Search Results (72)

Enhancing carbon sink capacity and optimizing urban blue-green infrastructure (UBGI) are crucial for urban planning and sustainable development. Based on the ArcGIS 10.8 platform and the InVEST model, this study comprehensively evaluates the spatiotemporal evolution characteristics of three ecosystem services (carbon storage, habitat quality, and water retention) in Guilin. By applying the coupling coordination degree model, bivariate spatial autocorrelation, and K-means clustering methods, it systematically reveals the synergistic and trade-off relationships among multiple ecosystem services in karst cities, identifies the spatial differentiation pattern of ecological spaces, and proposes UBGI optimization strategies. The results show that the three types of ecosystem services in Guilin exhibited a spatiotemporal differentiation pattern of stable high values in mountainous areas and continuous expansion of low values around urban areas from 1993 to 2023, with their changes mainly driven by the significant negative impact of human activity intensity (nighttime light, population density). Guilin’s ecological space can be divided into four functional zones: Ecological Core Cluster (77.50%), Degraded Carbon-Poor Cluster (1.47%), Habitat Protection Cluster (0.46%), and Buffer Balance Cluster (20.58%). Carbon storage, habitat quality, and water retention showed significant spatial gradient differences (Kruskal–Wallis nonparametric test, p < 0.001) and local decoupling characteristics. Furthermore, the study proposed key ecological management thresholds, such as impervious surface ratio < 15% and forestland ratio > 30%, and constructed a differentiated “zoning-classification-grading” UBGI optimization strategy system based on the four functional zones, including ecological corridor construction, promotion of vertical greening and sponge facilities, supplementary planting of native vegetation, and integration of ecological agriculture. These strategies aim to enhance the synergistic efficiency of ecosystem services, improve regional carbon sink capacity, and provide a scientific basis for Guilin’s ecological planning, the implementation of “dual carbon” goals, and the construction of the National Innovation Demonstration Zone for Sustainable Development Agenda. Full article

In species-rich forests, the integration of vegetative and reproductive traits defines plant ecological strategies and underpins community assembly. How these trait syndromes assemble into functional groups to facilitate species coexistence in ecotones remains unclear. To address this, we measured 17 key functional traits in 121 woody plant species, covering vegetative and reproductive traits, and used hierarchical clustering to classify these species into functional groups (FGs). We found the following: (1) The woody plant community exhibits distinct trait syndromes adapted to the ecotonal environment: evergreen species accounted for 84.3%, microphanerophytes dominated (95.04%), simple leaves and alternate phyllotaxy prevailed, and animal-mediated pollination (91.74%) and seed dispersal (77.69%) were the primary reproductive strategies. (2) The 121 species were classified into 10 optimal FGs based on integrated differences in vegetative traits (e.g., leaf morphology, life form, phyllotaxy) and reproductive traits (e.g., pollination/dispersal mode, inflorescence/fruit type). Most FGs were dominated by evergreen microphanerophytes, reflecting convergent adaptation to the subtropical ecotonal environment, while distinct adaptive strategies differentiated the groups: FG1 (solely Meliosma rigida) was distinguished by whorled phyllotaxy and large leaves, a specialization for high-light microhabitats; FG5, a unique deciduous group, comprised species (e.g., Nyssa sinensis) with alternate leaves and axillary inflorescences, adapting to seasonal resource fluctuations. (3) These FGs reflected adaptive strategies to diverse microhabitats: rare species in FG4 (e.g., Acer cordatum) adopted wind-dependent pollination/dispersal to cope with mountainous wind variability, while FGs 3, 7, 8, 10 relied on animal mutualism to ensure reproductive success, highlighting the role of plant–animal interactions in community structure. Our study clarifies the trait differentiation patterns and FG assembly mechanisms of woody plants in the mid-subtropical–south-subtropical ecotone. The integrated trait-based FG classification could provide insights into how species coexist via niche differentiation and offer a theoretical basis for biodiversity and ecosystem conservation. Full article

Human recreation is an increasingly popular activity; however, an increase in recreational pressure in wilderness areas can contribute to issues such as human–wildlife conflict, introduction of invasive species, vegetation and soil degradation, riparian area impacts, and anthropogenic waste. While remote camera studies are frequently used to assess the response of wildlife species (i.e., grizzly bears) or ecosites (i.e., coastal sand dunes) to human recreational disturbance, classifying and quantifying human recreational behavior, including differences in spatial, temporal, and recreation types, is less common and presents unique design challenges. Here, we present our practical design considerations and lessons learned from a study quantifying human recreation along trails in the northern Rocky Mountains of Alberta, Canada. We describe our standardized protocol to deploy our camera array, and image classification and analysis of recreation use by type and group size. Finally, we provide practical recommendations for future work attempting to evaluate human recreation in wilderness settings relative to landscape management outcomes. Full article

Different approaches have been developed to assess the phenological dynamics of ecosystems. However, diverse data sources and extraction methods for assessing ecosystem phenology can result in discrepant and inaccurate results, especially across different types of vegetation under various climate classifications. Based on the phenology of dominant plant species (Phe_plant) obtained from ground monitoring in an alpine shrub meadow at Haibei Station (HBS) on the Qinghai–Tibetan Plateau and in a broad-leaved Korean pine forest at Changbai Mountain (CBF) in Northeastern China, we extracted vegetation phenology from the Normalized Difference Vegetation Index (Phe_NDVI) and photosynthetic phenology from gross primary productivity (Phe_GPP) using five common methods. These methods included Gaussian fitting, single logistic function fitting, double logistic function fitting, and smoothing techniques combined with fixed threshold and derivative-based determination approaches. There was no consistent interannual trend in either plant phenology or environmental factors at the two sites. Among the three types of plant phenology, a similar interannual pattern in the start of the growing season (SOS) was observed, whereas the interannual patterns for the end of the growing season (EOS) and the growing season length (GSL) were asynchronous. Compared to Phe_plant, both Phe_NDVI and Phe_GPP exhibited an earlier SOS, a delayed EOS, and consequently an extended GSL. The SOS derived from both Phe_NDVI and Phe_GPP was advanced by increasing spring temperatures at both sites, while the relationship between EOS and air temperature was relatively weak. The discrepancy between Phe_NDVI and Phe_GPP was more pronounced at CBF than at HBS, likely due to the complex vegetation composition and structure of the mixed forest. The different extraction methods produced more consistent and less variable estimates of SOS compared to EOS and GSL at both sites. Among the five methods, the dynamic threshold approach showed a relatively small difference between Phe_NDVI and Phe_GPP, suggesting that it could provide a more consistent estimate of plant phenology across the two sites. This study clearly reveals the inherent discrepancies associated with using different types of phenological data and the influence of extraction methods on phenology across different plant functional types. More attention should be given to improving the accuracy of EOS and understanding the influence of vegetation composition on phenological variation in future studies. Full article

The Caucasus Mountains are recognized as a global center of biodiversity but currently face significant risks of degradation due to intensified economic development and the effects of climate change. Forest inventory and mapping are essential for biodiversity conservation in the Caucasus region. Geospatial modeling is a common method of thematic mapping, but its reliability depends heavily on the initial classification of reference data used for model training. Modern vegetation science features various classification approaches, most of which were developed independently of digital mapping practices and are rarely assessed for their suitability in geospatial modeling. To fill this gap, we classified the same dataset of vegetation relevés from mixed and broadleaf forests in the northwestern Caucasus using two approaches, based on floristic and dominant concepts, and compared the predictive performance of geospatial models trained on these datasets. We considered multiple types of geospatial variables, including optical satellite imagery, a digital elevation model (DEM), and bioclimatic and soil features, to evaluate their informativeness for spatial differentiation of the resulting forest types and to identify optimal variable combinations for modeling via multistage feature selection. We trained several models using different variable sets and machine learning methods for both classifications and evaluated their accuracy via nested cross-validation. The forest types produced by the two approaches scarcely matched, and the selected variable sets for model training differed accordingly. Unexpectedly, bioclimatic and soil variables were more effective than DEM- and satellite-derived variables, despite their coarser spatial resolution. Floristic-based geospatial models outperformed dominant-based models in terms of forest-type separability and predictive accuracy. Therefore, a floristic classification approach may be preferable for forests with complex species composition, both ecologically and in terms of the reliability of geospatial modeling and the derived mapping results. Full article

Classifying land cover in mountainous areas has always been challenging due to the high diversity of ecosystems and the complexity of the spectral–temporal–spatial relationships caused by the rugged terrain. This paper introduces multi-year synthesized phenology data to improve land cover classification in these regions. Using the Shennongjia Forestry District in Hubei Province, China, as a case study, we investigate how incorporating multi-year synthesized phenology data enhances the accuracy of land cover classification with single-temporal and multi-temporal remote sensing imagery, as well as how it aids in identifying different vegetation types in shaded areas of the mountains. The research results indicate that incorporating multi-year synthesized phenology data significantly improves the accuracy of land cover classification for single summer imagery, single autumn imagery, multi-temporal summer–autumn imagery, and mountain shadow areas. The Kappa coefficient (Kappa) increased by 1.57% to 9.93%, while overall accuracy (OA) improved by 1.4% to 8.75%. Notably, the improvement in classification accuracy was most pronounced for single summer imagery. Furthermore, the results demonstrate that, in the absence of terrain data, multi-year synthesized phenology data provide even greater enhancements in land cover classification accuracy using remote sensing imagery. Full article

Monsoon evergreen broad-leaved forests (MEBFs) represent one of the most species-rich and structurally complex vegetation types, and one of the most widely distributed forests in Yunnan Province, Southwest China. However, they have yet to undergo a comprehensive analysis on their community diversity, spatial differentiation patterns, and underlying drivers across Yunnan. Based on extensive field surveys during 2021–2024 with 548 MEBF plots, this study employed the Unweighted Pair Group Method for forest community classification and Non-metric Multidimensional Scaling for ordination and interpretation of community–environment association. A total of 3517 vascular plant species were recorded in the plots, including 1137 tree species, 1161 shrubs, and 1219 herbs. Numerical classification divided the plots into 3 alliance groups and 24 alliances: (1) Castanopsis–Schima (Lithocarpus) Forest Alliance Group (16 alliances), predominantly distributed west of 102°E in central-south and southwest Yunnan; (2) Castanopsis–Machilus (Beilschmiedia) Forest Alliance Group (6 alliances), concentrated east of 101°E in southeast Yunnan with limited latitudinal range; (3) Castanopsis–Camellia Forest Alliance Group (2 alliances), restricted to higher-elevation mountainous areas within 103–104° E and 22.5–23° N. Climatic variation accounted for 81.1% of the species compositional variation among alliance groups, with contributions of 83.5%, 57.6%, and 62.1% to alliance-level differentiation within alliance groups 1, 2, and 3, respectively. Precipitation days in the driest quarter (PDDQ) and precipitation seasonality (PS) emerged as the strongest predictors of community differentiation at both alliance group and alliance levels. Topography and soil features significantly influenced alliance differentiation in Groups 2 and 3. Collectively, the interaction between the monsoon climate and topography dominate the spatial differentiation of MEBF communities in Yunnan. Full article

This study used Jilin-1 satellite data and unmanned aerial vehicle (UAV)-collected visible-thermal infrared imagery to calculate twelve spectral indices and evaluate their effectiveness in distinguishing post-fire forest areas and identifying human-altered land-cover changes in Jinyun Mountain, Chongqing. The research goals included mapping wildfire impacts with M-statistic separability, measuring land-cover distinguishability through Jeffries–Matusita (JM) distance analysis, classifying land-cover types using the random forest (RF) algorithm, and verifying classification accuracy. Cumulative human disturbances—such as land clearing, replanting, and road construction—significantly blocked the natural recovery of burn scars, and during long-term human-assisted recovery periods over one year, the Red Green Blue Index (RGBI), Green Leaf Index (GLI), and Excess Green Index (EXG) showed high classification accuracy for six land-cover types: road, bare soil, deadwood, bamboo, broadleaf, and grass. Key accuracy measures showed producer accuracy (PA) > 0.8, user accuracy (UA) > 0.8, overall accuracy (OA) > 90%, and a kappa coefficient > 0.85. Validation results confirmed that visible-spectrum indices are good at distinguishing photosynthetic vegetation, thermal bands help identify artificial surfaces, and combined thermal-visible indices solve spectral confusion in deadwood recognition. Spectral indices provide high-precision quantitative evidence for monitoring post-fire land-cover changes, especially under human intervention, thus offering important data support for time-based modeling of post-fire forest recovery and improvement of ecological restoration plans. Full article

Distinguishing vegetation types from satellite images has long been a goal of remote sensing, and the combination of multi-source and multi-temporal remote sensing images for vegetation classification is currently a hot topic in the field. In species-rich mountainous environments, this study selected four remote sensing images from different seasons (two aerial images, one WorldView-2 image, and one UAV image) and proposed a vegetation classification method integrating hierarchical extraction and object-oriented approaches for 11 vegetation types. This method innovatively combines the Random Forest algorithm with a decision tree model, constructing a hierarchical strategy based on multi-temporal feature combinations to progressively address the challenge of distinguishing vegetation types with similar spectral characteristics. Compared to traditional single-temporal classification methods, our approach significantly enhances classification accuracy through multi-temporal feature fusion and comparative experimental validation, offering a novel technical framework for fine-grained vegetation classification under complex land cover conditions. To validate the effectiveness of multi-temporal features, we additionally performed Random Forest classifications on the four individual remote sensing images. The results indicate that (1) for single-temporal images classification, the best classification performance was achieved with autumn images, reaching an overall classification accuracy of 72.36%, while spring images had the worst performance, with an accuracy of only 58.79%; (2) the overall classification accuracy based on multi-temporal features reached 89.10%, which is an improvement of 16.74% compared to the best single-temporal classification (autumn). Notably, the producer accuracy for species such as Quercus acutissima Carr., Tea plantations, Camellia sinensis (L.) Kuntze, Pinus taeda L., Phyllostachys spectabilis C.D.Chu et C.S.Chao, Pinus thunbergii Parl., and Castanea mollissima Blume all exceeded 90%, indicating a relatively ideal classification outcome. Full article

Global climate change poses a serious threat to Torreya grandis, a rare and economically important tree species, making the accurate mapping of its spatial distribution essential for forest resource management. However, extracting forest-growing areas remains challenging due to the limited spatial and temporal resolution of remote sensing data and the insufficient classification capability of traditional algorithms for complex land cover types. This study utilized monthly Sentinel-2 imagery from 2023 to extract multitemporal spectral bands, vegetation indices, and texture features. Following minimum redundancy maximum relevance (mRMR) feature selection, a spatial–spectral fused attention network (SSFAN) was developed to extract the distribution of T. grandis in the Kuaiji Mountain area and to analyze the influence of topographic factors. Compared with traditional deep learning models such as 2D-CNN, 3D-CNN, and HybridSN, the SSFAN model achieved superior performance, with an overall accuracy of 99.1% and a Kappa coefficient of 0.961. The results indicate that T. grandis is primarily distributed on the western, southern, and southwestern slopes, with higher occurrence at elevations above 500–600 m and on slopes steeper than 20°. The SSFAN model effectively integrates spectral–spatial information and leverages a self-attention mechanism to enhance classification accuracy. Furthermore, this study highlights the joint influence of natural factors and human land-use decisions on the distribution pattern of T. grandis. These findings aid precision planting and resource management while advancing methods for identifying tree species. 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

Due to the high-density coverage of vegetation, the complexity of terrain, and occlusion issues, ground point extraction faces significant challenges. Airborne Light Detection and Ranging (LiDAR) technology plays a crucial role in complex mountainous areas. This article proposes a method for constructing fine terrain in high vegetation coverage areas based on implicit neural representation. This method consists of data preprocessing, multi-scale and multi-feature high-difference point cloud initial filtering, and an upsampling module based on implicit neural representation. Firstly, preprocess the regional point cloud data is preprocessed; then, K-dimensional trees (K-d trees) are used to construct spatial indexes, and spherical neighborhood methods are applied to capture the geometric and physical information of point clouds for multi-feature fusion, enhancing the distinction between terrain and non-terrain elements. Subsequently, a differential model is constructed based on DSM (Digital Surface Model) at different scales, and the elevation variation coefficient is calculated to determine the threshold for extracting the initial set of ground points. Finally, the upsampling module using implicit neural representation is used to finely process the initial ground point set, providing a complete and uniformly dense ground point set for the subsequent construction of fine terrain. To validate the performance of the proposed method, three sets of point cloud data from mountainous terrain with different features are selected as the experimental area. The experimental results indicate that, from a qualitative perspective, the proposed method significantly improves the classification of vegetation, buildings, and roads, with clear boundaries between different types of terrain. From a quantitative perspective, the Type I errors of the three selected regions are 4.3445%, 5.0623%, and 5.9436%, respectively. The Type II errors are 5.7827%, 6.8516%, and 7.3478%, respectively. The overall errors are 5.3361%, 6.4882%, and 6.7168%, respectively. The Kappa coefficients of the measurement areas all exceed 80%, indicating that the proposed method performs well in complex mountainous environments. Provide point cloud data support for the construction of wind and photovoltaic bases in China, reduce potential damage to the ecological environment caused by construction activities, and contribute to the sustainable development of ecology and energy. Full article

This article presents the application of novel cartographic methods of vegetation mapping with a case study of the Rif Mountains, northern Morocco. The study area is notable for varied geomorphology and diverse landscapes. The methodology includes ML modules of GRASS GIS ‘r.learn.train’, ‘r.learn.predict’, and ‘r.random’ with algorithms of supervised classification implemented from the Scikit-Learn libraries of Python. This approach provides a platform for processing spatiotemporal data and satellite image analysis. The objective is to determine the robustness of the “DecisionTreeClassifier” and “ExtraTreesClassifier” classification algorithms. The time series of satellite images covering northern Morocco consists of six Landsat scenes for 2023 with a bimonthly time interval. Land cover maps are produced based on the processed, classified, and analyzed images. The results demonstrated seasonal changes in vegetation and land cover types. The validation was performed using a land cover dataset from the Food and Agriculture Organization (FAO). This study contributes to environmental monitoring in North Africa using ML algorithms of satellite image processing. Using RS data combined with the powerful functionality of the GRASS GIS and FAO-derived datasets, the topographic variability, moderate-scale habitat heterogeneity, and bimonthly distribution of land cover types of northern Morocco in 2023 have been assessed for the first time. Full article

Overhead transmission lines are widely deployed across both mountainous and plain areas and serve as a critical infrastructure for China’s electric power industry. The rapid advancement of three-dimensional (3D) laser scanning technology, with airborne LiDAR at its core, enables high-precision and rapid scanning of the detection area, offering significant value in identifying safety hazards along transmission lines in complex environments. In this paper, five transmission lines, spanning a total of 160 km in the mountainous area of Sanmenxia City, Henan Province, China, serve as the primary research objects and generate several insights. The location and elevation of each power tower pole are determined using an Unmanned Aerial Vehicle (UAV), which assesses the direction and elevation changes in the transmission lines. Moreover, point cloud data of the transmission line corridor are acquired and archived using a UAV equipped with LiDAR during variable-height flight. The data processing of the 3D laser point cloud of the power corridor involves denoising, line repair, thinning, and classification. By calculating the clearance, horizontal, and vertical distances between the power towers, transmission lines, and other surface features, in conjunction with safety distance requirements, information about potential hazards can be generated. The results of detecting these five transmission lines reveal 54 general hazards, 22 major hazards, and an emergency hazard in terms of hazards of the vegetation type. The type of hazard in the current working condition is mainly vegetation, and the types of cross-crossing hazards are power lines and buildings. The detection results are submitted to the local power department in a timely manner, and relevant measures are taken to eliminate hazards and ensure the normal supply of power resources. The research in this paper will provide a basis and an important reference for identifying the potential safety hazards of transmission lines in Henan Province and other complex environments and solving existing problems in the manual inspection of transmission lines. Full article

The habitats protected by the European Union (EU) include most peat vegetation, such as mires, swamp mires, fens, and peat bogs—all belonging to the classes Oxycocco–Sphagnetea and Scheuchzerio–Caricetea fuscae and carrying the Habitat Codes 71xx and 72xx. These types of vegetation are typical of cold and cool temperate climates, while they become rarer in Southern Europe where Mediterranean influences prevail, representing relic fragments of the past glacial climatic conditions there. Because of their limited extension and the increasing warmth and drought due to climate change, they are seriously threatened. Even if many studies were performed, their richness and distribution across Europe are still not well–understood, and only a few examples are known from the Central and Southern Apennines to date. In order to provide the syntaxonomical classification of the alkaline fens referable to the EU Habitat 7230 found on the mountain plateaus of the Central Apennines, we analyzed their species structure and flora composition, together with their chorological and ecological characteristics. We also evaluated their conservation status, pressures, and threats. The alkaline fens of the Central Apennines are found to be poorer in diagnostic species when compared to similar communities of Central and Northern Europe. However, they are rich in the species of the surrounding meadows and pastures. Among them, the new subassociation Caricetum davallianae caricetosum hostianae is described. Full article