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Rapid replacement of vegetated land with impermeable land (built-up areas) is a major factor in the increase in Land Surface Temperature (LST), while increased LST worsens the temperature in cities and creates the Surface Urban Heat Island (SUHI) effect. The study aims to measure vegetation loss and Land Surface Temperature of the Gazipur district between 2000 and 2020 and explore the relationship among Normalized Difference Vegetation Index (NDVI), LST, and Urban Thermal Field Variance Index (UTFVI). The Landsat TM/OLI images with minimum cloud coverage have been used to derive different indices. The mean NDVI values are 0.21, 0.16, and 0.22 in 2000, 2010, and 2020 respectively which indicates a general improvement in the health of the vegetation. Besides, the highest LST values throughout 20 years, represent a general increasing trend. As a consequence, different land covers have experienced fluctuations in mean temperature. The result shows that the mean temperature of bare land, buildup, vegetation, and waterbody has increased by 4.77, 2.01, 2.25, and 2.23 °C respectively from 2000 to 2020. The strongest SUHI zone's area grew by about 28% between 2000 and 2020. Additionally, the highest index value of UTFVI was 0.39 in 2000 and grew to 0.43 in 2010. It changed to 0.49 in 2020, or ten years later. Thus, the SUHI effect's increasing intensity is visible. Also, regression analysis has been used to explore the correlation between the derived indices. Stakeholders from different sectors like urban planners and policymakers may take insights from this study to work to promote greenery for a healthy urban environment. [ABSTRACT FROM AUTHOR]

Aquatic insects are key to wetland ecological functioning, and the distribution of amphibious insects such as Odonata jointly depends on environmental conditions in the aquatic and terrestrial settings. Therefore, untangling the relative effects of within‐wetland and landscape composition can help predict the responses of Odonata to environmental alterations in wetlands. Using data from 19 wetlands spanning over the southern Brazilian Pampa (center‐western Rio Grande do Sul state), we assessed the relative importance of water chemistry and land‐cover variables to the richness and composition of larval Odonata assemblages (and suborders Anisoptera and Zygoptera). Anisoptera richness decreased with pH and bare soil area. Water pH and areas of bare soil, mosaic of agricultural land use, and waterbodies land cover were the main drivers of Odonata and Zygoptera composition. Our results indicate that land cover is the main driver of the assemblage structure of larval Odonata, although a complex interplay of mechanisms associated with land conversion and water quality drive the distribution of larval Odonata in freshwater wetlands of the Brazilian Pampa. The major implication of our findings is that land cover modification is the major threat to Odonata distribution in Pampean wetlands, with potential impacts on the trophic structure and functioning of these ecosystems. [ABSTRACT FROM AUTHOR]

Introduction: Crop Wild Relatives (CWR) have great socioeconomic importance for humans harbouring a broad spectrum of diversity and being important elements of different habitats. Beta vulgaris subsp. maritima ((L.) Arcangeli), also known as sea beet, is an important CWR of cultivated beets (GP-1). The high adaptability of this taxon to different environmental conditions, as well as its tolerance/resistance to different biotic and abiotic stresses, makes it a vital source for sugar beet improvement. Aim of this work was to analyse the in situ and ex situ status of sea beet population conservation in Italy, so as to guide protection activities and new collecting missions. Methods: Geographical distribution data of populations were that occur in the wild and conserved in genebanks were assembled from different databases and submitted to data quality control. Distribution, habitat characterization, land cover and use of the involved sites were then evaluated to provide insight into the current condition of areas hosting this CWR diversity. The presence of populations within vs. outside Italian protected areas was also evaluated. A density analysis of the records was performed and the adequateness of sea beet ex situ conservation, in terms of number of conserved accessions, was finally estimated. Results: A collection of 138 B. vulgaris subsp. maritima high quality georeferenced records were obtained, mainly distributed into Mediterranean Biogeographic Region. About 22% of the considered populations occurs in protected sites of the Natura 2000 Network, while about 15% in areas recorded in the Official list of protected natural areas (EUAP). Occurrences within protected areas are founded primarily in natural environments, whereas those outside are mainly located in urban and cultivated areas. The comparison of distribution and density analysis results revealed the presence of several gaps between sites hosting in situ populations and sites where ex situ conserved accessions were originally collected. Discussion: Here presented data indicates that the protection status for sea beet in Italy can be considered only partially adequate; more proactive protection measures should be foreseen to increase the role of protected areas in safeguarding in situ conservation. "Out of reserve management" for populations outside protected areas should also be developed, as well as new collecting missions carried out. [ABSTRACT FROM AUTHOR]

Untangling the abiotic and biotic feedback mechanisms driving animal movements and ranges is a core question in ecology, yet progress is hampered by inadequate modelling procedures. Here we show how a recently developed process‐based modelling approach, combining step‐selection functions and individual‐based models, enables a flexible method to infer movement drivers and multi‐scale emergent space use patterns. As a case study, we examine the movement behaviours of a highly invasive social generalist (wild pigs, Sus scrofa) in relation to conspecific space use and multiple land cover types in a complex agricultural landscape, showing that social interactions are predominantly more important to this species than selection for land cover. Thus, animal movement studies should not neglect the effects of sociality when inferring resource driven movements and, crucially, use multi‐scale techniques that incorporate movement processes to untangle drivers of animal space use. [ABSTRACT FROM AUTHOR]

Pansharpening is an important technology for obtaining high-resolution multispectral (HRMS) images by fusing low-resolution multispectral (LRMS) images and high-resolution panchromatic (PAN) images. Although many pansharpening models have emerged by taking advantage of deep learning (DL) technology, there remains a pressing need to further assess pansharpening accuracy and stability when LRMS images with complex land-cover types. What's more, these models often overlook the exploitation of PAN images' inherent high-frequency information. To address these issues, we propose a pansharpening model combining multi-level and multi-scale network architectures. The multi-level network architecture is used to build spatial-spectral dependence on LRMS-PAN pairs, and strengthen the network's feature capture capability by keeping the multi-level texture details. The multi-scale architecture is subsequently used to extract the spatial structure and deep texture of the PAN images at different scales. Downsampled experiments and real experiments in four standard datasets show that the proposed model achieves a state-of-the-art performance. [ABSTRACT FROM AUTHOR]

One of the challenges in global change research is the significant uncertainty in global historical land use and land cover (LUCC) datasets, which are widely used as foundational data. In addition to the regional cropland area reconstructions, improving the grid allocation method is another feasible way to raise the reliability of historical LUCC data. In this study, an integrated reconstruction of the national cropland areas over the past 200 years was developed for 36 European countries. After that, the allocation algorithm was built using physiogeographic variables and historical city sites for accounting for land suitability and cultivation preferences, respectively. Finally, cropland data in Europe with a spatial resolution of 5′×5′ at five time sections from AD 1800 to 2000 were generated using the optimal allocation algorithm in accordance with the stages of the regional history. The results were as follows: (1) The dominant factors governing the distribution of croplands in Europe vary at different agricultural stages, but the results can be merged together. Land suitability was more optimal for allocation during the modern agricultural stage (AD 1950 and 2000); the priority index combined with land suitability and cultivation preference was more reasonable for allocation during the traditional agricultural stage (AD 1800). The average of the allocations by priority index and the land suitability could be adopted as the allocation results during the transitional stage (AD 1850 and 1900) because the grids for absolute differences within ±10 and ±20 percentage points between the results obtained from the above two allocations were above 80% and 95%, respectively, which means the two allocation results could be merged. (2) Over the past 200 years, the total cropland area in Europe first increased to a peak in AD 1900 and then decreased. Spatially, the centre of the higher cropland fraction shifted from the western part of Europe in AD 1800 to the eastern part of the continent after AD 1950. (3) Both the cropland area and the spatial distribution in this study are more reasonable than the global dataset HYDE3.2. [ABSTRACT FROM AUTHOR]

One of the most prominent factors contributing to global landscape cover change is land use and land cover (LULC). Such changes are the outcome of several interrelated factors. This study assessed trends in land use and land cover within Hirpora from 1992 to 2006 and 2021 utilizing remote sensing data and satellite pictures from Landsat 5-TM in 1992, Landsat 7-enhanced TM in 2006, and Landsat 8-OLI in 2021. The images were analyzed using ArcGIS v10.1 and ERDAS Imagine v14. From 1992 to 2021, there was a substantial shift in land use trends in a few targeted classes, with snow cover losing the most (33.76%), followed by dense forests (6.80%), and economically grasslands (21.46%), barren/rocky (11.43%), and scrub (5.41%) gaining the most. During the observation period, moderately dense forests and build-up increased in area by 4.50 and 0.35%, respectively. Due to large-scale human intrusion and habitat fragmentation, the Hirpora Wildlife Sanctuary has swiftly transitioned from a natural to a cultural landscape. As part of the LULC change process, natural, demographic, and economic factors have impacted land and had environmental implications. [ABSTRACT FROM AUTHOR]

Spatiotemporal variations of biomass burning (BB) over the Indian region using satellite-based data from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the period 2003–2021 are analyzed and studied. We have used fire products with a high confidence level (≥ 80%), which is free from false alarm fires. The total fire counts (TFC), fire radiative power (FRP), and burned area (BA) for different land use and land cover (LULC) types over six different regions, namely Central India (CI), Indian Gangetic Plain (IGP), North-East India (NEI), North India (NI), South India (SI) and West India (WI) are studied. The biomass burning shows spatial, seasonal and inter-annual variations. Within the regions, different hotspots are identified for cropland burning, forest burning, etc. It is observed that in the IGP and WI regions, burning activity shows bi-modal seasonal behaviour, which coincides with crop burning after harvesting seasons, while other regions show a single mode. Non-parametric long-term analysis in TFC and TFRP (derived by adding FRP of all the fire hotspots in a respective year) shows a positive trend over all the regions except in the NEI region. The decreasing TFC with increasing precipitation is also observed in all the considered regions, which is attributed to enhanced moisture and decreased temperature. The present study provides the scientific basis for addressing the origin and type of biomass burning in different regions of India, and it is quite useful for developing procedures, awareness, and planning for reducing BB, which is quite harmful to human health as well as the environment. [ABSTRACT FROM AUTHOR]

The information on land use/land cover (LULC) is indispensable in regional planning, policy formulation and tracking land use/land cover changes (LULCC). The rapid urbanization of hilly terrains, driven by population growth, has significant implications for landslide risk reduction. Recognizing the need for an innovative approach for extracting LULC information, the present study uses a random forest (RF) classifier to develop a novel, pre-trained and universal tool that automatically generates LULC classification maps based on natural colour satellite imagery without any training input from the end-user. The proposed framework with an overall accuracy of 0.75 and an area under the curve (AUC) score of 0.95 in the receiver operating characteristic curve (ROC) approach was used for mapping built-up area expansion in regions susceptible to rainfall-induced landslides in Idukki block panchayat (administrative division), Kerala, India. By comparing the LULC information for the years 2012 and 2022, it was understood that the built-up area in the location has increased from 12.76% of the total area in 2012 to 26.48% in 2022. It is important to consider the rapid increase in built-up area expansion in the 'very high' landslide susceptibility zones in the study area. This clearly demonstrates the need for hazard inclusive planning and tracking of LULCC, for disaster risk reduction. Research Highlights: A pre-trained Land Use/Land Cover (LULC) classification tool is developed using the Random Forest (RF) classifier. Based on natural colour satellite imagery, the tool automatically generates LULC maps for various landscapes worldwide. The tool demonstrates a satisfactory performance, achieving an overall accuracy of 0.75 and an overall ROC AUC score of 0.95. The tool was used to understand the LULC changes in Idukki block panchayat between 2012 and 2022. [ABSTRACT FROM AUTHOR]

Convolutional neural networks trained with satellite image time series have demonstrated their potential in land cover classification in recent years. Nevertheless, the rationale leading to their decisions remains obscure by nature. Methods for providing relevant and simplified explanations of their decisions as well as methods for understanding their inner functioning have thus emerged. However, both kinds of methods generally work separately and no explicit connection between their findings is made available. This paper presents an innovative method for refining the explanations provided by channel-based attention mechanisms. It consists in identifying correspondence rules between neuronal activation levels and the presence of spatiotemporal patterns in the input data for each channel and target class. These rules provide both class-level and instance-level explanations, as well as an explicit understanding of the network operations. They are extracted using a state-of-the-art redescription mining algorithm. Experiments on the Reunion Island Sentinel-2 dataset show that both correct and incorrect decisions can be explained using convenient spatiotemporal visualizations. [ABSTRACT FROM AUTHOR]

• Improving Coastline Edge Detail: EDS module enhances edge fitting. • Bridging Semantic Gap: CSDS and AFM collaboration enhances semantic information. • Outperforms with mIoU: CSAFNet achieves remarkable 96.72% mIoU value. The extraction of coastlines from remote sensing images is vital for promoting sustainable development in coastal areas, conserving marine environments, strengthening disaster response capabilities, and supporting scientific research. However, current coastline detection approaches using remote sensing face challenges related to resolution, terrain, boundary, and data, requiring accurate solutions for reliability. Here, we introduce the Collaborative Supervision and Attention Fusion (CSAFNet) model for pixel-level sea-land segmentation, with a primary goal of improving the accuracy of coastline extraction. The model integrates the Edge Deep Supervision (EDS) module to enhance coastline edge detail fitting. Additionally, the Collaborative Semantic Deep Supervision (CSDS) module and Attention Fusion Module (AFM) collaborate to bridge the semantic gap between different hierarchical features, resulting in a more precise and detailed delineation of coastlines. Experimental validation on the publicly available SLSD 1 1 The dataset was initially labeled as "sea-land segmentation data" by the authors. For ease of discussion, we will adopt the practice of using the initial letters of each word as an abbreviation. dataset has demonstrated superiority over various advanced methods, with an impressive mIoU value of 96.72%. Through simple optimization, detailed and rich coastlines can be extracted, validating the feasibility of coastline extraction. [ABSTRACT FROM AUTHOR]

• Landsat bands alone may not give adequate quality of LULC mapping in a heterogeneous landscape. • Remote sensing-derived indices and auxiliary data improve the accuracy of LULC mapping. • LULC maps classified under different classification algorithms have shown a significant discrepancy. Land use/land cover (LULC) information is regarded as one of the most important variables in global change studies. Several LULC classification algorithms are available with different levels of accuracy in LULC mapping under different geographical setups. Furthermore, different remote sensing (RS) based indices and auxiliary data have been reported to have contributed to the accuracy of LULC mapping. Hence, the main aim of this study was to compare the different ML algorithms for the LULC mapping and explicitly assess the potential contribution of different RS-derived indices and auxiliary data on classification accuracy in the Upper Tekeze-Atbarah (UTA) river basin in Ethiopia. The overall classifiers evaluation result revealed that random forest (RF) performed best and followed by K-Nearest Neighbour (KNN), Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), and Classification and Regression Tree (CART). Compared to RF, the MLC method showed less accuracy by 16.2% (19.7%) in OA (kappa coefficient) respectively, which showed that the currently emerging ML classifiers contributed to an improvement in LULC mapping. The use of RS-derived indices and DEM as predictors together with the Landsat bands improved the overall accuracy and kappa coefficient by 5.5% and 7% respectively. Furthermore, the discrepancy in the classified LULC map from RF and other classifiers was compared at pixel-level and the result showed a considerable disagreement between the LULC maps. For instance, when the LULC map from RF and KNN are compared, only 80.6% of the area has the same LULC condition. This result reflects the potential discrepancy that could exist in LULC maps from different classifiers. The hydrological simulation under each of the classified map also resulted in a considerable difference in the major water balance components. In a conclusion, given the considerable difference in classifier performance and the potential propagation of errors in LULC maps to further applications such as water resources assessment, it is highly recommended to explicitly evaluate the available classifiers. Users should also harness the power of RS indices and auxiliary datasets for improved LULC mapping. [ABSTRACT FROM AUTHOR]

According to the 2018 report of the United Nations Office for Disaster Risk Reduction (UNISDR), the total number of recorded flood events between 1998 and 2017 was 3,148. This means there is an average of 13 monthly floods on our Earth. Satellite monitoring using Sentinel-1 synthetic aperture radar (SAR) can be effectively used to map floods. It provides a short revisit time, a high spatial resolution of 5 m, and a swath of up to 400 km. In this study, we compare two promising flood mapping algorithms to map floods which are common phenomena in the An Giang area, and to monitor the mapping difficulty of flood peaks. The two mapping algorithms are Global Flood Mapper (GFM), an open-source Google Earth Engine (GEE) application; and the other algorithm implemented the Otsu threshold method and change detection approach. They were visually compared using Sentinel-2 optical data and a land-use land cover (LULC) map of An Giang, Vietnam. The results show that the GFM overcomes the misclassification of flooded and non-flooded areas. A good statistical agreement showed that the performance of flood maps derived from the modified GFM algorithm on Sentinel-1 VH-VV-VH RGB composite and Sentinel-2 images have better accuracy than the Otsu Sentinel-1 technique by $\sim20\% $ ∼ 20 %. [ABSTRACT FROM AUTHOR]

Context: There has been a limited amount of research which comparatively examines the local and landscape scale ecological determinants of the community structure of both riparian and aquatic bird communities in floodplain ecosystems. Objectives: Here, we quantified the contribution of local habitat structure, land cover and spatial configuration of the sampling sites to the taxonomical and functional structuring of aquatic and terrestrial bird communities in a relatively intact floodplain of the river Danube, Hungary. Methods: We used the relative abundance of species and foraging guilds as response variables in partial redundancy analyses to determine the relative importance of each variable group. Results: Local-scale characteristics of the water bodies proved to be less influential than land cover and spatial variables both for aquatic and terrestrial birds and both for taxonomic and foraging guild structures. Purely spatial variables were important determinants, besides purely environmental and the shared proportion of variation explained by environmental and spatial variables. The predictability of community structuring generally increased towards the lowest land cover measurement scales (i.e., 500, 250 or 125 m radius buffers). Different land cover types contributed at each scale, and their importance depended on aquatic vs terrestrial communities. Conclusions: These results indicate the relatively strong response of floodplain bird communities to land cover and spatial configuration. They also suggest that dispersal dynamics and mass-effect mechanisms are critically important for understanding the structuring of floodplain bird communities, and should therefore be considered by conservation management strategies. [ABSTRACT FROM AUTHOR]

In this modern age of industrialization, waste from houses and factories has a major impact on the environment, and its management has become one of the most difficult tasks for the whole world. This study attempts to locate suitable sites for disposing of solid waste using geospatial techniques in Ranchi, Jharkhand. Moreover, as Ranchi is a part of the smart city project, it is important to find new sites for dumping to control the environmental issue that arises due to the increasing pressure of population growth and the rate of human migration. Site selection of solid waste dumping considers several parameters, such as land use, land cover, road, geomorphology, soil, slope, water bodies, forest, and built‐up. The present study has taken a multicriteria decision approach and used the analytical hierarchy process (AHP) to delineate the suitable landfill site for the medium‐sized city of Ranchi. The study adopts the overlay analysis operation for several factors and geospatial techniques to perform site suitability analysis. The result shows four classes of suitable dumping sites, where low or restricted area covers 1145.52 km2 (63.92%), moderately restricted covers 477.74 km2 (26.65%), whereas about 140 km2 (7.81%) of the area can be constructed but with restrictions, and 28.79 km2 (1.60%) of the area is highly recommended for dumping site construction. The site suitability map and the identified four new dumping sites are highly useful to the municipality corporation and decision‐makers to alleviate environmental pollution and human health problems. [ABSTRACT FROM AUTHOR]

Modeling the prediction of land use/land cover (LULC) change is necessary for sustainable management of natural resources and effective planning for proper urban development. The Markov chain model is the most commonly used prediction model for assessing LULC changes. This study aims to assess changes in LULC in Cuttack City from 1990 to 2020 and to predict LULC modification in 2050. For the analysis, Landsat satellite data were collected from the USGS website for 1990, 2005, and 2020. The satellite images were masked with our area of interest and classified using the maximum likelihood classifier tool in ArcGIS 10.2. The result of the multi‐temporal classification shows that the built‐up area percentage reached 45.927% in 1990 and 57.827% in 2020. Most areas of vegetation and water bodies were affected by this impromptu growth. Using the chi‐square test, the results of the Markov model are validated, which shows no significant differences between the classified and simulated areas. This model can, therefore, be accepted for our prediction. Built‐up growth has already achieved its peak in Cuttack City. There may be further growth in built‐up areas in the city shortly. These findings draw the attention of urban planners and policymakers to the stress of urban sprawl and prompt them to take stringent action to deal with this pressing issue. [ABSTRACT FROM AUTHOR]

Changes in hydrological processes in watersheds are triggered by various factors, including land use land cover changes (LULCC) emerging from human activities. Hence, it is crucial to elucidate the interaction among hydrological processes to improve sustainable water management plans. The current study explored the effects of LULCC on hydrological processes in the Gala Lake catchment over a 30‐year temporal period. In this context, to evaluate LULCC, land cover maps were obtained using the supervised classification method with Landsat images (5 TM, 7 ETM+, 8 OLI) of 1993, 2008, and 2023. Hydrological processes were assessed using the Soil and Water Assessment Tool (SWAT), a commonly applied eco‐hydrological model. The model was utilized to evaluate the potential effect of LULCC on processes like surface runoff, infiltration, evapotranspiration, and erosion. Between 1993 and 2023, significant LULCC have been observed in the study area. There has been a decrease of 4.3%, 1%, 1.6%, and 5.3% in agricultural lands, forested lands, inland marshes, and pasture lands, respectively, while artificial surfaces, rice fields, and water bodies have seen an increase of 7%, 3.4%, and 1.9%, respectively. The potential effects of these changes were observed as a decrease in the runoff, a decrease in permeability, and an increase in processes such as evapotranspiration and erosion throughout the basin and sub‐basin. In general, LULCC significantly affect the hydrological status of the catchment. Therefore, establishing different conservation and utilization strategies that consider land use dynamics to maintain the resilience of ecosystems in the basins is vital in the sustainable management of water resources. [ABSTRACT FROM AUTHOR]

Agricultural expansion is one of the main causes of deforestation of tropical montane forests in the central Andes of Peru. The hydrological impacts of converting forests to cropland are well known; however, an issue that is not entirely clear is related to the hydrological effects that can result from the conversion of montane forests to agroforestry systems and the recovery of the hydrological functions of montane forests after a disturbance. In this study, we compare the hydrological processes of different land covers previously modified by agricultural expansion, in order to determine the impact of the conversion of tropical montane forest to agricultural land on the ecosystem service of water provision and regulation. To achieve this, we establish study plots in four land cover types located in the central Andes of Peru (mature montane forest (BMC), natural regenerating secondary forest (BMR), coffee agroforestry systems (AF), and cropland (C)), for the purpose of measuring the vegetation structure and soil properties in them, and subsequently carry out a soil water balance in each plot to calculate the actual evapotranspiration, surface runoff, and groundwater recharge. The results revealed the following percentages (based on precipitation) for the hydrological components in the four land cover types: annual actual evapotranspiration—BMR (41.2%), AF (40.4%), BMC (40.0%), and C (38.0%); annual surface runoff—C (16.1%), BMC (8.3%), BMR (5.2%), and AF (4.6%); and annual groundwater recharge—AF (43.0%), BMR (41.6%), C (34.0%), and BMC (31.7%). Furthermore, the study also examined the relationship between vegetation structure and the hydrological components across the four land cover types. The findings indicated that the reduction of thicker and taller trees could increase surface runoff generation, whereas the presence of thinner and shorter trees could facilitate groundwater recharge. These results shed light on the complex interactions between land cover types, vegetation structure, and hydrological processes, emphasizing the importance of considering these factors in water resource management and land use planning. [ABSTRACT FROM AUTHOR]

The aim of this research was to simulate the future water balance of the Silwani watershed, Jharkhand, India, under the combined effect of land use and climate change based on the Soil and Water Assessment Tool (SWAT) and Cellular Automata (CA)-Markov Chain model. The future climate prediction was done based on daily bias-corrected datasets of the INMCM5 climate model with Shared Socioeconomic Pathway 585 (SSP585), which represent the fossil fuel development of the world. After a successful model run, water balance components like surface runoff, groundwater contribution to stream flow, and ET were simulated. The anticipated change in land use/land cover (LULC) between 2020 and 2030 reflects a slight increase (3.9 mm) in groundwater contribution to stream flow while slight decrease in surface runoff (4.8 mm). The result of this research work helps the planners to plan any similar watershed for future conservation. [ABSTRACT FROM AUTHOR]

A standalone Advanced Circulation (ADCIRC) model for the east coast of India with a high-resolution grid of 100 m near the coast is used to evaluate coastal inundation resulting from the storm tides generated by recent cyclones. A directional surface roughness parametrization that alters wind speed and Manning's n friction coefficient to compute bottom friction based on land use/land cover (LULC) at a particular location is incorporated in the model. ERA5 reanalysis winds highlight that the cyclonic winds over the land are less intensive than nearby coastal oceans, particularly during landfall time, by approximately 29%–50%. Experiments are also designed to quantify the impact of surface and bottom friction on wind speed and the inward propagation of storm tides. A comparison of cyclonic wind speed after incorporating LULC data in the model is made with an automatic surface observation system (ASOS). It suggests a 15%–29% reduction, consistent with ASOS. The inundated area computed for the cyclones advocates a significant reduction (15%–50%) due to LULC. Sensitivity experiments with LULC are performed to examine the impact of mangroves in the Krishna estuary located in between two concave-shaped coastal geometries. Replacing wetlands with Mangroves results in a simultaneous decline in wind speed (12.5%) and inundated area (13.4%). It also highlights that bottom friction contributes (9.4%) in the inundated area against surface friction (4%). This study infers that further investigation and planning are mandatory to ensure coastal mangrove restoration initiatives and effective coastal management practices. [ABSTRACT FROM AUTHOR]

The Bangkok Metropolitan Region (BMR), located in the Chao Phraya River basin delta, is particularly vulnerable to floods, with susceptibility heightened by geographical aspects and rapid urbanization. This study aimed to assess spatiotemporal flood exposure and allow proper flood‐risk recognition among all stakeholders through a three‐phase flood exposure assessment. First, land use and land cover (LULC) changes were identified based on a 30‐year Landsat time series. Second, built‐up areas that overlapped with past flood inundation maps were designated as flood exposure areas. Third, a rainfall‐runoff inundation (RRI) model simulated the 2011 Thailand Flood, the largest on record, by analyzing inundation depth implications across three decades. The findings revealed a dramatic increase in the use of built‐up areas and the associated flood exposure. In 1992, built‐up areas accounted for approximately 20% of the total area, sharply increasing to nearly 45% by 2022, according to the LULC classification. The flood exposure increased from 648.83 km2 in 1992 to 1681.26 km2 by 2022, demonstrating a linear trend. Notably, the catastrophic 2011 flood did not inhibit urbanization in flood‐prone areas, highlighting the need for robust policies, such as the segmentation of flood‐risk zones, to mitigate future exposure in the region. [ABSTRACT FROM AUTHOR]

Hawassa is a rapidly developing city in Lake Hawassa watershed of Ethiopia. Analyzing the effect of land cover dynamics on surface runoff remains imperative to adaptive urban stormwater management. This study quantified spatial variation of land cover and sensitivity of stormwater management response. Historical 30 years of daily annual rainfall, three satellite imageries, DEM, and hydrological soil group data were analyzed. A statistical‐based combined approach of geospatial techniques and Soil Conservation Service‐Curve Number (SCS‐CN) model was employed. CN and surface runoff depth for the delineated urban watersheds were determined. The result revealed that the built‐up area increased by 30.9 km2, where the rate varies spatially. The variation of impervious land cover explains 58.6% of change in CN with coefficient of 0.352. While CN is inversely correlated with agricultural and vegetation land cover variations. The finding suggests CN explains 96.78% of the change in surface runoff with a significant correlation coefficient of 3.91. The proposed integrated model approach justifies the potential to reorganize the relationship between the spatial effect of land cover variation on surface runoff at the urban watersheds. Thus, suitable local‐specific solutions can be devised for effective management of flood risk and optimize the drainage system of urban areas. [ABSTRACT FROM AUTHOR]

Amidst global concerns about land use change and its far-reaching impact on biodiversity and human well-being, there is a growing need to understand how land use stock and flow changes over time through land use accounting. While existing studies on land accounting have focused on historical land changes, little attention has been paid to future transitions. This study assessed historical patterns and projected future shifts in land use dynamics from 1987 to 2050 across Japan by combining high-resolution land use and land cover datasets, land change simulations, and land accounting. In the analyses, particular attention was paid to the historical and future trends of farmland abandonment by leveraging data at 100-m resolution built on national vegetation surveys. High-resolution analysis of farmland abandonment issue with national scale in Japan is a novelty. From 1987 to 1998, the land stock analysis results showed a pronounced marked increase in residential land (10.4%) and grassland (16.9%); the flow analysis results showed that urban residential sprawl expansion was mainly formed by secondary (32.6%) and plantation (21.1%) forest areas, coinciding with increasing population and economic growth. Projections from 2010 to 2050 indicate a marked increase in abandoned farmland (67.2% per decade), a trend influenced by rapid population decline and presumably agricultural policies, especially significant in regions such as Hokkaido and Kyushu. The findings of this study are crucial for shaping policy and decision-making, underlining the need for sustainable land management strategies that effectively balance urban growth, agricultural productivity, and environmental preservation in Japan. [ABSTRACT FROM AUTHOR]

This study analyses the decrease in soil organic carbon (SOC) stocks due to changes in land use following the earthquake in Düzce, Turkey, 1999. The primary objective of the study is to determine the changes in land use within Düzce and to provide a multi-dimensional approach to the spatial and quantitative distributions of SOC losses. Corine Land Use- Land Cover (LULC) within the study is used to determine the change in land use. The loss of LULC and carbon stocks were identified by means of LULC with transfer matrix method and GIS-based analysis. The study of land-use change caused by urbanisation and agricultural activity shows that the limited green spaces around the urban core created by degrading natural areas do not compensate for the loss of SOC. SOC stocks decline after the land use changes from agricultural regions to artificial areas (− 5%), Natural- Semi-natural (N-SN) regions to artificial areas (− 15%), N-SN areas to agricultural areas (− 20.9%) and agricultural areas to water bodies (− 9%), and SOC stocks increase after land use changes from artificial areas to N-SN areas (+ 29.6%), artificial areas to agricultural areas (+ 8%), agricultural areas to N-SN areas (+ 25%). However, in some agricultural areas, SOC stocks are similar to semi-natural and natural areas. For instance, in sparsely vegetated areas, SOC stocks from fruit and berry plantations may be poor. Although it is generally assumed that SOC loss can occur on land transformed from natural areas, this rule of thumb may be revised in some particular circumstances. Therefore, local ecological restoration decisions should not be based on land cover generalisations. [ABSTRACT FROM AUTHOR]

Urbanisation is a crucial issue for urban planners and researchers worldwide, especially in South Asia, where rapid urbanisation has led to several negative impacts on the environment, economy, and people. Land-use change is one of the associated problems of urbanisation that can lead to an increased land surface temperature (LST) and surface urban heat island (SUHI) effect. This study aimed to investigate the impact of urbanisation on the local climate in Dehradun city, India, by analysing the relationship between land-use and land-cover (LULC) change, LST, and the UHI effect. Geospatial analyses were carried out using Landsat multispectral data from 1996 to 2019. LULC classification using a machine learning-based supervised classification technique, support vector machine and LST were derived using a mono-window algorithm, and the surface UHI (SUHI) effect was quantified using the urban thermal field variance index (UTFVI). The study found that the built-up cover increased by 28%, resulting in seasonal LST increases of 7 °C and 2 °C for the summer and winter seasons, respectively. The findings show that the regions with no SUHI effect reduced significantly from 42.59% in 1996 to 21.55% in 2019, while the strongest SUHI regions increased from 0% in 1996 to 10.7% in 2019. This study contributes to delivering scientific information on the impact of urbanisation and land-use change on the urban thermal environment of the city, which may be useful for policymakers in developing appropriate strategies to reduce the impact of urbanisation on the environment and to improve the quality of life for citizens. [ABSTRACT FROM AUTHOR]

Soil erosion is a severe issue posing a number of adverse effects on the environment. It is a prominent hazard damaging the fertile agricultural land. Therefore, in this study, a spatio-temporal assessment of soil erosion was carried out in the Swat River Basin, Pakistan by employing the Revised Universal Soil Loss Equation (RUSLE). The parameters of the RUSLE model are rainfall erosivity, soil erodibility, slope length and steepness, land management and support practice. These factors were developed from monthly mean rainfall data obtained from the Regional Metrology Department Peshawar, FAO soil database, land use data prepared from Landsat-5 and 8 satellite imageries, topographic data obtained from the ALOS PALSAR Digital Elevation Model (DEM). The analysis discovered that 13% of the study area experienced severe erosion. Results of the spatial distribution and vulnerability to erosion within the Swat River Basin have been categorized into different zones such as very low (59.7%), low (19.5%), moderate (5.37%), high (6.86%), and very high (5.96%). These results accentuate the necessity for mitigation measures in the study area to mitigate the loss of valuable topsoil. This research possesses the potential to offer valuable insights into decision-making and planning to reduce the risk of erosion. [ABSTRACT FROM AUTHOR]

Background: Tea is a valuable economic plant grown extensively in several Asian countries. The accurate mapping of tea plantations is critical for the growth and development of the tea industry. In eastern India, tea plantations have a significant role in its economy. Sonitpur, Jorhat, Sibsagar, Dibrugarh, and Tinsukia are major tea-producing districts in Assam. Due to the rapid increase in tea plantations and the burgeoning population, a detailed mapping and regular monitoring of tea plantations are imperative for understanding land use alteration. Objectives: The present study aims to analyse the dynamics of tea plantations from 1990 to 2022 at a decadal scale, using satellite data, such as Landsat-5 and Sentinel-2. Methods: A supervised classifier called Random Forest (RF) was deployed in the Google Earth Engine (GEE) platform to classify tea plantations. Results: The results showed significant growth in tea plantations in the district of Dibrugarh (112%), whereas the remaining districts had a growth rate of 45–89%. During 32 years (1990–2022), about 1280.47 km2 (78.71%) of areas of tea plantations expanded across five districts of Assam. Precision and recall were used to measure the accuracy of tea plantations classification, which exhibited considerably high F1 scores (0.80 to 0.96). Conclusions: This study helps to demonstrate the application of remote sensing techniques to evaluate the dynamics of tea plantations which can help policymakers to manage the tea estates and underlying changes in land cover. [ABSTRACT FROM AUTHOR]

Freshwater and terrestrial biodiversity are linked through spatial resource flows. Key examples are detrital subsidies from the riparian vegetation that form the base of food webs in small streams. Despite the central role of detritivores in these food webs, the consequences of altered riparian vegetation type and resource availability on their trophic strategies are less known. Therefore, we experimentally tested direct and indirect effects of riparian vegetation type (i.e. riparian forests present vs. absent) on trophic interactions and dietary imbalances of aquatic detritivores. To characterise trophic strategies, we used stoichiometric and isotopic differences between consumers and resources as functional measures of trophic link strength in leaf litter bags naturally colonised by stream decomposers and detritivores. Our results show that an absence of riparian forests affected stoichiometric and isotopic properties in stream detrital food webs. Where local riparian forests were absent, leaf litter showed lower C:N ratios, which only trichopteran detritivores mirrored, whereas plecopteran and crustacean detritivores showed differences in their isotopic signatures. Diverging patterns between resources and consumers could lead to a rewiring of energy flow paths and weaken the coupling between aquatic detritivores and terrestrial detritus. Consequently, our findings demonstrate that riparian forests are essential for aquatic food webs by not only influencing organisms themselves but also their trophic interactions and energy flow paths. Aquatic detritivores depending on terrestrial subsidies can be highly sensitive to local changes in their resource environment. Therefore, functionally divergent patterns between resources and consumers in recipient systems highlight how habitat properties of the donor system can affect food webs connected across ecosystem boundaries. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

This study aimed at delineating the shoreline by using the Digital Shoreline Analysis System (DSAS 5.0) tool and detected the changes of land use/land cover (LULC) by Google Earth Engine (GEE) platform. The shoreline is divided into two zones, whereas Zone I covered 87.12km and Zone II possessed 168.05km. According to End Point Rate (EPR), the mean shoreline change rate of Zone I is 3.55m/year and Zone II is −6.84m/year. Likewise, based on Linear Regression Rate (LRR), the mean shoreline change rate of Zone I is 5.46m/year and Zone II is −4.71m/year, respectively. Apart from that, the Net Shoreline Movement (NSM) recorded in Zone I is 109.42m as well as Zone II is −213.25m, which also revealed how much the shoreline has changed during the last 32 years. This study also used the Kalman filter model to forecast the shoreline positions for 20 years. The most destructive signal is that more than 70% of the coastline is vulnerable due to erosion, whereas 6% is highly vulnerable. By contrast, the results of LULC changes demonstrated the increasing trend of water bodies, built up, and agricultural land while vegetation along with bare land is reduced continuously. The overall accuracy is recorded above 88%, and the kappa co-efficient is found above 0.87 for all three years. The outcome of this study will provide fruitful insight into coastal land use management and adaptation measures against the ongoing along with future threats of shoreline changes to coastal ecosystems and livelihoods. [ABSTRACT FROM AUTHOR]

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

The primary objective of this study was to investigate patterns of abundance and diversity among reptiles and amphibians within the protected area of Mount Chelmos, located in Peloponnese, Greece. Our aim was to examine the drivers influencing abundance (for lizards, snakes, chelonians, anurans, urodels) and diversity (for reptiles and amphibians) within the finely-scaled study area and identify biodiversity hotspots. Land cover significantly impacted the abundance and diversity of all studied groups. Elevation had a significant impact on the abundance of snakes, chelonians, and anurans, as well as on reptile and amphibian diversity. Climate influenced all groups except chelonians. Slope significantly impacted anuran abundance and amphibian diversity. Aspect primarily influenced the abundance of lizards, anurans, and urodels, as well as reptile diversity. We have also identified abundance and diversity hotspots, emphasizing the importance of conservation and management efforts. In reptiles, abundance hotspots were predicted in the west, east, and south of the mountainous massif, whereas amphibian abundance hotspots were mapped to the west and east-southeast of the massif. Regarding diversity, the patterns were similar for both reptiles and amphibians, with hotspots emerging in the west, south, and southeast boundaries of the study area, and a few spots north of the mountainous region. Notably, many of these hotspots coincided with areas susceptible to human exploitation and vehicular transit. [ABSTRACT FROM AUTHOR]

Background: Attractive Targeted Sugar Baits (ATSBs) are a proposed new vector control tool for malaria that contain sugar and an ingestion toxicant, and are designed to attract and kill sugar-feeding mosquitoes. During a two-arm cluster randomized Phase III trial conducted in Zambia to test the efficacy of ATSB stations on malaria incidence, ATSB stations deployed on eligible household structures within intervention clusters were routinely monitored to ensure their good physical condition and high coverage. This study investigates trends in prevalence and rate of damage to ATSB stations during year 2 of the two-year trial. Methods: The analysis was conducted using monitoring data collected in year 2, which included types of damage observed, location, and date of removal and/or replacement of ATSB stations. The study evaluated temporal trends in the prevalence of overall damage and different damage types among 68,299 ATSB stations deployed. A profile of all ATSB stations installed on each structure was constructed, and spatial analyses conducted on overall damage and different damage types observed on 18,890 structures. Mixed effects regression analyses were conducted to investigate drivers of damage to ATSB stations on these structures. Results: Prevalence of overall damage and different damage types was temporally and spatially heterogeneous. Among damaged ATSB stations observed during monitoring, tears and mold had the highest prevalences on average, with tears maintaining above 50.0% prevalence through most of the monitoring period, while mold prevalence increased steadily during the first few months, peaking in February. Overall, 45.6% of structures had at least one damaged ATSB station, however this varied spatially across the trial site. Both structure characteristics and environmental factors significantly impacted the odds and rate of damage to ATSB stations on structures, including: ATSB stations' level of protection from rainfall and sunshine; roof and wall material of the structure; night-time temperature; rainfall; enhanced vegetation index, and land cover. Conclusion: Damage to ATSB stations in this setting was common and was temporally and spatially heterogeneous. This has implications on operational feasibility, sustainability, and cost of future deployment. Further research is required to understand the mechanisms of damage, and to minimize prevalence and rate of damage to ATSB stations. [ABSTRACT FROM AUTHOR]

This study examines the relationship between the reclamation method (with topsoil and with bare rock), individual species, and land cover (woodland, grassland, and forbsland) on micro‐climate and retention properties, that is, volumetric water content (vwc) and water storage capacity (S). The large overburden dump "Sośnica" in Upper Silesia served as a research site, which makes it possible to investigate the planned experimental variants with relatively homogeneous properties of the deposited rock. The study uses statistical analysis and multilinear regressions to investigate the correlation between reclamation method, species, and land cover with parameters such as atmospheric temperature, relative humidity, vwc, soil temperature (st), and S. The experiments were carried out both in the field and in the laboratory. The results of this study indicate that the parameters related to retention properties were higher in the recultivated areas than in the areas left to natural succession. "S" was significantly higher in grassland and forbsland (15.67–16.52%) than in control plots where 10.23% was found, and in areas where an additional soil layer was applied (14.72%). Maximum vwc (22.17%) was on plots with topsoil than on bare rock (14.89%). Vwc were highest in the areas designated as grassland (28.54%), in comparison with woodlands and forblands (the control without vegetation cover had the worst thermal and retention properties). This study sheds light on the effects of reclamation method, species, and land cover on various parameters and helps to understand the importance of appropriate reclamation techniques for soil and water conservation. Further research on the impacts of reclamation techniques and land cover on micro‐climatic conditions in diverse post‐mining landscapes is crucial for developing globally applicable restoration strategies that promote ecological resilience. [ABSTRACT FROM AUTHOR]

Land use and cover change (LUCC) significantly impacts global carbon cycles and land surface properties, accounting for 25% of the historical atmospheric CO2 increase. We explore a previously overlooked role of LUCC in driving the land carbon cycle by using a three-level meta-analysis and Land Use Harmonization data to drive an ecosystem model. Our findings reveal that a loss of 39.2% of soil organic carbon (SOC) change in China due to LUCC, mitigated by afforestation, doubles gross primary productivity at 0.02 Pg C yr−1, countering central China's urbanization decline. Indirect climate effects, especially soil bulk density, significantly impact SOC compared to direct climate effects. LUCC has significantly increased the Chinese terrestrial carbon sink, with net ecosystem productivity reaching 0.02 ± 0.12 Pg C yr−1. Our study underscores the importance of reforestation and afforestation in addressing climate change and enhancing carbon sinks in future carbon management. [ABSTRACT FROM AUTHOR]

This study evaluates the land use and land cover (LULC) dynamics that play an indispensable role in the degradation and deterioration of soil and water quality affecting the natural resources throughout the Kulfo watershed in Ethiopia. Directed image classification is initiated for satellite images to study the watershed. The image classification is categorized into ten different LULC classes with validation of ground control points. A Revised Universal Soil Loss Equation (RUSLE) model was used to generate the average soil loss of the watershed. The model involves the Rainfall Erosivity factor (R), Soil Erodibility factor (K), Length and Slope factor (LS), Cover Management factor (C), and Support Practice factor (P). The dynamics of LULC change and rainfall erosivity over the past 30 years have been interpreted using maps from 1990, 2005, and 2020 using the C-factor and R-factor. The remaining factors, like K-factor, LS-factor, and Pfactor, were kept constant over the period. The results reveal that the average annual soil loss rate (A) of the watershed is estimated to be 138.8 t ha-1, 161.2 ha-1, and 173.25 t ha-1 per year, for the selected period intervals. During the past three decades, the soil loss rate in the watershed has increased by 34.4 t ha-1 per year. The watershed and sustainable soil and water conservation practices need special attention to mitigate the severity of soil erosion risks to avoid disaster. [ABSTRACT FROM AUTHOR]

Nature-based solutions are increasingly advocated to mitigate climate change and biodiversity loss, while improving ecosystem resilience and providing additional ecosystem services. In Scotland, woodland expansion and restoration of degraded peatlands are expected to play a major role in meeting net-zero emissions by 2045 and have prompted debates about the impact of increased woodland cover and prescribed fire on the biodiversity and ecosystem services provided by upland landscapes. In alignment with national policy, the Cairngorms National Park, the UK's largest national park, has committed to an ambitious programme of woodland expansion and peatland restoration in a landscape dominated by heather moorlands that is predominantly managed through prescribed burning for game management. Using the Native Woodland Model and the InVest modelling platform, we assessed the effects of five land cover and land use change scenarios, with different levels of prescribed fire regulation and woodland expansion, to evaluate their benefits and costs on biodiversity and carbon sequestration. Results show that changing the extent and management of habitats will result in different carbon sequestration pathways, as well as biodiversity winners and losers. The scenario presenting greater benefits for the conservation of biodiversity also has lower above-ground carbon sequestration potential and a larger negative impact on red grouse habitats, thus being less profitable to sporting estates. Hence, trade-offs will be necessary to achieve optimal carbon sequestration and biodiversity gains, with a potential role played by the continuation of prescribed fires and traditional moorland management practices as well as complementary grants and support measures based on biodiversity benefits rather than carbon sequestration. The results from this study could support discussions regarding future management of the uplands, trade-offs between loss of carbon in soils, carbon sequestration in woodlands and conservation of biodiversity, as well as stakeholders likely to be affected. [ABSTRACT FROM AUTHOR]

The upper Tekeze River Basin is facing challenges of widespread deforestation and natural vegetation cover degradation that could exacerbate the water scarcity, food insecurity and extreme poverty in the region. Using remote sensing and GIS, this study quantified the land use land cover change trend in the last three decades and analyzed the current land use / cover statues in the basin. A hybrid classification technique is applied to obtain better classification accuracy. Moreover, for automated cloud and cloud shadow detection the newly developed Mountainous Fmask is used. Using post classification change detection technique, seven major land use/cover classes were identified. These classes remained the dominant classes during the study period, showing marked changes in the area coverage within them. Based on the error matrix statistical indices, the classification accuracies of each class are found to be strong. The overall accuracy and the kappa coefficient for the 2021 map are 91% and 89%, respectively. The techniques used have contributed to improving the accuracy of the classification process and helped the classified images to practically match the ground truths. The analysis revealed settlement expansion by 570.31% in parallel with the expansion of farmland by 52.32% during the period 1991–2021. In contrast, the forestland decreased significantly, by 75.55%. The environmental degradation and unplanned use of land resources could have contributed to why the upper Tekeze basin is experiencing worsening poverty, water scarcity and food insecurity. Thus, land use/cover time series modeling is essential for various purposes, including land use planning and, managing natural resources. In this regards this study provides basic information for implementing sustainable environmental conservation strategies in the area. Furthermore, the applied methodologies may have practical applications in other similar areas. [ABSTRACT FROM AUTHOR]

The management of groundwater systems is essential for nations that rely on groundwater as the principal source of communal water supply (e.g., Mohmand District of Pakistan). The work employed Remote Sensing and GIS datasets to ascertain the groundwater recharge zones (GWRZ) in the Mohmand District of Pakistan. Subsequently, a sensitivity analysis was conducted to examine the impact of geology and hydrologic factors on the variability of the GWRZ. The GWRZ was determined by employing weighted overlay analysis on thematic maps derived from datasets about drainage density, slope, geology, rainfall, lineament density, land use/land cover, and soil types. The use of multi-criteria decision analysis (MCDA) involves the utilization of the multi-influencing factor (MIF) and analytical hierarchy procedure (AHP) to allocate weights to the selected influencing factors. The MIF data found that very high groundwater recharge spanned 1.20%, high zones covered 40.44%, moderate zones covered 50.81%, and low zones covered 7.54%. In comparison, the AHP technique results suggest that 1.81% of the whole area is very high, 33.26 is high, 55.01% is moderate, and 9.92% has low groundwater potential. The geospatial-assisted multi-influencing factor approach helps increase conceptual knowledge of groundwater resources and evaluate possible groundwater zones. [ABSTRACT FROM AUTHOR]

Land Use and Land Cover (LULC) maps are important tools for environmental planning and social-ecological modeling, as they provide critical information for evaluating risks, managing natural resources, and facilitating effective decision-making. This study aimed to generate a very high spatial resolution (0.5 m) and detailed (21 classes) LULC map for the greater Mariño watershed (Peru) in 2019, using the MORINGA processing chain. This new method for LULC mapping consisted in a supervised object-based LULC classification, using the random forest algorithm along with multi-sensor satellite imagery from which spectral and textural predictors were derived (a very high spatial resolution Pléiades image and a time serie of high spatial resolution Sentinel-2 images). The random forest classifier showed a very good performance and the LULC map was further improved through additional post-treatment steps that included cross-checking with external GIS data sources and manual correction using photointerpretation, resulting in a more accurate and reliable map. The final LULC provides new information for environmental management and monitoring in the greater Mariño watershed. With this study we contribute to the efforts to develop standardized and replicable methodologies for high-resolution and high-accuracy LULC mapping, which is crucial for informed decision-making and conservation strategies. [ABSTRACT FROM AUTHOR]

In this study, the impact of change in land use and land cover (LULC) on runoff estimation in the Kidangoor watershed was assessed using the SCS-CN technique. Recent flood-like natural disasters in Kerala are thought to be driven by changes in rainfall patterns and LULC. The accurate calculation of runoff from watersheds is urgently needed. In ArcGIS 10.5, the supervised classification approach is used to classify satellite images from 2000, 2011, 2013, and 2017. Similarly, the Inverse Distance Weighted (IDW) technique is used to produce spatial distribution maps of rainfall for each antecedent moisture condition (AMC). The runoff maps were generated by superimposing the distributed rainfall, LULC, and Hydrological Soil Group (HSG) maps. It was observed that the built-up area expanded by 168% between 2000 and 2017, whereas other classes decreased by 10-23%. However, compared to 2000, both with and without a change in LULC, runoff generation increased by just 31%, and 27% in 2017. The SCS-CN technique for runoff estimation indicates that the change in LULC in the Kidangoor watershed is insignificant. Thus, this study will help land use planners and decision-makers in limiting the potential damage from flooding when it comes to flood management techniques. [ABSTRACT FROM AUTHOR]

Wildfires are recognized as major contributors to forest loss and soil degradation on a global scale. Understanding the cumulative effects of fire regimes on forest ecosystems and soil dynamics necessitates a deeper exploration of wildfire-vegetation-soil interactions over the long term. This study delves into the wildfire-landscape dynamics within the "Baixa Limia Serra do Xurés" Natural Park, a region prone to fires in Galicia, Spain. By analyzing available statistical and remote sensing data, we identified significant shifts in fire regimes and landscape dynamics between the periods of 2000-2010 and 2010-2020. Our findings indicate a potential extension of the fire season, reflecting the impacts of climate change. Despite improvements in firefighting capabilities, the occurrence of large fires is on the rise in the Natural Park, underscoring the need for proactive management strategies in such areas. Notably, significant fire events in 2011, 2016, 2017, and 2020 extensively affected wooded areas, constituting the majority of the burned area. Shrubs and forests emerged as particularly vulnerable, with varying degrees of burn severity influencing post-fire vegetation recovery rates. While shrublands expanded their coverage between 2000 and 2010, rocky areas with sparse vegetation showed an increase over the subsequent decade (2010-2020), indicating soil degradation and potential desertification in areas affected by recurrent and severe fires, especially within zones designated for the highest levels of protection (with fire rotation periods of less than 1 year). In conclusion, this study provides valuable insights into the impacts of wildfires, changes in land cover, and post-fire soil-vegetation dynamics, which can inform management and conservation efforts in fire-prone mountainous regions. Leveraging advanced remote sensing techniques enables the monitoring of cumulative soil degradation resulting from repeated wildfires over extended periods. [ABSTRACT FROM AUTHOR]

It is evident that the means of subsistence of the community have a significant impact on the management of natural resources. This study examined the socio-economic drivers of LULCC and assessed the impacts of such changes on rural livelihoods in Shato forest, southwest Ethiopia. To map the land use and land cover, supervised classifications were used. The data were collected from 358 household heads through semi-structured questionnaires. A logistic regression model was employed to investigate the dependence of rural households on forest resources. LULC analysis results showed that about 308.29 ha of wetland and 3215.6 ha of natural forest were converted to other land use types during the last 30 years. The findings reveal that a household's education level, household size, distance from the market, total land owned, skills and social network significantly affect their dependency on forest resources. Respondents gave high rankings to covariates such as erratic rainfall (1.70), market price (1.53), low crop output (1.28), and inadequate infrastructure (1.24). These covariates force rural communities of the study area into two major livelihood diversification strategies. These were crop and income diversification. The study comes to conclude that, the extensive and imprudent use of natural resources is a result of changes in livelihood strategies to cope up with the aforementioned shocks. Thus, the decreasing nature of forest resources results in covariate shocks in the study area and needs serious intervention mechanisms to tackle this trajectory of catastrophe. [ABSTRACT FROM AUTHOR]

The rapid expansion of informal settlements poses significant challenges to urban ecosystems and their services, a pressing issue that this study addresses by examining its impacts in Addis Ababa. This study delves into the impact of informal settlements on ecosystem services in Addis Ababa, comparing them with formal settlements. Utilizing geographic information systems (GIS) and remote sensing, it employed Landsat imagery, aerial photography, and base maps to track spatiotemporal changes and analyze trends. Through a GIS overlay approach, informal settlements and associated land use changes were identified, while the benefit transfer method assessed ecosystem service values. The findings revealed a significant increase in informal settlements on the city's outskirts, expanding from 77 ha in 2009 (2% of the total area) to 765.6 ha in 2023 (21% of the total area). Nearly half (48.5%) of these informal settlements were established on previously natural land covers through encroachment. The research highlighted the ongoing conversion of agricultural land, forests, and urban green spaces into informal settlements, particularly on the city's outskirts, leading to significant encroachment on these areas. This transformation caused an annual loss of $1,665,033.7 in ecosystem services from 2009 to 2023. Significant impacts were identified on food production, climate regulation, and habitat ecosystem services. Overall, the annual loss in ecosystem services due to land use changes amounted to $1,933,320.08. Informal settlements accounted for 86.1% of this total loss, highlighting the urgent need for targeted interventions to mitigate their disproportionate effects on ecosystem services. The study underscores the urgency of addressing the impact of informal settlements on ecosystem services through effective urban planning, sustainable land management practices, prevention of encroachment on natural land covers, and promotion of sustainable ecosystem management and utilization. [ABSTRACT FROM AUTHOR]