Search Results (67)

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

Low-cost unmanned aerial vehicle (UAV) visible light remote sensing provides new opportunities for plant community monitoring, but its practical deployment in different ecosystems is still limited by the lack of standardized vegetation index (VI) optimization for multi-species coverage extraction. This study developed a universal method integrating four VIs—Excess Green Index (EXG), Visible Band Difference Vegetation Index (VDVI), Red-Green Ratio Index (RGRI), and Red-Green-Blue Vegetation Index (RGBVI)—to bridge UAV imagery with plant communities. By combining spectral separability analysis with machine learning (SVM), we established dynamic thresholds applicable to crops, trees, and shrubs, achieving cross-species compatibility without multispectral data. The results showed that all VIs achieved robust vegetation/non-vegetation discrimination (Kappa > 0.84), with VDVI being more suitable for distinguishing vegetation from non-vegetation. The overall classification accuracy for different vegetation types exceeded 92.68%, indicating that the accuracy is considerable. Crop coverage extraction showed a minimum segmentation error of 0.63, significantly lower than that of other vegetation types. These advances enable high-resolution vegetation monitoring, supporting biodiversity assessment and ecosystem service quantification. Our research findings track the impact of plant communities on the ecological environment and promote the application of UAVs in ecological restoration and precision agriculture. Full article

Plant species classification in semi-arid mining areas is of great significance in assessing the environmental impacts and ecological restoration effects of coal mining. However, in semi-arid mining areas characterized by mixed arbor–shrub–herb vegetation, the complex vegetation distribution patterns and spectral features render single-sensor approaches inadequate for achieving fine classification of plant species in such environments. How to effectively fuse hyperspectral images (HSI) data with light detection and ranging (LiDAR) to achieve better accuracy in classifying vegetation in semi-arid mining areas is worth exploring. There is a lack of precise evaluation regarding how these two data collection approaches impact the accuracy of fine-scale plant species classification in semi-arid mining environments. This study established two experimental scenarios involving the synchronous and asynchronous acquisition of HSI and LiDAR data. The results demonstrate that integrating LiDAR data, whether synchronously or asynchronously acquired, significantly enhances classification accuracy compared to using HSI data alone. The overall classification accuracy for target vegetation increased from 71.7% to 84.7% (synchronous) and 80.2% (asynchronous), respectively. In addition, the synchronous acquisition mode achieved a 4.5% higher overall accuracy than asynchronous acquisition, with particularly pronounced improvements observed in classifying vegetation with smaller canopies (Medicago sativa L.: 17.4%, Pinus sylvestris var. mongholica Litv.: 11.7%, and Artemisia ordosica Krasch.: 7.5%). This study can provide important references for ensuring classification accuracy and error analysis of land cover based on HSI-LiDAR fusion in similar scenarios. Full article

Forest ecosystems are important for biodiversity conservation, climate regulation and climate change mitigation, soil and water protection, and the recreation and provision of raw materials. This paper presents a dataset on forest type and tree species composition for 934 georeferenced plots located in Italy. The forest type is classified in the field consistently with the Italian National Forest Inventory (NFI) based on the dominant tree species or species group. Tree species composition is provided by the percent crown cover of the main five species in the plot. Additional data on conifer and broadleaves pure/mixed condition, total tree and shrub cover, forest structure, sylvicultural system, development stage, and local land position are provided. The surveyed plots are distributed in the central–eastern Alps, in the central Apennines, and in the southern Apennines; they represent a wide range of species composition, ecological conditions, and silvicultural practices. Data were collected as part of a project aimed at developing a classification algorithm based on hyperspectral data. The dataset was made publicly available as it refers to forest types and species widespread in many countries of Central and Southern Europe and is potentially useful to other researchers for the study of forest biodiversity or for remote sensing applications. Full article

Analyzing habitat conditions and mapping habitat structures are crucial for monitoring ecosystems and implementing effective conservation measures, especially in the context of declining open grassland ecosystems in Europe. The marsh fritillary (Euphydryas aurinia), an endangered butterfly species, depends heavily on specific habitat conditions found in these grasslands, making it vulnerable to environmental changes. To address this, we conducted a comprehensive habitat suitability analysis within the Hainich National Park in Thuringia, Germany, leveraging very high-resolution (VHR) airborne, red-green-blue (RGB), and color-infrared (CIR) remote sensing data and deep learning techniques. We generated habitat suitability models (HSM) to gain insights into the spatial factors influencing the occurrence of E. aurinia and to predict potential habitat suitability for the whole study site. Through a deep learning classification technique, we conducted biotope mapping and generated fine-scale spatial variables to model habitat suitability. By employing various modeling techniques, including Generalized Additive Models (GAM), Generalized Linear Models (GLM), and Random Forest (RF), we assessed the influence of different modeling parameters and pseudo-absence (PA) data generation on model performance. The biotope mapping achieved an overall accuracy of 81.8%, while the subsequent HSMs yielded accuracies ranging from 0.69 to 0.75, with RF showing slightly better performance. The models agree that homogeneous grasslands, paths, hedges, and areas with dense bush encroachment are unsuitable habitats, but they differ in their identification of high-suitability areas. Shrub proximity and density were identified as important factors influencing the occurrence of E. aurinia. Our findings underscore the critical role of human intervention in preserving habitat suitability, particularly in mitigating the adverse effects of natural succession dominated by shrubs and trees. Furthermore, our approach demonstrates the potential of VHR remote sensing data in mapping small-scale butterfly habitats, offering applicability to habitat mapping for various other species. Full article

Scyphiphora hydrophyllacea is an endangered mangrove species in China. Over-exploitation and coastal development have drastically reduced its distribution and population, now limited to the Qingmei Port (Sanya) and the Qinglan Port (Wenchang). Despite its critical status, research on its ecological roles remains limited. This study examines the characteristics of S. hydrophyllacea communities and their relationship with soil properties. A total of 17 species from 11 families and 14 genera were recorded. TWINSPAN classification identified two distinct community types: the Qinglan Port community and the Qingmei Port community. Significant biodiversity differences were found only in the tree layer, with no differences in shrub or herbaceous layers. The importance value of S. hydrophyllacea within the arbor layer exhibited variability across the two communities, serving as an associated species in the Qinglan Port community and as a dominant species in the Qingmei Port community, suggesting potential barriers to its natural regeneration. Redundancy analysis (RDA) revealed that key soil factors influencing S. hydrophyllacea’s distribution include electrical conductivity (EC), total phosphorus (TP), total nitrogen (TN), soil organic content (SOC), and carbon/nitrogen ratio (C/N). We propose that high soil salinity and nitrogen deficiency may act as key factors limiting the natural regeneration of S. hydrophyllacea. Full article

The purpose of this work was to assess the functional diversity of herb–shrub layer com munities determined by their dispersal mode in pine boreal forests depending on two factors: (i) the degree of technogenic heavy metal pollution and (ii) the time passed since the last fire. We tested two hypotheses: (1) the functional diversity of communities determined by their diaspore dispersal mode decreases in polluted forests and in forests disturbed by recent fires; (2) the abundance, i.e., participation of anemochorous species in communities, is relatively greater in polluted forests and in forests disturbed by recent fires than in unpolluted or in forests that have not burned for a long time. We analyzed 77 vegetation relevés made in polluted and unpolluted pine forests to obtain the impact gradient of the Karabash copper smelter (South Urals, Russia). The studied forests also had different durations of time since the last ground fire—from 1 to 60 years. Two classifications of the diaspore dispersal modes were used. We found that community functional diversity and predominant dispersal modes changed significantly in response to technogenic pollution and, to a lesser extent, in response to ground fires. In polluted forests, the importance of species with a long diaspore dispersal distance—anemochores and zoochores—increased. This result suggests conducting a specific study of long-distance diaspore migration as a possibly underestimated factor of community formation under severe technogenic disturbances. The importance of zoochores in a broad sense, including species with diaspores dispersed by vertebrates and invertebrates, increased in post-fire succession. This result coincides with the known pattern of increasing abundance of zoochorous plants in regenerative successions in tropical forests. Therefore, the data on plant–animal interactions can possibly provide valuable information on succession mechanisms in taiga forests. Full article

The Euphorbiaceae is a large, diverse, and cosmopolitan family of monoecious or dioecious trees, shrubs, herbs, and lianas. Their name comes from Euphorbia, one of the largest genera in the Angiosperms, with close to 2000 species and a complex taxonomy. Many of their members have an economic interest in multiple applications, including pharmaceutical, nutritional, and others. The seeds of the Euphorbiaceae develop in schizocarps and have a diversity of shapes that have proven useful for species identification and classification. Nevertheless, analytical quantitative methods can be the subject of further development for the application of seed morphology in the taxonomy of this family. With this objective, measurements of size (area, perimeter, length, and width) and shape (circularity, aspect ratio, roundness, and solidity) in seed images of 230 species representative of the main taxonomic groups of Euphorbiaceae are presented, and curvature analysis is applied to 19 species. Seed images corresponding to many species of this family present a tetragonal pattern with a curvature peak in the apical pole and three in the basal pole. The results of the curvature analysis are discussed in relation to other morphological properties, revealing new aspects of seed morphology of taxonomic application. Full article

High-elevation and cold regions have harsh natural conditions with low temperatures and intense ultraviolet radiation, which impede plant growth and maintenance. Therefore, soil and water conservation vegetation restoration models are of great significance. In this study, a site condition analysis was performed based on three main limiting factors, including climatic and meteorological, soil, and topographic and geomorphological factors, providing a basis for vegetation restoration. The study area was divided into different site types. After investigating the situation of nurseries distributed in places such as Tibet, Qinghai, and Sichuan, trees, shrubs, and grasses with ecological characteristics similar to those of the local vegetation, including strong stress resistance, good soil and water conservation benefits, and well-established artificial cultivation practices, were selected as alternative vegetation for late-stage planting of indigenous tree species. Combining the results of site condition analysis and site type classification, the configuration of trees, shrubs, and grasses for different off-site condition types and the corresponding greening methods are discussed, providing a scientific reference for ecological restoration in high-elevation and low-temperature regions. Full article

Cemeteries are often overlooked in ecological studies, yet they represent unique urban microhabitats that contribute to the preservation of diverse plant species, including those adapted to various ecological niches. This study aimed to assess the species composition, ecological classifications, and abundance of vascular plants in the cemetery and surrounding areas to explore cemeteries’ role in conserving plant ecobiomorph diversity in arid climates. This study identified 79 plant species from 23 families within the cemetery compared with 31 species from 11 families in the surrounding area. The plant community in the cemetery was dominated by mesophytes, suggesting favorable and stable conditions for plant growth, while xerophytes were more common in the surrounding areas, indicating harsher, drier conditions. The diversity of plant life forms, including perennial herbs, shrubs, and trees, was significantly higher within the cemetery, indicating a more complex and resilient ecosystem. Our study demonstrates that cemeteries act as vital refuges for plant biodiversity. They offer significantly higher species diversity and more complex ecosystem structures compared with the surrounding areas. These findings emphasize the critical role cemeteries play in urban biodiversity conservation, particularly in increasingly arid environments. Full article

Rangelands represent about 25% of the Earth’s land surface but are under severe pressure. Rangeland degradation is a gradually increasing global environmental problem, resulting in temporary or permanent loss of ecosystem functions. Ecological rangeland studies aim to determine the productivity of rangelands as well as the severity of their degradation. Rigorous in situ assessments comprising visual identification of plant species are required as such assessments are perceived to be the most accurate way of monitoring rangeland degradation. However, in situ assessments are expensive and time-consuming exercises, especially when carried out over large areas. In situ assessments are also limited to areas that are accessible. This study aimed to evaluate the effectiveness of multispectral (MS) and hyperspectral (HS) remotely sensed, unmanned aerial vehicle (UAV)-based data and machine learning (random forest) methods to differentiate between 15 dominant Nama Karoo plant species to aid ecological impact surveys. The results showed that MS imagery is unsuitable, as classification accuracies were generally low (37.5%). In contrast, much higher classification accuracies (>70%) were achieved when the HS imagery was used. The narrow bands between 398 and 430 nanometres (nm) were found to be vital for discriminating between shrub and grass species. Using in situ Analytical Spectral Device (ASD) spectroscopic data, additional important wavebands between 350 and 400 nm were identified, which are not covered by either the MS or HS remotely sensed data. Using feature selection methods, 12 key wavelengths were identified for discriminating among the plant species with accuracies exceeding 90%. Reducing the dimensionality of the ASD data set to the 12 key bands increased classification accuracies from 84.8% (all bands) to 91.7% (12 bands). The methodology developed in this study can potentially be used to carry out UAV-based ecological assessments over large and inaccessible areas typical of Karoo rangelands. Full article

The composition and spatial distribution of tree species are pivotal for biodiversity conservation, ecosystem productivity, and carbon sequestration. However, the accurate classification of tree species in subtropical forests remains a formidable challenge due to their complex canopy structures and dense vegetation. This study addresses these challenges within the Jiangxi Lushan National Nature Reserve by leveraging high-resolution GF-2 remote sensing imagery and UAV multispectral images collected in 2018 and 2022. We extracted spectral, texture, vegetation indices, geometric, and topographic features to devise 12 classification schemes. Utilizing an object-oriented approach, we employed three machine learning algorithms—Random Forest (RF), k-Nearest Neighbor (KNN), and Classification and Regression Tree (CART)—to identify 12 forest types in these regions. Our findings indicate that all three algorithms were effective in identifying forest type in subtropical forests, and the optimal overall accuracy (OA) was more than 72%; RF outperformed KNN and CART; S12 based on feature selection was the optimal feature combination scheme; and the combination of RF and Scheme S12 (S12) yielded the highest classification accuracy, with OA and Kappa coefficients for 2018-RF-S12 of 90.33% and 0.82 and OA and Kappa coefficients for 2022-RF-S12 of 89.59% and 0.81. This study underscores the utility of combining multiple feature types and feature selection for enhanced forest type recognition, noting that topographic features significantly improved accuracy, whereas geometric features detracted from it. Altitude emerged as the most influential characteristic, alongside significant variables such as the Normalized Difference Greenness Index (NDVI) and the mean value of reflectance in the blue band of the GF-2 image (Mean_B). Species such as Masson pine, shrub, and moso bamboo were accurately classified, with the optimal F1-Scores surpassing 89.50%. Notably, a shift from single-species to mixed-species stands was observed over the study period, enhancing ecological diversity and stability. These results highlight the effectiveness of GF-2 imagery for refined, large-scale forest-type identification and dynamic diversity monitoring in complex subtropical forests. Full article

As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity of wildfires. Recognizing the growing need to monitor the impacts of these smaller, low-severity fires, we leveraged Sentinel-2 imagery to reveal important inter- and intra-annual variation in grasslands before and after fires. Specifically, we explored three methodological approaches: (1) the complete time series of the normalized burn ratio (NBR), (2) annual summary metrics (mean, fifth percentile, and amplitude of NBR), and (3) maps depicting spatial patterns in these annual NBR metrics before and after fire. We also used a classification of pre-fire vegetation to stratify these analyses by three dominant vegetation cover types (grasses, shrubs, and trees). We applied these methods to a unique study area in which three adjacent grasslands had diverging fire histories and showed how grassland recovery from a low-severity intentional burn and a high-severity wildfire differed both from each other and from a reference site with no recent fire. On the low-severity intentional burn site, our results showed that the annual NBR metrics recovered to pre-fire values within one year, and that regular intentional burning on the site was promoting greater annual growth of both grass and shrub species, even in the third growing season following a burn. In the case of the high-severity wildfire, our metrics indicated that this grassland had not returned to its pre-fire phenological signals in at least three years after the fire, indicating that it may be undergoing a longer recovery or an ecological shift. These proposed methods address a growing need to study the effects of small, intentional burns in low-biomass ecosystems such as grasslands, which are an essential part of mitigating wildfires. Full article

Natural regeneration is deemed essential for maintaining biodiversity and ecosystem stability. Previous studies, however, have primarily concentrated on regions exhibiting limited environmental and climatic variability, overlooking the classification of natural regeneration based on age and source. Research conducted at the mesoscale, characterized by increased environmental variability and the incorporation of neighborhood competition and understory-associated vegetation, enhances our comprehension of the multifaceted influences on natural regeneration. To comprehend this issue, this study implemented 60 plots, each measuring 20 m × 20 m, across five distinct areas of Chongqing, China. Twenty explanatory variables were chosen from five diverse categories: understory vegetation, neighborhood competition, stand structure, climatic factors, and environmental factors. And the naturally regenerated species were classified into seedlings and saplings, as well as endogenous and exogenous species, based on their age and origin. We examined the response of the different categories of natural regeneration to various factors and constructed a structural equation model (SEM) for significant factors to investigate their direct and indirect effects on natural regeneration. A total of 61 regenerated tree species belonging to 29 families and 42 genera were found in the study area, and the naturally regenerating species with high importance values were Quercus fabri, Robinia pseudoacacia, Alangium chinense, Cunninghamia lanceolata, and Ligustrum lucidum. It was found that neighborhood competition and understory-associated vegetation explained the largest proportion (more than 50%) of the variation in the different categories of natural regeneration, and forests with clumped distribution (W), a high mingling index (M) and strong competition (H) had a reduced natural regeneration capacity. Understory-associated herbs significantly reduced natural regeneration and the crowdedness index (C) significantly inhibited the understory-associated herbs, thus indirectly promoting natural regeneration. The shrub cover is significantly and positively correlated with the number of naturally regenerated plants and can be used as an indicator of a forest community’s regeneration potential. Understanding the differences in the importance of various factors at the mesoscale, as well as their direct and indirect impacts, can help us further comprehend the mechanisms of natural regeneration and provide a foundation for the sustainable development of forests. Full article

This methodological study describes the adaptation of a new method in digital wood anatomy, pixel-contrast densitometry, for angiosperm species. The new method was tested on eight species of shrubs and small trees in Southern Siberia, whose wood structure varies from ring-porous to diffuse-porous, with different spatial organizations of vessels. A two-step transformation of wood cross-section photographs by smoothing and Otsu’s classification algorithm was proposed to separate images into cell wall areas and empty spaces within (lumen) and between cells. Good synchronicity between measurements within the ring allowed us to create profiles of wood porosity (proportion of empty spaces) describing the growth ring structure and capturing inter-annual differences between rings. For longer-lived species, 14–32-year series from at least ten specimens were measured. Their analysis revealed that maximum (for all wood types), mean, and minimum porosity (for diffuse-porous wood) in the ring have common external signals, mostly independent of ring width, i.e., they can be used as ecological indicators. Further research directions include a comparison of this method with other approaches in densitometry, clarification of sample processing, and the extraction of ecologically meaningful data from wood structures. Full article