Search Results (8,941)

The bush cricket Saga pedo, listed as Vulnerable globally by the IUCN and included in Annex IV of the EU Habitats Directive, is a parthenogenetic species highly sensitive to environmental changes, facing threats from forest expansion and agricultural intensification. S. pedo prefers dry, open habitats with sparse vegetation, and its pronounced thermo-heliophily makes it an indicator of xerothermic habitats. In many areas of Italy, including the Northern Apennines (Piedmont), semi-natural grasslands are fragmented. Open habitats have been reduced to small, isolated patches surrounded by forests due to the abandonment of agropastoral activities. Consequently, the occurrence of open habitat species is related to the quality and availability of suitable areas and ecological connectivity. We developed a spatial Bayesian framework to identify areas of occurrence for S. pedo. Using the inverse probability of occurrence, we derived ecological corridors among suitable patches. Our findings indicate that the occurrence and connectivity of S. pedo are reduced by intensive cultivation but favored by open habitats with 10–50% woody tree cover, suggesting sustainable land management is crucial for supporting the species. Given the extinction risk S. pedo faces, we urge local administrations to maintain and improve suitable areas and guarantee the network of ecological corridors identified. Full article

This paper aims to explore the willingness of farmers in the northern Namibia to adopt conservation agriculture (CA), employing the conditional logit model to estimate the probability of farmers choosing to adopt CA in different villages relative to all other alternatives and examining the effects of omitted variance and correlations on coefficient estimates, willingness to pay (WTP), and decision predictions. This study has practical significance, as agriculture plays a crucial role in the economic development of and livelihoods in Namibia, especially for those farmers who rely on small-scale farming as a means of subsistence. In terms of methodology, the data for the experimental choice simulation were collected using a structured questionnaire administered through a face-to-face survey approach. This paper adopts the conditional logit model to estimate the probability of farmers choosing to adopt CA in different villages, which is an appropriate choice as the model is capable of handling multi-option decision problems. This paper further enhances its rigor and reliability by simulating discrete choice experiments to investigate the impact of omitted variables and correlations on the estimation results. The research findings indicate that crop rotation and permanent soil cover are the main factors positively influencing farmers’ WTP for adopting CA, while intercropping, the time spent on soil preparation in the first season, and the frequency and rate of weeding consistently negatively influence the WTP for adopting CA. These discoveries provide valuable insights for formulating policy measures to promote the adoption of CA. In terms of policy recommendations, this paper puts forward targeted suggestions, including the appointment of specialized extension technicians by the Ministry of Agriculture, Water, and Land Reform to disseminate information as well as coordinate, promote, and personally implement CA activities across all regions. Additionally, to expedite the adoption of CA, stakeholders should ensure the availability of appropriate farming equipment, such as rippers and direct seeders, in local markets. Full article

This study integrates morphometric analysis, remote sensing, and GIS with the analytical hierarchical process (AHP) to identify high potential groundwater recharge areas in Wadi Abadi, Egyptian Eastern Desert, supporting sustainable water resource management. Groundwater recharge primarily comes from rainfall and Nile River water, particularly for Quaternary aquifers. The analysis focused on the Quaternary and Nubian Sandstone aquifers, evaluating 16 influencing parameters, including elevation, slope, rainfall, lithology, soil type, and land use/land cover (LULC). The drainage network was derived from a 30 m-resolution Digital Elevation Model (DEM). ArcGIS 10.8 was used to classify the basin into 13 sub-basins, with layers reclassified and weighted using a raster calculator. The groundwater potential map revealed that 24.95% and 29.87% of the area fall into very low and moderate potential categories, respectively, while low, high, and very high potential zones account for 18.62%, 17.65%, and 8.91%. Data from 41 observation wells were used to verify the potential groundwater resources. In this study, the ROC curve was applied to assess the accuracy of the GWPZ models generated through different methods. The validation results indicated that approximately 87% of the wells corresponded accurately with the designated zones on the GWPZ map, confirming its reliability. Over-pumping in the southwest has significantly lowered water levels in the Quaternary aquifer. This study provides a systematic approach for identifying groundwater recharge zones, offering insights that can support resource allocation, well placement, and aquifer sustainability in arid regions. This study also underscores the importance of recharge assessment for shallow aquifers, even in hyper-arid environments. Full article

Understanding the changes in land use and land cover (LULC) in national parks and their corresponding ecosystem service value (ESV) shifts is crucial for shaping future management policies and directions. However, comprehensive analyses in this research area that integrate tourism development perspectives are lacking. Therefore, this interdisciplinary study considers Akan-Mashu National Park in Japan as a case study. Using remote sensing data, LULC maps for the past 10 years were generated using the Google Earth Engine. The benefit transfer method was employed to calculate the corresponding ESV for each year, followed by a qualitative analysis of local tourism policy documents to explore how the park ecosystem has changed in the context of promoting tourism development. The results showed that LULC changes in Akan-Mashu National Park have been relatively stable over the past decade, with the most noticeable changes occurring in built-up areas. The results also confirm that tourism development has not had a significant negative impact on the ESV of the Akan-Mashu National Park. The recommendations proposed in this study can also be applied to other similar national parks or protected areas worldwide to achieve a dynamic balance between environmental protection and tourism development. Full article

The large-scale expansion of the golden jackal (Canis aureus) across Europe in recent decades has been strongly influenced by its successful space and habitat use. In this study, we analyzed the habitat selection of seven golden jackals tracked with GPS collars between 15 March 2021 and 25 November 2022 in a predominantly agricultural landscape in the southwestern part of the Pannonian Basin, Central Europe. Animals were tracked for an average of 29 weeks, and GPS collars recorded a total of 29,840 hourly localization points, which were compared to a high-resolution land cover dataset. We found that golden jackals maintain smaller home ranges in agricultural landscapes than in more pristine environments. Based on Jacobs’ index values calculated for monthly habitat preferences and the distribution of distances from land cover edges, we also found that preferences for the various habitat types differed significantly among individuals. Most of the time, golden jackals stayed near the edges of forests, agricultural lands, and shrublands, while they stayed away from artificial areas, wetlands, and water bodies. Forests and shrublands providing cover and safety were generally preferred by the golden jackals, especially during breeding and pup-rearing periods, while there was a strong avoidance of agricultural lands in general. Overall, our findings suggest that despite individual differences in the availability of habitat types within home ranges, forest–agricultural ecotones with relative proximity to food and shelter play a key role in the habitat selection of golden jackals. Full article

The Landsat archive stands as one of the most critical datasets for studying landscape change, offering over 50 years of imagery. This invaluable historical record facilitates the monitoring of land cover and land use changes, helping to detect trends in and the dynamics of the Earth’s system. However, the relatively low temporal frequency and irregular clear-sky observations of Landsat data pose significant challenges for multi-temporal analysis. To address these challenges, this research explores the application of a closed-form continuous-depth neural network (CFC) integrated within a recurrent neural network (RNN) called CFC-mmRNN for reconstructing historical Landsat time series in the Canadian Prairies region from 1985 to present. The CFC method was evaluated against the continuous change detection (CCD) method, widely used for Landsat time series reconstruction and change detection. The findings indicate that the CFC method significantly outperforms CCD across all spectral bands, achieving higher accuracy with improvements ranging from 33% to 42% and providing more accurate dense time series reconstructions. The CFC approach excels in handling the irregular and sparse time series characteristic of Landsat data, offering improvements in capturing complex temporal patterns. This study underscores the potential of leveraging advanced deep learning techniques like CFC to enhance the quality of reconstructed satellite imagery, thus supporting a wide range of remote sensing (RS) applications. Furthermore, this work opens up avenues for further optimization and application of CFC in higher-density time series datasets such as MODIS and Sentinel-2, paving the way for improved environmental monitoring and forecasting. Full article

The adoption of land cover standards is essential for resolving inconsistencies in global, regional, and national land cover datasets. This study examines the challenges associated with integrating existing datasets, including variations in land cover class definitions, classification methodologies, limited interoperability, and reduced comparability across scales. Focusing on Ghana as a case study, this research aims to develop a land cover legend and land cover map aligned with International Organization for Standardization (ISO) 19144-2 standards, evaluate the effectiveness of improving land cover classification and accuracy of data, and finally, assess the challenges and opportunities for the adoption of land cover standards. This study uses a multi-sensor remote sensing approach, integrating Sentinel-1 and Sentinel-2 optical imagery with ancillary data (elevation, slope, and aspect), to produce a national land cover dataset for 2023. Using the random forest (RF) algorithm, the land cover map was developed based on a land cover legend derived from the West African land cover reference system (WALCRS). The study also collaborates with national and international organizations to ensure the dataset meets global reporting standards for Sustainable Development Goals (SDGs), including those for land degradation neutrality. Using a survey form, stakeholders in the land cover domain were engaged globally (world), regionally (Africa), and nationally (Ghana), to assess the challenges to and opportunities for the adoption of land cover standards. The key findings reveal a diverse range of land cover types across Ghana, with cultivated rainfed areas (28.3%), closed/open forest areas (19.6%), and savanna areas (15.9%) being the most dominant classes. The classification achieved an overall accuracy of 90%, showing the robustness of the RF model for land cover mapping in a heterogeneous landscape such as Ghana. This study identified a limited familiarity with land cover standards, lack of documentation, cost implication, and complexity of standards as challenges to the adoption of land cover standards. Despite the challenges, this study highlights opportunities for adopting land cover standards, including improved data accuracy, support for decision-making, and enhanced capacity for monitoring sustainable land cover changes. The findings highlight the importance of integrating land cover standards to meet international reporting requirements and contribute to effective environmental monitoring and sustainable development initiatives. Full article

Rocky land cover provides vital habitat for many different species, including endemic, vulnerable, or threatened plants and animals; thus, various land management organizations prioritize the conservation of rocky habitat. Despite its importance, land cover classification maps rarely classify rocky land cover explicitly, and if they do, they are limited in spatial resolution or extent. Consequently, we used random forest models in Google Earth Engine (GEE) to classify rocky land cover at a high spatial resolution across a broad spatial extent in the Cascade Mountains and Columbia River Gorge in Washington, USA. The spectral indices derived from Sentinel-2 satellite data and NAIP aerial imagery, the specialized multi-temporal predictors formulated using time series of normalized burn ratio (NBR) and normalized difference in vegetation index (NDVI), and topographical predictors were especially important to include in the rocky land cover classification models; however, the predictors’ relative variable importance differed regionally. Beyond evaluating random forest models and developing classification maps of rocky land cover, we conducted three case studies to highlight potential avenues for future work and form connections to land management organizations’ needs. Our replicable approach relies on open-source data and software (GEE), aligns with the goals of land management organizations, and has the potential to be elaborated upon by future research investigating rocky habitats or other rare habitat types. Full article

Monitoring the snow cover variability and trends is crucial due to its significant contribution to river formation and sustenance. Using gap-filled MODIS data over the 2001–2024 period, the spatial distribution and temporal evolution of three snow cover metrics were studied: number of days, onset and end of the snow cover season across fourteen regions covering the Karakoram, Western Himalayas and Kunlun Mountains. The obtained signals exhibit considerable complexity, making it difficult to find a unique factor explaining their variability, even if elevation emerged as the most important one. The mean values of snow-covered days span from about 14 days in desert regions to about 184 days in the Karakoram region. Given the high interannual variability, the metrics show no significant trend across the study area, even if significant trends were identified in specific regions. The obtained results correlate well with the ERA5 and ERA5-Land values: the Taklamakan Desert and the Kunlun Mountains experienced a significant decrease in the snow cover extent possibly associated with an increase in temperature and a decline in precipitation. Similarly, the Karakoram and Western Himalayas region show a positive snow cover trend possibly associated with a stable temperature and a positive precipitation trend. Full article

Hydrology relates to many complex challenges due to climate variability, limited resources, and especially, increased demands on sustainable management of water and soil. Conventional approaches often cannot respond to the integrated complexity and continuous change inherent in the water system; hence, researchers have explored advanced data-driven solutions. This review paper revisits how artificial intelligence (AI) is dramatically changing the most important facets of hydrological research, including soil and land surface modeling, streamflow, groundwater forecasting, water quality assessment, and remote sensing applications in water resources. In soil and land modeling, AI techniques could further enhance accuracy in soil texture analysis, moisture estimation, and erosion prediction for better land management. Advanced AI models could also be used as a tool to forecast streamflow and groundwater levels, therefore providing valuable lead times for flood preparedness and water resource planning in transboundary basins. In water quality, AI-driven methods improve contamination risk assessment, enable the detection of anomalies, and track pollutants to assist in water treatment processes and regulatory practices. AI techniques combined with remote sensing open new perspectives on monitoring water resources at a spatial scale, from flood forecasting to groundwater storage variations. This paper’s synthesis emphasizes AI’s immense potential in hydrology; it also covers the latest advances and future prospects of the field to ensure sustainable water and soil management. Full article

The northern slope of the Tianshan Mountains city cluster (NSTM), as a key urban agglomeration for the development of western China, has experienced rapid regional economic development and high population concentration since the twenty-first century. Accompanied by the increase in human activities in the NSTM, it has significantly altered the land use structure, leading to varying levels of habitat disturbance and degradation. In this paper, based on the land use and land cover (LULC) of NSTM from 2000 to 2020. The InVEST model was employed to assess habitat quality, revealing notable spatial and temporal variations. A geoprobe was further employed to explore the key drivers of the spatially distributed pattern of habitat quality in the research region. The results show that (1) from 2000 to 2020, the NSTM was largely characterized by grassland, unused land, and cropland in terms of land use, with a notable expansion of cropland and construction land; (2) the overall habitat quality in the study area is poor, with a clear spatial distribution pattern of high in the south and low in the north, with a predominance of low grades, and a trend of decreasing and then increasing is shown in the temporal direction; (3) under the influence of rapid urbanization in the region, the degradation degree of habitat quality on the NSTM shows a distinct radial structure, with high degradation in the middle and low degradation at the edges, and shows the trend of “increase-decrease-increase” over time; and (4) the results of the geodetector show that altitude and land use type have the greatest influence on habitat quality on the NSTM, indicating that the habitat quality of the research region is primarily influenced by the type of land use. Full article

Physical soil crust (PSC) is a dense structural layer formed on the surface of bare or very low-cover land due to raindrop splashes or runoff. The formation of crust changes the properties of the soil and strongly affects water infiltration and runoff and sediment production processes on slopes. The irrational use of soil and water resources and frequent human production activity under the influence of urbanization increase the possibility of inducing erosion. Studying the formation and structural characteristics of PSC to predict terrestrial hydrological processes and improve models for predicting erosion is very important. Many studies of PSC have been carried out in China and abroad, but they are mainly unilateral discussions of the basic properties and characteristics of crust and its effects on runoff and sediment yield on slopes. Studies systematically analyzing and synthesizing the progress of crust research, however, are lacking. By reading the literature and analyzing the developmental history of PSC, we provide a comprehensive review of the following: (1) the meaning, main types, and classification of PSC, (2) the mechanism of formation and the characteristics and dynamic development of crust, (3) the factors affecting the formation of crust, including natural and anthropogenic factors and comprehensive effects, and (4) the development and formation of crust in the soil environment, i.e., hydrological processes and erosion. We also summarize the potential directions for future research on PSC: (1) studying the dynamics of soil structure during the development of crust, (2) developing an objective and standardized quantitative method for studying crust formation, (3) using models of erosion influenced by crust development, (4) improving the scale of the degree of crust development and structural characteristics, and (5) rationalizing the management of crust to optimize land structure and increase crop yield. Full article

Accurate identification of tea plantation distribution is critical for optimizing agricultural practices, informing land-use policies, and preserving ecological balance. However, challenges persist in mountainous regions with persistent cloud cover and heterogeneous vegetation, where conventional methods relying on single-source remote sensing features face limitations due to spectral confusion and information redundancy. This study proposes a novel framework integrating multisource remote sensing data and feature optimization to address these challenges. Leveraging the Google Earth Engine (GEE) cloud platform, this study synthesized 108 spectral, textural, phenological, and topographic features from Sentinel-1 SAR and Sentinel-2 optical data. SVM-RFE (support vector machine recursive feature elimination) was employed to identify the optimal feature subset, prioritizing spectral indices, radar texture metrics, and terrain parameters. Comparative analysis of three classifiers, namely random forest (RF), support vector machine (SVM), and decision tree (DT), revealed that RF achieved the highest accuracy, with an overall accuracy (OA) of 95.03%, a kappa coefficient of 0.95. The resultant 10 m resolution spatial distribution map of tea plantations in Shiyan City (2023) demonstrates robust performance in distinguishing plantations from forests and farmlands, particularly in cloud-prone mountainous terrain. This methodology not only mitigates dimensionality challenges through feature optimization but also provides a scalable solution for large-scale agricultural monitoring, offering critical insights for sustainable land management and policy formulation in subtropical mountainous regions. Full article

Historically, the Wei River has served as part of the Yongji Canal section of the Grand Canal, playing a crucial role in connecting northern and southern China. However, with the acceleration of urbanization in China, issues such as excessive land development and ecological landscape fragmentation have emerged. Exploring the mechanisms of landscape fragmentation evolution in the Wei River basin and proposing optimization strategies is of significant importance for land use and ecological stability within small- to medium-sized river basins. This study selected land use data from the Weihe River basin between 2000 and 2020, using landscape pattern indices to analyze the trend of landscape fragmentation. The principal component analysis (PCA) and geographical detector methods were employed to explore the distribution characteristics and driving factors of landscape fragmentation. The research results indicate that: (1) The degree of landscape fragmentation in the Wei River basin has progressively intensified over time. The edge density index (ED), the landscape division index (DIVISION), the landscape shape index (LSI), and the Shannon diversity index (SHDI) have increased annually, while the contagion index (CONTAG) and area-weighted mean patch size (Area_AM) have continuously decreased; (2) Landscape fragmentation in the Wei River basin is characterized by stable changes in the source and tributary fragmentation areas, a concentrated distribution of fragmentation in the tributaries, and a significant increase in fragmentation in the main stream; (3) The analysis using the geographic detector method indicates that vegetation coverage (FVC), human activity intensity (HAI), and land use/land cover change (LUCC) are the main driving factors of landscape fragmentation in the Wei River basin. The findings explore the mechanisms of landscape fragmentation in the basin and provide a reference for land use planning and ecological restoration in the region. Full article

Sabkha (inland and coastal—saline beds or saline lands) are widespread in Saudi Arabia and are distinguished by their hypersaline nature. These hypersaline habitats are commonly covered by halophytic vegetation. Moreover, Arbuscular mycorrhizal fungi (AMF) are an essential component of these habitats and exhibit a unique adaptation and contribute significantly to ecosystem variability, diversity, and function. Additionally, AMF from saline habitats are an essential component for the successful rehabilitation of salinity-affected areas. Despite their importance, little is known about the distribution and abundance of AMF along inland and coastal sabkhat of Saudi Arabia. Therefore, the main objective of this study was to investigate the abundance and diversity of AMF in the coastal and inland sabkhat of Saudi Arabia. Five soil samples, each from five randomly selected spots (considering the presence of dominant and co-dominant halophytic species), were collected from every location and were used to assess the AMF abundance and diversity. The study indicated that the highest number of AMF spores was recorded from Jouf, averaging ≈ 346 spores 100 g⁻¹ dry soil, and the lowest from Uqair, averaging ≈ 96 spores 100 g⁻¹ dry soil. A total of 25 AMF species were identified, belonging to eight identified genera viz., Acaulospora, Diversispora, Gigaspora, Scutellospora, Claroideoglomus, Funneliformis, Glomus, and Rhizophagus and five families. Of the total identified species, 52% belonged to the family Glomeraceae. Moreover, the highest number of species was isolated from the sabkha in Qasab. Additionally, Glomeraceae was abundant in all the studied locations with the highest relative abundance in Uqair (48.34%). AMF species Claroideoglomus etunicatum, Funneliformis mosseae, Glomus ambisporum, and Rhizophagus intraradices were the most frequently isolated species from all the Sabkha locations with isolation frequency (IF) ≥ 60%, and Claroideoglomus etunicatum (Ivi ≥ 50%) was the dominant species in all the studied locations. Furthermore, data on the Shannon–Wiener diversity index showed that the highest AMF species diversity was in Qaseem and Qasab habitats. The highest Pielou’s evenness index was recorded in Jouf. Moreover, the soil parameters that positively affected the diversity of identified species included Clay%, Silt%, HCO₃¹⁻, OM, MC, N, and P, while some soil parameters such as EC, Na⁺, SO₄²⁻, and Sand% had a significant negative correlation with the isolated AMF species. This study revealed that AMF can adapt and survive the harshest environments, such as hypersaline sabkhas, and thus can prove to be a vital component in the potential restoration of salinity-inflicted/degraded ecosystems. Full article