Search Results (406)

As a fundamental metric for assessing carbon sequestration, Net Primary Productivity (NPP) and the mechanisms driving its spatiotemporal dynamics constitute a critical research domain within global change science. This research centered on the Huang–Huai–Hai Plain (HHHP), combining 2001–2023 MODIS-NPP data with natural (landform, temperature, precipitation, soil) and socio-economic (population density, GDP density, land use) drivers. Trend analysis, coefficient of variation, and Hurst index were applied to clarify the spatiotemporal evolution of NPP and its future trends, while geographic detectors and structural equation models were used to quantify the contribution of drivers. Key findings: (1) Across the HHHP, the multi-year average NPP ranged between 30.05 and 1019.76 gC·m⁻²·a⁻¹, with higher values found in Shandong and Henan provinces, and lower values concentrated in the northwestern dam-top plateau and central plain regions; 44.11% of the entire region showed a statistically highly significant increasing trend. (2) The overall fluctuation of NPP was low-amplitude, with a stable center of gravity and the standard deviation ellipse retaining a southwest-to-northeast direction. (3) Future changes in NPP exhibited persistence and anti-persistence, with 44.98% of the region being confronted with vegetation degradation risk. (4) NPP variations originated from the synergistic impacts of multiple elements: among individual elements, precipitation, soil type, and elevation had the highest explanatory capacity, while synergistic interactions between two elements notably enhanced the explanatory capacity. (5) Climate variation exerted the strongest influence on NPP (direct coefficient of 0.743), followed by the basic natural environment (0.734), whereas human-related activities had the weakest direct impact (−0.098). This research offers scientific backing for regional carbon sink evaluation, ecological security early warning, and sustainable development policies. Full article

Net primary productivity (NPP) is a vital indicator of carbon sequestration and ecosystem resilience. However, the dynamics of NPP across different land use types and especially the interactive function of natural drivers remain insufficiently quantified in regions with significant land use change. Therefore, this study selected Heilongjiang Province in China as the research area. Utilizing multi-source data from 2001 to 2022, it identified the primary land use types, analyzed the mean values and trends of vegetation NPP for each type, and quantified the driving effects of natural factors on NPP across these land types. Results show that forests had the highest mean NPP (514.01 gC m⁻²·a⁻¹) and shrub–grass–wetland composites the lowest (269.2 gC m⁻²·a⁻¹); cropland-to-forest transitions boosted NPP most notably. Critically, precipitation–temperature interactions dominated NPP variation, while elevation acted mainly through modulating other factors. This study offers a strategic framework for spatial planning and ecosystem management, supporting climate mitigation and carbon sequestration policies. Full article

Extreme drought events may become more frequent with climate change. Understanding the impact of extreme drought on grassland ecosystems is therefore crucial for the long-term sustainability of ecosystems. Here, we identified extreme drought events in the Inner Mongolia grasslands of China using long-term standardized precipitation evapotranspiration index (SPEI) data and evaluated drought resistance of the vegetation under extreme drought based on net primary production (NPP). The impact of consecutive extreme drought events and multiple discontinuous one-year extreme drought events on grasslands were further analyzed to investigate the response strategies of different grassland types to different drought conditions. We found that the frequency and area of extreme drought in 2000–2011 were significantly higher than those in 2012–2020, and the Xilingol League region showed the highest frequency of extreme drought events. Under extreme drought, vegetation resistance was positively correlated, where annual precipitation > 300 mm. The mean resistance of different grassland types followed the order: upland meadow (UM) > lowland meadow (LM) > temperate meadow steppe (TMS) > temperate desert (TD) > temperate steppe (TS) > temperate steppe desert (TSD) > temperate desert steppe (TDS). In the analysis of two cases of consecutive two-year extreme drought, all grassland types except TSD and TD showed obvious decreased resistance in the final drought year, with the highest reduction (0.16) in LM during 2010–2011, implying the widespread and significant inhibition of grassland growth by continuous drought. However, under the multiple discontinuous extreme drought events, the resistance of all grassland types showed a fluctuating but an overall increasing trend, suggesting the adaptability of grassland to drought. The results emphasize that management departments should pay more attention to regions with low resistance and enhance the stability of grassland production by increasing the proportion of drought-resistant plants in reaction to future extreme drought scenarios. Full article

Morocco, located in an arid region and increasingly affected by climate change, faces chronic water stress. This structural vulnerability places mounting pressure on the country’s water resources. International trade contributes significantly to this pressure, particularly through the export of water-intensive agricultural products. This study investigates the virtual water trade flows of the 32 most-traded agricultural products between Morocco and its primary trading partner, the European Union, over the period of 2000–2020. This study adopts a bottom-up approach, employing the FAO’s CROPWAT 8.0 software based on the Penman–Monteith climatic model to estimate crop water requirements. The results indicate that Morocco was a net importer of virtual water in its agricultural trade with EU countries, with a cumulative net virtual water of 51,839.171 million cubic meters (Mm³). During the study period, Morocco exported a total of 3393.791 Mm³ of virtual water to the EU, primarily through fruits (2903.028 Mm³; 85.539%) and vegetables (467.928 Mm³; 13.788%), notably those with high water footprints. The top three EU importers of Moroccan virtual water were France (1138.785 Mm³), the Netherlands (874.323 Mm³), and the United Kingdom (430.872 Mm³). Conversely, virtual water imports by Morocco amounted to 55,232.963 Mm³, overwhelmingly dominated by cereals, which accounted for 99.697% of the total. These imports originated mainly from France (37,154.090 Mm³), Germany (4980.296 Mm³), and Poland (2330.039 Mm³). The analysis of Morocco’s virtual water balance with EU countries revealed that Morocco was a net virtual importer in trade with most of them. Furthermore, the crop-level virtual water trade balance revealed a tendency to export water-intensive crops that offer relatively low economic water productivity. However, four agricultural products recorded a high economic return per unit of Virtual Water Exported: tomatoes returned 19.80 USD/m³, strawberries 16.02 USD/m³, carrots 13.06 USD/m³, and watermelons 8.11 USD/m³. These findings underscore the importance of integrating water footprint analysis into national agricultural policy to maximize the economic productivity of water and ensure the sustainability of resources in a water-stressed country. Full article

Vegetation serves as a crucial indicator for monitoring ecosystems and plays a vital role. This paper employs remote sensing techniques to monitor vegetation in Taojiang County, aiming to explore the effects of ecological restoration projects on vegetation in mining areas. The study uses the Theil–Sen median slope and Mann–Kendall tests to analyze the trend of fractional vegetation coverage (FVC) changes in mining areas, the CASA model to estimate net primary productivity (NPP) in mining areas, and random forest models to assess the importance of influencing factors. Overall, FVC in the study area has slightly increased from 0.729 to 0.847. The FVC in mining areas reached its lowest point at 0.423 in 2011 and recovered to 0.718 in 2023 due to artificial restoration. From 2004 to 2011, FVC in mining areas showed an overall downward trend, while from 2013 to 2023, it showed an overall upward trend. The trend of NPP in mining areas is similar to that of FVC, with NPP being 939.8 g/m² y in 2004, 2011, and 2020, 788.3 g/m² y in 2011, and 855.7 g/m² y in 2020. Results from the random forest simulation indicate that the primary factor affecting FVC in mining areas is distance from roads, followed by elevation. This study finds that ecological restoration projects play a significant role in achieving ecological recovery and sustainable development in mining areas. Full article

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

In the context of global climate change, a comprehensive understanding of the spatiotemporal impacts of drought on vegetation productivity is essential for assessing terrestrial ecosystem stability. Utilizing outputs from six global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study systematically assessed historical and projected drought probability, the drought vulnerability of Net Primary Productivity (NPP), and overall drought risk across the Mongolian Plateau under three Shared Socioeconomic Pathway scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Results revealed that the Standardized Precipitation Evapotranspiration Index (SPEI) exhibited a declining trend, whereas NPP showed an overall increasing trend. These changes were most pronounced under the SSP5-8.5 scenario, with the SPEI decreasing at a rate of −0.39/10a and NPP increasing at 25.8/10a. Drought severity exhibited strong spatial heterogeneity, intensifying from northeast to southwest, whereas NPP demonstrated an inverse spatial pattern. The spatial distribution of high-drought-risk zones varied markedly across scenarios: the southwestern region was most affected under SSP1-2.6, the northwestern region under SSP2-4.5, and the southeastern region under SSP5-8.5. Based on 12-month SPEI values and NPP derived from the Carnegie–Ames–Stanford Approach (CASA) model, SSP2-4.5 presented the highest overall drought risk, despite lower emissions. The annual mean NPP drought vulnerability ranked as follows: SSP2-4.5 (0.60 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$) > SSP1-2.6 (−1.03 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$) > SSP5-8.5 (−1.24 $\mathrm{g}\mathrm{C}{\mathrm{m}}^{-2}{\mathrm{y}\mathrm{r}}^{-1}$). Projections indicated a substantial increase in drought occurrence probability during the period 2061–2100, particularly under SSP2-4.5 and SSP5-8.5. Under higher emissions, the spatial extent of areas with negative drought vulnerability values was expected to expand 68%. Wind speed was the dominant factor influencing drought risk under SSP1-2.6 and SSP2-4.5, whereas precipitation became the primary driver (45.34%) under SSP5-8.5. These findings offer critical insights for early drought warning systems and for strengthening ecosystem resilience across the Mongolian Plateau. Full article

Land degradation is one of the significant ecological and environmental issues threatening regional sustainable development. Datong Coalfield is located in an arid and semi-arid ecologically fragile area and is also an important energy base, the mining of coal resources and natural factors have caused serious land degradation problems. Therefore, dynamic monitoring and influencing factor analysis of land degradation in the Datong Coalfield is particularly important for land degradation prevention and land reclamation in mining areas. This study focuses on the Datong Coalfield, using remote sensing technology to dynamically extract soil erosion, net primary productivity of vegetation, land desertification, soil moisture content. Based on the Analytic Hierarchy Process (AHP), a comprehensive assessment model for land degradation was constructed to analyze the spatiotemporal evolution of land degradation in the Datong Coalfield from 2000 to 2021, and the influencing factors of land degradation were explored using a geographic detector. The results indicate that (1) from 2000 to 2021, the land degradation level in Datong Coalfield changed to mild degradation and non degradation, with the mild degradation area increasing by 30.48% and the non degradation area increasing by 13.9%, and spatially expanding contiguously from localized areas outwards. (2) Over the past 21 years, the land degradation situation in Datong Coalfield predominantly showed an improving trend, accounting for 69.11%, indicating an overall positive trajectory. However, 0.54% of the area experienced significantly intensified land degradation, scattered in the eastern and southwestern parts of the Datong Coalfield, which are areas requiring focused governance efforts. (3) Vegetation and land use are the main factors affecting land degradation in Datong Coalfield. At the same time, the influence of land use has gradually increased over the years, and the influence of vegetation and land use interaction is the highest in the two-factor interaction. Full article

Continental ecosystems are crucial constituents of the worldwide carbon process, and their carbon source and sink processes are highly sensitive to human-induced climate change. However, the spatiotemporal changes and principal determinants of carbon source/sink in Shandong Province remain unclear. This study constructs six dominant control modes of carbon sources/sinks based on three carbon sink indicators (gross primary production (GPP), net primary production (NPP), and net ecosystem productivity (NEP)) and three carbon source indicators (autotrophic respiration (Ra), heterotrophic respiration (Rh), and total ecosystem respiration (Rs)), revealing the main control characteristics of the spatiotemporal dynamics of carbon source/sink in the continental ecosystems of Shandong Province. Additionally, the principal determinants of carbon sources and sinks are quantitatively analyzed using cloud models. The research findings are as follows: (1) From 2001 to 2020, the continental ecosystem of Shandong Province demonstrated a weak carbon sink overall, with both carbon sinks and sources showing fluctuating growth trends (growth rate: GPP, NEP, NPP, Rs, Ra, and Rh consist of 15.55, 6.14, 6.09, 9.59, 9.47, and 0.07 gCm⁻²a⁻¹). (2) The dominant control characteristics of carbon source/sink in Shandong Province exhibit significant spatial differentiation, which can be classified into absolute carbon sink cities (Jinan, Zibo, Rizhao, Jining, Liaocheng, Zaozhuang, Binzhou, Dezhou, Tai’an) and relative carbon source cities (Weifang, Yantai, Weihai, Linyi, Qingdao, Heze, and Dongying). GPP is the dominant control factor in carbon sink areas and is widely distributed across the province, while Rs and GPP are the dominant control factors in carbon source fields, focused on the eastern coastal and southwestern inland sites. (3) Landscape modification and rainfall are the main driving elements influencing the carbon sink and source variations in Shandong Province’s continental ecosystems. (4) The spatial differentiation of the driving factors of carbon producers and reservoirs is significant. In absolute carbon sink cities, land-use change and vegetation cover are the dominant factors for carbon sinks and sources, with significant changes in both range and spatial differentiation. In relative carbon source cities, land-use change is the leading factor for carbon source/sink, and the range of changes and spatial differentiation is most notable. The observations from this study supply scientific underpinnings and reference for enhancing carbon sequestration in continental ecosystems, urban ecological safety management, and achieving carbon neutrality goals. Full article

Net primary productivity (NPP) is a key metric for assessing ecosystem functionality and sustainability. This study utilized MOD17A3 NPP data in conjunction with trend analysis, a gravity center model, and the Geodetector method to examine the spatiotemporal evolution and driving mechanisms of NPP across the Yellow River Basin (Shaanxi section) from 2000 to 2022. Results revealed: (1) The average NPP over the study period was 353.01g C m⁻² with an upward trend of 9.7 g C m⁻²yr⁻¹; spatially, NPP increased from north to south, with significant variability in northern Shaanxi and a 17.89 km northeastward shift in NPP’s gravity center. (2) Areas exhibiting significant NPP increases (slope > 0, p < 0.01) comprised 97.83% of the region, while declines were mainly observed in Guanzhong. (3) Normalized Difference Vegetation Index (NDVI) was the dominant factor, with the strongest synergistic, nonlinear interaction with land use type reflecting human activities (q = 0.831), indicating that the combined influence of climate factors, land surface factors, and human activities amplifies the explanatory effect on NPP variability. The study demonstrates an overall improvement in NPP, although local declines occurred, and its spatial distribution was influenced by a combination of natural and human factors. These findings will provide data support for the high-quality development of the Yellow River Basin. Full article

While technologies like renewable energy and low-carbon transportation are known to mitigate carbon emissions from urban expansion, achieving carbon neutrality during this process remains a critical unresolved challenge. This issue is particularly pressing for developing countries striving to balance urbanization with carbon reduction. Taking Qionglai City as a case study, this study simulated the territorial spatial functional patterns (TSFPs) and carbon emission distribution for 2025 and 2030. Based on the key drivers of carbon emissions from urban expansion identified through the Geographical and Temporal Weighted Regression (GTWR) model, carbon-neutral pathways were designed for two scenarios: urban expansion scenarios under historical evolution patterns (Scenario I) and urban expansion scenarios optimized under carbon neutrality targets (Scenario II). The results indicate that (1) urban space is projected to expand from 6094.73 hm² in 2020 to 6249.77 hm² in 2025 and 6385.75 hm² in 2030; (2) total carbon emissions are forecasted to reach 1.25 × 10⁶ t (metric tons) and 1.40 × 10⁶ t in 2025 and 2030, respectively, exhibiting a spatial pattern of “high in the central-eastern regions, low in the west”; (3) GDP, Net Primary Productivity (NPP), and the number of fuel vehicles are the dominant drivers of carbon emissions from urban expansion; and (4) a four-pronged strategy, optimizing urban green space vegetation types, replacing fuel vehicles with new energy vehicles, controlling carbon emissions per GDP, and purchasing carbon credits, proves effective. Scenario II presents the optimal pathway: carbon neutrality in the expansion zone can be achieved by 2025 using the first three measures (e.g., optimizing 66.73 hm² of green space, replacing 800 fuel vehicles, and maintaining emissions at 0.21 t/10⁴ CNY per GDP). By 2030, carbon neutrality can be achieved by implementing all four measures (e.g., optimizing 67.57 hm² of green space, replacing 1470 fuel vehicles, and achieving 0.15 t/10⁴ CNY per GDP). This study provides a methodological basis for local governments to promote low-carbon urban development and offers practical insights for developing nations to reconcile urban expansion with carbon neutrality goals. 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

Wetlands play a critical role in modulating the global carbon cycle and significantly contribute to climate change mitigation. China’s wetlands are characterized by high diversity, a large total area, wide distribution, and strong regional variability. However, the carbon exchange dynamics across different wetland types and their controlling mechanisms remain poorly understood. Here, we quantified and compared CO₂ fluxes (gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem productivity (NEP)) among China’s wetland types using eddy covariance measurements, analyzing spatial patterns and controlling mechanisms. Coastal wetlands exhibited higher annual GPP, ER, and NEP compared with inland wetlands. Among all wetland types, mangrove ecosystems had the highest carbon uptake capacity. The carbon conversion efficiency (CCE) of inland wetlands (0.89 ± 0.24) was higher than that of coastal wetlands (0.66 ± 0.12), suggesting that inland wetlands are less efficient at carbon fixation than coastal wetlands. However, due to their larger total area than that of coastal wetlands, inland wetlands in China likely constitute a greater overall CO₂ sink. Spatially, GPP and NEP showed significant differences between the tropical/subtropical zones and the temperate/plateau zones (p < 0.05), indicating the influence of climatic conditions. Climate factors influenced carbon fluxes primarily through their regulation of vegetation and soil features. The cascading relationships among climate, vegetation, and soil, as revealed by structural equation modeling (SEM), explained 61–71% of the spatial variation in GPP and ER, and 68% in NEP. Our findings provide valuable theoretical insights into the role of China’s wetland ecosystem in the global carbon cycle. Full article

Net primary productivity (NPP) reflects the carbon sequestration capacity of terrestrial ecosystems and it is used as an important indicator for measuring ecosystem quality. However, due to the effects of “warming and humidification” and “oasisization”, the spatiotemporal evolution and driving mechanisms of the NPP of vegetation in the northern slope of the Tianshan Mountains (NSTM), a typical arid area in China, are still unclear. Thus, in this study, we used remote sensing data and meteorological data to construct a Carnegie–Ames–Stanford–Approach (CASA) model for estimating the NPP of vegetation in the study area. Trend analysis, partial correlation analysis, and optimal parameter-based geographic detector (OPGD) methods were combined to explore the spatiotemporal evolution and driving mechanisms to changes in the NPP. The results showed that from 2001 to 2020, the annual average NPP on the NSTM exhibited an overall significant upward trend, increasing from 107.33 gC⋅m⁻²⋅yr⁻¹ to 156.77 gC⋅m⁻²⋅yr⁻¹, with an increase of 2.47 gC⋅m⁻² per year and 46.06% year-on-year. Over the past 20 years, climate change and human activities generally positively affected the changes in NPP in the study area. Human activities in the study area are mainly manifested in the large-scale conversion of other land use types into farmland, with a total increase of 16,154 km² in farmland area, resulting in a net increase of 6.01 TgC in NPP. Precipitation has the strongest correlation with NPP in the study area, with a partial correlation coefficient of 0.30, temperature and solar radiation have partial correlation coefficients with NPPs of 0.17 and 0.09, respectively. Therefore, increases in precipitation, temperature, and solar radiation have a promoting effect on the growth of NPP on the NSTM. During the study period, the land use type and soil moisture were the main factors that affected the spatial differentiation of vegetation NPP, and the effects of human interference on natural environmental conditions had significant impacts on vegetation NPP in the area. Therefore, in this study, we accurately determined the spatiotemporal variations in the NPP on the NSTM and comprehensively explored the driving mechanisms to provide a theoretical basis for sustainable development in arid areas and achieving carbon neutrality goals. Full article

As an important ecological barrier and animal husbandry resource base in arid and semi-arid areas, grassland degradation directly affects regional ecological security and sustainable development. Ili Prefecture is located in the western part of Xinjiang, China, and is a typical grassland resource-rich area. However, in recent years, driven by climate change and human activities, grassland degradation has become increasingly serious. In view of the lack of comprehensive evaluation indicators and the inconsistency of grassland evaluation grade standards in remote sensing monitoring of grassland resource degradation, this study takes the current situation of grassland degradation in Ili Prefecture in the past 20 years as the research object and constructs a comprehensive evaluation index system covering three criteria layers of vegetation characteristics, environmental characteristics, and utilization characteristics. Net primary productivity (NPP), vegetation coverage, temperature, precipitation, soil erosion modulus, and grazing intensity were selected as multi-source indicators. Combined with data sources such as remote sensing inversion, sample survey, meteorological data, and farmer survey, the factor weight coefficient was determined by analytic hierarchy process. The Grassland Degeneration Comprehensive Index (GDCI) model was constructed to carry out remote sensing monitoring and evaluation of grassland degradation in Yili Prefecture. With reference to the classification threshold of the national standard for grassland degradation, the GDCI grassland degradation evaluation grade threshold (GDCI reduction rate) was determined by the method of weighted average of coefficients: non-degradation (0–10%), mild degradation (10–20%), moderate degradation (20–37.66%) and severe degradation (more than 37.66%). According to the results, between 2000 and 2022, non-degraded grasslands in Ili Prefecture covered an area of 27,200 km², representing 90.19% of the total grassland area. Slight, moderate, and severe degradation accounted for 4.34%, 3.33%, and 2.15%, respectively. Moderately and severely degraded areas are primarily distributed in agro-pastoral transition zones and economically developed urban regions, respectively. The results revealed the spatial and temporal distribution characteristics of grassland degradation in Yili Prefecture and provided data basis and technical support for regional grassland resource management, degradation prevention and control and ecological restoration. Full article