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

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

A study of land use and land cover (LULC) mapping using satellite data was conducted in the Congo Basin rainforest in the Sangha area of the northern Congo Republic, which is crucial for sustainable land management. However, the heterogeneous nature of LULC features makes data classification difficult in this area because persistent cloud cover is challenging for observing the land surface and tropical humid conditions characterizing the study area. This study compares and evaluates the potential of two of the most widely used and popular satellite image sources, the recently launched Landsat 9 and Sentinel-2, to generate land-use land cover (LULC) maps in the Sangha area using pixel-based supervised machine learning algorithms, Random Forest (RF), for classification. All procedures were performed using ArcGIS Pro. The potential to improve Landsat 9 performance was tested using the nearest resampling technique, and different bands were resampled from 30 × 30 to 10 × 10 m. Owing to the heterogeneous characteristics of the Sangha study area, six classes were defined: dense forest, open forest, wetland forest, water bodies, urban areas, and bare soils. To classify and compare Sentinel-2 and Landsat 9, the shortwave infrared (SWIR), Near Infrared (NIR), and Red, Green, Blue (RGB) bands were used. These two satellite images were compared to test the quality of their results, particularly for assessing the accuracy of the LULC classifications and identifying which dataset had the highest accuracy. Results show that overall accuracy was 93.80% for Sentinel-2 while it was 91.60% for Landsat 9, similarly, the Kappa coefficient was calculated 0.89 and 0.85 for Sentinel-2 and Landsat 9, respectively. Therefore, Sentinel-2 exhibits significant classification ability compared to the nearest resample technique Landsat 9, despite the improved resolution of the latter, which offers a scientific basis for choosing the appropriate satellite imagery to create accurate LULC maps. [ABSTRACT FROM AUTHOR]

River hydrology shapes the sources, concentration, and stoichiometry of organic matter within drainage basins. However, our understanding of how the microbes process dissolved organic matter (DOM) and recycle nutrients in tropical rivers needs to be improved. This study explores the relationships between elemental DOM composition (carbon/nitrogen/phosphorus: C/N/P), C and N uptake, and C mineralization by autochthonous bacterioplankton in the Usumacinta River, one of the most important fluvial systems in Mexico. Our study investigated changes in the composition and concentration of DOM and evaluated carbon dioxide (CO2)production rates (C–CO2) through laboratory experiments. We compared three sites representing the middle and lower river basins, including their transitional zones, during the rainy and dry seasons. After incubation (120 h at 25°C), the DOM decreased between 25% and 89% of C content. Notably, the initial high proportion of C in DOM in samples from the middle–forested zone and the transition led to elevated C–CO2 rates (>10 mg l−1 day−1), in contrast to the lower initial C proportion and subsequent C–CO2 rates (<7 mg l−1 day−1) in the lower river basin. We also found that dissolved organic carbon uptake and NO3− and NH4+ production were higher during the dry season than in the rainy season. The low water flow in the river during the dry season accentuated the differences in elemental composition and microbial processing of DOM among the sites, while the high water flow of the rainy season homogenized these factors. Our findings indicate that microbial metabolism operates with reduced efficiency in C-rich environments like forests, particularly when faced with high C/N and C/P ratios in DOM. This study highlights the influence of the tropical hydrological regime (rainy and dry seasons) and the longitudinal changes in the river basin (middle and lower) topography and land cover on microbial metabolism by constraining DOM characteristics, emphasizing the crucial role of elemental ratios in river DOM processing. [ABSTRACT FROM AUTHOR]

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

ABSTRACT Aedes vexans (Meigen, 1830) is a floodwater mosquito species that may cause significant nuisance and can serve as a vector for multiple arboviruses. Its distribution is expected to shift in the future as a result of changes in climate and land use. Understanding these shifts is important for estimating future disease risk. This study aims to identify habitat suitability and probability of occurrence of A. vexans. Using the Netherlands as a case study, we utilised an occurrence dataset generated by the Netherlands Centre for Monitoring of Vectors. We employed an auto machine learning approach to model generation, using a variety of modelling methodologies, determining the optimal ratio of presence: Absence datapoints in the training data and ultimately creating a 10‐model ensemble. We selected predictor variables relating to weather, land use, soil properties, flood risk and salinity. The probability of A. vexans presence was predicted on a 1 km grid for both the current Dutch situation and for four scenarios for 2050. Our analysis identified temperature, soil type and land cover as the primary determinants influencing the probability of A. vexans occurrence. Future projections reveal an increase in the likelihood of A. vexans occurrence in the study area, particularly along major river corridors and in regions with increasing amounts of artificial and natural areas. Additionally, the mosquito season is predicted to become longer under all future scenarios. Insights provided in our study can also be applied to other similar areas, such as other north‐western European countries or other urban deltas. This study shows for the first time detailed future occurrence predictions and also future seasonal predictions for this mosquito species. Seasonal predictions allow researchers to study how disease risk changes throughout the year, something which is particularly valuable given the predicted lengthening of the mosquito (and thus disease transmission) season. [ABSTRACT FROM AUTHOR]

This study presents the model setup and results from the first calibrated, physically-based, spatially-distributed hydrological modelling of combined land cover and climate change impacts on a large sample of UK river catchments. The SHETRAN hydrological model was automatically calibrated for 698 UK catchments then driven by the 12 regional climate model projections from UKCP18, combined with urban development and natural flood management scenarios. The automatic calibration of SHETRAN produces a median Nash-Sutcliffe efficiency value of 0.82 with 581 catchments having a value greater than 0.7. 24 summary metrics were calculated to capture changes to important aspects of the flow regime. The UKCP18 realisations in SHETRAN indicate that a warming climate will cause river flows, on average, to decrease. These decreases are simulated to be greatest in the south and east of the UK, with droughts becoming longer and more severe. While high flows also decrease on average, an increased number of extremes are exhibited, implying a greater number of extreme flood events in the future, particularly in the north and west of the UK. In the urban development scenarios, for flood events there is an increase in flow with the increased urbanization, with the 1 in 3-year peak flow event showing the greatest increase. The natural flood management scenarios consider the effect of increasing woodland and adding surface water storage ponds. The inclusion of these features produces a complex response but overall, the modelling shows a reduction in low, median, and high flows, although the more extreme the flow event the smaller the percentage change in flow. Simulated timeseries and summary metric datasets are freely available on the CEDA archive. [ABSTRACT FROM AUTHOR]

China is undergoing rapid urbanization, which brings about drastic land cover changes and thus has an important impact on land carbon stocks. Therefore, it is of great significance to study the driving factors of land cover changes in typical cities and simulate their carbon stocks in multiple scenarios, in order to promote the development of sustainable use of land resources and to achieve the goal of "dual-carbon." In this study, based on the synergistic relationship between land cover and carbon stock (CS), a coupled modeling framework based on MOP-FLUS-InVEST (MFI) is proposed, which integrates the advantages of three models: targeted optimization of the land cover (LC) structure, patch-level simulation of the layout, and rapid probing of spatial and temporal evolutions of CS. In addition, based on the 30 m resolution surface cover data, we analyzed the land cover change characteristics of Shijiazhuang, a city undergoing rapid urbanization in China, from 2000 to 2020 using a dynamic attitude model. The results show that the rate of surface cover change in Shijiazhuang City is relatively fast, but the rate of surface cover change gradually slows down during the 20-year period. The LC change is mainly manifested in the mutual transfer of cropland, woodland and grassland. In the future, the area of cropland, water bodies and bare land decreases, the business-as-usual development (BAU) scenario has the most drastic increase in construction land, and the changes in woodland and grassland are weak, with an increase in economic benefits. In the Ecological Priority Development (EDP) scenario, woodland and grassland expand significantly while construction land growth stagnates, and ecological functions are restored. In the Ecologically and Economically Balanced Development (EEB) scenario, ecological land increases and the growth of built-up land slows down, realizing both economic and ecological benefits. The continuous shrinkage of water bodies is a pressing issue. The coupled model can provide scientific references for the simulation of spatial and temporal changes of LC and CS, the early warning of ecological risks, and the development of land cover planning. [ABSTRACT FROM AUTHOR]

As the main component of terrestrial ecosystem, vegetation plays a very important role in regional ecosystem environmental change, global carbon cycle and climate regulation. The Lower Mekong region (LMR) is at the core of Southeast Asia, its vegetation changes will affect the regional ecosystem and climate. Five countries of LMR were selected as the study area, based on MODIS (Moderate-Resolution Imaging Spectroradiometer) NDVI(Normal Difference Vegetation Index) data from 2000 to 2022, using the Sen's slope estimator, Mann–Kendall trend test and geographic detector to study the spatial and temporal variation trends and driving forces of vegetation coverage. The results showed that:(1) From 2000 to 2022,the vegetation coverage in the LMR showed an overall fluctuating upward trend, the annual average Fractional Vegetation Cover(FVC) value was 0.70, mainly with high vegetation coverage and relatively high vegetation coverage. Vegetation distribution had obviously spatial heterogeneity, and the vegetation of Myanmar, Laos and Vietnam was significantly larger than Thailand and Cambodia.(2) The variation trend analysis of Sen_MK showed that the proportion of improved and degraded vegetation coverage areas in the LMR were 56.33% and 37.55% respectively. The vegetation improvement area was much larger than the vegetation degradation area during 2000–2022. According to the variation trend analysis of different countries, the vegetation coverage improvement area in Vietnam, Myanmar and Thailand were larger than the degraded , the overall vegetation coverage variation trend were good. However, in Laos and Cambodia, the degraded areas were larger than the improved, the overall variation trends of coverage were not good.(3) The results of geographic detector showed that the Land Use and Land Cover(LULC) had the greatest influence on vegetation coverage in the study area.The influencing factors of vegetation coverage were different in the LMR. For Vietnam, Thailand and Laos,elevation and slope factors were second only to LULC, for Myanmar and Cambodia, the influence of precipitation factor was second only to LULC. The results provide scientific data support for understanding the ecological environment status and future changes in the research area. [ABSTRACT FROM AUTHOR]

Introduction: The Common Agricultural Policy (CAP) is a vital policy framework implemented by the European Union to regulate and support agricultural production within member states. The Land Parcel Identification System (LPIS) is a key component that provides reliable land identification for administrative control procedures. On-the-spot checks (OTSC) are carried out to verify compliance with CAP requirements, typically relying on visual interpretation or field visits. However, the CAP is embracing advanced technologies to enhance its efficiency. Methods: This study focuses on using Sentinel-2 time series data and a two-level approach involving recurrent neural networks (RNN) and convolutional neural networks (CNN) to accurately identify permanent pastures. Results: In the first step, using RNN, the model achieved an accuracy of 68%, a precision of 36%, a recall of 97% and a F1-score of 52%, which indicates the model's ability to identify all the true positive parcels (correctly identified permanent pasture parcels) and minimize the false negative parcels (non-identified permanent pasture parcels). This occurs due to the difficulty in distinguishing between permanent pastures and other similar land covers (such as temporary pastures and shrublands). In the second step, it was possible to distinguish the permanent pasture parcels from the others. The obtained results improved significantly from the first to the second step. Using CNN, an accuracy of 93%, a precision of 89%, and a recall of 98% were achieved for the "Permanent pasture" class. The F1-score was 94%, indicating a balanced measure of the model's performance. Discussion: The integration of advanced technologies in the CAP's control mechanisms, as demonstrated, has the potential to automate the verification of farmers' declarations and subsequent subsidy payments. [ABSTRACT FROM AUTHOR]

Urbanization is one of the important drivers of increasing local temperatures. As cities and urban areas evolve, extensive land use and land cover (LULC) changes alter the physical characteristics of surface materials. This modification results in reduced evapotranspiration rates, ultimately contributing to higher surface and air temperatures. This study investigated the impact of urbanization on urban temperature in Jakarta. Urban temperature was simulated for a 20-year time period (1995–2014) by the urban boundary layer climate model UrbClim, using LULC data for both 1995 and 2014. Temperature changes were analysed by assessing the temperature anomaly across different LULC change classes divided into four main classes namely no built-up changes (BB), no green spaces changes (GG), built-up to green spaces (BG), and green spaces to built-up (GB). The study revealed that the conversion of green spaces to built-up areas (GB) had the most significant impact on the increase in air temperature. This was indicated by the mean values of the temperature anomaly of GB of about 0.24°C followed by GG, BB, and BG with the mean values of the temperature anomaly of about 0.20°C, 0.19°C, 0.17°C, respectively. The different temperature anomalies of the LULC change classes indicate that green spaces have an important role in maintaining local climate. Hence, it is important for local government to effectively manage the composition, the quantity, as well as the distribution of green spaces within a city. By looking at temperature anomalies of LULC change classes, the present study provides an alternative approach to many existing methods that provide general information about temperature changes, without specifically analyzing the effects of LULC transformations. [ABSTRACT FROM AUTHOR]

In recent years, deep learning-based multi-source data fusion, e.g., hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion, has gained significant attention in the field of remote sensing. However, the traditional convolutional neural network fusion techniques always provide poor extraction of discriminative spatial–spectral features from diversified land covers and overlook the correlation and complementarity between different data sources. Furthermore, the mere act of stacking multi-source feature embeddings fails to represent the deep semantic relationships among them. In this paper, we propose a cross attention-based multi-scale convolutional fusion network for HSI-LiDAR joint classification. It contains three major modules: spatial–elevation–spectral convolutional feature extraction module (SESM), cross attention fusion module (CAFM), and classification module. In the SESM, improved multi-scale convolutional blocks are utilized to extract features from HSI and LiDAR to ensure discriminability and comprehensiveness in diversified land cover conditions. Spatial and spectral pseudo-3D convolutions, pointwise convolutions, residual aggregation, one-shot aggregation, and parameter-sharing techniques are implemented in the module. In the CAFM, a self-designed local-global cross attention block is utilized to collect and integrate relationships of the feature embeddings and generate joint semantic representations. In the classification module, average polling, dropout, and linear layers are used to map the fused semantic representations to the final classification results. The experimental evaluations on three public HSI-LiDAR datasets demonstrate the competitiveness of the proposed network in comparison with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The Greater Amanzule Peatlands (GAP) in Ghana is an important biodiversity hotspot facing increasing pressure from anthropogenic land-use activities driven by rapid agricultural plantation expansion, urbanisation, and the burgeoning oil and gas industry. Accurate measurement of how these pressures alter land cover over time, along with the projection of future changes, is crucial for sustainable management. This study aims to analyse these changes from 2010 to 2020 and predict future scenarios up to 2040 using multi-source remote sensing and machine learning techniques. Optical, radar, and topographical remote sensing data from Landsat-7, Landsat-8, ALOS/PALSAR, and Shuttle Radar Topography Mission derived digital elevation models (DEMs) were integrated to perform land cover change analysis using Random Forest (RF), while Cellular Automata Artificial Neural Networks (CA-ANNs) were employed for predictive modelling. The classification model achieved overall accuracies of 93% in 2010 and 94% in both 2015 and 2020, with weighted F1 scores of 80.0%, 75.8%, and 75.7%, respectively. Validation of the predictive model yielded a Kappa value of 0.70, with an overall accuracy rate of 80%, ensuring reliable spatial predictions of future land cover dynamics. Findings reveal a 12% expansion in peatland cover, equivalent to approximately 6570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses have increased, evidenced by an 85% rise in rubber plantations (from 30,530 ± 110.96 hectares to 56,617 ± 220.90 hectares) and a 6% reduction in natural forest cover (5965 ± 353.72 hectares). Sparse vegetation, including smallholder farms, decreased by 35% from 45,064 ± 163.79 hectares to 29,424 ± 114.81 hectares. Projections for 2030 and 2040 indicate minimal changes based on current trends; however, they do not consider potential impacts from climate change, large-scale development projects, and demographic shifts, necessitating cautious interpretation. The results highlight areas of stability and vulnerability within the understudied GAP region, offering critical insights for developing targeted conservation strategies. Additionally, the methodological framework, which combines optical, radar, and topographical data with machine learning, provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands that is applicable to other regions facing similar environmental challenges. [ABSTRACT FROM AUTHOR]

Clouds and their shadows significantly affect satellite imagery, resulting in a loss of radiometric information in the shadowed areas. This loss reduces the accuracy of land cover classification and object detection. Among various cloud shadow detection methods, the geometric-based method relies on the geometry of the sun and sensor to provide consistent results across diverse environments, ensuring better interpretability and reliability. It is well known that the direction of shadows in raw satellite images depends on the sun's illumination and sensor viewing direction. Orthoimages are typically corrected for relief displacements caused by oblique sensor viewing, aligning the shadow direction with the sun. However, previous studies lacked an explicit experimental verification of this alignment, particularly for cloud shadows. We observed that this implication may not be realized for cloud shadows, primarily due to the unknown height of clouds. To verify this, we used Rapideye orthoimages acquired in various viewing azimuth and zenith angles and conducted experiments under two different cases: the first where the cloud shadow direction was estimated based only on the sun's illumination, and the second where both the sun's illumination and the sensor's viewing direction were considered. Building on this, we propose an automated approach for cloud shadow detection. Our experiments demonstrated that the second case, which incorporates the sensor's geometry, calculates a more accurate cloud shadow direction compared to the true angle. Although the angles in nadir images were similar, the second case in high-oblique images showed a difference of less than 4.0° from the true angle, whereas the first case exhibited a much larger difference, up to 21.3°. The accuracy results revealed that shadow detection using the angle from the second case improved the average F1 score by 0.17 and increased the average detection rate by 7.7% compared to the first case. This result confirms that, even if the relief displacement of clouds is not corrected in the orthoimages, the proposed method allows for more accurate cloud shadow detection. Our main contributions are in providing quantitative evidence through experiments for the application of sensor geometry and establishing a solid foundation for handling complex scenarios. This approach has the potential to extend to the detection of shadows in high-resolution satellite imagery or UAV images, as well as objects like high-rise buildings. Future research will focus on this. [ABSTRACT FROM AUTHOR]

Accurate burn severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where the vegetation experiences above-ground combustion but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush burn severity and woody vegetation post-burn coppicing/regeneration events in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km2 plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.71% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a >50% probability of woody vegetation regrowth in high-severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine-scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas. [ABSTRACT FROM AUTHOR]

The forest floor is an integral component of forest soils, which constitutes a significant yet underaccounted terrestrial carbon pool. Comprehensive studies were conducted at a test site in the Kivach State Nature Reserve, which represents little disturbed middle-taiga ecosystems of Eastern Fennoscandia. Forest floor properties were studied following the methods guidelines of the project "National system for monitoring the dynamics of climate-active substances in terrestrial ecosystems of the Russian Federation". Organic carbon content (Сorg) was determined using Perkin Elmer 2400 Series II CHNS/O Analyzer (USA). The forest floor is a heterogeneous formation with quite variable composition and state. There predominantly (80% of all sampling points) forms a fermentation-type raw-humus forest floor with average thickness of 5.9 ± 0.2 cm and a stock of 49.8±1.9 t/ha in the study area. Organic matter is lost gradually in the course of dead plant biomass degradation, so Сorg content in the dwarf shrub–true moss habitats predominating at the test site was the highest in the top layer of the forest floor, OL, reaching 52.8 ± 0.6%. Carbon content in the lower floor subhorizons declined to 40.8 ± 2.0%. Where the proportion of forb species increased, average Сorg content significantly decreased, to 19%. Average estimated forest-floor Сorg stock in the studied ecosystems is 20.9 ± 0.9 t/ha. It exhibited high spatial variation of 1.5 to 45 t/ha. The predictors of forest-floor Сorg variation in the study area include land cover characteristics, dominant tree species, and location relative to the tree. Thorough estimation of the contribution of forest-floor carbon is necessary for accurate quantification of the stock of this pool within the new national system for monitoring carbon pools and greenhouse gas fluxes. [ABSTRACT FROM AUTHOR]

The objective of this study is to develop a methodological approach to assess ecosystem functioning in terms of carbon uptake and sequestration. To achieve this, a model was developed to forecast Net Primary Productivity (NPP) and tested in a rural area, the Thrace region (NE Greece). Monthly net photosynthesis data from July 2015 to February 2024 was estimated using the MODerate Resolution Imaging Spectroradiometer (MODIS) Aqua MYD17A2H v061 dataset for the study area. This dataset was used as the dependent variable in an Autoregressive Integrated Moving Average (ARIMA) model. The predictive ability of monthly Land Cover (LC) and Soil Moisture (SM) data at various time lags was examined, and variables found to be significant predictors were introduced into the model as external predictors. The study found that crop areas and SM conditions exert the greatest influence on NPP, with areas containing trees exerting a lesser impact. Additionally, NPP was estimated for seven different LC and SM scenarios from March 2023 to February 2024. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

Investigating the effects of spatial scales on the uncertainty and sensitivity analysis of the social vulnerability index (SoVI) model output is critical, especially for spatial scales finer than the census block group or census block. This study applied the intelligent dasymetric mapping approach to spatially disaggregate the census tract scale SoVI model into a 300‐m grids resolution SoVI map in Davidson County, Nashville. Then, uncertainty analysis and variance‐based global sensitivity analysis were conducted on two scales of SoVI models: (a) census tract scale; (b) 300‐m grids scale. Uncertainty analysis results indicate that the SoVI model has better confidence in identifying places with a higher socially vulnerable status, no matter the spatial scales in which the SoVI is constructed. However, the spatial scale of SoVI does affect the sensitivity analysis results. The sensitivity analysis suggests that for census tract scale SoVI, the indicator transformation and weighting scheme are the two major uncertainty contributors in the SoVI index modeling stages. While for finer spatial scales like the 300‐m grid's resolution, the weighting scheme becomes the uttermost dominant uncertainty contributor, absorbing uncertainty contributions from indicator transformation. [ABSTRACT FROM AUTHOR]

Climate change, urbanization, and land subsidence are increasing risks of dangerous and costly flooding in Africa's coastal regions. As the intensity and the number of flooding occurrences increase, real-time solutions for flood risks and hazards mapping are required to assess flooding and its potential impacts on the environment and humans. Flood risk mapping has become an important tool for assessing flood risks in coastal areas to guide planning and mitigation efforts by local officials. However, this tool has not been leveraged to support flood risk analysis. Using GIS weighted sum method, this study mapped and assessed flood risk zones in Limbe, Cameroon, by utilizing a Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM). Flood risk zones were delineated based on several environmental factors, including precipitation, elevation, slope, soil texture, land use/land cover, and flow accumulation. The findings classified about 50% of land in Limbe as having either high or very high vulnerability to flooding. The highest risk areas were concentrated in the upstream and downstream areas of Ejengele and Limbe Rivers. Only about 9% of Limbe was classified as very low risk, suggesting that most of the land area is susceptible to flooding. The study reveals that more than 90% of the Limbe's urban areas are in flood-prone areas, which requires special attention from stakeholders to take necessary steps toward the preparation and mitigation of extreme flood events. The resulting flood risk maps provide vital information for urban and risk management planning professionals. [ABSTRACT FROM AUTHOR]

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

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

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

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

Spatial modelling approaches to aid land‐use decisions which benefit both wildlife and humans are often limited to the comparison of pre‐determined landscape scenarios, which may not reflect the true optimum landscape for any end‐user. Furthermore, the needs of wildlife are often under‐represented when considered alongside human financial interests in these approaches.We develop a method of addressing these gaps using a case‐study of wild bees in the UK, an important group whose declines may adversely affect both human economies and surrounding ecosystems. By combining the genetic algorithm NSGA‐II with a process‐based pollinator model which simulates bee foraging and population dynamics, Poll4pop, we 'evolve' a typical UK agricultural landscape to identify optimum land cover configurations for three different guilds of wild bee. These configurations are compared to those resulting from optimisations for farm income alone, as well as optimisations that seek a compromise between bee populations and farm income objectives.We find that the land cover proportions in landscapes optimised for each bee guild reflect their nesting habitat preferences rather than foraging preferences, highlighting a limiting resource within the study landscape. The spatially explicit nature of these optimised landscapes illustrates how improvement for a given target species may be limited by differences between their movement range and the scale of the units being improved. Land cover composition and configuration differ significantly in landscapes optimised for farm income and bee population growth simultaneously and illustrate how human agents are required to compromise much more when the multifaceted nature of biodiversity is recognised and represented by multiple objectives within an optimisation framework. Our methods provide a way to quantify the extent to which real‐life landscapes promote or compromise objectives for different landscape end‐users.Our investigation suggests that optimisation set‐up (decision‐unit scales, traditional choice of a single biodiversity metric) can bias outcomes towards human‐centric solutions. It also demonstrates the importance of representing the individual requirements of different actors with different landscape‐level needs when using genetic algorithms to support biodiversity‐inclusive decision‐making in multi‐functional landscapes. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

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

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

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

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

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

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

Land use and land cover change (LUCC) significantly influences the dynamics of carbon storage in thin terrestrial ecosystems. Investigating the interplay between land use alterations and carbon sequestration is crucial for refining regional land use configurations, sustaining the regional carbon balance, and augmenting regional carbon storage. Using land use data from the Pearl River Delta Urban Agglomeration (PRDUA) from 2010 to 2020, this study employed PLUS-InVEST models to analyze the spatiotemporal dynamics of land use and carbon storage. Projections for the years 2030, 2040, and 2050 were performed under three distinct developmental scenarios, namely, natural development (ND), city priority development (CPD), and ecological protection development (EPD), to forecast changes in land use and carbon storage. The geographic detector model was leveraged to dissect the determinants of the spatial and temporal variability of carbon storage, offering pertinent recommendations. The results showed that (1) during 2010–2020, the carbon storage in the PRDUA showed a decreasing trend, with a total decrease of 9.52 × 106 Mg, and the spatial distribution of carbon density in the urban agglomeration was imbalanced and showed an overall trend in increasing from the center to the periphery. (2) Clear differences in carbon storage were observed among the three development scenarios of the PRDUA between 2030 and 2050. Only the EPD scenario achieved an increase in carbon storage of 1.10 × 106 Mg, and it was the scenario with the greatest potential for carbon sequestration. (3) Among the drivers of the evolution of spatial land use patterns, population, the normalized difference vegetation index (NDVI), and distance to the railway had the greatest influence on LUCC. (4) The annual average temperature, annual average rainfall, and GDP exerted a significant influence on the spatiotemporal dynamics of carbon storage in the PRDUA, and the interactions between the 15 drivers and changes in carbon storage predominantly manifested as nonlinear and double-factor enhancements. The results provide a theoretical basis for future spatial planning and achieving carbon neutrality in the PRDUA. [ABSTRACT FROM AUTHOR]

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

The application of remote sensing technology in water body detection has become increasingly widespread, offering significant value for environmental monitoring, hydrological research, and disaster early warning. However, the existing methods face challenges in multi-scene and multi-temporal water body detection, including the diverse variations in water body shapes and sizes that complicate detection; the complexity of land cover types, which easily leads to false positives and missed detections; the high cost of acquiring high-resolution images, limiting long-term applications; and the lack of effective handling of multi-temporal data, making it difficult to capture the dynamic changes in water bodies. To address these challenges, this study proposes a novel network for multi-scene and multi-temporal water body detection based on spatiotemporal feature extraction, named TSAE-UNet. TSAE-UNet integrates convolutional neural networks (CNN), depthwise separable convolutions, ConvLSTM, and attention mechanisms, significantly improving the accuracy and robustness of water body detection by capturing multi-scale features and establishing long-term dependencies. The Otsu method was employed to quickly process Sentinel-1A and Sentinel-2 images, generating a high-quality training dataset. In the first experiment, five rectangular areas of approximately 37.5 km2 each were selected to validate the water body detection performance of the TSAE-UNet model across different scenes. The second experiment focused on Jining City, Shandong Province, China, analyzing the monthly water body changes from 2020 to 2022 and the quarterly changes in 2022. The experimental results demonstrate that TSAE-UNet excels in multi-scene and long-term water body detection, achieving a precision of 0.989, a recall of 0.983, an F1 score of 0.986, and an IoU of 0.974, significantly outperforming FCN, PSPNet, DeepLabV3+, ADCNN, and MECNet. [ABSTRACT FROM AUTHOR]

This study was designed to develop a 30 m resolution land cover dataset to improve the performance of regional weather forecasting models in East China. A 10-class land cover mapping scheme was established, reflecting East China's diverse landscape characteristics and incorporating a new category for plastic greenhouses. Plastic greenhouses are key to understanding surface heterogeneity in agricultural regions, as they can significantly impact local climate conditions, such as heat flux and evapotranspiration, yet they are often not represented in conventional land cover classifications. This is mainly due to the lack of high-resolution datasets capable of detecting these small yet impactful features. For the six-province study area, we selected and processed Landsat 8 imagery from 2015–2018, filtering for cloud cover. Complementary datasets, such as digital elevation models (DEM) and nighttime lighting data, were integrated to enrich the inputs for the Random Forest classification. A comprehensive training dataset was compiled to support Random Forest training and classification accuracy. We developed an automated workflow to manage the data processing, including satellite image selection, preprocessing, classification, and image mosaicking, thereby ensuring the system's practicality and facilitating future updates. We included three Weather Research and Forecasting (WRF) model experiments in this study to highlight the impact of our land cover maps on daytime and nighttime temperature predictions. The resulting regional land cover dataset achieved an overall accuracy of 83.2% and a Kappa coefficient of 0.81. These accuracy statistics are higher than existing national and global datasets. The model results suggest that the newly developed land cover, combined with a mosaic option in the Unified Noah scheme in WRF, provided the best overall performance for both daytime and nighttime temperature predictions. In addition to supporting the WRF model, our land cover map products, with a planned 3–5-year update schedule, could serve as a valuable data source for ecological assessments in the East China region, informing environmental policy and promoting sustainability. [ABSTRACT FROM AUTHOR]

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

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

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