Search Results (1,936)

As an important coal-producing region in China, open-pit coal mining in Shaoyang, Hunan Province, has a significant impact on the ecological environment. This study focuses on the three major open-pit mining areas in the city, utilizing remote sensing data from 1998 to 2024. By calculating the normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC), and combining climate factors such as temperature and precipitation with Net Primary Productivity (NPP), this study analyzes the spatiotemporal evolution characteristics of vegetation cover and carbon sinks, and explores the impact of climate and environmental policies on vegetation recovery. The study employed trend analysis and autoregressive integrated moving average (ARIMA) model predictions, which showed that vegetation cover in the mining areas decreased overall from 1998 to 2011, gradually recovered after 2011, and reached a relatively high level by 2024. Changes in carbon sinks were consistent with the trends in vegetation cover. Spatially, the north mining area experienced the most severe vegetation degradation in the early stages, the middle area recovered earliest, and the south area had the fastest vegetation cover recovery rate. Climate factors had a certain influence on vegetation recovery, but precipitation, temperature, and FVC showed no significant correlation. The study indicates that vegetation recovery in mining areas is jointly influenced by mining intensity, climate conditions, and policy interventions, with geological environment management policies in Hunan mining areas playing a key role in promoting vegetation recovery. Full article

Analyzing the status of food production in Qinghai Province and exploring the nexus between its ecological conservation and food supply are of critical significance. This study systematically synthesizes the evolution of ecological protection policies in Qinghai Province from 2000 to 2020 and delineates the spatiotemporal evolutionary patterns of grain production in Qinghai Province and their underpinning driving factors. The key findings are as follows. (1) From 2000 to 2020, the corpus of policies governing ecological governance measures in Qinghai Province exhibited a sustained growth trend, with management-oriented policies predominating. (2) The primary grain and meat-producing regions in Qinghai Province are predominantly clustered in the northeastern part, displaying a gradual intensification of concentration. From 2000 to 2020, grain production showed an upward trajectory in the northern region and a downward trend in the southern region, whereas meat production exhibited an ascending trend in both the northern and western regions. (3) Agricultural production conditions represent the principal drivers of grain and meat production in Qinghai Province. Specifically, two driving factors—common cultivated area and total power of agricultural machinery—have exerted significant positive effects on grain and meat production across over 30 counties. Ecological protection conditions have manifested heterogeneous effects across different regions of Qinghai Province; the normalized difference vegetation index (NDVI) has exerted a negative influence on grain and meat production in the eastern region while exerting a positive influence in the western region. 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

The vegetation cover in the Red River Basin (RRB) has undergone considerable changes over the past 20 years. Identifying vegetation cover and its transboundary differences is crucial for assessing the ecological health of the region. This study utilized normalized difference vegetation index (NDVI) data (2000–2023) to analyze the spatiotemporal dynamics of fractional vegetation cover (FVC) and its transboundary differences within the RRB. The results revealed the following: (1) From 2000 to 2023, overall FVC in the basin increased, with a mean value of 0.64, indicating favorable vegetation conditions. (2) In terms of spatial distribution, the RRB in China (RRBC) generally exhibited higher FVC in the west and lower FVC in the east, whereas the RRB in Vietnam and Laos (RRBVL) exhibited higher FVC in the east and lower FVC in the west. Regarding spatiotemporal changes, in RRBC, the changes were primarily characterized by both non-significant improvement (56.01%) and extremely significant improvement (21.45%). Conversely, RRBVL exhibited both areas of extremely significant improvement (25.4%) and areas of extremely significant degradation (18%). (3) Anthropogenic activities exerted a stronger influence than precipitation on both spatiotemporal changes and transboundary differences in FVC. In conclusion, an overall increase in FVC is observed throughout the RRB, with notable transboundary variations. Full article

The Loess Plateau (LP), Earth’s largest loess deposit, has experienced significant vegetation recovery since 2000 despite water scarcity. Using 2001–2022 satellite-derived normalized difference vegetation index (NDVI) and solar-induced chlorophyll fluorescence (SIF) data, we analyze vegetation structural (greenness) and functional (photosynthesis) responses, addressing critical knowledge gaps in cover expansion—functional enhancement relationships during ecological restoration. Sustained warming and increased moisture have consistently enhanced both the NDVI and SIF across the LP, with water availability remaining the key limiting factor for vegetation structure and function. Notably, the relative trend of SIF (RT_SIF: 3.92% yr⁻¹) significantly exceeded that of the NDVI (RT_NDVI: 1.63% yr⁻¹), producing a mean divergence (ΔRT_SIF-NDVI) of 2.38% yr⁻¹ (p < 0.01) across the LP. This divergence indicates faster functional enhancement relative to structural expansion during vegetation recovery, with grasslands exhibiting the most pronounced difference in ΔRT_SIF-NDVI compared to forests and shrublands. Hydrothermal conditions regulated vegetation structural–functional divergence, with regions experiencing stronger water stress exhibiting significantly greater ΔRT_SIF-NDVI values. These findings demonstrate substantial hydrological constraint alleviation since 2001. Increased precipitation enhanced light use efficiency, accelerating photosynthetic function—especially in grasslands due to their rapid precipitation response. In contrast, forests maintained higher structure–function synchrony (lower values of ΔRT_SIF-NDVI) through conservative strategies. Our findings indicate that grasslands may evolve as carbon sink hotspots via photosynthetic overcompensation, whereas forests remain reliant on sustaining current vegetation and are constrained by deep soil water deficits. This contrast highlights the value of ΔRT_SIF-NDVI as a physiologically based indicator for quantifying restoration quality and predicting carbon sequestration potential across the LP. Full article

The exploitation of mineral resources often necessitates groundwater drainage, which may impact surrounding ecosystems, particularly vegetation. In this study, the effects of passive drainage in the Kornica-Popówka chalk mine in eastern Poland were analyzed using Sentinel-2 satellite images and the NDVI vegetation index. Groundwater monitoring wells were used to delineate the extent of the depression cone, representing areas of potentially altered hydrological conditions. NDVI values were analyzed across multiple time points between 2023 and 2024 to assess the condition of vegetation both inside and outside the depression cone. The results indicate no significant difference in NDVI values during the 2023–2024 study period for this specific chalk mine case between areas affected and unaffected by the depression cone, suggesting that vegetation in this region is not experiencing stress due to lowered groundwater levels. This outcome highlights the influence of other environmental factors, such as rainfall and land use, and suggests that the local geological structure allows plants to maintain sufficient access to water despite hydrological alterations. This study confirms the utility of integrating remote sensing with hydrogeological data in environmental monitoring and underlines the need for continued observation to assess long-term trends in vegetation response to mining-related groundwater changes. Full article

Drought is a complex and impactful natural hazard, with sometimes catastrophic impacts on small or subsistence agriculture and water security. In Pacific Island countries, there lacks an agreed approach for monitoring agricultural drought hazard with satellite-derived remote sensing data. This study addresses this gap through a framework for agricultural drought monitoring in the Pacific using freely available space-based observations. Applying World Meteorological Organization’s (WMO) recommendations and a set of objective selection criteria, three remotely sensed drought indicators were chosen and combined using fuzzy logic to form a composite drought hazard index: the Standardised Precipitation Index, Soil Water Index, and Normalised Difference Vegetation Index. Each indicator represents a subsequential flow-on effect of drought on agriculture. The index classes geographic areas as low, medium, high, or very high levels of drought hazard. To test the drought hazard index, two case studies for drought in the western Pacific, Papua New Guinea (PNG), and Vanuatu, are assessed for the 2015–2016 El Niño-related drought. Findings showed that at the height of the drought in October 2015, 58% of PNG and 72% of Vanuatu showed very high drought hazard, compared to 6% and 40%, respectively, at the beginning of the drought. The hazard levels calculated were consistent with conditions observed and events that were reported during the emergency drought period. Application of this framework to operational drought monitoring will promote adaptive capacity and improve resilience to future droughts for Pacific communities. Full article

Burrowing mammals function as ecosystem engineers by creating spatial heterogeneity in the soil structure and vegetation composition, thereby providing microhabitats for a wide range of organisms. These keystone species play a crucial role in maintaining local ecosystem functions and delivering ecosystem services. However, in Mongolia, where overgrazing has accelerated due to the expansion of a market-based economy, scientific knowledge remains limited regarding the impacts of human activities on such species. In this study, we focused on the Siberian marmot (Marmota sibirica), an ecosystem engineer inhabiting typical Mongolian steppe ecosystems. We assessed the relationship between the spatial distribution of marmot burrows and vegetation conditions both inside and outside Hustai National Park. Burrow locations were recorded in the field, and the Normalized Difference Vegetation Index (NDVI) was calculated, using Planet Lab, Dove-2 satellite imagery (3 m spatial resolution). Through a combination of remote sensing analyses and vegetation surveys, we examined how the presence or absence of anthropogenic disturbance (i.e., livestock grazing) affects the ecological functions of marmots. Our results showed that the distance between active burrows was significantly shorter inside the park (t = −2.68, p = 0.0087), indicating a higher population density. Furthermore, a statistical approach, using beta regression, revealed a significant interaction between the burrow type (active, non-active, off-colony area) and region (inside vs. outside the park) on the NDVI (e.g., outside × non-active: z = −5.229, p < 0.001). Notably, in areas with high grazing pressure outside the park, the variance in the NDVI varied significantly as a function of burrow presence or absence (e.g., July 2023, active vs. off-colony area: F = 133.46, p < 0.001). Combined with vegetation structure data from field surveys, our findings suggest that marmot burrowing activity may contribute to the enhancement of vegetation quality and spatial heterogeneity. These results indicate that the Siberian marmot remains an important component in supporting the diversity and stability of steppe ecosystems, even under intensive grazing pressure. The conservation of this species may thus provide a promising strategy for utilizing native ecosystem engineers in sustainable land-use management. Full article

Understanding vegetation dynamics and climate variability in the vicinity of Internally Displaced Person (IDP) camps is critical due to the high dependency of displaced populations on local natural resources. This study investigates vegetation cover changes and long-term climate variability around the Sortony IDP camp in Darfur, Sudan, using satellite and climate data spanning 1980 to 2024. High-resolution imagery from PlanetScope and Sentinel–2 Level 2A was used to assess vegetation cover changes from 2015 to 2024, while precipitation, temperature, and drought trends were analyzed over 44 years (1980–2024). Vegetation changes were quantified using the Normalized Difference Vegetation Index (NDVI), and drought conditions were assessed through the Standardized Precipitation Evapotranspiration Index (SPEI) at 6-, 9-, and 12-month timescales. Future precipitation predictions were modeled using the Autoregressive Integrated Moving Average (ARIMA) model. The results revealed a substantial increase in vegetative cover: the dense vegetation class increased by 3.50%, moderate vegetation by 17.33%, and low vegetation by 30.22%. In contrast, sparse and non-vegetated areas declined by 4.55% and 46.51%, respectively. The SPEI analysis indicated a marked reduction in drought frequency and severity after 2015, following a period of prolonged drought from 2000 to 2014. Forecasts suggest continued increases in rainfall through 2034, which may further support vegetation regrowth. These findings underscore the complex interplay between climatic factors and human activity in conflict-affected landscapes. The observed vegetation recovery highlights the region’s potential for ecological resilience, reinforcing the urgent need for sustainable land-use planning and climate-adaptive management strategies in humanitarian and post-conflict settings such as Darfur. Full article

Coastal wetlands in the Laurentian Great Lakes of North America are under increasing stress due to numerous threats. Restoration and management of the remaining wetlands are necessary to ensure that ecosystem functions, critical for fisheries, persist. This study used long-term monitoring datasets for one of the Laurentian Great Lakes, Lake Ontario, including 138 sampling events from 31 different wetlands, to examine the relationship between fish community health and select abiotic and vegetation habitat variables. Eight of 13 habitat variables were found to have significant relationships with fish community health, including total, submerged, and emergent vegetation; submerged aquatic vegetation IBI; water depth; turbidity; conductivity; and water-quality index. Ranges for each significant variable were summarized for each fish community health group to provide guidance when diagnosing impairment or setting restoration goals. An ordination of the fish and environmental data revealed high amounts of variation at sites with poor fish community health relative to excellent health, suggesting a multimetric approach provides valuable insight into community variability. The results from this study provide additional information and alternative methods for assessment of current conditions, target setting, and restoration success assessment for coastal wetland managers. Full article

Accurately assessing the spatiotemporal dynamics and influencing factors of ecosystem stability in arid desert regions (ADR) is crucial for ecological conservation and the achievement of high-quality regional development. However, existing assessment frameworks generally fail to adapt to the extremely fragile ecological conditions of ADR. Therefore, the Alxa Desert, a typical region, was selected as the research region, and an ecosystem stability assessment framework tailored to regional characteristics (perturbation–resilience–function) was constructed. Perturbation represents external pressure, resilience reflects the capacity for recovery and adaptation, and function serves as the supporting foundation. The three dimensions are dynamically coupled and jointly determine the stability status of the ecosystem in the Alxa Desert. Methodologically, this study innovatively introduces the Cloud Model–Analytic Hierarchy Process (CM-AHP) to calculate indicator weights, which more effectively addressed the widespread fuzziness and uncertainty inherent in ecosystem assessments compared to traditional methods. In addition, spatial autocorrelation methods was applied to reveal the spatial and temporal evolution characteristics of ecosystem stability from 2000 to 2020. Furthermore, the optimal parameters geographical detector model (OPGDM) was applied to analyze the effects of natural and human factors on the spatial differentiation of ecosystem stability in Alxa Desert. In addition, the Markov–FLUS model was employed to simulate the future trends of ecosystem stability over the next two decades. The results indicate that ecosystem stability in Alxa Desert from 2000 to 2020 was primarily characterized by vulnerable and moderate levels, with the area classified as extremely vulnerable decreasing significantly by 10% relative to its extent in 2000. Spatially, higher stability was observed in oasis regions and southeastern mountainous regions, while lower stability was concentrated in the desert hinterlands. Overall, ecosystem stability shifted from vulnerable toward moderate levels, reflecting a trend of gradual improvement. From 2000 to 2020, the Moran’s I varied between 0.78 and 0.81, showing strong spatial clustering. Surfce Soil moisture content (SSMC), Soil organic carbon (SOC), and enhanced vegetation index (EVI) were the primary factors influencing the spatial differentiation of ecosystem stability in Alxa Desert. The interaction between these factors further enhanced their explanatory power. Future forecasting results indicate that ecosystem stability will further improve by 2030 and 2040, particularly in the northern and southern areas of Alxa Left Banner and Alxa Right Banner. The findings can offer a theoretical foundation for future ecological conservation and environmental management in ADR. Full article

With the advancement of precision agriculture, Unmanned Aerial Vehicle (UAV)-based remote sensing has been increasingly employed for monitoring crop water and nutrient status due to its high flexibility, fine spatial resolution, and rapid data acquisition capabilities. This review systematically examines recent research progress and key technological pathways in UAV-based remote sensing for crop water and nutrient monitoring. It provides an in-depth analysis of UAV platforms, sensor configurations, and their suitability across diverse agricultural applications. The review also highlights critical data processing steps—including radiometric correction, image stitching, segmentation, and data fusion—and compares three major modeling approaches for parameter inversion: vegetation index-based, data-driven, and physically based methods. Representative application cases across various crops and spatiotemporal scales are summarized. Furthermore, the review explores factors affecting monitoring performance, such as crop growth stages, spatial resolution, illumination and meteorological conditions, and model generalization. Despite significant advancements, current limitations include insufficient sensor versatility, labor-intensive data processing chains, and limited model scalability. Finally, the review outlines future directions, including the integration of edge intelligence, hybrid physical–data modeling, and multi-source, three-dimensional collaborative sensing. This work aims to provide theoretical insights and technical support for advancing UAV-based remote sensing in precision agriculture. Full article

Net ecosystem productivity (NEP) serves as a key indicator for assessing regional carbon sink potential, with its dynamics regulated by nonlinear interactions among multiple factors. However, its driving factors and their coupling processes remain insufficiently characterized. This study investigated terrestrial ecosystems in Yunnan Province, China, to elucidate the drivers of NEP using 14 environmental factors (including topography, meteorology, soil texture, and human activities) and 21 remote sensing features. We developed a research framework based on “Feature Selection–Machine Learning–Mechanism Interpretation.” The results demonstrated that the Variable Selection Using Random Forests (VSURF) feature selection method effectively reduced model complexity. The selected features achieved high estimation accuracy across three machine learning models, with the eXtreme Gradient Boosting Regression (XGBR) model performing optimally (R² = 0.94, RMSE = 76.82 gC/(m²·a), MAE = 55.11 gC/(m²·a)). Interpretation analysis using the SHAP (SHapley Additive exPlanations) method revealed the following: (1) The Enhanced Vegetation Index (EVI), soil pH, solar radiation, air temperature, clay content, precipitation, sand content, and vegetation type were the primary drivers of NEP in Yunnan. Notably, EVI’s importance exceeded that of other factors by approximately 3 to 10 times. (2) Significant interactions existed between soil texture and temperature: Under low-temperature conditions (−5 °C to 12.15 °C), moderate clay content (13–25%) combined with high sand content (40–55%) suppressed NEP. Conversely, within the medium to high temperature range (5 °C to 23.79 °C), high clay content (25–40%) coupled with low sand content (25–43%) enhanced NEP. These findings elucidate the complex driving mechanisms of NEP in subtropical ecosystems, confirming the dominant role of EVI in carbon sequestration and revealing nonlinear regulatory patterns in soil–temperature interactions. This study provides not only a robust “Feature Selection–Machine Learning–Mechanism Interpretation” modeling framework for assessing carbon budgets in mountainous regions but also a scientific basis for formulating regional carbon management policies. Full article

With the purpose of understanding the trophic relationships between three herbivores that use humid high-mountain grassland and evaluating a possible interspecific competition between them and depending on the importance of the hydromorphic vegetation formations of high-mountain areas, relations were established between the attributes of these grasslands and the botanical composition of the diet of grey-breasted seedsnipe (Thinocorus orbignyianus), brown hares (Lepus europaeus), and horses (Equus ferus caballus). For two summer seasons, the botanical composition of the grassland and dry matter availability were assessed. In parallel, the botanical composition of the diets of the three herbivores was estimated through fecal microhistology. Based on the botanical composition data for both the grasslands and herbivores’ diets, their relative diversity was estimated. The Pianka index was established among the three herbivores. Hares showed greater dietary diversity (J) than horses and grey-breasted seedsnipes, factors that were negatively correlated in all three cases with the vegetation diversity patch. The same response amplitude was found when analyzing the food web. The dietary diversity for all species showed no relation to the dry matter productivity of the vegetable patches. Through analyzing the correlation of the abundance of two species of Cyperaceae in the grassland with the presence of the same in the diet of herbivores, we found a negative relationship between the abundance of Carex sp. and grey-breasted seedsnipe diet, and a positive relationship between the Eleocharis pseudoalbibracteata species abundance and frequency in the diet of hares and horses. About the group of species content of graminoids in the diet, a dietary overlap of 30% was determined in the animal species assessed; depending on that, it could identify the existence of interspecific competition between herbivores, which would be conditioned by the response of individuals to the environment. However, and according to the magnitude of the dietary overlap, a low probability of interspecific trophic competition among the studied herbivore species can be expected, which enables the use of the highland wet grassland habitat in sympatry. Full article

Excessive or improper fertilization not only salinizes soil but also reduces crop yield and quality. The objective of this study was to determine the optimum N, P, and K levels capable of improving tomato fruit quality and reducing environmental pollution for tomato plants under brackish water irrigation conditions. The ‘Jingcai 8’ tomato was used as the research object, and an orthogonal experimental design was used to set up three nutritional factors of N, P, and K. Each factor was set at three levels: N (mmol·L⁻¹): 2.00 (N1), 4.00 (N2), and 8.00 (N3); P (mmol·L⁻¹): 0.67 (P1), 1.33 (P2), and 2.00 (P3); K (mmol·L⁻¹): 8.00 (K1), 12.00 (K2), and 16.00 (K3). The effects of different levels of N, P, and K on plant growth indexes, root vigor and antistress enzymes, biomass and nutrients of plants and fruits, yield, quality, soil nutrients, and soil enzymes were investigated, and metabolomic measurements were performed on treatments ranked first (N:P:K ratio was 2:1.33:12) and ninth (N:P:K ratio was 8:1.33:8) for overall quality. In general, a N concentration of 8 mmol·L⁻¹ promoted plant vegetative growth and plant biomass accumulation by promoting the accumulation of aboveground nitrogen content, but it reduced the weight of single fruit and tomato quality due to an increase in soil EC and pH. In contrast, 0.67 mmol·L⁻¹ of P and 12 mmol·L⁻¹ of K were able to promote both plant vegetative growth and tomato quality formation. In addition, 0.67 mmol·L⁻¹ of P enhanced soil nutrient availability and enzyme activity, while 16 mmol·L⁻¹ of K reduced nutrient availability and enzyme activity and increased soil EC. The concentrations of ferulic acid, cinnamic acid, caffeic acid, coumarin, and (-)-epigallocatechin were generally higher in tomatoes from the T2 treatment (N:P:K ratio was 2:1.33:12) than in those from other treatments. Together, the optimum N:P:K ratio (2:1.33:12) of fertilization enhances tomato yield and quality under brackish water irrigation. Full article