Search Results (892)

Habitat loss and fragmentation threaten forest-dependent ungulates in subtropical mountain systems, yet integrative assessments linking habitat quality and landscape configuration remain limited. Here, we evaluated habitat suitability and identified core habitat patches for the endangered black muntjac (Muntiacus crinifrons) in Tongboshan National Nature Reserve using an Analytic Hierarchy Process–Habitat Suitability Index (AHP–HSI) framework integrated with camera-trap validation and landscape pattern analysis. Vegetation-related indicators (NDVI and vegetation type) were the dominant suitability drivers, and highly suitable habitats accounted for 62.9% of the reserve (8646.97 ha), forming three major forest blocks with low disturbance levels. Camera-trap detections (n = 58) showed strong concordance with model predictions (98.28% within moderately suitable or higher classes). Landscape metrics revealed contrasting spatial configurations between overall high-suitability habitats and optimal core patches, indicating that demographic source areas are embedded within fragmented peripheral mosaics. Medium patches and forested ridges may function as potential stepping stones and corridors facilitating movement across habitat clusters. These findings highlight the importance of maintaining functional connectivity and mitigating edge disturbances in buffer and experimental zones to ensure long-term population persistence and effective protected-area management for forest ungulates. Full article

Since land use considerably affects the spatial variation of PM_2.5 levels, it is crucial to predict PM_2.5 concentrations under future land use changes. However, prior research has primarily concentrated on meteorological factors influencing PM_2.5 predictions, while neglecting the effect of land use configurations. Consequently, in our study, a novel Patch-generating Land Use Simulation–Land Use Regression (PLUS-LUR) method was developed by integrating the PLUS model’s dynamic prediction capability with the LUR model’s spatial interpretation strength. The incorporation of landscape indices as key variables was essential for predicting PM_2.5 concentrations. First, the random forest-optimized LUR method was trained with PM_2.5 datasets from the Pearl River Delta (PRD) monitoring stations and multi-source spatial datasets. We assessed the modeling accuracy with and without considering landscape indices using the test dataset. Subsequently, the PLUS approach was applied to forecast land use as well as associated landscape indices in 2028. Based on these projections, grid-scale influencing factors were input into the previously constructed LUR model to forecast future PM_2.5 distributions at a grid scale. The results reveal a spatial pattern with higher PM_2.5 levels in central areas and lower levels in peripheral regions. Furthermore, the PM_2.5 concentrations in the PRD are all below the Grade II threshold of the China Ambient Air Quality Benchmark in 2028. Notably, the predictions incorporating landscape indices demonstrate higher accuracy and reliability compared to those excluding them. These results provide methodological support for future PM_2.5 assessment and land use management. Full article

In this paper, we investigate the asymptotic behavior of solutions to a diffusive forest kinematic model, which describes the interactions among young trees, old trees, and airborne seeds. Our study focuses on the stability of the positive equilibrium, the occurrence of Hopf bifurcation yielding spatially homogeneous periodic solutions, and the subsequent Turing instability induced by diffusion in these periodic states. The analysis highlights that the juvenile tree mortality rate, represented by a quadratic function of mature tree density, plays a central dynamical role. Specifically, the parameter corresponding to the mature tree density at which juvenile mortality is minimized serves as a key Hopf bifurcation parameter. This indicates that the system’s transition to periodic solutions and later to diffusion-driven pattern formation can be effectively regulated through this parameter. From an ecological perspective, these results suggest that forest management strategies capable of indirectly influencing factors related to this critical parameter could help control the emergence of spatial patterns, such as forest patches. Furthermore, the functional form of the mortality rate offers a useful foundation for future studies examining how different assumptions regarding tree interaction morphology may influence ecosystem patterning. Full article

Accurate estimation of Leaf Area Index (LAI) is essential for monitoring vegetation structure and ecosystem services in urban and peri-urban environments, particularly in small, heterogeneous patches typical of semi-arid cities. This study presents a comparative assessment of four empirical LAI estimation methods—NDVI-based, NDVI-advanced, SAVI-based, and EVI-based methods—applied to atmospherically corrected Sentinel-2 Level-2A imagery (10 m spatial resolution) over a 0.045 km² urban–agricultural polygon in the Tashkent region, Uzbekistan. Multi-temporal observations acquired during the 2023 growing season (June–August) were used to examine intra-seasonal vegetation dynamics. In the absence of field-measured LAI, a Random Forest regression model was implemented as an inter-method consistency analysis to assess agreement among index-derived LAI estimates rather than to perform external validation. Statistical comparisons revealed highly systematic and practically significant differences between methods, with the EVI-based approach producing the highest and most dynamically responsive LAI values (mean LAI = 1.453) and demonstrating greater robustness to soil background and atmospheric effects. Mean LAI increased by 66.7% from June to August, reflecting irrigation-driven crop phenology in the semi-arid study area. While the results indicate that EVI provides the most reliable relative LAI estimates for small urban–agricultural patches, the absence of ground-truth data and the influence of mixed pixels at 10 m resolution remain key limitations. This study offers a transferable methodological framework for comparative LAI assessment in data-scarce urban environments and provides a basis for future integration with field measurements, higher-resolution imagery, and LiDAR-based 3D vegetation models. Full article

Agricultural expansion is a major driver of biodiversity loss in tropical regions, yet human-dominated landscapes also hold potential for biodiversity conservation when managed as multifunctional mosaics that retain patches of native vegetation. We assessed how natural and semi-natural habitats contribute to avian diversity in a tropical livestock farm in northern Costa Rica. Over one year, bird assemblages were sampled across three habitat types (live fences, pastures and riparian forest) at La Balsa farm. Using point counts surveyed every month during the year, we recorded 165 bird species, including 20 migratory and 6 species of global conservation concern, and 4 regionally endemic species. Species richness and overall abundance were lower in the riparian forest compared to live fences and pastures, and bird assemblage composition differed markedly among habitats, with the community in the riparian forest exhibiting a distinct assemblage structure. These results indicate that though the riparian forest hosts fewer species and individuals, it harbors a characteristic bird assemblage, highlighting its irreplaceable ecological role in providing habitat to forest-dependent species. Overall, the findings underscore that structurally diverse agricultural mosaics can sustain remarkably high bird diversity when complemented by habitats including native vegetation. Full article

Accurate, high-resolution information on economic forest resources, here referring to fruit-tree plantations and economic tree crops, is essential for land-use planning and resource management in arid regions. Xinjiang, China—one of the country’s most important fruit-producing areas—exhibits highly fragmented and heterogeneous distributions of economic tree plantations, posing challenges for large-scale and long-term monitoring. In this study, we integrated multi-source remote sensing data by combining multi-temporal Sentinel-2 optical imagery with Sentinel-1 SAR backscatter and texture features to characterize the spatial and temporal distribution patterns of major economic tree plantations from 2019 to 2024. An optimized Random Forest classifier was applied across five key production regions (Aksu, Bazhou, Hotan, Kashgar, and Turpan–Hami). The mapping results achieved overall accuracies ranging from 0.85 to 0.97, with Kappa coefficients between 0.80 and 0.95. The results indicate that economic tree plantations are predominantly distributed along oasis corridors of the Tarim Basin and the alluvial plains on both sides of the Tianshan Mountains, forming belt- and patch-like spatial patterns. While the overall spatial configuration remained relatively stable during the study period, localized expansion was observed, mainly associated with walnut, jujube, and grape plantations. These findings provide insights into the spatial dynamics of economic tree plantations and support land-use optimization and agricultural planning in arid and semi-arid regions. Full article

Assessing the dynamics between native forests and exotic tree plantations is key to understanding the drivers of native forest transformation and conservation challenges. We examined these dynamics across four zones in the Los Ríos and Los Lagos regions of southern Chile: the Coastal Range, Central Valley, Andes, and Chiloé. Changes from 2002–2012 and 2012–2022 were analyzed using satellite image classifications and landscape metrics (total area, mean patch size, number of patches, patch density, mean Euclidean nearest-neighbor distance). In both periods, in zones with strong human influence, such as the Coastal Range and Central Valley, native forest area decreased and became more fragmented, whereas exotic tree plantations initially expanded and then declined, resulting in a net increase. Transitions between native forests and exotic plantations showed strong bidirectional substitutions. In less disturbed zones, such as the Andes and Chiloé, native forests expanded in area and connectivity. Overall, native forest cover increased in the Andes (+12.85 km²) and Chiloé. (+6.19 km²) but declined in the Coastal Range (−0.65 km²) and Central Valley (−7.75 km²), whereas exotic plantations showed a net expansion across all zones. These contrasting trajectories underscore the need for reliable monitoring tools to support effective forest management. Full article

(1) Forest fragmentation and associated edge effects can strongly modify the diversity and distribution of soil invertebrates, yet their responses in temperate floodplain forests remain poorly understood. We investigated myriapod (centipede and millipede) assemblages in a fragmented forest mosaic in the protected landscape area Litovelské Pomoraví (Czech Republic), focusing on the role of stand age, ecotones and key microhabitat variables. (2) Myriapods were sampled continuously during two years using pitfall traps arranged along transects crossing four neighboring patches (clear-cut with seedlings, 10-year-old stand, 87-year-old and 127-year-old Querco–Ulmetum forests). Species diversity was quantified using the Shannon–Wiener index, and patterns were analyzed by t-tests, canonical correspondence analysis and generalized additive models. (3) We collected over six thousand individuals (10 centipede and 10 millipede species). Diversity peaked in old-growth stands and adjacent ecotones, and two of the three ecotones supported particularly high species abundances. Litter cover and thickness, stand age, and the structure of the herb and shrub layers were the most important predictors of species distributions. Dominant species (e.g., Glomeris tetrasticha Brandt, 1833, Lithobius mutabilis L. Koch, 1862, L. forficatus (Linnaeus, 1758)) showed contrasting habitat preferences, reflecting niche differentiation along microhabitat and stand-age gradients. (4) Our findings indicate that conserving a fine-grained mosaic of stand ages, together with structurally complex forest interiors and ecotones, is essential for maintaining myriapod diversity and the ecosystem functions they provide in Central European forests. Full article

Under the intensifying water shortages in the vegetation season, early identification of Ips typographus L. damage is crucial for preventing wide outbreaks, which undermine the economic potential of commercial stands of Norway spruce (Picea abies Karst.) across Europe. For this purpose, remote sensing based on satellite images is considered one of the most efficient methods, particularly in homogenous and wide forested landscapes. However, under highly heterogeneous seminatural managed forest landscapes in lowland Central and Northern Europe, as illustrated by the eastern Baltic region and Latvia in particular, the efficiency of such an approach can lack the desired accuracy. Hence, the identification of smaller damage patches by I. typographus, which can act as a source of wider outbreaks, can be overlooked, and situational awareness can be further aggravated by infrastructure artefacts. In this study, the accuracy of satellite imaging for the identification of I. typographus damage was evaluated, focusing on the occurrence of false positives and particularly false negatives obtained from the comparison with UAV imaging. Across the studied landscapes, correct or partially correct identification of damage patches larger than 30 m² occurred in 73% of cases. Still, the satellite image analysis of the highly heterogeneous landscape resulted in quite a common occurrence of false negatives (up to one-third of cases), which were related to stand and patch properties. The high rate of false negatives, however, is crucial for the prevention of outbreaks, as the sources of outbreaks can be underestimated, burdening prompt and hence effective implication of countermeasures. Accordingly, elaborating an analysis of satellite images by incorporating stand inventory data could improve the efficiency of early detection systems, especially when coupled with UAV reconnaissance of heterogeneous landscapes, as in the eastern Baltic region. Full article

Accurate mapping of wetland vegetation is essential for ecosystem monitoring and conservation planning. Traditional workflows combining Sentinel-1 SAR, Sentinel-2 optical imagery, and topographic data have advanced vegetation classification but require extensive preprocessing and often yield fragmented boundaries and “salt-and-pepper” noise. In this study, we compare a conventional multi-sensor classification framework with a novel embedding-based approach derived from the AlphaEarth foundation model, using a cluster-guided Random Forest classifier applied to the dynamic wetland system of Narran Lake, New South Wales. Both approaches achieved high accuracy ac with test performance typically in the ranges: OA = 0.985–0.991, Cohen’s κ = 0.977–0.990, weighted F1 = 0.986–0.991, and MCC = 0.977–0.990. Embedding based maps showed markedly improved spatial coherence (lower edge density, local entropy, and patch fragmentation), producing smoother, ecologically consistent boundaries while requiring minimal preprocessing. Differences in class delineation were most evident in fire-affected and agricultural areas, where embeddings demonstrated greater resilience to spectral disturbance and post-fire variability. Although overall accuracies exceeded 0.98, these high values reflect the use of spectrally pure, homogeneous training samples rather than overfitting. The results highlight that embedding-driven methods can deliver cleaner, more interpretable vegetation maps with far less data preparation, underscoring their potential to streamline large-scale ecological monitoring and enhance the spatial realism of wetland mapping. Full article

Forests play a vital role in the global carbon cycle but face growing anthropogenic pressures, with climate change and forest fragmentation among the most critical. In West Africa, particularly in Ghana, the interaction between increasing aridity and forest fragmentation remains underexplored, despite its significance for forest biomass dynamics and carbon storage processes. This study examined how spatial variation in climatic aridity (Aridity Index, AI) affects above-ground biomass (AGB) in Ghana’s ecological zones, both directly and indirectly through forest fragmentation and biodiversity, using structural equation modeling (SEM) and generalized additive models (GAMs). Results from this study show that AGB declines along the aridity gradient, with humid zones supporting the highest biomass and semi-arid zones the lowest. The SEM analysis revealed that areas with a lower aridity index (drier conditions) had significantly lower AGB, indicating that arid conditions are associated with lower forest biomass. Fragmentation patterns align with this relationship, while biodiversity (as measured by species richness) showed weak associations, likely reflecting both ecological and data limitations. GAMs highlighted nonlinear fragmentation effects: mean patch area (AREA_MN) was the strongest predictor, showing a unimodal relationship with biomass, whereas number of patches (NP), edge density (ED), and landscape shape index (LSI) reduced AGB. Overall, these findings demonstrate that aridity and spatial configuration jointly control biomass, with fragmentation acting as a key mediator of this relationship. Dry and transitional forests emerge as particularly vulnerable, emphasizing the need for management strategies that maintain large, connected forest patches and integrate restoration into climate adaptation policies. Full article

The reliability and economic operation of power systems increasingly depend on renewable energy, making accurate short−term photovoltaic (PV) power prediction essential. Conventional approaches struggle with the nonlinear and stochastic characteristics of PV data. This study proposes an enhanced prediction framework integrating Artificial Fish Swarm Algorithm–Isolation Forest (AFSA–IF) anomaly detection, Generative Adversarial Network−based feature extraction, multimodal data fusion, and a Patch Time Series Transformer (PatchTST) model. The framework includes advanced preprocessing, fusion of meteorological and historical power data, and weather classification via one−hot encoding. Experiments on datasets from six PV plants show significant improvements in mean absolute error, root mean square error, and coefficient of determination compared with Transformer, Reformer, and Informer models. The results confirm the robustness and efficiency of the proposed model, especially under challenging conditions such as rainy weather. Full article

Quantifying forest patch dynamics is essential for understanding how forest patch characteristics vary in relation to ancient tree locations. This study developed a satellite-based framework to analyze the differences among forest patches associated with natural and planted ancient trees across the Sichuan–Chongqing region, China. Using monthly LandTrendr on Google Earth Engine, we analyzed long-term (1990–2024) and high-frequency observations of forest dynamics at a 180 m × 180 m (6 × 6 pixels) spatial scale. Disturbance and recovery events were characterized by their magnitude, rate, timing, and duration. Patches were classified into six categories based on ancient tree type and proximity and further subdivided by land use type. The results show that in natural forests, patches with natural ancient trees are associated with more stable change signatures, whereas in planted forests, patches containing planted ancient trees are associated with stronger recovery-related change patterns. Over 60% of detected changes were short-lived (≤5 years), indicating that most disturbances and recovery processes were transient rather than persistent. These findings show that the presence and spatial context of ancient trees are associated with differences in patch change patterns. The proposed workflow provides a scalable approach for integrating multi-temporal remote sensing into large-scale monitoring and management of ancient trees and their associated forest patches. Full article

Pavement macrotexture, quantified by mean texture depth (MTD) and mean profile depth (MPD), is a critical parameter for road safety and performance. The traditional sand patch test is labor-intensive and slow, creating a bottleneck for modern pavement management systems. Accurately translating the rich point cloud data into reliable MTD values using the 3D scanning method remains a challenge, with current methods often relying on oversimplified correlations. This research addresses this gap by developing and validating a novel machine learning framework to predict MTD and MPD directly from high-resolution 3D laser scans. A comprehensive dataset of 127 pavement samples was created, combining traditional sand patch measurements with detailed 3D point clouds. From these point clouds, 27 distinct surface features spanning statistical, spatial, spectral, and geometric domains were developed. Six machine learning algorithms, consisting of Random Forest, Gradient Boosting, Support Vector Regression, k-Nearest Neighbor, Artificial Neural Networks, and Linear Regression, were implemented. The results demonstrate that the ensemble-based Random Forest model achieved superior performance, predicting MTD with an R² of 0.941 and a mean absolute error (MAE) of 0.067 mm, representing a 56% improvement in accuracy over traditional digital correlation methods. Model interpretation via SHAP analysis identified root mean square height (Sq) and surface skewness (Ssk) as the most influential features. Full article

Forests provide a wide range of ecosystem services, and their importance in supporting human well-being is widely recognized. As goods and benefits from forests are exhaustible, it is therefore essential to gather sound data for their monitoring and management. Remote sensing has gained increasing importance in collecting data on forests, driven by the growing demand for regularly updated environmental data. However, remote sensing modeling of vegetation requires reference data to be collected in the field. This article presents a dataset on tree crown cover—both total and by species—of 528 georeferenced forest plots located in the Eastern Alps, Italy, an area affected by extensive wind and snow damage and subsequent widespread damage caused by bark beetles. The characteristic species of the forest types in the dataset are widely distributed over the Eurasian continent, making the dataset potentially useful to many users and researchers studying forest biodiversity or remote sensing applications to monitor forest cover changes. Data were collected within a still ongoing project aimed at detecting crown cover changes in small forest patches. Full article