75.4% respectively, compared to baseline models, the improvement is about 2.6 percentage points and 3.2 percentage points respectively. This also validates the effectiveness of the proposed algorithm in addressing the issue of blurry segmentation edges. [ABSTRACT FROM AUTHOR]

Groundwater recharge zone identification is vital for managing water resources, particularly in semi-arid and dry climates. Accurate and quantifiable assessment is necessary for the sustainable management of groundwater resources, and it is possible to carry this method out using modern techniques and technical standards. To identify likely groundwater locations in the Diyala River Catchment, Iraq, which serves as an example study basin, the current research examines a new methodology that employs a geographic information system, and an Analytical Hierarchy Process connected with remote sensing data. The technique of ArcGIS was employed to generate spatially distributed thematic layers of rainfall, lithology, slope, drainage density, land use/land cover, relief and soil. The raster data from these layers were then converted and categorized. The weights assigned to thematic strata depended on their significance relative to groundwater occurrence. A pairwise judgement matrix for the Analytical Hierarchy Process was used, with the categorized ranking, to assess the standardized weights of the layers under consideration. The layers for the formation of groundwater zones have then been placed using the overlay-weighted summation approach. Three regions, which are classed as excellent, good and moderate, have been identified on the resulting groundwater potential zones map, representing roughly 29, 69 and 2% of the basin's total area, respectively. The study's conclusions indicate that, in such a climate, the adopted strategy would produce favourable results to promote the organizing of opinions and the sustainable use of groundwater resources. [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]

The purpose of this study is to establish basic policies for managing the impacts of climate change on water resources using the integration of machine learning and land cover modeling. We predicted future changes in land cover within the water management and assessed its vulnerability to climate change. After confirming this vulnerability, we considered measures to improve climate resilience and presented future water resource parameters. We reviewed the finances available to promote climate projects, noting the major river management funds. The future project will serve as a stepping stone to promote climate resilience projects addressing water resource challenges exacerbated by future climate change. The study examined the results of analyzing changes in land cover maps due to climate change and assessed vulnerability in water management areas until 2050. According to the analysis results, the regulations for our study areas were set lower than those for other water management zones, resulting in a high rate of urbanization. Therefore, the climate resilience project in the water management area should be implemented first, despite the need for a long-term view in adapting to climate change. [ABSTRACT FROM AUTHOR]

The urban functional zone, serving as a bridge to understanding the complex interactions between human spatial activities and surface thermal environmental changes, explores the driving force information of its internal temperature changes, which is crucial for improving the urban thermal environment. However, the impacts of the current urban functional zones on the thermal environment, based on the delineation of human activities, have yet to be sufficiently investigated. To address the issue, we constructed a two-factor weighted dominant function vector model of "population heat--land use scale" to identify urban functional zones. This model is based on multisource data and considers the perspective of urban functional supply and demand matching. We then analyzed the spatial differentiation and driving factors of the relationship between urban functional zones and the surface thermal environment using the random forest algorithm, bivariate spatial autocorrelation, geographical detectors, and geographically weighted regression models. The results showed that there are significant differences in the Land Surface Temperature among different urban functional zones in the central urban area of Lanzhou. Among these, the life service zone has the greatest impact on the surface thermal environment, followed by the industrial zone and catering service zone, while the green space zone has the least impact. The surface thermal environment exhibits high-high clusters in localized spatial clustering patterns with life service, industrial, catering service, and residential zones. In contrast, it tends to exhibit low-high clusters with green spaces. Significant spatial clustering and dependence exist between various functional zones and the surface thermal environment. The land cover types characterized by the Normalized Difference Bare Land and Building Index, the vegetation coverage represented by the Fraction of Vegetation Cover, and the density of industrial activities indicated by the Industrial POI Kernel Density Index are the main drivers of the surface thermal environment in the various functional zones of the central urban area of Lanzhou, and all exhibit significant spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Urban green spaces play a vital role in enhancing the well-being of communities and mitigating environmental challenges such as air pollution and global warming. Despite their importance, effective models to allocate these green spaces are often overlooked, particularly in developing countries. This study utilises GIS-based Multi-Criteria Analysis and the Analytical Hierarchy Process to recommend optimal locations for green space interventions in Lilongwe City, Malawi, based on nine factors: population density, proximity to roads, slope, Digital Elevation Model (DEM), Normalized Difference Vegetative Index (NDVI), land cover, existing green space, proximity to water bodies, and nitrogen dioxide concentration. The results show that 0.57% (23,776 hectares) of Lilongwe city is highly suitable while 14.50% (604,596 hectares) is unsuitable for green space interventions, where population density was the most determining factor. The suitability varied across the city, with highly suitable areas predominantly located in the southern part. The study highlights the importance of informed decision-making in urban green space planning, setting a standard for equitable access to green spaces and sustainable urban development. [ABSTRACT FROM AUTHOR]

The spatial distribution and data quality of curve number (CN) values determine the performance of hydrological estimations. However, existing CN datasets are constrained by universal-applicability hypothesis, medium resolution, and imbalance between specificity CN tables to generalized land use/land cover (LULC) maps, which hinder their applicability and predictive accuracy. A new annual CN dataset named CUSCN30, featuring an enhanced resolution of 30 meters and accounting for temporal variations in climate and LULC in the continental United States (CONUS) between 2008 and 2021, was developed in this study. CUSCN30 demonstrated good performance in surface runoff estimation using CN method when compared to observed surface runoff for the selected watersheds. Compared with existing CN datasets, CUSCN30 exhibits the highest accuracy in runoff estimation for both normal and extreme rainfall events. In addition, CUSCN30, with its high spatial resolution, better captures the spatial heterogeneity of watersheds. This developed CN dataset can be used as input for hydrological models or machine learning algorithms to simulate rainfall-runoff across multiple spatiotemporal scales. [ABSTRACT FROM AUTHOR]

In this study, we analyzed the surface temperature of Chuncheon from 2014 to 2020 using Landsat 8 images. The findings revealed that man-made structures such as asphalt roads and buildings cause significant increases in land surface temperatures in urbanized, arid, and residential areas. Industrial areas exhibited the highest relative land surface temperature (RLST) index, while densely populated residential areas showed relatively high RLST values. Conversely, areas with high-rise apartments had lower land surface temperatures due to shade and green space. Natural land cover areas demonstrated negative mean RLST, highlighting the usefulness of RLST in distinguishing temperature differences between land cover types. A 0 °C reference surface temperature was used in RLST to standardize land surface temperature measurements, which helped understand land cover status. As the urban heat island phenomenon continues, land surface temperatures are gradually rising. To build a sustainable urban ecosystem, policies and plans to expand and improve green spaces and waterfront environments are essential. To achieve these goals, a comprehensive strategy for expanding and improving green spaces is needed, as well as various policies to improve water quality. [ABSTRACT FROM AUTHOR]

Large-scale, high-resolution land cover classification is a prerequisite for constructing Earth system models and addressing ecological and resource issues. Advancements in satellite sensor technology have led to improvements in spatial resolution and wider coverage areas. Nevertheless, the lack of high-resolution labelled data is still a challenge, hindering the large-scale application of land cover classification methods. In this study, a Transformer-based weakly supervised method for cross-resolution land cover classification using outdated data is proposed. First, to capture long-range dependencies without overlooking the fine-grained details of objects, a U-Net-like Transformer based on a reverse difference mechanism (RDM) using dynamic sparse attention is designed. Second, an anti-noise loss calculation module based on optimal transport (OT) is proposed. The anti-noise loss calculation identifies confident areas and vague areas based on the OT matrix, which relieves the effect of noises on outdated land cover products. By introducing a weakly supervised loss with weights and using an unsupervised loss, the RDM-based U-Net-like Transformer was trained. Remote sensing images with 1 m resolutions and the corresponding ground truths of six states in the United States were used to validate the performance of the proposed method. The experiments used outdated land cover products with 30 m resolutions from 2013 as training labels and produced land cover maps with 1 m resolutions from 2017. The results showed the superiority of the proposed method over state-of-the-art methods. The code is available at https://github.com/yu-ni1989/ANLC-Former. [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]

Open biomass burning has significant adverse effects on regional air quality, climate change, and human health. Extensive open biomass burning is detected in most regions of China, and capturing the characteristics of open biomass burning and understanding its influencing factors are important prerequisites for regulating open biomass burning. The characteristics of open biomass burning have been widely investigated at the national scale, with regional studies often focusing on northeast China, but few studies have examined regional discrepancies in spatiotemporal variations over a long timescale in Guangxi province. In this study, we used the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG), combined with land cover data and high-resolution remote sensing images, to extract open biomass burning (crop residue burning and forest fire) fire points in Guangxi province from 2012 to 2023. We explored the spatial density distribution and temporal variation of open biomass burning using spatial analysis methods and statistical methods, respectively. Furthermore, we analyzed the driving forces of open biomass burning in Guangxi province from natural (topography, climate, and plant schedule), policy, and social (crop production and cultural customs) perspectives. The results show that open biomass burning is concentrated in the central, eastern, and southern parts of the study area, where there are frequent agricultural activities and abundant forests. At the city level, the highest numbers of fire points were found in Baise, Yulin, Wuzhou, and Nanning. The open biomass burning fire points exhibited large annual variation, with high levels from 2013 to 2015 and a remarkable decrease from 2016 to 2020 under strict control measures; however, inconsistent enforcement led to a significant rebound in fire points from 2021 to 2023. Forest fires are the predominant type of open biomass burning in the region, with forest fires and crop residue burning accounting for 76.82% and 23.18% of the total, respectively. The peak period for crop residue burning occurs in the winter, influenced mainly by topography, planting schedules, crop production, and policies, while forest fires predominantly occur in the winter and spring, primarily influenced by topography, climate, and cultural customs. The results indicate that identifying the driving forces behind spatiotemporal variations is essential for the effective management of open biomass burning. [ABSTRACT FROM AUTHOR]

Filtering approaches on Global Ecosystem Dynamics Investigation (GEDI) data differ considerably across existing studies and it is yet unclear which method is the most effective. We conducted an in‐depth analysis of GEDI's vertical accuracy in mapping terrain and canopy heights across three study sites in temperate forests and grasslands in Spain, California, and New Zealand. We started with unfiltered data (2,081,108 footprints) and describe a workflow for data filtering using Level 2A parameters and for geolocation error mitigation. We found that retaining observations with at least one detected mode eliminates noise more effectively than sensitivity. The accuracy of terrain and canopy height observations depended considerably on the number of modes, beam sensitivity, landcover, and terrain slope. In dense forests, a minimum sensitivity of 0.9 was required, while in areas with sparse vegetation, sensitivity of 0.5 sufficed. Sensitivity greater than 0.9 resulted in an overestimation of canopy height in grasslands, especially on steep slopes, where high sensitivity led to the detection of multiple modes. We suggest excluding observations with more than five modes in grasslands. We found that the most effective strategy for filtering low‐quality observations was to combine the quality flag and difference from TanDEM‐X, striking an optimal balance between eliminating poor‐quality data and preserving a maximum number of high‐quality observations. Positional shifts improved the accuracy of GEDI terrain estimates but not of vegetation height estimates. Our findings guide users to an easy way of processing of GEDI footprints, enabling the use of the most accurate data and leading to more reliable applications. Plain Language Summary: The Global Ecosystem Dynamics Investigation (GEDI) collected terrain and canopy observations using laser altimetry. The quality of terrain and canopy observations is influenced by acquisition conditions and land (cover) characteristics. Consequently, a considerable amount of GEDI observations is discarded as noise, and further filtering is necessary to retain only high‐quality observations. Our objective was to assess how environmental and acquisition characteristics influence the accuracy of terrain and canopy height of GEDI observations. Although the main objective of the GEDI mission was to map forests, we also focused on grasslands. GEDI serves not only as an essential source of information on canopy height but also provides accurate terrain observations. Furthermore, it is important to know that GEDI does not overestimate the height of low vegetation as this can result in an overestimation of carbon storage. We distinguished four steps in the GEDI data processing: (a) removal of noise observations, (b) removal of low‐quality data, (c) effect of additional acquisition characteristics, and (d) mitigation of geolocation error. We found that the accuracy of terrain and canopy height observations depended considerably on the number of detected modes, beam sensitivity, landcover, and terrain slope. Key Points: Terrain is crucial for estimates of canopy height, however only 20%–30% of footprints have an absolute error of terrain estimates <3 mThe quality of terrain and canopy height estimates depends on the interplay of number of modes, sensitivity, land cover, and terrain slopeNoise and low‐quality footprints can be successfully removed using number of modes, sensitivity, quality flag and difference from TanDEM‐X [ABSTRACT FROM AUTHOR]

Objectives: In the land cover classification study from multi-source remote sensing images, domain adaptation method can align images or extracted image features from source and target images, thus improves the generalization ability of deep learning models and plays an important role in intelligent remote sensing image interpretation. Methods: A self-training domain adaptation method based on information entropy uncertainty estimation for pseudo label correction is proposed, its core is an entropy uncertainty loss function for land cover classification between cross source remote sensing images. First, a land cover classification model is pretrained on the source domain training set with ground truth, and is applied on the target domain images without ground truth labels to generate pseudo labels. Then, the pseudo labels are used to further train the model, the information entropy of the prediction result is calculated and used as the uncertainty estimation of the pseudo labels to further correct the pseudo labels with self-training, so as to obtain weights of the classification model more suitable for the target domain dataset. Finally, a cross domain classification experiment was conducted on three data sets, namely, the WHU building change detection data set, the ISPRS 2D semantic annotation competition data set, and the Wuhan land cover classification data set. Results: Experimental results show that:(1) The proposed method improved the mean intersection over union(mIoU) and overall accuracy(OA) of semantic segmentation network by 0.3%-3.1% and 1.2%-4.5%, respectively. (2) Compared with the traditional self-training method, the proposed method can improve the mIoU and OA by 0.1%-1.5%. (3) Compared with the most recent uncertainty estimation method based on Kullback-Leibler divergence, the proposed method can improve the mIoU and OA about 0.6% in average. Conclusions: The proposed method can further improve the performance of a trained segmentation model for the target domain images without the requirement of target labels. There is also no need to introduce additional modules or parameters on the existing segmentation model. [ABSTRACT FROM AUTHOR]

Cities are facing increased heat-related health risks (HHRs) due to the combined effects of global warming and rapid urbanization. However, few studies have focused on HHR assessment based on fine-scale information. Moreover, most studies only analyze spatial HHR patterns and do not explore the potential driving factors. In this study, we estimated the potential HHRs based on the "hazard–exposure–vulnerability" framework by using multisource data, including the modified thermal–humidity index (MTHI), population density, and land cover. Then, the variations in the HHRs among different local climate zones (LCZs) at the fine spatial scale were analyzed in detail. Finally, we compared the different contributions of the LCZs and types of land cover to the HHRs and their three components by using multiple linear regression models. The results indicate that the spatial pattern of the HHRs was different from those of the individual components, and high-hazard regions do not mean high HHRs. There were huge variations in the HHRs among the different LCZs. The built-up LCZs typically had much higher HHRs than the natural ones, with compact LCZs facing the most severe risk. LCZ 6 (open low-rise buildings) had a relatively low HHR and should be paid more attention in future urban planning. Compared to the LCZs, the land covers better explained the variations in the HHR. In contrast, the LCZs better predicted the land surface temperatures. However, both the LCZs and land covers made only slight contributions to the heat exposure and vulnerability. Furthermore, the manmade buildings and impervious surface areas contributed much more to the HHR than the natural land covers. Therefore, the arrangement of the warming LCZs and land cover types is worthy of further investigation from the perspective of HHR mitigation. [ABSTRACT FROM AUTHOR]

In field driving activities, off-road areas usually lack existing paths that can be directly driven on by ground vehicles, but their surface environments can still satisfy the planning and passage requirements of some off-road vehicles. Additionally, the existing path planning methods face limitations in complex field environments characterized by undulating terrains and diverse land cover types. Therefore, this study introduces an improved A* algorithm and an adapted 3D model of real field scenes is constructed. A velocity curve is fitted in the evaluation function to reflect the comprehensive influences of different slopes and land cover types on the traffic speed, and the algorithm not only takes the shortest distance as the basis for selecting extension nodes but also considers the minimum traffic speed. The 8-neighborhood search method of the traditional A* algorithm is improved to a dynamic 14-neighborhood search method, which effectively reduces the number of turning points encountered along the path. In addition, corner thresholds and slope thresholds are incorporated into the algorithm to ensure the accessibility of path planning, and some curves and steep slopes are excluded, thus improving the usability and safety of the path. Experimental results show that this algorithm can carry out global path planning in complex field environments, and the planned path has better passability and a faster speed than those of the existing approaches. Compared with those of the traditional A* algorithm, the path planning results of the improved algorithm reduce the path length by 23.30%; the number of turning points is decreased by 33.16%; and the travel time is decreased by 38.92%. This approach is conducive to the smooth progress of various off-road activities and has certain guiding significance for ensuring the efficient and safe operations of vehicles in field environments. [ABSTRACT FROM AUTHOR]

Contact with environmental microbial communities primes the human immune system. Factors determining the distribution of microorganisms, such as dispersal, are thus important for human health. Here, we used the relative number of bacteria shared between environmental and human samples as a measure of bacterial dispersal and studied these associations with living environment and lifestyles. We analyzed amplicon sequence variants (ASVs) of the V4 region of 16S rDNA gene from 347 samples of doormat dust as well as samples of saliva, skin swabs, and feces from 53 elderly people in urban and rural areas in Finland at three timepoints. We first enumerated the ASVs shared between doormat and one of the human sample types (i.e., saliva, skin swab, or feces) of each individual subject and calculated the shared ASVs as a proportion of all ASVs in the given sample type of that individual. We observed that the patterns for the proportions of shared ASVs differed among seasons and human sample type. In skin samples, there was a negative association between the proportion of shared ASVs and the coverage of built environment (a proxy for degree of urbanization), whereas in saliva data, this association was positive. We discuss these findings in the context of differing species pools in urban and rural environments. IMPORTANCE Understanding how environmental microorganisms reach and interact with humans is a key question when aiming to increase human contacts with natural microbiota. Few methods are suitable for studying microbial dispersal at relatively large spatial scales. Thus, we tested an indirect method and studied patterns of bacterial taxa that are shared between humans and their living environment. [ABSTRACT FROM AUTHOR]

Land cover change research is salient in providing information for land use planning and management in urban areas. This study therefore analysed land cover change in four local government areas in Ogun State, Nigeria. Using remote sensing and geographic information system, Landsat images were analysed between 2001, 2013 and 2020 with both unsupervised and supervised image classifications. Results showed that between 2002 and 2013, there was a 41.14% decrease in forest area which further reduced by 41.92% by 2020. Agricultural land increased by 66.47% between 2002 and 2013 but reduced by 43.24% by 2020. Urban areas increased by 108.66% between 2002 and 2013 while by 2020 they had gained 42.19% additional land. It is concluded that the 2005–2025 Ogun State regional planning policy has so far been less than effective due to massive ecological change caused by urban expansion in the region. [ABSTRACT FROM AUTHOR]

This study reveals the temporal and spatial evolution characteristics of the winter nighttime urban heat island (UHI) effect in the case of Beijing, China. The land surface temperature (LST) is retrieved by radiative transfer equation by using the remote sensing data from Landsat ETM+/OLITIRS from 2007 to 2017 for the winter nighttime period, and LST is then divided by the mean-standard deviation method into different levels of thermal landscapes. A combination of the migration calculation of gravity center and multi-directional profile analysis is used to study the directional differentiation characteristics of LST and the migratory characteristics of the gravity center of UHI. Finally, the overall temporal and spatial evolution characteristics of winter nighttime surface urban heat island (SUHI) in Beijing are studied, and the possible reasons for the changes are discussed. Results show that Beijing's UHI effect first increased and subsequently decreased from 2007 to 2017. The winter heat island in the urban area developed from low-density agglomeration to high-density agglomeration to low-density diffusion. Additionally, the high-level thermal landscapes migrated to the southwest along with the city center of gravity, and the expansion rate is fastest in the southwest, which is directly linked to the changes in the urban construction land. Moreover, the overall spatial distribution of winter nighttime LST is high in the east and south and low in the west and north, and is influenced by topography, land cover, urbanization, anthropogenic heat, and other factors as well. [ABSTRACT FROM AUTHOR]

The post‐globalization of the Indian economy marks the beginning of a new era for various industries, including manufacturing and the service sector. As a result, people begin to migrate from sub‐urban/rural area to nearby major cities in search of employment and access to modern conveniences. Land Use Land Cover (LULC) pattern change is caused by human and natural factors which include geology, elevation, and slope. LULC change assessment is best ways to manage and understand landscape transformation. Bilari block in Moradabad is India's brass city and one of Uttar Pradesh's major cities, its population growth has pushed the rapid urbanization of city in last two decades. In the present study Landsat 7 Enhanced Thematic Mapper (ETM) and Landsat 8 Operational Land Manager (OLI) data for years 2000 and 2023 have been employed to assess. ArcGIS Pro 2.9.0 software is used to perform layer stacking and on the raw Landsat data for pre‐processing. To find LULC changes in the study area between 2000 and 2023, supervised classification is done by adopting Maximum Likelihood Classification (MLC). From the results, it is observed that the area from pervious to impervious changes very quickly in Bilari. This research will aid in regional and urban planning, as well as agricultural management, in the years ahead. [ABSTRACT FROM AUTHOR]

A flash flood is a natural phenomenon generally occurring in regions with dense and compact rainfall. The arid Far-North Region of Cameroon (FNRC) is subject to such climate conditions which result in recurrent flash flood events. Those events often cause numerous deaths and important property damage. This article aims at mitigating and reducing flood risks in the FNRC using a GIS-based multicriteria decision-making technique. For this, data were collected from the radar sensor ALOS PALSAR 2, the optical sensor Landsat 9 Operational Land Imager (OLI), and WorldClim 2. From the aforementioned datasets, ten influencing layers, namely curvature, drainage density, elevation, distance to rivers, distance to lakes, land use/land cover (LULC), rainfall, slope, stream power index (SPI) and topographic witness index (TWI) were prepared, normalized, and combined on a GIS environment. The resulting map of the flood susceptible zones (FSZ) reveals two-fifths of the FNRC is seriously threatened by flash flood events. FSZ are clearly demarcated and mapped, and this map is of paramount importance for sustaining safe settlements in the FNRC. In the context of scarce ground data, as in the FNRC where there is a single rain gauge located at the airport, a combined remote sensing-analytical hierarchy process is effective for flash flood investigation. This approach can help in flash flood analysis in other regions of the world. [ABSTRACT FROM AUTHOR]