Search Results (3,196)

Siziwang Banner in Inner Mongolia is a typical arid and semi-arid grassland region where ecological environmental quality is highly sensitive to climate variability and land use and land cover change (LULCC). Clarifying the long-term coupling relationship between LULCC and ecological environmental quality is essential for regional ecological protection and sustainable land management. Based on the Google Earth Engine (GEE) platform, this study integrated multi-temporal Landsat imagery and CLCD-based land use datasets, including an updated 2024 land use layer, to construct a Remote Sensing Ecological Index (RSEI) using standardized and direction-corrected principal component analysis. land use transition matrix analysis, spatial autocorrelation analysis, ecological contribution rate calculation, and GeoDetector were further applied to reveal the spatiotemporal evolution patterns, ecological effects, and driving mechanisms of LULCC in Siziwang Banner from 2000 to 2024. The results showed that: (1) grassland was consistently the dominant land use type, accounting for more than 90% of the total area. The overall land use pattern was characterized by stable grassland dominance, decreasing farmland and unused land, and slight increases in grassland and construction land; forestland showed a high relative growth rate but remained very small in absolute area. (2) The regional ecological environmental quality remained at a lower-to-medium level, with mean RSEI values ranging from 0.27 to 0.47. RSEI showed a phased pattern of initial improvement, subsequent decline, and partial recovery; the marked decline around 2015 was associated with the combined effects of drought stress and land use degradation rather than a single driving factor. RSEI exhibited significant positive spatial autocorrelation, with Moran’s I values ranging from 0.898 to 0.993. High-value clusters were mainly distributed in the southern region, whereas low-value clusters were concentrated in the central and northern regions. (3) Different land use transitions produced differentiated ecological effects. The conversion of unused land to grassland contributed positively to ecological restoration, while grassland degradation and construction land expansion exerted negative effects. The positive RSEI response of some grassland-to-farmland transitions should be interpreted cautiously in relation to local irrigation and intensive farmland management. (4) GeoDetector results indicated that land use type and DEM were the dominant factors controlling the spatial differentiation of RSEI, with average q values of 0.7188 and 0.6178, respectively. The interaction between DEM and land use type showed the strongest explanatory power, indicating that ecological quality was jointly shaped by land use structure and natural background conditions. This study provides a scientific basis for grassland protection, unused-land restoration, farmland management, and spatially differentiated ecological restoration in Siziwang Banner and similar ecologically fragile arid and semi-arid grassland regions. Full article

Rurbanization and peri-urbanization are among the most dynamic territorial processes affecting metropolitan regions in Morocco, particularly within the Casablanca–Settat region. These transformations, driven by rapid urban growth, demographic pressure, and socio-economic change, generate complex transitional spaces between rural and urban environments. In this context, the present study proposes a hybrid methodology for detecting, classifying, and analyzing the rural–urban continuum by using remote sensing data and artificial intelligence techniques. The approach integrates Sentinel-2 satellite imagery, spectral indices, Global Human Settlement Layer datasets, and socio-demographic indicators derived from the Moroccan census. Two models, Self-Organizing Maps (SOM) and Graph Neural Networks (GNN), were applied to classify territories into four categories: urban, peri-urban, rurban, and rural. Model outputs were combined with expert-based decision rules to improve classification robustness and interpretability. The SOM model achieved up to $89.3\%$ agreement with expert classifications and a Cohen’s Kappa coefficient of $0.842$, demonstrating strong interpretability and consistency, while the GNN model reached $53\%$ agreement and effectively modeled spatial dependencies and neighborhood interactions. Diachronic analysis between 2014 and 2024 revealed a $54\%$ increase in peri-urban municipalities, a $24\%$ decrease in rurban territories, and a decline in rural municipalities, highlighting intensified urban sprawl and fragmentation of agricultural landscapes. Beyond its scientific contribution, this study provides a valuable decision-support framework for urban planners, environmental agencies, and policy makers involved in territorial governance and sustainable development. It can support land-use planning, monitoring of urban sprawl, protection of agricultural lands, and the implementation of adaptive territorial policies aimed at improving the resilience and sustainability of rurban environments. Full article

Climate change and increasing water scarcity are intensifying pressure on irrigated agriculture, which currently represents 70% of global freshwater withdrawals. Remote sensing technologies have become essential tools for monitoring soil moisture, evapotranspiration, crop growth, and irrigation performance across multiple spatial and temporal levels. This review synthesizes 83 peer-reviewed studies published between 2002 and 2025, focusing on the use of optical, thermal, and microwave sensors to support irrigation water management under climate variability. The analysis highlights progress in multi-sensor integration, UAV-based monitoring, crop and agro-hydrological modeling, and emerging machine learning approaches that enhance irrigation scheduling, soil moisture estimation, and crop water stress detection. Despite these advancements, several methodological challenges persist, including data integration constraints, sensor-specific limitations, model transferability issues, insufficient ground validation, and difficulties in translating remote sensing outputs into operational decision support systems. In addition, structural gaps at the policy level restrict the evaluation of irrigation efficiency and climate resilience. This review aims to clarify current limitations and outline priority research directions to enhance the climate resilience and sustainability of irrigated agricultural systems. Full article

Monitoring inland surface water quality is essential for water security, ecosystem conservation, public health, and sustainable water resource management. Although in situ measurements remain indispensable, they are often limited by high costs, restricted spatial coverage, low temporal frequency, and discontinuous monitoring networks. This study presents a systematic literature review, guided by the PRISMA 2020 framework, of empirical studies published between 2021 and 2025 on the integration of artificial intelligence (AI) and remote sensing (RS) for inland surface water quality monitoring. Searches were conducted in the Web of Science database, resulting in a final corpus of 367 peer-reviewed articles. Preliminary bibliometric characterization and qualitative content analysis were performed to identify sensors, platforms, AI paradigms, algorithms, estimated parameters, validation strategies, limitations, challenges, trends, and research gaps. The results show rapid growth in the field, with Sentinel-2 and Landsat-8 as the most recurrent sensors and multispectral data as the dominant spectral source. Machine learning approaches, especially Random Forest, Artificial Neural Networks, XGBoost, and Support Vector Machine, predominated, while deep learning, multi-source integration, hybrid models, and Explainable AI emerged as relevant trends. AI–RS integration shows strong potential to complement conventional monitoring, but persistent challenges remain regarding in situ data dependence, limited external and temporal validation, model transferability, generalization, uncertainty reporting, validation robustness, and interpretability. Full article

Smart technologies and artificial intelligence (AI) are increasingly reshaping viticulture by improving vineyard monitoring, supporting data interpretation, and enabling more targeted management decisions. This review examines how sensor networks, remote sensing, machine learning, deep learning, and decision-support systems contribute to more sustainable vineyard management and the production of high-quality grapes. Particular attention is paid to applications in grapevine stress monitoring, disease and pest detection, irrigation and nutrient management, yield estimation, grape quality prediction, and emerging automation. The review also highlights the main barriers that still limit broader adoption in commercial vineyards, including data quality issues, limited transferability across sites and seasons, interoperability gaps, vendor lock-in, and concerns related to governance, privacy, and cybersecurity. Although these constraints remain significant, the available evidence shows that smart viticulture can improve resource-use efficiency, support more precise interventions, and help growers respond more effectively to environmental variability. Future progress will depend on stronger validation under field conditions, better integration into practical vineyard workflows, interoperable digital infrastructures, and decision-support tools that are transparent, reliable, and useful for end users. Full article

In arid and semi-arid regions, groundwater plays an important ecohydrological role in sustaining ecosystem stability under climate-warming-induced surface-water uncertainty. Disentangling precipitation and groundwater recharge effects on vegetation growth remains challenging, limiting robust identification of groundwater-dependent vegetation ecosystems (GDVEs) and quantitative ecological groundwater level estimation. Taking the Daihai Basin, a typical inland closed-lake basin, as a case study, we integrated multi-source remote-sensing data (2005–2025) with in situ groundwater monitoring to develop a comprehensive framework for ecohydrological response analysis and management quantification. Using an improved Mann–Kendall test together with spatiotemporal correlation analyses, we analyzed the spatial relationships between vegetation dynamics and groundwater depth. Results show: (1) basin-wide vegetation exhibits a greening trend (Sen’s slope = 0.00014) with spatial heterogeneity; (2) vegetation dependence on groundwater displays a clear threshold behavior, with low-cover areas (fractional vegetation cover, FVC < 0.3) showing relatively strong groundwater dependency (r = 0.698) whereas high-cover areas exhibit a weaker relationship; and (3) approximate ecological groundwater reference thresholds are estimated as 1.0 m (90% assurance) for forest land and 0.6 m for grass land (80% assurance). The proposed GDVE identification scheme provides a scientific reference for adaptive groundwater management and ecological assessment. Full article

Landscape Ecological Risk (LER) reflects the potential adverse effects of landscape change on ecological structure, function, and stability. In rapidly urbanizing megacities such as Dhaka, vegetation loss and built-up expansion have intensified environmental pressure over recent decades. This study examines the spatiotemporal dynamics of LER in Dhaka from 2004 to 2024 under the combined influence of vegetation change and urban expansion. Multi-temporal remote sensing data were used to generate land cover maps, derive Fractional Vegetation Cover (FVC), and quantify urbanization intensity using Nighttime Light (NTL) data. The Landscape Ecological Risk Index (LERI) was calculated using landscape pattern metrics, while bivariate spatial autocorrelation and geographically weighted regression (GWR) were applied to examine spatial associations and local spatial heterogeneity. The results show that vegetation degradation affected 34.39% of the study area during 2004–2024, while high-risk zones increased from 24.36% in 2004 to 42.95% in 2024. Land cover analysis further indicates a substantial expansion of built-up areas, accompanied by the contraction and fragmentation of vegetation, agricultural land, and lowland classes. Spatial analyses reveal that the relationships among vegetation cover, urbanization intensity, and ecological risk vary across the city and became increasingly spatially differentiated over time. These findings suggest that vegetation loss and urban expansion are spatially associated with increasing ecological risk in Dhaka. However, the results should be interpreted with caution because of uncertainties related to remotely sensed data, unsupervised land cover classification, resampling procedures, and limited ground validation. Despite these limitations, the study provides a spatially explicit framework for understanding ecological risk dynamics and offers useful evidence for green-space conservation, ecological restoration, and sustainable urban planning in rapidly urbanizing regions. Full article

Across semi-arid and environmentally vulnerable regions, intensifying climate pressures, land degradation, and resource scarcity are placing growing demands on institutions, communities, and land users. However, the knowledge and technical skills required to respond effectively remain uneven and often poorly aligned with local needs. This study presents a comparative skills audit in Kimberley, Upington, and Rietfontein in the Northern Cape, identifying capacity gaps, stakeholder-specific training priorities, and structural barriers in natural resource and sustainable land management. Using questionnaires, semi-structured interviews, participatory site visits, and multi-stakeholder consultations, competencies were assessed across GIS and remote sensing, climate resilience, soil and land restoration, water conservation, sustainable agriculture, and policy literacy. Results show significant disparities in skills proficiency. GIS and remote sensing (0.8) and climate resilience strategies (1.0) were weakest, while policy literacy (1.5) and soil management (2.0) were also limited. Sustainable agriculture (4.0) and water conservation (2.8) showed relatively stronger capacity. Training needs varied by stakeholder, with government prioritizing geospatial tools and governance, and farmers emphasizing climate adaptation and resource management. Key barriers include limited digital infrastructure (83%), insufficient government support (80%), high training costs (78%), and contextual mismatches (50%). Integrated, place-based capacity development is essential to strengthen adaptive governance and long-term resilience. Full article

Monitoring biodiversity in coastal and marine ecosystems is essential for supporting conservation strategies, sustaining ecosystem services, and meeting policy commitments at multiple scales, including the European Union’s Habitats Directive, Sustainable Development Goal 14 (SDG 14, Life Below Water), and the Kunming–Montreal Global Biodiversity Framework (GBF). However, many benthic habitats remain insufficiently mapped or monitored due to the spatial, temporal, and logistical limitations of traditional field-based approaches. Optical Remote Sensing (ORS), based on the use of optical sensors to retrieve spectral information from shallow-water environments, has emerged as a powerful tool for mapping and monitoring these ecosystems. This study presents a systematic review aimed at providing a comprehensive synthesis of above-water ORS applications for benthic biodiversity and habitat monitoring over the period 2014–2023. A total of 179 peer-reviewed studies were analyzed to identify temporal trends, geographic patterns, target ecosystems, and methodological workflows. The review considered observation platforms including satellite, airborne, unmanned aerial vehicles (UAVs), and field spectrometry systems, together with key preprocessing procedures required for reliable benthic detection, such as atmospheric correction, water column correction, and sunglint removal, alongside validation using independent measurements. The analysis reveals a rapid expansion of ORS applications, with a strong geographic concentration in tropical and subtropical regions. Studies focusing on specific benthic groups predominantly target coral reefs and seagrass ecosystems, although many adopt integrative benthic habitat classifications that incorporate multiple benthic components at the habitat level. However, significant limitations persist, including inconsistent preprocessing workflows, limited reporting transparency, and the underrepresentation of several ecologically important taxa (e.g., annelids, mollusks, echinoderms). Despite these challenges, ORS has become a cornerstone of large-scale and repeatable coastal monitoring. By analyzing methodological practices, ecological targets, and geographic biases, this review provides a critical foundation for improving the robustness, scalability, and global applicability of ORS in benthic habitat mapping, biodiversity monitoring, and ecosystem-based management. Full article

Ecologically fragile hilly areas are key regions for safeguarding national ecological security and advancing ecological civilization construction. Accurate assessment of ecosystem service value (ESV) and future scenario simulations in these regions is crucial for improving regional land use and attaining sustainable development. Based on high-resolution remote sensing data of the Longji Mountain area in Guangxi, China, from 2013 to 2023, this study systematically assesses the spatiotemporal evolution characteristics of ESV using the equivalent factor method with localized corrections. This study adopts spatial autocorrelation analysis, geographic modeling, and scenario simulation. It predicts the spatial patterns of ESV for 2028 and 2033 under three scenarios: ecological protection, natural development, and tourism development. The results reveal that: (1) from 2013 to 2023, the total ESV in the Longji Mountain area showed an overall fluctuating trend. It increased first, then declined and recovered slightly, with an average annual growth rate of −0.15%. Spatially, the ESV presented a heterogeneous pattern, characterized by “high-value agglomeration in forest land, medium-value transition in terraced fields, and low-value interpolation in constructed areas”, with distinct clustering features; (2) regional ecological functions are mainly dominated by regulating and supporting services. Climate regulation contributes the highest value. Water supply is the only service with negative value, indicating a persistent water ecological deficit that remains unaddressed; (3) scenario simulations reveal that the total ESV is highest and spatial connectivity is strongest under the ecological protection scenario. Furthermore, a consistent trend is observed across all three scenarios: high-value ESV areas tend to become dominant, while spatial connectivity shows progressive enhancement. The human–land system coupling framework for the ecologically fragile hilly region suggests that ecologically oriented decision-making is the core pathway to sustainably improve ecosystem services and realize regional sustainable development. This study offers scientific support for regional ecological conservation and sustainable advancement. Full article

Managed aquifer recharge (MAR) is increasingly being realized as an important approach to improve water security in arid and semi-arid environments where there is a low amount of surface water and high climatic variability. This paper introduces a unified approach to the process of locating appropriate MAR locations and estimating recharge potential in Central Kazakhstan through a multi-criteria analysis using geographic information systems (GIS) and hydrogeological field exploration, water balance modelling. Remote sensing datasets and evapotranspiration (ET) analyses were conducted for the 2014–2024 period, while field investigations, infiltration tests, and hydrochemical sampling were performed during the 2025 field campaign. The suitability testing was preliminarily performed in the Google Earth Engine (GEE; Google LLC, Mountain View, CA, USA) environment as a weighted overlay test with the combination of terrain, vegetation, hydrological, and land cover parameters. According to the suitability map obtained and patterns of activity in agricultural activities, eleven candidate sites were identified, out of which eight were found to be suitable after hydrochemical analysis. The Nesterov and Boldyrev techniques of field-based infiltration tests produced a range of 0.05 to 1.42 m/day of hydraulic conductivity. Water balance analysis shows that the total amount of water that could potentially be added to groundwater recharge is about 40.2 million m³/year and that the effective amount of water could be recharged is about 11.0 million m³/year, which is limited by the infiltration processes. This means that about 27 percent of the available water is added into ground water recharge, which is a significant boost to the original estimates. The assessment of the storage capacity of the aquifers indicates that at all locations, the pore space is much greater than the recharge volumes that have been calculated and, therefore, storage is not a limiting factor in the implementation of MAR. It is estimated that the potential MAR rates range between 174 and 5282 m³/day depending on local hydrogeological conditions. The suggested method offers a powerful and generalizable site selection and measurement framework of MAR in arid areas with limited data. The findings highlight the significance of combining remote sensing, field measurements, and process-based modeling to aid sustainable groundwater management and climate adaptation strategies. Full article

The urban tree canopy is an important resource that spans public and private property and whose form and quantity change over short distances. Although remote sensing and deep learning approaches have been used to map urban tree canopy, the high cost of commercial imagery and the technical complexity of model development have limited their adoption by urban forestry practitioners. We developed a structured and reproducible deep learning workflow optimized for freely available USDA National Agriculture Imagery Program (NAIP) imagery. The workflow incorporates a reproducible U-Net segmentation model for canopy delineation and a YOLOv9e object detection model for individual tree identification, enabling complementary estimation of the canopy extent and individual tree locations. Across two neighborhoods in Los Angeles, the optimized U-Net achieved a Dice coefficient of 0.824 for canopy segmentation, while YOLOv9e reached an F1-score of 0.687 for individual tree detection on a held-out test set with 17,466 annotated trees. A data sufficiency experiment showed that model performance stabilizes when approximately 130 trees are annotated per 320 × 320 pixel (px) tile, corresponding to about 25,379 training and 2641 validation labels, providing a practical target for annotation effort. Additional experiments demonstrate a structured workflow for spatial sampling, training data requirements, and the use of model inferences to estimate tree canopy extent and individual tree locations. The workflow also shows encouraging evidence of transferability to previously unseen urban areas without retraining. By relying solely on NAIP-optimized approaches, this new workflow bridges the gap between complex deep learning techniques and the practical needs of urban foresters; empowers local stakeholders to create accurate, affordable, and timely urban tree inventories; and fosters data-driven decision-making for the sustainable management of urban green infrastructure. Full article

The human–environment interaction on the Qinghai–Xizang Plateau determines the direction of global human sustainable development, making it necessary to propose a refined prediction for this relationship. Currently, there is a lack of a predictive method for human–environment relationships, especially at the grid scale. This study focuses on Qinghai Province and proposes a human–environment relationship simulation method based on cellular automata (CA), utilizing land-use data and a remote sensing-based ecological (RSEI) index. The method enables grid-scale explicit predictions of human–environment relationships. The results show that by 2030, the human–environment relationship in Qinghai Province will become more diverse, with the coordination ratio rising to 11% and the degradation ratio to 7%. The ecological protection scenario serves a defensive role, preventing 3835 km² of land from degradation. In contrast, the urban development scenario plays a revitalizing role, achieving a coordinated area 2% larger than the business-as-usual scenario. By 2030, about 8956 km² of land in Qinghai will be suitable for agricultural revitalization, and 54,340 km² must be reserved for ecological protection. Due to the high-altitude environment, the human–environment relationship aligns only with the right half of the Environmental Kuznets Curve, namely, development brings greater harmony. We further discover the lag in the natural system’s response, for artificially increasing vegetation cover will not quickly improve habitat quality. Likewise, leapfrogging expansion in the urban development scenario may conceal long-term ecological risks behind short-term coordination. For stakeholders and policymakers, this study provides refined and differentiated governance measures at the grid scale, while highlighting the need to focus on underdeveloped regions and remain vigilant about the lag in human–environment relationship responses. Full article

Understanding the adaptive dynamics of social-ecological systems (SESs) is critical for regional sustainability as human–environment interactions intensify. However, existing indicator-based research frequently lacks a clear theoretical framework and methodological clarity when analyzing SES adaptation. Using complex adaptive system (CAS) theory as an interpretive lens, this research creates a social-ecological system (SES) adaptability evaluation framework that incorporates the pressure–state–response (PSR) model from a CAS perspective. This study examines the Huaihe River Ecological and Economic Belt (HREEB) as a case study, combining remote sensing (RS) and geographic information system (GIS) data from 28 prefecture-level cities from 2005 to 2020. The entropy-weight approach is used to create a composite adaptability index, and obstacle-degree analysis is used to identify key limiting factors, followed by an examination of spatiotemporal evolution patterns. The study found that: (1) SES adaptability in the HREEB increased steadily (mean annual growth rate: 3.97%), with the social subsystem exhibiting a larger connection with the overall trend and the ecological subsystem displaying greater volatility; (2) there was significant spatial heterogeneity, forming a “high in the east and west, low in the center” pattern (supported by a global Moran’s I = 0.535, p < 0.05); (3) obstacle degree analysis identified per capita afforestation area (ecological response), per capita GDP (social state), and population density (ecological pressure) as persistent key constraints. Full article

The detection technology of crop diseases and pests is transitioning from single sensor monitoring to intelligent perception and multimodal fusion. This paper follows the PRISMA 2020 standard and systematically reviews the relevant core literature. This paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases. The focus is on the architectural evolution of deep learning models, including lightweight convolutional neural networks (CNNs), vision transformers (ViTs) with long-range dependency modeling capabilities, and the efficient computing state space model Mamba. In addition, the research progress of spatial spectral joint learning, heterogeneous data fusion, and vision-language models (VLMs) in improving system robustness and interpretability are introduced. By synthesizing the integrated applications of UAV remote sensing, Internet of Things (IoT) edge computing and intelligent robots in staple and cash crops, this paper summarizes the implementation of the integrated system of perception, decision-making and execution. To address the issues of insufficient cross-domain generalization ability and uneven allocation of computing resources in existing models, this paper provides perspectives on the future development of agricultural artificial intelligence (AI) towards foundation model-driven, edge-intelligent collaboration, and green sustainable direction, which can provide theoretical reference for engineering applications in the field of intelligent plant protection. Full article