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Information on Land Use and Land Cover Map (LULCM) is essential for environment and socioeconomic applications. Such maps are generally derived from Multispectral Remote Sensing Images (MRSI) via classification. The classification process can be described as information flow from images to maps through a trained classifier. Characterizing the information flow is essential for understanding the classification mechanism, providing solutions that address such theoretical issues as "what is the maximum number of classes that can be classified from a given MRSI?" and "how much information gain can be obtained?" Consequently, two interesting questions naturally arise, i.e. (i) How can we characterize the information flow? and (ii) What is the mathematical form of the information flow? To answer these two questions, this study first hypothesizes that thermodynamic entropy is the appropriate measure of information for both MRSI and LULCM. This hypothesis is then supported by kinetic-theory-based experiments. Thereafter, upon such an entropy, a generalized Jarzynski equation is formulated to mathematically model the information flow, which contains such parameters as thermodynamic entropy of MRSI, thermodynamic entropy of LULCM, weighted F1-score (classification accuracy), and total number of classes. This generalized Jarzynski equation has been successfully validated by hypothesis-driven experiments where 694 Sentinel-2 images are classified into 10 classes by four classical classifiers. This study provides a way for linking thermodynamic laws and concepts to the characterization and understanding of information flow in land cover classification, opening a new door for constructing domain knowledge. [ABSTRACT FROM AUTHOR]

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Cropland (CRL) abandonment is a worldwide phenomenon of land use change with significant impacts on agro‐ecosystems. This research attempts to deepen the analysis of the positive and negative effects arising by focusing on the changes in soil properties and the amount and composition of soil particles detached after several periods of rainfall and the resulting exported sediment that occurs in abandoned areas with natural revegetation compared to CRL. The study was carried out in an agroforestry catchment with a temperate climate, on which CRL and abandoned land with natural vegetation were compared. The soil was sampled along two representative hillslopes integrating elements of the landscape and land use/land covers. Sediments were collected after seven periods in which flood events were recorded during the period July 2016 to December 2017, using artificial‐lawn mats. Soil and sediment composition (texture, soil organic carbon [SOC] and nutrients [total nitrogen and total phosphorous]) under both land uses were assessed and compared and related to rainfall characteristics using principal component analysis. Nutrient enrichment factors in the sediments compared to soils were also evaluated. The results highlight that after abandonment, SOC increased significantly, reaching contents almost three times higher than in CRL. Consequently, soil erodibility decreased, resulting in substantially lower sediment generation after erosive rainfall events. On average, sediment generation was three times lower in abandoned areas than in CRL, despite their steeper slopes. Soil total nitrogen also increased on abandoned lands, reaching values about twice as high as those in CRL. However, total phosphorous content was almost twice as high in CRL than in abandoned land posing a potential risk for water due to higher erosion rates recorded in CRL. The results confirmed the association of phosphorous with smaller particles and also demonstrated the total phosphorous‐SOC link in abandoned land. Furthermore, the effect of rainfall intensity on phosphorous mobilisation was confirmed, whilst nitrogen losses were mainly related to the total amount of rainfall recorded. In a scenario of increasing extreme precipitation, both total amount and increased precipitation intensity may exacerbate water pollution problems following nutrient loss in cultivated areas. This research contributes to identifying the impacts on agroforestry systems within the current context of converting natural areas into cultivated land, whilst in other regions revegetated natural areas are developed from abandoned agricultural land. [ABSTRACT FROM AUTHOR]

Due to climate change and human activities, land desertification, a major environmental issue in arid regions, has exerted an impact on ecosystem services (ES). However, previous ES assessments in arid regions lacked the integrated assessment and trade‐off analysis of soil‐related ecosystem services (S‐ES). To address this, we developed a S‐ES‐Bayesian network (S‐ES‐BN) model to analyze the trade‐offs and synergies between the complex relationships between S‐ES and their multiple influences in Central Asia (CA). The results showed that (1) there is a large spatial and temporal heterogeneity in S‐ES, with the most significant changes in water yield service (WY, −30%) and soil conservation service (SC, −26%) in the past 30 years (1990–2020); (2) land use and land cover change (LUCC) area shifts can have a gaining or weakening effect on the S‐ES. Among them, water to wetland had the most obvious gain effect on SC (+5.1 t·km2·yr−1) and water to forestland had the most pronounced effect on carbon fixation service (CF, +251.22 t); and (3) based on the S‐ES‐BN, an optimal configuration of S‐ES in CA demonstrated that the intensification of a future "warming and wetting" scenario in CA might lead to significant decreases in WY (−37%) and sand fixation (−7%). In addition, the clarification of the land allocation structure (cropland, forestland, and grassland) in LUCC will enhance the spatial demand for CF. [ABSTRACT FROM AUTHOR]

To understand how ectotherms will respond to warming temperatures, we require information on thermal habitat quality at spatial resolutions and extents relevant to the organism. Measuring thermal habitat quality is either limited to small spatial extents, such as with ground‐based 3D operative temperature (Te) replicas, representing the temperature of the animal at equilibrium with its environment, or is based on microclimate derived from physical models that use land cover variables and downscale coarse climate data. We draw on aspects of both these approaches and test the ability of unoccupied aerial vehicle (UAV) data (optical RGB) to predict fine‐scale heterogeneity in sub‐canopy lizard (Anolis bicaorum) Te in tropical forest using random forest models. Anolis bicaorum is an endemic, critically endangered, species, facing significant threats of habitat loss and degradation, and work was conducted as part of a larger project. Our findings indicate that a model incorporating solely air temperature, measured at the centre of the 20 × 20 m plot, and ground‐based leaf area index (LAI) measurements, measured at directly above the 3D replica, predicted Te well. However, a model with air temperature and UAV‐derived canopy metrics performed slightly better with the added advantage of enabling the mapping of Te with continuous spatial extent at high spatial resolutions, across the whole of the UAV orthomosaic, allowing us to capture and map Te across the whole of the survey plot, rather than purely at 3D replica locations. Our work provides a feasible workflow to map sub‐canopy lizard Te in tropical environments at spatial scales relevant to the organism, and across continuous areas. This can be applied to other species and can represent species within the same community that have evolved a similar thermal niche. Such methods will be imperative in risk modelling of such species to anthropogenic land cover and climate change. [ABSTRACT FROM AUTHOR]

Glaciers have experienced a global trend of recession within the past century. Quantification of glacier variations using satellite imagery has been of great interest due to the importance of glaciers as freshwater resources and as indicators of climate change. Spatiotemporal glacier dynamics must be monitored to quantify glacier variations. The potential methods to quantify spatiotemporal glacier dynamics with increasing complexity levels include detecting the terminus location, measuring the length of the glacier from the accumulation zone to the terminus, quantifying the glacier surface area, and measuring glacier volume. Although some deep learning methods designed purposefully for glacier boundary segmentation have achieved acceptable results, these models are often localized to the region where their training data were acquired and further rely on the training sets that were often curated manually to highlight glacial regions. Due to the very large number of glaciers, it is practically impossible to perform a worldwide study of glacier dynamics using manual methods. As a result, an automated or semi-automated method is highly desirable. The current study has built upon our previous works moving towards identification methods of the 2D glacier profile for glacier area segmentation. In this study, a deep learning method is proposed for segmentation of temporal Landsat images to quantify the glacial region within the Mount Cook/Aoraki massif located in the Southern Alps/Kā Tiritiri o te Moana of New Zealand/Aotearoa. Segmented glacial regions can be further utilized to determine the relationship of their variations due to climate change. This model has demonstrated promising performance while trained on a relatively small dataset. The permanent ice and snow class was accurately segmented at a 92% rate by the proposed model. [ABSTRACT FROM AUTHOR]

The research presented in this study introduces a novel methodology for delineating flood-prone regions within the Kosi River Basin, utilizing a multi-criteria decision-making approach. This method integrates multi-criteria analysis, Geographical Information Systems (GIS), and Remote Sensing (RS). The specific process involves the creation of flood susceptibility maps based on five crucial factors: rainfall, land use/cover, slope, drainage density, and distance from the river. Expert judgments were incorporated and translated into weighted values to ascertain the relative significance of each factor in determining flood susceptibility. Weight calculations were performed using the Fuzzy Analytic Hierarchy Process (FAHP). The findings of this study indicate that across all 10 districts in the region, a varying degree of land area is classified as high-risk, with Madhubani displaying the highest percentage of land area categorized as of very high-risk. Key challenges include data accuracy and model generalization, with potential applications in other flood-prone areas. This approach not only improves the precision of flood susceptibility mapping, but also offers valuable insights for disaster management and planning in areas with limited data availability. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The relationship between urban heat island (UHI) and land use/land cover (LULC), and local climate zone (LCZ) is apparent and takes rising attention in the current literature. This study aimed to investigate the relationship between meteorological data collected from 30 stations between 2016 and 2022 and Istanbul's LULCs and LCZs. Several notable findings were uncovered, providing light on the UHI phenomenon and its consequences for the city's characteristics. The stations in urban areas (typology A) had higher temperatures than stations in rural/suburban (typology B) and forested landscape (typology C). Those yearly values were ∼1°C for monthly mean temperatures and ∼1.5°C for monthly minimum temperatures. Moreover, urban areas possessed +4 and +2 hot days (35°C and above) for typologies B and C, respectively. Another remarkable result was that stations situated close to water surfaces exhibited a lower tendency to exceed temperatures of 35°C. Furthermore, built-type LCZs wind velocity achieved a lower value than land cover type, and humidity in typology A was 5% and 10% less than the typologies B and C, respectively. Consequently, the southern part of Istanbul emerged as the most vulnerable location to the UHI phenomena, suffering greater temperatures. [ABSTRACT FROM AUTHOR]

The garden-city model, characterised by sprawling urban expansion and extensive open spaces, features green and water surfaces alongside substantial road networks, large parking lots and unshaded lands. This diverse landscape contributes to uneven air temperature distribution within the city. Our study investigated urban heat island (UHI) patterns in various urban environments within Putrajaya, focusing on rural, business, residential and near-water areas. Atmospheric data were collected over a year. Putrajaya exhibits two distinct UHI patterns: cool-day-hot-night in residential zones and hot-noon-temperate-elsewhere in impermeable areas. Business areas display alternating patterns based on seasonal climatic conditions and anthropogenic factors. The efficacy of artificial water bodies as a UHI countermeasure in garden cities depends on the size of the water body and the nature of the surrounding land cover. Shaded and green areas exhibit UHI intensities limited to 3°C, while impermeable areas, regardless of proximity to water surfaces, experience UHI up to 6°C. Overall, the garden-city model tends to restrict UHI variations within −1 to 3°C compared to −2 to 4°C in nearby Kuala Lumpur. [ABSTRACT FROM AUTHOR]

The presence of ice cover on temperate lakes is a crucial factor in determining the functioning of these ecosystems. The isolation of water from atmospheric influences significantly alters physical, chemical, and biological processes, and the intensity of this impact depends on the duration of the ice cover. This study analyzed the basic parameters of ice cover on several dozen lakes in Northeastern Poland. The aim of this study is to investigate the influence of morphometric parameters, alongside environmental factors, on the variation of ice cover characteristics in lakes located within the Eastern Baltic Lakeland. Characterization of ice conditions in the analyzed lakes was based on basic statistics such as minimum and maximum values, mean, standard deviation, coefficients of variation, skewness, and kurtosis. Given that the dataset contains variables describing ice phenomena in the studied lakes and data describing location, morphometric parameters, and land cover directly adjacent to the lake (treated as independent variables), a method of Spearman's rank correlations and constrained ordination method were decided upon. Despite the relatively small study area, significant variability was observed, with average differences as follows: 26 days for the onset of ice cover, 17 days for the end date, 15 cm for ice thickness, and a 30-day difference in the average duration of ice cover. Key factors included parameters such as lake volume, average depth, and land use (urbanized and agricultural areas). Understanding parameters such as the onset and end of ice cover is essential for lake ecosystems, both from an ecological and economic perspective. This knowledge is crucial for interpreting the behavior of living organisms, water quality, and economic considerations. [ABSTRACT FROM AUTHOR]

Uncontrolled human activity and nature are causing the deterioration of Saint Martin Island, Bangladesh's only tropical island, necessitating sustainable land use strategies and ecological practices. Therefore, the present study measures the land use/cover transition from 1974 to 2021, predicts 2032 and 2042, and constructs the spatiotemporal features of the Landscape Ecological Risk Index based on land use changes. The study utilized Maximum Likelihood Classification (MLC) on Landsat images from 1974, 1988, 2001, 2013, and Sentinel 2B in 2021, achieving ≥ 80% accuracy. The MLP-MC approach was also used to predict 2032 and 2042 LULC change patterns. The eco-risk index was developed using landscape disturbance and vulnerability indices, Bayesian Kriging interpolation, and spatial autocorrelations to indicate spatial clustering. The research found that settlements increased from 2.06 to 28.62 ha between 1974 and 2021 and would cover 41.22 ha in 2042, causing considerable losses in agricultural areas, waterbodies, sand, coral reefs, and vegetation. The area under study showed a more uniform and homogenous environment as Shannon's diversity and evenness scores decreased. The ecological risk of Saint Martin Island increased from 4.31 to 31.05 ha between 1974 and 2042 due to natural and human factors like erosion, tidal bores, population growth, coral mining, habitat destruction, and intensive agricultural practices and tourism, primarily in Nazrul Para, Galachipa, and Western Dakhin Para. The findings will benefit St. Martin Island stakeholders and policymakers by providing insights into current and potential landscape changes and land eco-management. [ABSTRACT FROM AUTHOR]

The exploitation and utilization of coal resources have significantly contributed to global energy security. However, this mining activity has inflicted considerable damage on the ecological environment, particularly on the Tibetan Plateau, where the impact on ecosystems may be even more detrimental. The implementation of high-intensity mining activities leads to rapid changes in land cover/land use. Consequently, it is essential to accurately and effectively monitor mining disturbances. In this study, we propose an approach to capture surface mining disturbances using spatial–temporal rules and time series stacks of Landsat data. First, a time series of annual mining disturbance probability was generated based on Landsat temporal-spectral metrics and random forest. Second, the Landsat-based detection of Trends in Disturbance and Recovery (LandTrendr) algorithm was employed to segment the time series and detect breakpoints. Finally, mining disturbances were captured by further restricting the output of LandTrendr based on spatial–temporal rules of mining disturbances. This approach was applied and evaluated in the Muli mining area of the northeastern Tibetan Plateau, which experienced large-scale and rapid mining disturbances from 2004 to 2014, and identified a disturbed mining area of 43.62 km2. The mining sites have been reclaimed after mining, and all reclamation work was done after 2016, with a total reclaimed area of 22.28 km2. The validation results indicated that the overall accuracy of mining disturbance and reclamation mapping ranges from 0.7333 to 0.8667, and the F1 scores for mining disturbances and reclamation range from 0.7551 to 0.8723. This study provides a reliable framework for monitoring mining disturbances and reclamation in surface mines, promising to be useful in realizing disturbance monitoring in surface mines for a wide range of mineral types. [ABSTRACT FROM AUTHOR]

Soil and water characteristics in micro basins with different land uses/land cover (LULC) can influence riparian vegetation diversity, stream water quality, and benthic diatom diversity. We analyzed 18 streams in the upper part of the La Antigua River basin, México, surrounded by cloud forests, livestock pastures, and coffee plantations. Concentrations of P, C, and N were elevated in the humus of forested streams compared to other land uses. In contrast, cations, ammonium, and total suspended solids (TSS) of water streams were higher in pastures and coffee plantations. These results indicate that LULC affects stream chemistry differently across land uses. Vegetation richness was highest (86–133 spp.) in forest streams and lowest in pastures (46–102), whereas pasture streams had the greatest richness of diatoms (9–24), likely due to higher light and temperatures. Some soil and water characteristics correlated with both true diversity and taxonomic diversity; soil carbon exchange capacity (CEC) correlated with vegetation diversity (r = 0.60), while water temperature correlated negatively (r = − 0.68). Diatom diversity was related to soil aluminum (r = − 0.59), magnesium (r = 0.57), water phosphorus (r = 0.88), and chlorophyll (r = 0.75). These findings suggest that land use affects riparian vegetation, while physical and chemical changes influence diatom diversity in stream water and soil. The lack of correlation between vegetation and diatom diversity indicates that one cannot predict the other. This research is an essential first step in understanding how land use changes impact vegetation and diatom diversity in mountain landscapes, providing valuable insights for environmental monitoring and conservation efforts in tropical cloud forests. [ABSTRACT FROM AUTHOR]

Drought is one of the common natural disasters with a wide range of occurrences in terms of space and time, and with varying levels of severity, that may result in economic damage and health issues to humans. This study focuses on assessing drought severity in the Central Highlands of Vietnam based on ground meteorological stations and multispectral remote sensing data. A Modification of the Normalized Difference Drought Index (MNDDI) was developed to enhance the effectiveness of remote sensing indices in the drought assessment. Results indicate that MNDDI outperforms Normalized Difference Drought Index and other investigated indicators, such as Normalized Difference Vegetation Index, Normalized Difference Latent Heat Index, and Normalized Difference Water Index, in representing the Earth's surface response to drought events. Correlations ranging from 0.85 to 0.63 were identified between MNDDI and various time scales of the commonly used meteorological drought indicator, namely the Standardized Precipitation Index, during the drought year of 2015. This work also reveals the superiority of MNDDI in portraying the response of land cover types to drought situations. The finding of a severe drought phenomenon in critical agricultural zones is highly consistent with the report from the Ministry of Agriculture and Rural Development of Vietnam. This study contributes valuable insights to the preliminary assessment of drought through remote sensing data, offering a foundation for precise drought outlooks and effective risk management strategies. [ABSTRACT FROM AUTHOR]

Groundwater depletion and water scarcity are pressing issues in water-limited regions worldwide, including Pakistan, where it ranks as the third-largest user of groundwater. Lahore, Pakistan, grapples with severe groundwater depletion due to factors like population growth and increased agricultural land use. This study aims to address the lack of comprehensive groundwater availability data in Lahore's semi-arid region by employing GIS techniques and remote sensing data. Various parameters, including Land Use and Land Cover (LULC), Rainfall, Drainage Density (DD), Water Depth, Soil Type, Slope, Population Density, Road Density, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Moisture Stress Index (MSI), Water Vegetation Water Index (WVWI), and Land Surface Temperature (LST), are considered. Thematic layers of these parameters are assigned different weights based on previous literature, reclassified, and superimposed in weighted overlay tool to develop a groundwater potential zones index map for Lahore. The groundwater recharge potential zones are categorized into five classes: Extremely Bad, Bad, Mediocre, Good, and Extremely Good. The groundwater potential zone index (GWPZI) map of Lahore reveals that the majority falls within the Bad to Mediocre recharge potential zones, covering 33% and 28% of the total land area in Lahore, respectively. Additionally, 14% of the total area falls under the category of Extremely Bad recharge potential zones, while Good to Extremely Good areas cover 19% and 6%, respectively. By providing policymakers and water supply authorities with valuable insights, this study underscores the significance of GIS techniques in groundwater management. Implementing the findings can aid in addressing Lahore's groundwater challenges and formulating sustainable water management strategies for the city's future. [ABSTRACT FROM AUTHOR]

Monitoring the land use/land cover (LU/LC) changes that have occurred with rapid population growth and urbanization since the Industrial Revolution is important for the optimal configuration of landscape patterns and ensuring the sustainability of ecological functions. Spatiotemporal dynamic pattern of LU/LC change using high-resolution land use data is an indicator to evaluate the landscape ecological risk through landscape pattern index analysis. In this study, the landscape ecological risk index (LERi) based on LU/LC change was calculated using remote sensing images of Landsat TM (Thematic Mapper) and OLI (Operational Land Imager) Rdata of a Gediz Mainstream Sub-basin in Turkiye between 1992 and 2022, and the spatial distribution regularity of LERi values was determined with spatial statistical analysis. According to the results, it was determined that the LERi values of the study area changed by 45% in 30 years. The highest change is in the very high-risk class, with an increase of 10.96%, and the least change occurred in the very low-risk class, with a decrease of 1.29%. According to the obtained statistical analysis results, it was determined that the global spatial autocorrelation values analyzed at different grain levels showed positive autocorrelation for both years and that the LERi values tended to have strong spatial clustering. As a result, it is emphasized that strict control measures should be taken for areas showing High-High (HH) autocorrelation type located in the southeast and north-southwest line of the study area at the local level, and ecological restoration applications should be given priority in these areas. [ABSTRACT FROM AUTHOR]

As the number of humans living in cities has grown, interest in the value of community gardens to provide agricultural products has increased. However, neighborhoods with different land cover patterns and socioeconomic characteristics often differ in their ecological attributes, leading to potential differences in biodiversity-mediated ecosystem services (e.g., pollination and pest control). Here we ask, how do impervious surface and socioeconomic features of the urban matrix around community gardens impact arthropod biodiversity and pollination and pest control services? We collected arthropods (insects, arachnids, myriapods, and isopods) across community gardens in Boulder Co., CO, and used experimental jalapeño pepper plants as a sentinel crop to measure herbivory damage and pollination services. We categorized arthropods into functional guilds to see how impervious surface and neighborhood wealth in the urban matrix surrounding a site impacts the abundance of three focal groups – pollinators, herbivorous pests, and predators. We also looked at how bee Hill-Simpson diversity responded to these variables. Through structural equation modeling, we found that fruit size increased as bee biodiversity increased, and bee biodiversity and overall pollinator abundance were negatively related to neighborhood wealth. Additionally, pollinator abundance was lower in gardens surrounded by higher amounts of impervious surfaces. Neighborhood wealth and impervious surfaces were positively correlated with herbivore and predator abundances, but these abundances had no relationship with herbivory damage in our plants. This research shows that reducing the amounts of impervious surface in the urban matrix can help increase bee biodiversity and abundance and improve pollination services in urban community gardens. [ABSTRACT FROM AUTHOR]

Peri-urban landscapes are transitional areas between urban and rural areas that are constantly changing. They are characterised by land use mixes and land cover changes, leading to significant changes in wildlife habitats. These changes, combined with an increasing anthropogenic presence, turn peri-urban landscapes into arenas of intensified human-wildlife interactions. In many scientifically documented cases, scientists are focusing on negative interactions. Furthermore, research about appropriate policy instruments for managing human-wildlife interactions is rare. This study focused on case studies and their characteristics from around the world on positive, neutral, or negative interactions between humans and wildlife at local level. In addition, influencing factors of human-wildlife interactions and policy instruments for managing human-wildlife interactions in peri-urban landscapes were addressed. A survey was conducted with an international group of scientists and practitioners working in this field. The results showed that various species were involved in human-wildlife interactions in peri-urban landscapes worldwide, with mammals as being the most common taxon. Contemporary changes in land use and land cover could be identified as a significant factor for increasing human-wildlife interactions in peri-urban landscapes. It can be highlighted that a policy mix consisting mainly of social and cultural instruments in combination with legal and regulatory instruments could be most suitable to address this situation. [ABSTRACT FROM AUTHOR]

Jointly building the Economic Corridor of the Eurasia Continent (ECEC), which is one of the most important parts of the Silk Road Economic Belt, is a pivotal initiative for fostering regional development. Forests, which serve as a green foundation of economic resilience, underpin this effort. However, there is an imbalance in ecological status due to differences in natural resources and the social economy along the economic corridor. This imbalance has led to alterations in landscapes, yet the specific changes and their underlying relationships are still much less understood. Here, we quantitatively detected changes in the forest landscape and its ecological efforts over the ECEC via widespread, satellite-based and long-term land cover maps released by the European Space Agency (ESA) Climate Change Initiative (CCI). Specifically, the coupling between changes in forest coverage and landscape patterns, e.g., diversity, was further examined. The results revealed that forest coverage fluctuated and declined over the ECEC from 1992 to 2018, with an overall reduction of approximately 9784.8 km2 (i.e., 0.25%). Conversions between forests and other land cover types were widely observed. The main displacements occurred between forests and grasslands/croplands (approximately 48%/21%). Moreover, the landscape diversity in the study area increased, as measured by the effective diversity index (EDI), during the study period, despite obvious spatial heterogeneity. Notably, this pattern of landscape diversity was strongly associated with forest displacement and local urban development through coupling analysis, consequently indicating increasing fragmentation rather than biological diversity. This study highlights the coupled relationship between quantitative and qualitative changes in landscapes, facilitating our understanding of environmental protection and policy management. [ABSTRACT FROM AUTHOR]

Background: Fire activity in the UK and comparable regions of northwest Europe is generally out of phase with peak fire weather conditions. Aims: Here, we assess the potential effect of phenology on fire occurrence patterns for the UK. Methods: We examined fire occurrence and vegetation phenology in the UK for 2012–2023, mapped onto the main fire-affected vegetation cover types within distinct precipitation regions, allowing the fire occurrence for fuels in different phenological phases to be explored across distinct 'fuel' types and regions. Key results: The UK's fire regime is characterised by burning in semi-natural grasslands and evergreen dwarf shrub ecosystems in early spring when vegetation is still dormant. During the high-greenness phase in late spring and summer, fire activity is reduced by a factor of 5–6 despite typically elevated fire weather conditions within that period. Conclusions and implications: Semi-natural vegetation in the UK is very resistant to burning during the high-greenness phase. However, this 'fire barrier' is diminished during severe drought episodes, which are predicted to become more extreme in the coming decades. Incorporating phenology information into models therefore has great potential for improving future fire danger and behaviour predictions in the UK and comparable humid temperate regions. We examined fire activity and phenology for major vegetation types across the UK for 2012–2023 and found that phenology rather than fire weather was the main driver for fire occurrence. Fire weather dominated only in years with extreme drought and heatwave events, which are likely to become more frequent with climate change. [ABSTRACT FROM AUTHOR]

Flashiness is a hydrologic signature used to describe the responsiveness of streamflow to precipitation. In practise, flashiness has most often been correlated with rainfall intensity and the presence of urban land cover or imperviousness. However, a number of watershed characteristics can influence flashiness, necessitating further investigation of how this particular watershed behaviour and its associated drivers vary at regional scales. We investigated how ten measurable watershed characteristics are empirically related to flashiness of 195 gauged streams in the Mid‐Atlantic region using linear regression analysis. The selected gauges had drainage areas ranging in size from 15 to 250 km2. To explore multiple quantifications of flashiness, we determined both the Richards‐Baker flashiness index, calculated from the continuous hydrograph, and number of peaks‐over‐threshold, for all watersheds for a 10‐year period. As found in other studies, the percentage of watershed development was strongly correlated with stream flashiness, though significant inverse relationships also existed for forest cover, water and wetlands and carbonate geology. Differences in flashiness values between rural and suburban watersheds were not significant, with a significant increase in flashiness (p < 0.01) occurring at 80% development or ~ 25% imperviousness. In addition, we found significant differences in relationships between watershed characteristics and flashiness when differentiated by physiographic regions. For the study area and each of its five physiographic sub‐regions, we identified the four most important predictors through multiple regression. The overall mid‐Atlantic model suggested that development, wetlands and compactness ratio as the most important predictors (adj R2 = 0.57). Significant predictors of stream flashiness varied among regions; however, wetlands served as a significant buffer of flashiness in four of five sub‐regions demonstrating the importance of wetlands in modulating flash‐flooding. In all physiographic regions, empirical models of flashiness values outperformed the Mid‐Atlantic regional model, underscoring the value of local landscape characteristics in augmenting or modulating flashy watershed responses. Our findings suggest the potential for improvements to operational flash‐flood forecasting using hydrologic characteristics of the landscape. [ABSTRACT FROM AUTHOR]

Satellite data are increasingly used to provide information to support forest monitoring and reporting at varying levels of detail and for a range of attributes and spatial extents. Forests are dynamic environments and benefit from regular assessments to capture status and changes both locally and over large areas. Satellite data can provide products relevant to forest science and management on a regular basis (e.g. annually) for land cover, disturbance (i.e. date, extent, severity, and type), forest recovery (e.g. quantification of return of trees following disturbance), and forest structure (e.g. volume, biomass, canopy cover, stand height), with products generated over large areas in a systematic, transparent, and repeatable fashion. While pixel-based outcomes are typical based upon satellite data inputs, many end users continue to require polygon-based forest inventory information. To meet this information need and have a spatial context for forest inventory attributes such as tree species assemblages, we present a new work-flow to produce a novel spatially explicit, stand-level satellite-based forest inventory (SBFI) in Canada applying image segmentation approaches to generate spatially unique forest stands (polygons), which are the fundamental spatial unit of management-level inventories. Thus, SBFI offers spatial context to aggregate and generalize other pixel-based forest data sets. Canada has developed a National Terrestrial Ecosystem Monitoring System (NTEMS) that utilizes medium spatial resolution imagery, chiefly from Landsat, to annually characterize Canada's forests at a pixel level from 1984 until present. These NTEMS datasets are used to populate SBFI polygons with information regarding status (e.g. current land cover type, dominant tree species, or total biomass) as well as information on dynamics (e.g. has this polygon been subject to change, when, by what, and if so, how is the forest recovering). Here, we outline the information drivers for forest monitoring, present a set of products aimed at meeting these information needs, and follow to demonstrate the SBFI concept over the 650-Mha extent of Canada's forest-dominated ecosystems. In so doing, the entirety of Canada's forest ecosystems (managed and unmanaged) were mapped using the same data, attributes, and temporal representation. Moreover, the use of polygons allows for the generation of attributes such as tree species composition, and total biomass and wood volume in a stand-scale format familiar to landscape managers and suitable for strategic planning. The data, methods, and outcomes presented here are portable to other regions and input data sources, and the national SBFI outcomes for Canada are available via open access. [ABSTRACT FROM AUTHOR]

The increase in flood events observed in West African countries, and often in specific river basins, can be influenced by several factors, including anthropogenic land use and land-cover changes. However, the potential contribution of land cover changes to flood events still needs to be explored, especially in West Africa. Here, the fully coupled atmosphere-hydrology WRF-Hydro system, which comprises an atmospheric model and additionally incorporates the surface, subsurface, overland flow, and channel routing, is used to investigate the potential impact of a land cover change scenario on flood events in the Senegal River basin. The simulation was performed from 2010 to 2020, with a calibration period spanning from 2011 to 2012 and a validation period from 2013 to 2020. Several skill scores, including Nash-Sutcliffe Efficiency (NSE), BIAS, and Kling-Gupta Efficiency (KGE), were utilized to assess the calibration and validation performances. Additionally, two planetary boundary layer schemes (PBL5 and PBL7) were used to determine their associated uncertainty. Our results show that the best calibration results (NSE = 0.70; KGE = 0.83; PBIAS = -7% and BE = 0.67) in the Senegal River basin are obtained with PBL5 when the calibration is performed with a SLOPE parameter 0.03. A similar good performance was also obtained for the validation with NSE = 0.74, KGE = 0.84, and PBIAS = -8%. Likewise, our findings indicate that converting savanna to woody savannas can elevate water resources, with a 2% rise in precipitation and a 4% increase in runoff. This transition also correlates with an increase in moderate flood events (3500-4000 m3/s), a decrease in severe floods (4000-5000 m3/s), and their associated occurrence of extreme floods (>5000 m3/s) in the Senegal River basin. [ABSTRACT FROM AUTHOR]

River systems act as conveyor belts carrying sediments in huge quantities from upstream to downstream regions of the basins. Suspended sediment (SS) is a vital element in a river system due to its importance in the planning, design, and management of hydraulic structures. Abrupt variations in sediment dynamics have been observed recently, attributing these to both changes in climate and different anthropogenic actions. A proper understanding of the dominance and impact of these anthropogenic and natural factors on sediment dynamics will help to maintain the sediment equilibrium of any basin. In this study, we present a comprehensive analysis of different changed regimes of suspended-sediment concentration (SSC) and quantify the dominant factors during such changed regimes in Indian river basins. Declining trends are evident in the SSC profiles of most of the gauge stations (∼80%) and can be attributed to increasing anthropogenic activities, especially the construction of water retaining structures throughout India. On average, more than a 50% reduction in sediment quantity was recorded at most gauge stations over the past three decades. It is apparent that natural factors such as discharge and rainfall still are the most dominant factors; but quite interestingly, their relative importance is decreasing after each change point, whereas the influence from anthropogenic factors such as changes in the cropland and grassland are increasing. Large river basins show more influence of anthropogenic activities on sediment dynamics when compared with other smaller basins. Also, changes in land use land cover (LULC) have increasing impact on upstream/midstream parts of the river basins. It is evident that the completion/commissioning of water retaining structures brings abrupt changes in the sediment dynamics, but once the system stabilizes, other natural and anthropogenic factors take over as dominant factors until the next structure comes up and changes the system dynamics again. [ABSTRACT FROM AUTHOR]

The habitat suitability for pollinators is an important factor for biodiversity and crop-based ecosystem services. Most flowering plants, including wild plants, rely on pollination ecosystem services for fructification and reproduction. Suitable nesting sites and accessible floral food resources are crucial to the abundance of pollinator insects. Therefore, the suitability for pollinators and the pollination service itself are influenced by the type of land use, the composition of the land cover and structures in the landscape. One way to estimate pollinator habitat suitability is to use computer models such as ESTIMAP and InVEST. Both models calculate the habitat suitability based on spatial land use data and their suitability as nesting and feeding habitats. Besides the decision for a model, the selected spatial dataset also has important effects on the modelling results. In Germany, a large number of Land Use and Land Cover (LULC) datasets is available, such as the European CORINE Land Cover, CORINE Urban Atlas, Biotope types, ATKIS or Open Street Map. These datasets differ in terms of spatial and thematic resolutions, LULC types, and abundance of structural elements, which are crucial input factors for modelling with ESTIMAP and InVEST. We applied and tested both models on the basis of two different datasets in the study area Hannover region in central Germany. A literature-based estimation and expert-based questionnaire determined the biophysical properties required for modelling with ESTIMAP and InVEST. The differences between the results based on two different spatial datasets and the differences between the models were estimated and the results that can be obtained by using freely available data were investigated and compared with the results based on non-publicly available data. The comparison of the results shows that the proportion of near-natural structures in the landscape are a decisive factor for modelling results. The comparison of the models shows that ESTIMAP estimates a higher influence of small structures in the landscape than InVEST, resulting in a higher pollinator habitat suitability. The median similarity index of the two models is between 0.68 to 0.93 for the highly detailed biotope type dataset and 0.40 to 0.79 for the less detailed Corine dataset. The results provide a guidance on how to choose the appropriate model and data to assess pollination ecosystem services. [ABSTRACT FROM AUTHOR]

Dryland precipitation depends on upwind and local moisture sources via moisture recycling. How upwind moisture variations affect interannual variations of downwind precipitation and vegetation in China's drylands remains unclear. We used high‐resolution moisture tracking data sets and found terrestrial moisture (93%) was the dominant moisture source for China's drylands, especially from drylands themselves (46%). In most dryland grids, we observed strong correlations between precipitation and upwind moisture sources from 2003 to 2022 (median r = 0.55), with a more significant effect in drier areas. These demonstrated the upwind moisture control on interannual variations of dryland precipitation, in which internal moisture from drylands exceeds the influence of external terrestrial sources. The upwind moisture variations, especially the recycled moisture of drylands, propagate to influence downwind vegetation greenness in precipitation‐sensitive dryland areas. Our findings revealed that upwind moisture variations induced by climate or land‐cover changes have important implications for water and food security in China's drylands. Plain Language Summary: Moisture recycling refers to the hydrological cycling in which winds transport evapotranspiration water from upwind source regions and generate precipitation in downwind regions. Precipitation in China's drylands depends on moisture from their upwind source regions, mainly Eurasia and the drylands themselves. Therefore, variations in evaporated water from upwind source regions may affect precipitation in China's drylands and vegetation activities that require water. This study used moisture tracking data sets and found the crucial influence of upwind moisture on dryland precipitation across years. The year‐by‐year changes in upwind moisture, especially from drylands themselves, influence precipitation and vegetation activities in dryland areas where vegetation growth is sensitive to water. The moisture changes due to climate or land cover changes in the upwind source regions could affect water and food security in China's drylands. Key Points: Upwind moisture source regions and downwind China's drylands are tele‐connected through moisture recyclingUpwind moisture variations induce interannual variations in dryland precipitation and vegetation greennessUpwind moisture variations have implications for water and food security in China's drylands [ABSTRACT FROM AUTHOR]

The present work was carried out on River Yamuna passing through Delhi-National Capital Region (Delhi-NCR) of India for a stretch of about 125 km to assess the fluvial characteristics in the highly human-dominated region for a period of 200 years with the help of historical maps, topographical maps and satellite data with integration of geographic information system (GIS) environment. Erosion, deposition and channel stability data were analyzed for 1977–1986, 1986–1996, 1996–2006, 2006–2016, and 2016–2022 period. Digital Shoreline Analysis System (DSAS) application was utilized to quantify river channel movement with average channel migration of 22.8 m/year. Westward migration is 4149.2 m maximum in length and eastward migration is about 4083.8 m. The river has migrated a total of 32.26 km2 of area during 1955-2022. The findings indicate that various human activities, such as engineering structures, sand mining, embankments, urbanization, land use/land cover changes, and canal networks, have significantly impacted the river. The DSAS application was also used to predict the position of river channel centerline in future for 2032 and 2042 with channel length of 132.5 and 141.6 km respectively. [ABSTRACT FROM AUTHOR]

Understanding the effects of human disturbance on the bedload transport regime of anthropised rivers is a topic of growing importance, as such information is of interest for adequate river diagnosis, correct implementation of restoration measures and appropriate design of post‐action monitoring programs. However, such assessments are complex, especially in sites where multiple factors simultaneously influence the bedload transport regime, so that it is difficult to establish simple causal relationships between human disturbances and changes in the sediment transport regime, notably on bedload. To overcome this, there is a need for rigorous hypothesis‐driven approaches to assess the isolated effects of each driver. With this in mind, we have characterised the dynamics of bedload transport in the Upper Garonne (Central Pyrenees, Spain‐France), a river impacted by sediment retention, flow diversion and mining that influence its morphological conditions and transport regime. We assessed the effects of (1) surface grain size distribution, (2) river morphology, (3) sediment supply and (4) flow diversion on the bedload transport regime. Four sites with different degrees of river anthropisation were selected. After defining hypotheses on the most likely bedload transport conditions for each site, we proposed a set of discriminating criteria to test these hypotheses, based on temporal within‐site and spatial between‐site comparisons of coarse particle tracking measurements over four years. The results of this research showed that the hydrosedimentary regime of the Garonne is controlled by a complex combination of drivers such as valley physiography, which exerts a first‐order control on differences in reach‐scale bedforms and bedload dynamics; and human disturbances which contribute to a reduction in sediment supply through changes in land cover and hydropower dams, or to changes in hydrology (i.e., flow competence) due to water diversion and abstraction. [ABSTRACT FROM AUTHOR]

Reliable information plays a pivotal role in sustainable urban planning. With advancements in computer technology, geoinformatics tools enable accurate identification of land use and land cover (LULC) in both spatial and temporal dimensions. Given the need for precise information to enhance decision-making, it is imperative to assess the performance and reliability of classification algorithms in detecting LULC changes. While research on the application of machine learning algorithms in LULC evaluation is widespread in many countries, it remains limited in Zambia and Sri Lanka. Hence, we aimed to assess the reliability and performance of support vector machine (SVM), random forest (RF), and artificial neural network (ANN) algorithms for detecting changes in land use and land cover taking Lusaka and Colombo City as the study area from 1995 to 2023 using Landsat Thematic Mapper (TM), and Operational Land Imager (OLI). The results reveal that the RF and ANN models exhibited superior performance, both achieving Mean Overall Accuracy (MOA) of 96% for Colombo and 96% and 94% for Lusaka, respectively. Meanwhile, the SVM model yielded Overall Accuracy (OA) ranging between 77% and 94% for the years 1995 and 2023. Further, RF algorithm notably produced slightly higher OA and kappa coefficients, ranging between 0.92 and 0.97, when compared to both the ANN and SVM models, across both study areas. A predominant land use change was observed as the expansion of vegetation by 11,990 ha (60.4%), primarily through the conversion of 1,926 ha of bare lands into vegetation in Lusaka during 1995-2005. However, a noteworthy shift was observed as built-up areas experienced significant growth from 2005 to 2023, with a total increase of 25,110 ha (71%). However, despite the conversion of vegetation to built-up areas during the entire period from 1995 to 2023, there was still a net gain of over 11,000 ha (53.4%) in vegetation cover. In case of Colombo, built-up areas expanded by 1,779 ha (81.5%), while vegetation land decreased by 1,519 ha (62.3%) during concerned period. LULC simulation also indicated a 160-ha expansion of built-up areas during the 2023-2035 period in Lusaka. Likewise, Colombo saw a rise in built-up areas by 337 ha within the same period. Overall, the RF algorithm outperformed the ANN and SVM algorithms. Additionally, the prediction and simulation results indicate an upward trend in built-up areas in both scenarios. The resultant land cover maps provide a crucial baseline that will be invaluable for urban planning and policy development agencies in both countries. [ABSTRACT FROM AUTHOR]

Introduction: Groundwater demand has been considerably heightened due to rapid urban growth, specifically in arid areas that rely primarily on groundwater. This study aims to utilize remote sensing and aeromagnetic data, combined with the Analytic Hierarchy Process (AHP) based GIS, to evaluate potential groundwater zones in the Sohag area, Egypt. Methods: Nine thematic layers, including soil moisture, rainfall, lithology, normalized difference vegetation index (NDVI), drainage density, lineament density, slope, and land use/land cover, were developed using various remote sensing datasets. Besides the remote sensing-derived thematic layers, a geophysics-derived thematic layer represented by the RTP aeromagnetic map was included. The aeromagnetic data were analyzed and interpreted to outline the subsurface structure affecting groundwater storage and flow. Also, the aeromagnetic data analysis helps estimate the basement depth that constitutes the Nubian Aquifer's base and identifies regions with considerable thick sedimentary deposits and significant water reserves. Results and discussion: The groundwater potentiality map was consistent with production wells in the area, and sites for drilling new wells were predicted, especially in the Nile Valley around the Tahta, El-Hamimia, and west Sohag cities. The most promising sites are clustered along the Nile Valley, and the study area's northwestern and northeastern parts. The results indicate that the predominant magnetic structural trends are NW-SE, NE-SW, N-S, and E-W, which contribute to the formation of a series of subsurface horsts (H) and grabens (G). Three main basins (A, B, and C) were identified as the most profound areas. These basins represent the most promising areas for groundwater accumulation, making them attractive for future hydrogeological exploration. This integrated approach strongly offers a powerful and effective tool to assist in developing an appropriate plan to manage groundwater in arid regions. [ABSTRACT FROM AUTHOR]

Biogenic volatile organic compounds (BVOCs) are regarded as important precursors for ozone and secondary organic aerosol, mainly from vegetation emissions. In the context of the expanding trend of vegetation greening, the development of high‐precision vegetation data and accurate BVOC emission estimates are essential to develop effective air pollution control measures. In this study, by integrating the multi‐source vegetation cover data, we established a high‐resolution vegetation distribution (HRVD) data set to develop a high spatio‐temporal resolution emission inventory and investigated the impact of different land cover data sets on emission simulation and impact of land cover change on BVOC emissions during 2001–2020. The annual total BVOC emissions in China for 2020 was 15.66 Tg, which were mainly from trees. The emissions simulated by CNLUCC and MODIS data sets were 1.53% and 1.72% higher than those simulated by HRVD data sets, respectively. The spatial distribution of emission differences was consistent with that of land cover differences. The simulated BVOC emissions by the HRVD data set had the best accuracy as they improved the bias between modeling and observation from 69.06% to 65.35% and decreased the underprediction of observations by a factor of 2.13 compared with simulation by MEGAN default vegetation data. The annual BVOC emissions caused by changing vegetation distribution and LAIv (LAI of vegetation covered surfaces) enhanced at a rate of 72.06 Gg yr−1 during 2001–2020. LAIv was the main driver of emission variations. The total OH reactivity of the resulted BVOC emissions increased at a rate of 1.59 s−1 yr−1, with isoprene contributed the most. Plain Language Summary: Biogenic Volatile organic compounds (BVOCs) are the key precursors of fine particulate matter and ozone, that mainly from vegetation emissions. To help to develop effective air pollution control measures in the context of expanding vegetation coverage for realizing carbon neutralization in China, it is urgent to develop highly precise vegetation data and accurately estimate BVOC emission. A high‐resolution vegetation distribution data set was established through integrating multi‐source vegetation cover data. Using it, the simulated annual BVOC emission in China was 15.66 Tg and mainly emitted from trees. Emissions from varied growth forms had different compound compositions. The BVOC emission simulated using the high‐resolution vegetation distribution data set we developed had better accuracy than that using the single vegetation databases. The annual BVOC emissions caused by changing vegetation cover and leaf area index (LAI) enhanced at a rate of 72.06 Gg yr−1 during 2001–2020. LAI was the main driver of BVOC emission variations. The interannual variation and its spatial pattern of the OH loss rates of BVOCs during 2001–2020 were consistent with that of BVOC emissions, especially isoprene. Key Points: Annual total BVOC emissions in China for 2020 was 15.66 Tg and emissions from varied growth forms had different compositionsBVOC emission inventory simulated by the developed high‐resolution vegetation distribution (HRVD) data set had better accuracyBVOC emission enhanced at a rate of 72.06 Gg yr−1 during 2001–2020 caused by land cover change, mainly driven by changing leaf area index [ABSTRACT FROM AUTHOR]

Arctic permafrost thaw holds the potential to drastically alter the Earth's surface in Northern high latitudes. We utilize high‐resolution large eddy simulations to investigate the impact of the changing surfaces onto the neutrally stratified atmospheric boundary layer (ABL). A stochastic surface model based on Gaussian Random Fields modeling typical permafrost landscapes is established in terms of two land cover classes: grass land and open water bodies, which exhibit different surface roughness length and surface sensible heat flux. A set of experiments is conducted where two parameters, the lake areal fraction and the surface correlation length, are varied to study the sensitivity of the boundary layer with respect to surface heterogeneity. Our key findings from the simulations are the following: The lake areal fraction has a substantial impact on the aggregated sensible heat flux at the blending height where surface heterogeneities become horizontally homogenized. The larger the lake areal fraction, the smaller the sensible heat flux. This result gives rise to a potential feedback mechanism. When the Arctic dries due to climate heating, the interaction with the ABL may accelerate permafrost thaw. Furthermore, the blending height shows significant dependency on the correlation length of the surface features. A longer surface correlation length causes an increased blending height. This finding is of relevance for land surface models concerned with Arctic permafrost as they usually do not consider a heterogeneity metric comparable to the surface correlation length. Plain Language Summary: The study explores how thawing permafrost (frozen soil) could change the Earth's surface in the Arctic region. The researchers used advanced simulations to study how different types of surfaces, like grassland and lakes, affect the atmosphere above. They found that the proportion of lakes in the area has a big impact on the heat exchange with the atmosphere, suggesting a potential feedback mechanism: as the Arctic dries due to climate change, it might speed up permafrost thaw. Additionally, the study highlights that the arrangement of surface features matters, which is important for models predicting permafrost changes in the Arctic. Key Points: Large eddy simulations with the EULAG research model of the atmospheric boundary layer over stochastic lake landscapesLake areal fraction has significant impact on land‐atmosphere energy exchangeLonger surface correlation length causes higher blending altitude [ABSTRACT FROM AUTHOR]

The direction and magnitude of soil organic carbon stock (SOCS) change following land use and land cover change (LULCC) are highly uncertain, largely due to the lack of relevant global soil data. Great efforts have been made to build SOCS database at regional, national and even sub-continental scales following LULCC; however, a comprehensive and open-access global database has not yet been developed, hindering a deep understanding of LULCC impact on SOCS dynamics. In this study, we introduce a new global SOCS database for LULCC, compiled from 639 articles documented in the Web of Science through the end of 2023. Targeting five major land uses (cropland, grasslands, forest, plantation, and savanna), this database – named the Global Soil Organic Carbon Stock dataset after Land Use and Land Cover Change (GSOCS-LULCC) – include 1,206 sites with 5,982 records at various sampling depths. The database will enable users to assess the global impact of LULCC on SOCS dynamics and identify the factors that control SOCS changes for specific types of LULCC. The GSOCS-LULCC database is freely available from the Zenodo platform at https://doi.org/10.5281/zenodo.11183819 (Chen et al., 2024). [ABSTRACT FROM AUTHOR]

[Objective] Taking Jingzhou City as an example, this paper discussed the relationship between urban expansion and farmland loss and its impact on economic growth. [Method] Using a 30-meter annual land cover dataset in China, the spatial and temporal patterns and regional differences of urban expansion and farmland loss during 1990-2019 were analyzed. The environmental Kuznets curve (EKC) hypothesis was introduced to test the relationship between urban expansion and economic growth through regression analysis of provincial panel data, and the future urban land expansion (FULE) dataset was used to predict farmland pressure under future scenarios. [Result] Urban expansion was the main cause of farmland loss, accounting for 76% of newly built urban land and 28% of farmland loss. Cities above the county level were more inclined to use farmland for construction, while cities below the county level had more farmland converted to other uses. There was an inverted U-shaped relationship between urban expansion and farmland loss, indicating that the loss of farmland increased first and then decreased with economic growth, and began to decouple after reaching an inflection point in 2012. Some coastal and large cities had entered a stage of reduced farmland loss, while some inland and small and medium-sized cities were still in a stage of increased farmland loss. It was estimated that by 2050, 0.3-0.4 million hectares of arable land in Jingzhou will be occupied by urban land, and third-and fourth-tier cities will suffer the most losses. [Conclusion] It was recommended to strengthen land planning and management, control urban expansion, optimize land use structure, improve land use efficiency, protect arable land resources, promote regional coordinated development, and achieve a balance between economy and environment. [ABSTRACT FROM AUTHOR]

Land use and land cover change (LULCC) have profoundly altered land surface properties and ecosystem functions, including carbon and water production. While mapping these changes from local to global scales has become more achievable due to advancements in earth observations and remote sensing, linking land cover changes to ecosystem functions remains challenging, especially at regional scale. Our study attempts to fill this gap by employing a computationally efficient method and two types of widely used high-resolution satellite images. We first investigated the contribution of landscape composition to ecosystem function by examining how land cover and proportion affected gross primary production (GPP) and evapotranspiration (ET) at six macro-landscapes in Mongolia and Kazakhstan. We hypothesized that both ecosystem and landscape GPP and ET are disproportionate to their composition and, therefore, changes in land cover will have asymmetrical influences on landscape functions. We leveraged a computational-friendly linear downscaling approach to align the coarse spatial resolution of MODIS (500 m) with a fine-grain and localized land cover map developed from Landsat (30 m) for six provinces in countries where intensive LULCC occurred in recent decades. By establishing two metrics—function to composition ratio (F/C) and function to changes in composition change (ΔF/ΔC)—we tested our hypothesis and evaluated the impact of land cover change on ecosystem functions within and among the landscapes. Our results show three major themes. (1) The five land cover types have signature downscaled ET and GPP that appears to vary between the two countries as well as within each country. (2) F/C of ET and GPP of forests is statistically greater than 1 (i.e., over-contributing), whereas F/C of grasslands and croplands is close to or slightly less than 1 (i.e., under-contribution). F/C of barrens is clearly lower than 1 but greater than zero. Specifically, a unit of forest generates 1.085 unit of ET and 1.123 unit of GPP, a unit of grassland generates 0.993 unit of ET and GPP, and a unit of cropland produces 0.987 unit of ET and 0.983 unit of GPP. The divergent F/C values among the land cover classes support the hypothesis that landscape function is disproportionate to its composition. (3) ΔET/ΔC and ΔGPP/ΔC of forests and croplands showed negative values, while grasslands and barrens showed positive values, indicating that converting a unit of forest to other land cover leads to a decrease in ET and GPP, while converting units of grassland or barren to other land cover classes will result in increased ET and GPP. This linear downscaling approach for calculating F/C and ΔF/ΔC is labor-saving and cost-effective for rapid assessment on the impact of land use land cover change on ecosystem functions. [ABSTRACT FROM AUTHOR]

We introduce CAMELS-INDIA (Catchment Attributes and MEteorology for Large-sample Studies – India), the hydrometeorological time series, and catchment attributes for 472 catchments in Peninsular India. Peninsular India covers 15 intrastate river basins defined by the Central Water Commission (CWC), where river flow and water level datasets are available for several gauge stations through the open-source India Water Resources Information System (India-WRIS). However, many of these gauge stations lack reliable metadata, and data are not in an analysis-ready format for large-sample hydrological studies. Therefore, we utilized 472 gauge stations and their catchment boundaries, characterized as stations with reliable metadata, from the 'Geospatial dataset for Hydrologic analyses in India (GHI)' (Goteti, 2023). For each of these catchments, the CAMELS-INDIA provides a catchment mean time series of meteorological forcings for 41 years (1980–2020) and around 211 catchment attributes representing hydroclimatic and land cover characteristics extracted from multiple data sources (including ground-based observations, remote sensing-based products, and reanalyses datasets). The CAMELS-INDIA follows the same standards of the previously developed CAMELS datasets for the USA, Chile, Brazil, Great Britain, Australia, Switzerland, Germany, and Denmark to facilitate comparisons with catchments of those countries and inclusion in global hydrological studies. Notably, the CAMELS-INDIA includes available observed streamflow and catchment mean time series of 19 meteorological forcings, including precipitation, maximum, minimum, and average temperature, long-wave and short-wave radiation flux, U and V-components of wind, relative humidity, evaporation rates from canopy and soil surface, actual and potential evapotranspiration, and soil moisture of four layers (covering depth up to 3 m below ground) for detailed hydrometeorological studies. We also derived catchment attributes representing human influences, including the number of dams and their utilization, total volume contents of dams in catchments, population density, and increase in urban and agricultural land covers to facilitate studies to understand human influences on catchment hydrology. Furthermore, the dataset includes predicted streamflow time series from a regionally trained Long-Short Term Memory (LSTM)-based hydrological model, which can fill gaps in observed streamflow data or serve as a benchmark for testing and developing new hydrological models. We envision that CAMELS-INDIA will provide a strong foundation for a community-led effort toward gaining new hydrological insights from hydrologically distinct Indian catchments and solving pertinent issues related to water management, quantification and risk assessment of hydrologic extremes, unraveling regional-scale hydrologic functioning, and climate change impact assessment of catchments across India. The CAMELS-INDIA dataset is available at https://doi.org/10.5281/zenodo.13221214 (Mangukiya et al., 2024). [ABSTRACT FROM AUTHOR]

Introduction: The selection of an optimal scale or granularity in landscape analysis is pivotal for uncovering inherent patterns and changes driven by processes. Variations in spatial resolution can significantly alter the proportions and distributions of various landscape types, thereby impacting the assessment of landscape patterns. Despite its importance, the scale factor is frequently neglected in studies focusing on long-term landscape dynamics. Methods: Bridging this gap, we utilized remote sensing imagery data from 1986 to 2020 for Lushui City, integrating remote sensing (RS) and geographic information system (GIS) technologies to generate land cover maps. Our focus centered on investigating the sensitivity of landscape pattern indices within the 30-1000m scale. Combining the first scale domain with an information loss assessment model, we identified the optimal granularity for the analysis, conducting a detailed spatiotemporal examination of landscape pattern from 1986 to 2020 using the index analysis method. Results and discussion: The results show that: (1) The dominance of forests in Lushui City, yet reveal a significant increase in construction land area over the study period, primarily driven by the conversion of forest and grassland. (2) Among the 10 examined indices, four (PD, ED, TE, and LSI) demonstrated predictable responses to changes in granularity, while three (PAFEAC, COHESION, AI) exhibited unpredictable stepwise reactions. Three indices (LPI, SHDI, PLAND) displayed minimal regularity to granularity changes. (3) The optimal long-term landscape analysis granularity for Lushui was identified as 100 m. (4) Before 1996, the city's landscape exhibited characteristics of aggregation, good connectivity, and minimal anthropogenic disturbance. However, post-1996, the landscape experienced disruptions, leading to an overall increase in fragmentation. The expansion of cultivated land and construction land due to urbanization has intensified landscape fragmentation. However, policies such as converting cropland to forest and planned ecological civilization initiatives have restored forest coverage and improved landscape cohesion and connectivity in Lushui City. This research offers vital insights for ecological planning and resource management in alpine valley watershed cities, deepening our grasp of landscape pattern evolution. [ABSTRACT FROM AUTHOR]

Satellite image time series (SITS) segmentation is crucial for many applications, like environmental monitoring, land cover mapping, and agricultural crop type classification. However, training models for SITS segmentation remains a challenging task due to the lack of abundant training data, which requires fine-grained annotation. We propose S4, a new self-supervised pretraining approach that significantly reduces the requirement for labeled training data by utilizing two key insights of satellite imagery: (a) Satellites capture images in different parts of the spectrum, such as radio frequencies and visible frequencies. (b) Satellite imagery is geo-registered, allowing for fine-grained spatial alignment. We use these insights to formulate pretraining tasks in S4. To the best of our knowledge, S4 is the first multimodal and temporal approach for SITS segmentation. S4's novelty stems from leveraging multiple properties required for SITS self-supervision: (1) multiple modalities, (2) temporal information, and (3) pixel-level feature extraction. We also curate m2s2-SITS, a large-scale dataset of unlabeled, spatially aligned, multimodal, and geographic-specific SITS that serves as representative pretraining data for S4. Finally, we evaluate S4 on multiple SITS segmentation datasets and demonstrate its efficacy against competing baselines while using limited labeled data. Through a series of extensive comparisons and ablation studies, we demonstrate S4's ability as an effective feature extractor for downstream semantic segmentation. [ABSTRACT FROM AUTHOR]

As a major agricultural hazard, drought frequently occurs due to a reduction in precipitation resulting in a continuously propagating soil moisture (SM) deficit. Assessment of the high spatial-resolution SM-derived drought index is crucial for monitoring agricultural drought. In this study, we generated a downscaled random forest SM dataset (RF-SM) and calculated the soil water deficit index (RF-SM-SWDI) at 30 m for agricultural drought monitoring. The results showed that the RF-SM dataset exhibited better consistency with in situ SM observations in the detection of extremes than did the SM products, including SMAP, SMOS, NCA-LDAS, and ESA CCI, for different land cover types in the U.S. and yielded a satisfactory performance, with the lowest root mean square error (RMSE, below 0.055 m3/m3) and the highest coefficient of determination (R2, above 0.8) for most observation networks, based on the number of sites. A vegetation health index (VHI), derived from a Landsat 8 optical remote sensing dataset, was also generated for comparison. The results illustrated that the RF-SM-SWDI and VHI exhibited high correlations (R ≥ 0.5) at approximately 70% of the stations. Furthermore, we mapped spatiotemporal drought monitoring indices in California. The RF-SM-SWDI provided drought conditions with more detailed spatial information than did the short-term drought blend (STDB) released by the U.S. Drought Monitor, which demonstrated the expected response of seasonal drought trends, while differences from the VHI were observed mainly in forest areas. Therefore, downscaled SM and SWDI, with a spatial resolution of 30 m, are promising for monitoring agricultural field drought within different contexts, and additional reliable factors could be incorporated to better guide agricultural management practices. [ABSTRACT FROM AUTHOR]

The Taihu Lake basin is one of the fastest-growing regions in China, where the natural environment has been seriously affected by humans. The plant area index (PAI) is an important parameter reflecting the change in vegetation growth, which plays a crucial role in studying vegetation growth and protecting the ecological environment. Advancements in remote sensing technology, complemented by machine learning techniques, have facilitated the accurate and efficient acquisition of PAI over large areas. In this study, the Taihu Lake Basin vegetation area was taken as the research object. Global Ecosystem Dynamics Investigation (GEDI) point cloud data and Landsat-8 remote sensing images were the primary information sources. MODIS land cover types were utilized to classify the vegetation into six categories. Three classical machine learning models, namely, Random Forest (RF), Support Vector Regression (SVR), and Back Propagation Neural Network (BPNN), were used to estimate the PAI in the Taihu Lake Basin. It was found that the RF model showed the best performance. The determination coefficients (R2) for grassland, evergreen forest, mixed forest, deciduous forest, farmland, and wetland were 0.71, 0.67, 0.69, 0.66, 0.65, and 0.69, respectively. Over 2000-2022, the PAI exhibited an absolute change rate of 0.035, with an overall increasing trend. The area of improved and degraded vegetation accounted for 58.33% and 41.67% of the total area, respectively. The study also revealed that PAI was positively correlated with precipitation (R = 0.64, P < 0.05) and negatively correlated with temperature (R = -0.58, P < 0.05). Different land types' effects on PAI were also analyzed, with wetland PAI having the smallest mean value and evergreen forest PAI having the most considerable mean value. This research underscores the effectiveness of integrating GEDI data and Landsat-8 imagery in PAI assessment, providing valuable insights for environmental monitoring and analysis in the Taihu Lake Basin. [ABSTRACT FROM AUTHOR]

Urban flooding problem has been exacerbated in recent times, especially in developing nations, due to haphazard changes in land use and land cover (LULC) resulting from rapid urban expansion, coupled with river encroachments and inadequately engineered river management structures. Kathmandu Valley Watershed (KVW), encompassing Kathmandu, Bhaktapur and Lalitur districts, the fastest growing cities in South Asia, is constantly growing, with a significant increase in urban areas. Due to urbanization, the watershed's water storage capacity is diminishing, while surface runoff volume and rate are accelerating. We evaluated the isolated as well as the integrated impact of multiple scenarios of LULC change and river encroachment on flood inundation characteristics in KVW. LULC prediction revealed an increase in built-up areas by 113% between 1990 and 2020, which are further projected to increase by 29% by 2050. Inundation modeling using Rainfall-Runoff Inundation (RRI) model showed that rather than the increase in inundation extent, the depth of inundation is projected to increase in future as a result of increasing urban areas. Furthermore, our research highlighted that the impact of river width encroachment had a more substantial effect on flooding compared to changes in LULC alone. Similarly, integrated impact of LULC change and river encroachment was more pronounced than the impact of change in LULC alone. The aggregate of observations leads to the conclusion that the encroachment of rivers is the predominant factor contributing to the flooding issue within the KVW. The findings of the study is anticipated to assist policymakers in effective land use planning and in proposing appropriate development initiatives concerning the river environment. [ABSTRACT FROM AUTHOR]

Coastal landslides pose significant threats to critical infrastructure in energy-dependent regions like the Assaluyeh anticline. To safeguard communities and economic stability, it is crucial to address uncertainties in coastal landslide susceptibility assessments. This necessity extends beyond scientific exploration and requires pioneering fuzzy logic methodologies, to enhance predictive accuracy. The Assaluyeh anticline is in the Persian Gulf's Zagros intercontinental region is of paramount importance, influencing Iran's energy infrastructure. To comprehend coastal landslide risk, our innovative study focuses on the Assaluyeh anticline's coastal areas. We combine 13 conditioning factors, blending traditional geological characteristics with advanced technology, including lithology, land-cover, topographic wetness index (TWI), elevation, and slope angle, using AI-driven modeling to create predictive landslide susceptibility maps. This approach surpasses conventional techniques, offering improved early warning systems and disaster management. Our research's groundbreaking aspect is its forward-looking approach, utilizing fuzzy logic to adapt to real-world conditions by embracing imprecision and uncertainty in data. We integrate historical development data, local knowledge, and forward-thinking fuzzy logic techniques to promote community-driven risk management and resilience in coastal environments. This study envisions the future, revolutionizing coastal landslide assessment and management, inspiring interdisciplinary collaboration, community engagement, and fuzzy logic-driven innovation in coastal disaster risk reduction on a global scale. [ABSTRACT FROM AUTHOR]

After the 2008 Wenchuan earthquake (MS 8.0), a large number of debris flow source areas formed and further affected the occurrence of debris flows. Identifying the source area that may participate in debris flow activities requires understanding its dynamically changing conditions of location and probability. Based on this, the Bayesian geomorphology method was used to analyze the variation probability in the sediment supply from coseismic landslide deposits to subsequent debris flows and to undertake a quantitative assessment as well. The segment of the Min River, stretching from the urban area of the Wenchuan County to the Yingxiu town, China, was selected as the study area. Geomorphologic characteristics, including elevation, distance to faults, seismic intensity, land cover, gradient, monthly precipitation, and lithostratigraphy, were determined as assessment factors. The method was applied and verified in three clusters of debris flow events from 2010 to 2019 in 37 debris flow catchments. The results indicate that the method is applicable for identifying the dynamic debris flow source areas in earthquake-affected regions with a high accuracy of over 87% and a refined resolution of 30 m. This study finds that the sediment supply types have changed since 2019, that is, the sediment supply from Wenchuan earthquake-induced landslide deposit zones occupied by the debris flow-prone area has dropped from an average of 95.9% in 2013 to approximately 44.7% in 2019. Still, a large number of coseismic landslide deposits exist in the debris flow basins; the probability of debris flow source areas could thus be used as an important criterion for evaluating the long-term evolution of landslides and subsequent debris flow activities in earthquake-affected regions. [ABSTRACT FROM AUTHOR]

This paper develops a method integrating Geographic Information Systems (GIS) and the Decision-Making Trials and Evaluation Laboratory (DEMATEL) for the analysis of factors influencing urban flood risk and the identification of flood-prone areas. The method is based on nine selected factors: land use/land cover (LULC: the ratio of built-up areas, the ratio of greenery areas), elevation, slope, population density, distance from the river, soil, Topographic Wetness Index (TWI), and Normalized Difference Vegetation Index (NDVI). The DEMATEL method is used to determine the cause–effect relationship between selected factors, allowing for key criteria and their weights to be determined. LULC and population density were identified as the most important risk factors for urban floods. The method was applied to a case study—the Serafa River watershed (Poland), an urbanized catchment covering housing estates of cities of Kraków and Wieliczka frequently affected by flooding. GIS analysis based on publicly available data using QGIS with weights obtained from DEMATEL identified the vulnerable areas. 45% of the total catchment area was classified as areas with a very high or high level of flood risk. The results match the actual data on inundation incidents that occurred in recent years in this area. The study shows the potential and possibility of using the DEMATEL-GIS method to determine the significance of factors and to designate flood-prone areas. [ABSTRACT FROM AUTHOR]

Hydrological processes can be highly influenced by changes in land use land cover (LULC), which can make hydrological modelling also very sensitive to land cover characterization. Therefore, obtaining up-to-date LULC data is a crucial process in hydrological modelling, and as such, different sources of LULC data raises questions on their quality and applicability. This is especially true with new data sources, such as citizen science-based land cover maps. Therefore, this research aims to explore the influence of LULC data sources on hydrological models via their parameterization and by performing sensitivity analyses. Kiffissos catchment, in Greece, a poorly gauged and highly urbanized basin including the city of Athens, is the case study area. In total, 12 continuous hydrological models were developed by mainly varying their structure and parametrization (lumped and gridded) and using three LULC datasets: coordination of information on the environment (CORINE), Urban Atlas and Scent (citizen-based). It was found that excess precipitation is negligibly contributed to by soil saturation and is dominated by the runoff over impervious areas. Therefore, imperviousness was the main parameter influencing both sensitivity to land cover and parameterization. Lastly, although the parametrization as lumped and gridded models affected the representation of hydrological processes in pervious areas, it was not relevant in terms of excess precipitation. [ABSTRACT FROM AUTHOR]

Quantifying the attribution of climate change and human activities on runoff and suspended sediment load is crucial for formulating future watershed management measures, especially in the ecologically fragile alpine region, which is more susceptible to climate change and human activities. In this study, the temporal changes in runoff‐suspended sediment load and the potential impact factors (i.e., precipitation, potential evapotranspiration (PET), land use/land cover change (LULC) and reservoir operation) were investigated in the Lhasa River Basin (LRB) from 1956 to 2020. In addition, the contributions of those factors to the changes in runoff and suspended sediment load were quantitatively evaluated based on physically‐based Soil and Water Assessment Tools (SWAT). The results indicated that annual runoff and suspended sediment load showed an increasing but insignificant trend during 1956–2020, while annual precipitation and PET showed a significant increasing trend at a rate of 0.94 and 1.07 mm/yr, respectively. LULC mainly presented an increase in forestland area followed by a decrease in grassland and bare land area due to the implementation of ecological projects. Runoff and suspended sediment load changed abruptly at approximately 1995 and 2005 based on three abrupt change identification methods, thus, the study period was further divided into three substages: baseline period (P0, 1956–1994), dramatically increased period (P1, 1995–2004) and slightly decreased period (P2, 2005–2020). The attribution analysis showed that climate change was the dominant contributor to runoff and suspended sediment load increments during P1. LULC caused the decline in runoff and suspended sediment load in both P1 and P2, with their contribution increasing significantly from 0.40% and 4.73% in P1 to 50.24% and 51.79% in P2, respectively. Reservoir operation was the second key factor in runoff and suspended sediment load reduction in P2, contributing 42.07% and 43.72%, respectively. These findings provide the scientific foundation for reasonably allocating water resources and statistical support for the benefit evaluation of implementing ecological projects in the basin. [ABSTRACT FROM AUTHOR]

Featured Application: Determination the scale of hazard caused by episodic water runoff in rural areas will allow better management of the land and limit its negative effects. There has been an increase in the frequency of hazards associated with meteorological and hydrological phenomena. One of them is flash floods occurring episodically in areas of concentrated runoff—valleys without permanent drainage. In the opinion of residents and local authorities, these are potentially safe areas—they are not threatened by floods and are therefore often occupied by buildings. The importance of addressing flash floods in land use planning is essential for sustainable development and disaster risk reduction. The objective of this research was to assess the level of the hazard and to evaluate its presence in land use planning activities. This manuscript fills a research gap, as to date flash flood threats have not been analyzed for individual buildings located in catchments of dry valleys in temperate climates. More than 12,000 first-order catchments were analyzed. The study covered an upland area located in East Poland, which is characterized by high population density and dispersed rural settlement. Within the 10 municipalities, buildings located on potential episodic runoff lines were identified. Qualitative assessment was applied to ascertain the susceptibility of catchments to flash floods. Such criteria as slopes, size, shape of the catchment area, and land cover, among others, were used. Between 10 and 20% of the buildings were located on episodic runoff lines, and about 900 sub-catchments were highly or very highly susceptible to flash floods. The way to reduce the negative effects of these phenomena is to undertake proper land use planning based on knowledge of geohazards, including flash floods. However, an analysis of available planning documents shows that phenomena of this type are not completely taken into account in spatial management processes. [ABSTRACT FROM AUTHOR]

Accurate signal path loss models for predictions are crucial in current cellular communication networks. Recently, numerous path loss estimation methods have been presented to improve the efficiency of networks. However, most of these existing models do not include spatial data such as land use/land cover, terrain elevation, building height, and the effect of topography. To address this issue, this study proposes a GeoAI-based technique for path loss estimation in cellular communication networks, addressing existing models' lack of spatial data integration. Support Vector Regression, K-Nearest Neighbor, Random Forest, and multi-layer perceptron (MLP) artificial neural network models are evaluated using field measurements in an urban, suburban area in Van, Turkey, across various frequencies. Among the models, MLP with three hidden layers, nine input variables, hyperbolic tangent activation function, and Adam optimization method performs best. At 900 MHz, MLP has been observed with MSE, RMSE, MAE, and R values of 0.22 dB, 0.47 dB, 0.46 dB, and 0.99 dB, respectively. Lastly, a comparison of the developed model to the Free space, COST 231, Ericsson, and SUI models revealed that the GeoAI-based path loss models outperformed the empirical models regarding prediction accuracy and generalization. This study underscores the significance of integrating spatial data into path loss prediction, particularly in diverse urban and suburban environments, for optimizing cellular communication networks. [ABSTRACT FROM AUTHOR]

Appropriate environment management requires an understanding of how mining activity alters environmental characteristics and how those changes affect an area. Therefore, to reduce the adverse effects of mining activity on the land, it becomes crucial to have relevant information about responses to environmental degradation. This study aims to assess the impact of semi-mechanised and artisanal mining activities on the land cover using remote sensing data and photogrammetric analysis, in the Mbale locality, Northern Cameroon. For this purpose, the maximum likelihood classification algorithm of the supervised classification method combined with field surveys was used to map environmental changes, based on Sentinel-2 images of 2019, 2021, and 2023. Normalized Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI), Brithness index (BI), and Soil crust index (SCI), were calculated to assess changes in vegetation, bare soil, water body, and exploited area. The orthophoto obtained from photogrammetric processing was performed to outline river network change through visual interpretation techniques and to calculate the volume of pits created by mining. The result of classified images indicated that vegetation cover decreased by 11.74% over the studied years. However, bare soil and exploited areas increased by 9.2% and 5.4% respectively. The calculated spectral indices show that between 2019 and 2023 the locality of Mbale considerably lost its vegetation cover, in favor of bare soil. The color of the soil and the granulometric size of the topsoil have also changed. The photogrammetry analysis highlighted the deviation of the main river and estimated the volume of pits created by mining activity to 22188.7 m3. The mining activities caused a loss of the vegetation cover, generated big pits, and multiple deviations of the Lom River from its natural course, which have a substantial negative influence on the ecosystem. Such data can be used for long-term environmental management, reclamation and rehabilitation monitoring, and mining area restoration. [ABSTRACT FROM AUTHOR]