Search Results (252)

To address land space issues in the West Qinling Mountains—including habitat degradation, ecosystem damage, spatial pattern imbalance and unsustainable resource use—this study employed the InVEST habitat quality model and spatial autocorrelation analysis. Based on land use remote sensing data from 1990 to 2020, we simulated and evaluated habitat quality and degradation over this 30-year period to propose scientific recommendations and optimization strategies. The results showed that: (1) The area of grassland and farmland in the West Qinling Mountains decreased significantly, the area of construction land, bare land and forest land increased mainly; (2) The habitat quality of the West Qinling Mountains was generally high, and the average of the habitat quality showed an overall decreasing trend in the period of 1990–2020. The proportion of worst habitat increased from 4.11% to 5.21%. The habitat quality is in the process of polarization, the spatial distribution of habitat quality in West Qinling shows a pattern of “high in the west, low in the north and southeast”; (3) The hot and cold spots of habitat quality in West Qinling are spatially manifested as “hotter in the west and the south; colder in the center and the east”; (4) The spatial clustering of habitat quality in the West Qinling Mountains is obvious, with the area of the high–high area and the low–low area increasing with time, the high–low area decreasing, and the low–high area slightly increasing. (5) The degree of habitat degradation in the West Qinling Mountains is generally low, the average value of degradation from 1990 to 2020 showed an upward trend, habitat degradation is in the process of converging to medium risk. The area of medium habitat degradation expanded by nearly 1.5 times between 1990 and 2020. The spatial distribution of habitat degradation in the West Qinling Mountains generally shows a pattern of low in the west and high in the north and high in the southeast. In future planning and management, the west Qinling Mountains should formulate and carry out scientific ecological restoration plans and projects in terms of improving the quality of habitats, curbing habitat degradation, optimizing the direction of regional land use and reasonably protecting land resources, in an effort to balance urban development and ecological protection, curbing ecological degradation, guaranteeing the sustainable development of the habitats in a benign direction. Full article

This study aimed to assess, compare, and validate a satellite-based plant health and water stress indicator: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) over Uganda. We used a direct agricultural drought indicator—the Standardized Precipitation and Evapotranspiration Index at scale 3 (SPEI-03)—and a plant water stress indicator—the crop water stress index (CWSI)—for the period of 1983–2013. Novel approaches such as spatial variability and trend analysis, along with correlation analysis, were used to achieve this. The results showed that there are six classes of highly variable photosynthetic activity over Uganda, dominated by class 4 (0.36–0.45). This dominant class encompassed 45% of the total land area, mainly spanning cropland. In addition, significant increases in monthly photosynthetic activity (FAPAR) and FAPAR-centered stress indicators (SFI < −1) were observed over 85% and 60% of total land area, respectively. The Standardized FAPAR Index (SFI) had a strong positive correlation with SPEI-03 over cropland, grassland, and forest lands, while SFI had a strong negative correlation with CWSI over 80% of the total area. These results highlight the state and variation in plant health and water stress, generate insights on ecosystem dynamics and functionality, and weigh in on the usability and reliability of satellite-based variables such as FAPAR in plant water monitoring over Uganda. We thus recommend satellite-based FAPAR as a robust proxy for vegetation health and water stress monitoring over Uganda, with potential application in crop yield prediction and irrigation management to inform effective agricultural planning and improve productivity. Full article

Tropical forests play an essential role in the carbon and water cycles of terrestrial ecosystems, but they are increasingly threatened by human activities and climate change. For places where ground observations are scarce, like in Equatorial Africa, remote sensing is a key source of information for monitoring the temporal and spatial dynamics of forests over large areas. Several Earth Observation-based global maps were developed in recent decades using different definitions of the land-use/land-cover (LULC) classes. While such products are widely used for monitoring land use and planning land management, the consistency of these LULC maps for the Congo Basin has never been analyzed and quantified at the ecosystem level. Here, we selected seven of the most-used global maps and analyzed their consistency over the Congo Basin. After reclassification into forest/non-forest masks and spatial resampling, we assessed the agreement and disagreement percentage across the different tropical ecoregions of Africa, from moist forest to miombo, including savanna. The datasets showed differences in forest area as a function of spatial resolution, with higher forest area levels at coarser resolutions (e.g., from 74.1% to 88.5% forest cover when upscaling the GLCLU from 30 m to 1 km over the Congo Basin). A higher agreement between the datasets was found for forest area over moist forest (between 88.18% and 99.38%) in comparison to savanna (32.82%–99.84%) and miombo (53.83%–99.7%). These discrepancies led to large differences in forest cover, varying from a net loss of 205,704 km² to a net gain of 50,726 km² over 2001–2019 depending on the dataset used. This study draws attention to the uncertainty associated with these products with regard to forests, particularly in regions of biological importance, such as the miombo and savanna regions, which remain poorly understood. Indeed, the two major uncertainties affecting the quality of LULC products are related to the different spatial resolutions and biological definition of “forest” adopted by each product. Full article

Reliable spatio-temporal rainfall prediction is a key element in disaster mitigation and water resource management in dynamic tropical regions such as the Bengawan Solo River Watershed. However, high climate variability and data limitations often pose significant challenges to the accuracy of conventional prediction models. This study introduces an innovative approach by applying ensemble stacking, which combines machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Light Gradient-Boosting Machine (LGBM) and deep learning models like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Networks (TCN), Convolutional Neural Network (CNN), and Transformer architecture based on monthly Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data (1981–2024). The novelty of this research lies in the systematic exploration of various model combination scenarios—both classical and deep learning and the evaluation of their performance in projecting rainfall for 2025–2030. All base models were trained on the 1981–2019 period and validated with data from the 2020–2024 period, while ensemble stacking was developed using a linear regression meta-learner. The results show that the optimal ensemble scenario reduces the MAE to 53.735 mm, the RMSE to 69.242 mm, and increases the R² to 0.795826—better than all individual models. Spatial and temporal analyses also indicate consistent model performance at most locations and times. Annual rainfall projections for 2025–2030 were then interpolated using IDW to generate a spatio-temporal rainfall distribution map. The improved accuracy provides a strong scientific basis for disaster preparedness, flood and drought management, and sustainable water planning in the Bengawan Solo River Watershed. Beyond this case, the approach demonstrates significant transferability to other climate-sensitive and data-scarce regions. Full article

Understanding the temporal dynamics and spatial distribution of key soil properties is essential for sustainable land management and informed decision-making. This study assessed the spatial variability and decadal changes (2013–2023) of topsoil properties in Córdoba, central Argentina, using digital soil mapping (DSM) and machine learning (ML) algorithms. Three ML methods—Quantile Regression Forest (QRF), Cubist, and Support Vector Machine (SVM)—were compared to predict soil organic matter (SOM), extractable phosphorus (P), and pH at 0–20 cm depth, based on environmental covariates related to site climate, vegetation, and topography. QRF consistently outperformed the other models in prediction accuracy and uncertainty, confirming its suitability for DSM in heterogeneous landscapes. Prediction uncertainty was higher in marginal mountainous areas than in intensively managed plains. Over ten years, SOM, P, and pH exhibited changes across land-use classes (cropland, pasture, and forest). Extractable P declined by 15–35%, with the sharpest reduction in croplands (−35.4%). SOM decreased in croplands (−6.7%) and pastures (−3.1%) but remained stable in forests. pH trends varied, with slight decreases in croplands and forests and a small increase in pastures. By integrating high-resolution mapping and temporal assessment, this study advances DSM applications and supports regional soil monitoring and sustainable land-use planning. Full article

Urban growth models often prioritize environmental and accessibility factors while underestimating behavioral and functional dynamics. This study develops a POI-enhanced Cellular Automata (CA) framework to simulate urban expansion by incorporating three semantic indicators derived from Point-of-Interest (POI) data—density (PD), diversity (PDI), and functional centrality (FC). Taking Yan’an, China, as a case, the model integrates these indicators with terrain and infrastructure variables via logistic regression to estimate land-use transition probabilities. To ensure robustness, spatial block cross-validation was adopted to reduce spatial autocorrelation bias. Results show that the POI-based model outperforms the baseline in both Kappa and Figure of Merit metrics. High-density and mixed-function POI zones correspond with compact infill growth, while high-centrality zones predict decentralized expansion beyond administrative cores. These findings highlight how functional semantics sharpen spatial prediction and uncover latent behavioral demand. Policy implications include using POI-informed maps for adaptive zoning, ecological buffer protection, and growth hotspot management. The study contributes a transferable workflow for embedding behavioral logic into spatial simulation. However, limitations remain: the model relies on static POI data, omits vertical (3D) development, and lacks direct comparison with alternative models like Random Forest or SVM. Future research could explore dynamic POI trajectories, integrate 3D building forms, or adopt agent-based modeling for richer institutional representation. Overall, the approach enhances both the accuracy and interpretability of urban growth modeling, providing a flexible tool for planning in functionally evolving and ecologically constrained cities. Full article

Power outages pose considerable risks to the reliability of electric grids, affecting both consumers and utilities through service disruptions and potential economic losses. This study analyzes a historical outage dataset from a Regional Transmission Organization (RTO) to reveal key patterns and trends that suggest outage management strategies. By integrating exploratory data analysis, predictive modeling, and a Large Language Model (LLM)-based interface integration, as well as data visualization techniques, we identify and present critical drivers of outage duration and frequency. A random forest regressor trained on features including planned duration, facility name, outage owner, priority, season, and equipment type proved highly effective for predicting outage duration with high accuracy. This predictive framework underscores the practical value of incorporating planning information and seasonal context in anticipating outage timelines. The findings of this study not only deepen the understanding of temporal and spatial outage dynamics but also provide valuable insights for utility companies and researchers. Utility companies can use these results to better predict outage durations, allocate resources more effectively, and improve service restoration time. Researchers can leverage this analysis to enhance future models and methodologies for studying outage patterns, ensuring that artificial intelligence (AI)-driven methods can contribute to improving management strategies. The broader impact of this study is to ensure that the insights gained can be applied to strengthen the reliability and resilience of power grids or energy systems in general. Full article

With the increasing importance of urban green spaces in leisure, ecology, emergency management, and other functions, urban forest parks play a key role in enhancing urban tourism resilience. Tourists are closely related to this, but current research lacks discussion on the sustainable development of urban forests and tourism resilience from the perspective of tourist demand. Therefore, this study took Kunming Xishan Forest Park as an example, conducted a questionnaire survey of 385 tourists, and identified tourist demands and weights through in-depth analysis using the KANO model and AHP. The results data show that among the 23 demand indicators across five dimensions, six are must-be qualities, eight are one-dimensional qualities, six are attractive qualities, and three are indifferent qualities. Based on the AHP analysis, we further investigated the weight of each demand indicator. The results of this study not only provide practical support and strategic guidance for the spatial planning and design of urban forests, thereby enhancing the sustainable development of urban tourism resilience, but also contribute to theories of urban tourism resilience and offer a reference source for other cities with similar aspirations. Full article

The satellite-derived climatic variables offer extensive spatial and temporal coverage for research; however, their inherent biases can subsequently reduce their accuracy for water balance estimate. This study evaluates the effectiveness of bias correction in improving the Tropical Rainfall Measuring Mission (TRMM) rainfall and the Global Land Data Assimilation System (GLDAS) land surface temperature (LST) data and illustrates their long-term (2000–2019) hydrological assessment. The novelty lies in coupling the bias-corrected climate variables with the Thornthwaite–Mather water balance model as well as land use land cover (LULC) for improved predictive hydrological modeling. Bias correction significantly improved the agreement with ground observations, enhancing the R² value from 0.89 to 0.96 for temperature and from 0.73 to 0.80 for rainfall, making targeted inputs ready to predict hydrological dynamics. LULC mapping showed a predominance of agricultural land (64.5%) in the area followed by settlements (20.0%), forest (7.3%), barren land (6.5%), and water bodies (1.7%), with soils being silt loam, clay loam, and clay. With these improved datasets, the model found seasonal rise in potential evapotranspiration (PET), peaking at 120.7 mm in June, with actual evapotranspiration (AET) following a similar trend. The annual water balance showed a surplus of 523.8 mm and deficit of 121.2 mm, which proves that bias correction not only enhances the reliability of satellite data but also reinforces the credibility of hydrological indicators, with a direct, positive impact on evidence-based irrigation planning and flood mitigation and drought management, especially in data-scarce regions. Full article

Effective forest planning in Mediterranean environments requires tools capable of managing ecological complexity, socio-economic pressures, and fragmented governance. This study develops and applies a GIS- and GNSS-based methodological framework for regional forest planning, tested in the “Bosco della Ficuzza, Rocca Busambra, Bosco del Cappelliere, Gorgo del Drago” Regional Nature Reserve (western Sicily, Italy). The main objective is to create a multi-layered Territorial Information System (TIS) that integrates high-resolution cartographic data, a Digital Terrain Model (DTM), and GNSS-based field surveys to support adaptive, participatory, and replicable forest management. The methodology combines the following: (i) DTM generation using Kriging interpolation to model slope and aspect with ±1.2 m accuracy; (ii) road infrastructure mapping and classification, adapted from national and regional forestry survey protocols; (iii) spatial analysis of fire-risk zones and accessibility, based on slope, exposure, and road pavement conditions; (iv) the integration of demographic and land use data to assess human–forest interactions. The resulting TIS enables complex spatial queries, infrastructure prioritization, and dynamic scenario modeling. Results demonstrate that the framework overcomes the limitations of many existing GIS-based systems—fragmentation, static orientation, and limited interoperability—by ensuring continuous data integration and adaptability to evolving ecological and governance conditions. Applied to an 8500 ha Mediterranean biodiversity hotspot, the model enhances road maintenance planning, fire-risk mitigation, and stakeholder engagement, offering a scalable methodology for other protected forest areas. This research contributes an innovative approach to Mediterranean forest governance, bridging ecological monitoring with socio-economic dynamics. The framework aligns with the EU INSPIRE Directive and highlights how low-cost, interoperable geospatial tools can support climate-resilient forest management strategies across fragmented Mediterranean landscapes. Full article

Carbon stock plays a crucial role in regulating atmospheric carbon dioxide concentrations and represents a vital ecological function for mitigating climate change and supporting long-term environmental sustainability. Jiangsu Province, a typical region experiencing rapid urbanization and land-use transformation in eastern China, serves as a representative case for regional-scale carbon assessment. This study employs the InVEST model, integrated with multi-source remote sensing data, a random forest algorithm, and a control variable approach, to simulate the spatiotemporal dynamics of carbon stock in Jiangsu Province under a set of climate, productivity, and population scenarios. Three scenario groups were designed to isolate the individual effects of climate change, gross primary productivity, and population density from 2020 to 2060, enabling a clearer understanding of the dominant drivers. The results indicate that the coupled model estimates Jiangsu’s 2020 carbon stock at 1.52 × 10⁹ t C, slightly below the 1.82 × 10⁹ t C estimated by the standalone InVEST model, with the coupled results closer to previous estimates. Compared with InVEST alone, the integrated model significantly improves numerical accuracy and spatial resolution, allowing for finer-scale pattern recognition. By 2060, carbon stock is projected to decline by approximately 24.4% across all scenarios. Among the features, climate change exerts the most significant influence, with an elasticity coefficient range of −37.76–1.01, followed by productivity, while population density has minimal impact. These findings underscore the dominant role of climate drivers and highlight that model integration improves both predictive accuracy and spatial detail, offering a more robust basis for scenario-based assessment. The proposed approach provides valuable insights for supporting sustainable carbon management, real-time monitoring, and provincial-scale decarbonization planning. Full article

Artificial afforestation plays a critical role in ecological restoration, but its implementation involves multiple strategies—such as new afforestation, densification, and replacement afforestation. Long-term spatial and temporal identification of these tree planting program scenarios (TPPSs) is key to evaluating ecological restoration policies, yet existing pixel-based time series change detection methods still face challenges in discriminating these patterns on a large scale. To address these challenges, we propose CFDC, the first framework that synergistically integrates Continuous Change Detection (CCD) for temporal spectral trajectories and Focal Context (FC) analysis for spatial neighborhood context. A Spatiotemporal Coupling Index (STCI) is proposed to abstractly summarize the two modules, and a rule-based model classifies TPPSs by their unique temporal–spatial signatures. Implemented on Google Earth Engine (GEE) for Bayi District, Tibet, CFDC delivered overall accuracies of 76.0–82.5% from 2007 to 2022, with user’s accuracies for all TPPS types exceeding 75% in most years. Detected TPPS timelines coincide with documented ecological restoration projects within a ±1-year tolerance. Overall, CFDC offers a novel mechanism that fuses spatiotemporal features to effectively distinguish new afforestation, densification, and replacement afforestation scenarios, addressing the limitations of previous methods and enabling more accurate and scalable TPPS monitoring, thereby supporting scalable artificial forest management and ecological restoration planning. Full article

Traditional Forest Management Plans (FMPs), which often span hundreds of pages on paper, present significant challenges due to their extensive length and lack of clear spatiotemporal context. This study aimed to integrate complex data from FMPs into an interactive, spatially referenced database. Using Gârda Forest in Romania’s Apuseni Mountains as a case study, we gathered raw data, developed the geodatabase’s spatial and alphanumerical components, and conducted spatial analyses related to ecological and production factors. Our GIS was designed to accommodate multiple attributes within the compartment layer’s attribute table. Unlike previous studies, we incorporated the full range of information from the Compartment Description, not just isolated management aspects. This comprehensive approach enabled spatial analysis to highlight, in maps, key features across the 50 compartments (totaling 752.5 ha) including dominant species (Norway spruce, silver fir, beech), target species composition (Norway spruce as the predominant target), land protection needs (required for 4% of the area), median forest volume (1565 m³ per compartment), elevation range (1020–1420 m), compartments with production functions, and silvicultural treatments. These thematic maps provide a tool for further analyses and clear spatial visualization. Our GIS-based methodology supports rapid condition assessments and aids forest professionals and decision-makers in promoting sustainable forest management. Full article

Accurate mapping of deforestation in regions characterized by complex, heterogeneous landscapes and frequent cloud cover remains a major challenge in remote sensing. This study presents a phenology-informed, spatiotemporal data fusion framework for robust deforestation mapping in North Korea, focusing particularly on hillside fields and unstocked forests—two dominant deforested land cover types in the region. By integrating multi-temporal satellite observations with variables derived from phenological dynamics, our approach effectively distinguishes spectrally similar classes that are otherwise challenging to separate. The Flexible Spatiotemporal Data Fusion Algorithm (FSDAF) was employed to generate high-frequency, Landsat-like time-series from MODIS data, thereby ensuring fine spatial detail alongside temporal consistency. Key classification features—including NDVI, NDSI, NDWI, and snowmelt timing—were identified and ranked using the Random Forest (RF) algorithm. The classification results were validated against reference Landsat imagery, achieving high correlation coefficients (R > 0.8) and structural similarity index values (SSIM > 0.85). The RF-based land cover classification reached an overall accuracy of 86.1% and a Kappa coefficient of 0.837, reflecting strong agreement with ground reference data. Comparative analyses demonstrated that this method outperformed global land cover products, such as MCD12Q1, in capturing the spatial variability and fragmented patterns of deforestation at the regional scale. This research underscores the value of combining spatiotemporal fusion with phenological indicators for accurate, high-resolution deforestation monitoring in data-limited environments, providing practical insights for sustainable forest management and ecological restoration planning. Full article

The Ecological Green Heart Area of the Chang–Zhu–Tan Urban Agglomeration in Central China faces increasing forest health threats due to rapid urbanization and land use change. This study assessed the spatiotemporal dynamics and drivers of forest health from 2005 to 2023 using a multi-dimensional framework based on vitality, organizational structure, and anti-interference capacity. A forest health index (FHI) was constructed using multi-source data, and the optimal parameter geographic detector (OPGD) model was applied to identify dominant and interacting factors. The results show the following: (1) FHI declined from 0.62 (2005) to 0.55 (2015) and rebounded to 0.60 (2023). (2) Healthier forests were concentrated in the east and center, with degradation in the west and south; (3) Topography was the leading driver (q = 0.17), followed by climate, while socioeconomic factors gained influence over time. (4) Interactions among factors showed strong nonlinear enhancement. This research demonstrates the effectiveness of the OPGD model in capturing spatial heterogeneity and interaction effects, underscoring the need for differentiated, spatially informed conservation and land management strategies. This research provides scientific support for integrating ecological protection with urban planning, contributing to the broader goals of ecosystem resilience, sustainable land use, and regional sustainability. Full article