Search Results (1,440)

The assessment of microplastics (MPs) in terrestrial ecosystems has garnered increasing global attention due to their accumulation and migration in soils, which may have potential impacts on soil health, biodiversity, and agricultural productivity. However, research on their distribution and interactions in soil remains limited, especially in tropical regions. This study aimed to characterize MPs extracted from tropical soil samples and relate their abundance to soil and terrain attributes under different land uses (forest, grassland, and agriculture). Soil samples were collected from an experimental farm in Lavras, Minas Gerais, Southeastern Brazil, to determine soil physical and chemical attributes and MP abundance in a micro-watershed. These locations were also used to obtain terrain attributes from a digital elevation model and the normalized difference vegetation index (NDVI). The majority of microplastics found in all samples were identified as polypropylene (PP), polyethylene (PE), polyethylene terephthalate (PET), and vinyl polychloride (PVC). The spatial distribution of MP was rather heterogeneous, with average abundances of 3826, 2553, and 3406 pieces kg⁻¹ under forest, grassland, and agriculture, respectively. MP abundance was positively related to macroporosity and sand content and negatively related to clay content and most chemical attributes. Regarding terrain attributes, MP abundance was negatively correlated with plan curvature, convergence index, and vertical distance to channel network, and positively related to topographic wetness index. These findings indicate that continuous water fluxes at both the landscape and soil surface scales play a key role, suggesting a tendency for higher MP accumulation in lower-lying areas and soils with greater porosity. These conditions promote MP transport and accumulation through surface runoff and facilitate their entry into the soil. Full article

Linpan ecosystems, distinct to western Sichuan, China, are integral to regional biodiversity and carbon cycling. However, comprehensive biomass estimation for these systems has not been thoroughly investigated. This study seeks to fill this gap by enhancing the accuracy and precision of biomass estimation in these ecologically vital landscapes through the application of multi-source remote sensing techniques, specifically by integrating the strengths of optical and radar remote sensing data. The focus of this research is on the forest biomass of Linpan, encompassing the tree layer, which includes the trunk, branches, leaves, and underground roots. Specifically, the research focused on the Linpan ecosystems in the Wenjiang District of western Sichuan, utilizing an integration of Sentinel-1 SAR, Sentinel-2 multispectral, and GF-2 high-resolution data for multi-source remote sensing-based biomass estimation. Through the preprocessing of these data, Pearson correlation analysis was conducted to identify variables significantly correlated with the forest biomass as determined by field surveys. Ultimately, 19 key modeling factors were selected, including band information, vegetation indices, texture features, and phenological characteristics. Subsequently, three algorithms—multiple stepwise regression (MSR), support vector machine (SVM), and random forest (RF)—were employed to model biomass across mixed-type, deciduous broadleaved, evergreen broadleaved, and bamboo Linpan. The key findings include the following: (1) Sentinel-2 spectral data and Sentinel-1 VH backscatter coefficients during the summer, combined with vegetation indices and texture features, were critical predictors, while phenological indices exhibited unique correlations with biomass. (2) Biomass displayed a marked north–south gradient, characterized by higher values in the south and lower values in the north, with a mean value of 161.97 t ha⁻¹, driven by dominant tree species distribution and management intensity. (3) The RF model demonstrated optimal performance in mixed-type Linpan (R² = 0.768), whereas the SVM was more suitable for bamboo Linpan (R² = 0.892). The research suggests that integrating multi-source remote sensing data significantly enhances Linpan biomass estimation accuracy, offering a robust framework to improve estimation precision. Full article

The betel palm is a critical economic crop on Hainan Island. Accurate and timely maps of betel palms are fundamental for the industry’s management and ecological environment evaluation. To date, mapping the spatial distribution of betel palms across a large regional scale remains a significant challenge. In this study, we propose an integrated framework that combines knowledge-based and machine learning approaches to produce a map of betel palms at 10 m spatial resolution based on Sentinel-1/2 data and Google Earth Engine (GEE) for 2023 on Hainan Island, which accounts for 95% of betel nut acreage in China. The forest map was initially delineated based on signature information and the Green Normalized Difference Vegetation Index (GNDVI) acquired from Sentinel-1 and Sentinel-2 data, respectively. Subsequently, patches of betel palms were extracted from the forest map using a random forest classifier and feature selection method via logistic regression (LR). The resultant 10 m betel palm map achieved user’s, producer’s, and overall accuracy of 86.89%, 88.81%, and 97.51%, respectively. According to the betel palm map in 2023, the total planted area was 189,805 hectares (ha), exhibiting high consistency with statistical data (R² = 0.74). The spatial distribution was primarily concentrated in eastern Hainan, reflecting favorable climatic and topographic conditions. The results demonstrate the significant potential of Sentinel-1/2 data for identifying betel palms in complex tropical regions characterized by diverse land cover types, fragmented cultivated land, and frequent cloud and rain interference. This study provides a reference framework for mapping tropical crops, and the findings are crucial for tropical agricultural management and optimization. Full article

Aboveground biomass (AGB) serves as a crucial indicator for assessing vegetation carbon sequestration capacity. While AGB levels vary significantly across different vegetation types and regions, the spatial distribution of AGB for specific wetland communities remains poorly characterized. To address this, we integrated field-collected data with Sentinel-2 spectral bands and remote sensing indices, employing random forest (RF) regression and Backpropagation Neural Network (BPNN) for AGB modeling. Through comparative evaluation of their inversion performance, the optimal model was selected to estimate vegetation AGB in the Nanji Wetland. By incorporating wetland classification data, we further generated spatial distribution maps of AGB for four dominant vegetation types during the dry season. The main findings are as follows. Important variables for the RF model included spectral bands B12, B11, B3, B2, B9, B1, B8, B6, and B4 and the Modified Normalized Difference Water Index (MNDWI), Normalized Difference Water Index (NDWI), Kernel Normalized Difference Vegetation Index (KNDVI), and Simple Ratio Index (SR). RF demonstrated significantly higher predictive accuracy (R² = 0.945, RMSE = 109.205 g·m⁻²) compared to the BPNN (R² = 0.821, RMSE = 176.025 g·m⁻²). The total estimated AGB reached 4.03 × 10⁹ g; Carex spp. dominated AGB accumulation (1.49 × 10⁹ g), followed by P. australis spp. (6.69 × 10⁸ g), M. lutarioriparius spp. (4.60 × 10⁸ g), and Polygonum spp. (3.61 × 10⁸ g). The AGB exhibited a clear spatial gradient, decreasing from higher-elevation lakeshore areas towards the central lake. The results provide detailed spatial quantification of AGB stocks across dominant vegetation types, revealing distinct spatial characteristics and interspecies variations in AGB. This study offers a valuable baseline and methodological framework for monitoring wetland carbon dynamics. Full article

Diptera belonging to the families Calliphoridae and Sarcophagidae are known for their diversified trophic preferences and for their forensic and medical-veterinary relevance. The ecological preferences (distribution, abundance and habitat) of these two families were investigated along four years in the Region Calabria (Southern Italy) in 17 sampling sites located in four areas: the Aspromonte National Park, the Sila National Park, the Natural Regional Park of Serre, and a suburban area at the University of Calabria (Rende, Cosenza, Italy). A total of 39,537 individuals were collected, with 36,253 belonging to 14 species of Calliphoridae and 3284 belonging to 35 species of Sarcophagidae. The most abundant species among Calliphoridae was Calliphora vomitoria (Linnaeus, 1758); among Sarcophagidae, it was Sarcophaga (Sarcophaga) croatica Baranov, 1941. The highest species richness and abundance of Calliphoridae were observed in forest areas and those of Sarcophagidae in open and humid environments. The results also show a close association between the distribution of both families and environmental factors such as altitude, vegetation type, season, and temperature. Full article

Timely and accurate winter crop distribution maps are crucial for agricultural monitoring, food security, and sustainable land use planning. However, conventional methods relying on field surveys are labor-intensive, costly, and difficult to scale across large regions. To address these limitations, this study presents an automated winter crop mapping framework that integrates phenology-based sample generation and machine learning classification using time-series Sentinel-2 imagery. The Winter Crop Index (WCI) is developed to capture seasonal vegetation dynamics, and the Otsu algorithm is employed to automatically extract reliable training samples. These samples are then used to train three widely used machine learning classifiers—Random Forest (RF), a Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)—with hyperparameters optimized via Bayesian optimization. The framework was validated in three diverse agricultural regions in China: the Erhai Basin in Yunnan Province, Shenzhou City in Hebei Province, and Jiangling County in Hunan Province. The experimental results demonstrate that the combination of the WCI and Otsu enables a reliable initial classification, facilitating the generation of high-quality training samples. XGBoost achieved the best performance in the Erhai Basin and Shenzhou City, with overall accuracies of 0.9238 and 0.9825 and F1-scores of 0.9233 and 0.9823, respectively. In contrast, the SVM performed best in Jiangling County, yielding an overall accuracy of 0.9574 and an F1-score of 0.9525. The proposed approach enables high-precision winter crop mapping without reliance on manually collected samples, demonstrating strong generalizability and providing a promising solution for large-scale, automated agricultural monitoring. Full article

Aboveground biomass (AGB) in grasslands is a vital metric for assessing ecosystem functioning and health. Accurate and efficient AGB estimation is essential for the scientific management and sustainable use of grassland resources. However, achieving low-cost, high-efficiency AGB estimation via remote sensing remains a key challenge. This study integrates Sentinel-1 and Sentinel-2 imagery to derive 38 multi-source feature variables, including backscatter coefficients, texture, spectral reflectance, vegetation indices, and topographic factors. These features are combined with AGB data from 112 field plots in the Three Parallel Rivers area. Feature selection was performed using Pearson correlation, Random Forest (RF), and SHAP values to identify optimal variable sets. Genetic Programming (GP) was then applied for nonlinear optimization of the selected features. Three machine learning models—RF, GBRT, and KNN—were used to estimate AGB and generate spatial distribution maps. The results revealed notable differences in model accuracy, with RF performing best overall, outperforming GBRT and KNN. After GP optimization, all models showed improved performance, with the RF model based on RF-selected features achieving the highest accuracy (R² = 0.90, RMSE = 0.31 t/ha, MAE = 0.23 t/ha), improving R² by 0.03 and reducing RMSE and MAE by 0.05 and 0.03 t/ha, respectively. Spatial mapping showed the AGB ranged from 0.41 to 3.59 t/ha, with a mean of 1.39 t/ha, closely aligned with the actual distribution characteristics. This study demonstrates that the RF model, combined with multi-source features and GP optimization, provides an effective approach to grassland AGB estimation and supports ecological monitoring in complex areas. Full article

With increasing energy demand and advancing carbon neutrality goals, arid regions—key areas for centralized photovoltaic (PV) station development in China—urgently require efficient and accurate remote sensing techniques to support spatial distribution monitoring and ecological impact assessment. Although numerous studies have focused on PV station extraction, challenges remain in arid regions with complex surface features to develop extraction frameworks that balance efficiency and accuracy at a regional scale. This study focuses on the Inner Mongolia Yellow River Basin and develops a PV extraction framework on the Google Earth Engine platform by integrating spectral bands, spectral indices, and topographic features, systematically comparing the classification performance of support vector machine, classification and regression tree, and random forest (RF) classifiers. The results show that the RF classifier achieved a high Kappa coefficient (0.94) and F1 score (0.96 for PV areas) in PV extraction. Feature importance analysis revealed that the Normalized Difference Tillage Index, near-infrared band, and Land Surface Water Index made significant contributions to PV classification, accounting for 10.517%, 6.816%, and 6.625%, respectively. PV stations are mainly concentrated in the northern and southwestern parts of the study area, characterized by flat terrain and low vegetation cover, exhibiting a spatial pattern of “overall dispersion with local clustering”. Landscape pattern indices further reveal significant differences in patch size, patch density, and aggregation level of PV stations across different regions. This study employs Sentinel-2 imagery for regional-scale PV station extraction, providing scientific support for energy planning, land use optimization, and ecological management in the study area, with potential for application in other global arid regions. Full article

Urban greening distribution critically impacts residents’ quality of life and environmental sustainability. While the Green View Index (GVI), derived from street view imagery, is widely adopted for urban green space assessment, its limitation lies in the inability to capture non-street-area vegetation. Remote sensing imagery, conversely, provides full-coverage urban vegetation data. This study focuses on Beijing’s Third Ring Road area, employing DeepLabv3+ to calculate a street-view-based GVI as a predictor. Correlations between the GVI and Sentinel-2 spectral bands, along with two vegetation indices, such as the Normalized Difference Vegetation Index (NDVI) and Fractional Vegetation Cover (FVC), were analyzed under varying buffer radius. Regression and classification models were subsequently developed for GVI prediction. The optimal classifier was then applied to estimate green perception levels in non-street zones. The results demonstrated that (1) at a 25 m buffer radius, the near-infrared band, NDVI, and FVC exhibited the highest correlations with the GVI, reaching 0.553, 0.75, and 0.752, respectively. (2) Among the five machine learning regression models evaluated, the random forest algorithm demonstrated superior performance in GVI estimation, achieving a coefficient of determination (R²) of 0.787, with a root mean square error (RMSE) of 0.063 and a mean absolute error (MAE) of 0.045. (3) When evaluating categorical perception levels of urban greenery, the Extremely Randomized Trees classifier (Extra Trees) demonstrated superior performance in green vision perception level estimation, achieving an accuracy (ACC) score of 0.652. (4) The green perception level in non-road areas within Beijing’s Third Ring Road is 56.8%, which is considered relatively poor. Moreover, the green perception level within the Second Ring Road is even lower than that in the area between the Second and Third Ring roads. This study is expected to provide valuable insights and references for the adjustment and optimization of green perception distribution in Beijing, thereby supporting more informed urban planning and the development of sustainable, human-centered green spaces across the city. Full article

Monitoring salt marsh vegetation in the Yellow River Delta (YRD) wetland is the basis of wetland research, which is of great significance for the further protection and restoration of wetland ecological functions. In the existing remote sensing technologies for wetland salt marsh vegetation classification, the object-oriented classification method effectively produces landscape patches similar to wetland vegetation and improves the spatial consistency and accuracy of the classification. However, the vegetation classes of the YRD are mixed with uneven distribution, irregular texture, and significant color variation. In order to solve the problem, this study proposes a fine-scale classification of dominant vegetation communities using color-enhanced aerial images. The color information is used to extract the color features of the image. Various features including spectral features, texture features and vegetation features are extracted from the image objects and used as inputs for four machine learning classifiers: random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN) and maximum likelihood (MLC). The results showed that the accuracy of the four classifiers in classifying vegetation communities was significantly improved by adding color features. RF had the highest OA and Kappa coefficients of 96.69% and 0.9603. This shows that the classification method based on color enhancement can effectively distinguish between vegetation and non-vegetation and extract each vegetation type, which provides an effective technical route for wetland vegetation classification in aerial imagery. Full article

Climate change poses unprecedented challenges to forest ecosystems. Betula alnoides, a tree species with significant ecological and economic value in southern China, has been the subject of studies on its distribution pattern and response to climate change. However, research on the distribution pattern of B. alnoides and its response to climate change remains relatively limited. In this study, we developed a MaxEnt model incorporating multiple environmental variables, including climate, topography, soil, vegetation, and human activities, to evaluate model performance, identify key factors influencing the distribution of B. alnoides, and project its potential distribution under various future climate scenarios. Species occurrence data and environmental layers were compiled for China, and model parameters were optimized using the ENMeval package. The results showed that the optimized model achieved an AUC value of 0.956, indicating extremely high predictive accuracy. The four key factors affecting the distribution of B. alnoides were standard deviation of temperature seasonality (Bio4), normalized difference vegetation index (NDVI), mean temperature of driest quarter (Bio9), and annual precipitation (Bio12). Among them, the cumulative contribution rate of climatic factors reached 68.9%, but the influence of NDVI was significantly higher than that of precipitation factors. The current suitable habitat of B. alnoides is mainly concentrated in the southwestern region, covering an area of 179.32 × 10⁴ km², which accounts for 18.68% of China’s land area. Under the SSP126 scenario, the suitable habitat area first decreases and then increases in the future, while under the SSP370 and SSP585 scenarios, the suitable habitat area continues to shrink, with significant losses in high-suitability areas. In addition, the centroid of the suitable habitat of B. alnoides shows an overall trend of shifting northwestward. This indicates that B. alnoides is highly sensitive to climate change and its distribution pattern will undergo significant changes in the future. In conclusion, the distribution pattern of B. alnoides shows a significant response to climate change, with particularly prominent losses in high-suitability areas in the future. Therefore, it is recommended to strengthen the protection of high-suitability areas in the southwestern region and consider B. alnoides as an alternative tree species for regions facing warming and drying trends to enhance its climate adaptability. Full article

We quantified monthly changes in land surface temperature (LST) over the Sierra Madre Occidental (SMO) in Mexico from 2000 to 2024 using MODIS satellite imagery (MOD11B3). The SMO is the longest continuous mountain complex in Mexico, covering an area of 251,648 km². It is an area of great importance for biodiversity conservation, as it is home to numerous endemic flora and fauna species. The Intergovernmental Panel on Climate Change (IPCC) has stated that high mountain areas are among the regions most affected by climate change and are a key element of the water cycle. We calculated an anomaly index by vegetation type in the SMO and applied change detection to spatially identify where changes in LST had taken place. The lowest LST values were in December and January (20 to 25 °C), and the highest LST values occurred in April, May, and June (>40 °C). Change detection applied to the time series showed that the months with the highest positive LST changes were May to July, and that November was notable for increases of up to 5.86 °C. The time series that showed the greatest changes compared to 2000 was the series for 2024, where LST increases were found in all months of the year. The maximun average increase was 6.98 °C from 2000 to June 2005. In general, LST anomalies show a pattern of occurrence in the months of March through July for the three vegetation types distributed in the Sierra Madre Occidental. In the case of the pine forest, which is distributed at 2000 m above sea level, and higher, it was expected that there would be no LST anomalies; however, anomalies were present in all time series for the spring and early summer months. The LST values were validated with in situ data from weather stations using linear regression models. It was found that almost all the values were related, with R² > 0.60 (p < 0.001). In conclusion, the constant increases in LST throughout the SMO are probably related to the loss of 34% of forest cover due to forest fires, logging, land use changes, and increased forest plantations. Full article

Crop classification plays a vital role in acquiring the spatial distribution of agricultural crops, enhancing agricultural management efficiency, and ensuring food security. With the continuous advancement of remote sensing technologies, achieving efficient and accurate crop classification using remote sensing imagery has become a prominent research focus. Conventional approaches largely rely on empirical rules or single-feature selection (e.g., NDVI or VV) for temporal feature extraction, lacking systematic optimization of multimodal feature combinations from optical and radar data. To address this limitation, this study proposes a crop classification method based on feature-level fusion of multimodal remote sensing data, integrating the complementary advantages of optical and SAR imagery to overcome the temporal and spatial representation constraints of single-sensor observations. The study was conducted in Story County, Iowa, USA, focusing on the growth cycles of corn and soybean. Eight vegetation indices (including NDVI and NDRE) and five polarimetric features (VV and VH) were constructed and analyzed. Using a random forest algorithm to assess feature importance, NDVI+NDRE and VV+VH were identified as the optimal feature combinations. Subsequently, 16 scenes of optical imagery (Sentinel-2) and 30 scenes of radar imagery (Sentinel-1) were fused at the feature level to generate a multimodal temporal feature image with 46 channels. Using Cropland Data Layer (CDL) samples as reference data, a U-Net deep neural network was employed for refined crop classification and compared with single-modal results. Experimental results demonstrated that the fusion model outperforms single-modal approaches in classification accuracy, boundary delineation, and consistency, achieving training, validation, and test accuracies of 95.83%, 91.99%, and 90.81% respectively. Furthermore, consistent improvements were observed across evaluation metrics, including F1-score, precision, and recall. Full article

Structural traits of vegetation, derived from the three-dimensional distribution of plant elements, are closely linked to ecosystem functions such as productivity and habitat provision. While extensively studied in forest ecosystems, these traits remain understudied in low-stature systems such as Mediterranean-type shrublands. In this study we explore the use of structural metrics derived from small unmanned aerial system (UAS)-based 3D point clouds, generated using the structure-from-motion (SfM) photogrammetry technique, to assess post-fire vegetation structure and biodiversity in the fynbos biome of the Cape Floristic Region (CFR), South Africa. Fynbos is a fire-adapted shrubland that represents nearly 80% of plant species in the CFR, making post-disturbance monitoring critical for conservation. We extracted three structural metrics—canopy height, top rugosity, and surface gap ratio—and achieved ~85% accuracy in classifying 5 × 5 m subplots by burn year using a Multi-Layer Perceptron (MLP), with canopy height as the strongest predictor. Additionally, top rugosity and gap ratio significantly contributed to modeling percentage cover-based species diversity. Our findings demonstrate that UAS-derived structural metrics provide valuable information for characterizing vegetation recovery and biodiversity patterns in low-stature, fire-prone ecosystems. This approach can support ecological monitoring and inform conservation strategies in Mediterranean-type shrublands. Full article

Under the backdrop of global climate warming, both forest vegetation greening and resilience decline coexist, and the consistency of these trends at the regional scale remains controversial. This study uses the kNDVI (Kernel Normalized Difference Vegetation Index) and TAC (Temporal Autocorrelation) index framework, combined with BEAST and Random Forest methods, to quantify and analyze the spatiotemporal evolution of forest resilience and its driving factors in Southwest China from 2000 to 2022. The results show the following: (1) Forest resilience exhibits a “high in the northwest and low in the southeast” spatial distribution, with a temporal pattern of “increase-decrease-increase.” The years 2010 and 2015 are key turning points. Trend shift analysis divides resilience into six types. (2) Although forest vegetation shows a clear greening trend, resilience does not necessarily increase with greening, and in some areas, an “increase in greening—decline in resilience” asynchronous pattern appears. (3) The annual average temperature, precipitation, and solar radiation are the main climate factors and their influence on resilience follows a nonlinear relationship. Higher temperatures and increased radiation may suppress resilience, while increased precipitation can enhance it. This study suggests incorporating the TAC indicator into ecological monitoring and early warning systems, along with applying trend classification results for region-specific management to improve the scientific basis and adaptability of forest governance under climate change. Full article