Artificial grassland is a key intervention for restoring degraded grasslands, and rapid large-scale mapping via remote sensing is essential. Current approaches remain highly dependent on field surveys and have notable limitations. To address these issues, we developed a framework that exploits multi-temporal Landsat series imagery together with the Continuous Change Detection and Classification algorithm to identify candidate locations and establishment dates of artificial grasslands. By integrating inferred planting times with annual phenological peak metrics to construct discriminative features, and applying a Random Forest classifier, we delineated planted artificial grassland areas. Ground validation yielded an overall accuracy of 89.87%, and comparisons with previous studies showed higher accuracies (>82.93%). Applying the framework to map 2021-2023 plantings in Qinghai Province produced a correlation coefficient r = 0.8843 (p < 0.05) with statistical records. These results indicate strong generalizability and accuracy across regions and years, demonstrating the framework's reliability for advancing artificial grassland mapping.

Enhancing the terrestrial ecosystem carbon sink represents a critical strategy for mitigating climate change and advancing sustainable development. In this study, we developed a comprehensive framework that integrated ecosystem service interactions across temporal, spatial, and functional dimensions to identify pathways for enhancing carbon sinks, using Northeast China as a case study. We employed the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, spatiotemporal difference comparison methods, Geodetector, and the Multiscale Geographically Weighted Regression (MGWR) model to identify the most relevant ESs for carbon storage (CS) and to analyze their interactions. By comparing the independent effects of single ecosystem services and the interaction effects of multiple ecosystem services on CS from 2000 to 2020, we proposed strategies for enhancing carbon sinks and improving spatial sustainability. The results showed that the interaction effects of ESs on CS were stronger than their independent effects, with the largest effect coefficients observed between soil retention (SR) and habitat quality (HQ) (0.66), followed by food production (FP) and HQ (0.63). Reducing trade-offs and enhancing synergies between SR-HQ and FP-HQ were identified as key pathways to increase carbon sinks. These findings are especially important for managing black soil resources and optimizing agroforestry systems in Heilongjiang and the Inner Mongolia Autonomous Region. This study highlighted the importance of amplifying feedback among ESs to improve CS and proposed targeted measures for the protection and restoration of black soils, thereby providing a scientific foundation and practical guidance for sustainable ecosystem management and climate change mitigation.

Protected areas contribute to poverty alleviation through the provision of non-timber forest products (NTFPs), but relatively few studies have researched the recreational context and the socio-ecological interactions that influence household NTFPs income. Using household surveys in the Yading biosphere reserve (2020-2022), our study investigated factors affecting income of main NTFPs including caterpillar fungus (Ophiocordyceps sinensis) and matsutake mushroom (Tricholoma matsutake). The generalized linear mixed model is used to explain the difference of NTFPs utilization patterns among households. The finding indicates that NTFPs account for 30.0% of household income in the protected area, comparing to tourism income (35.3%). A significant trade-off relationship was found between NTFPs harvesting and tourism income. Meanwhile, harvesting activities serve as an approach for asset accumulation to enable a transition to higher-value livelihoods. Elevation and tourism income emerged as the primary determinants of overall NTFP income. In addition, household landholdings and the number of males were distinctly associated with income from T. matsutake, whereas the educational attainment of household labor was a key predictor of income from O. sinensis. This research provides an empirical analysis of the factors that affect NTFP incomes, enabling the potential role of wild production in supporting sustainable livelihoods to be explored. Our findings have important implications for understanding the alpine protected area household livelihoods and designing conservation interventions that affect access to and use of wild natural resources.

Karst regions exhibit heterogeneous water distribution attributable to their unique topographical features. Research concerning water absorption, efficiency dynamics and the underlying mechanisms within commercial plantation forests remains limited, which constrains our understanding of ecohydrological processes. In this study, three typical commercial forests (Juglans regia, Prunus salicina and Prunus persica) were selected as research subjects. We investigated seasonal variations in water uptake patterns and water use efficiency (WUE) of each species by combining stable isotopes (delta H-2, delta O-18 and delta C-13) with the MixSIAR model. We hypothesise that these forests adjust their water absorption depth in response to seasonal variations in water availability, consequently influencing WUE. Our results showed that the most important water source for J. regia and P. salicina was 0-30 cm soil water, contributing 55% and 41.6%, respectively. Groundwater provided the next highest contributions, at 25.2% and 30.6%, respectively. In contrast, P. persica mainly used groundwater in spring and autumn (51.2% and 42.3%), 30-60 cm soil water and 0-30 cm soil water in summer (39.5% and 39.1%). Groundwater is an indispensable water source for commercial forests, with a minimum use proportion of 18.8% in summer and a maximum that can reach 51.2% in spring, highlighting the 'stabiliser' role groundwater provides during seasonal droughts in karst areas. The WUE of P. persica is significantly higher than that of J. regia during the spring and autumn seasons (p < 0.05). These results suggest groundwater is a crucial water source for commercial forests in humid subtropical zones, and vegetation with a greater dependence on groundwater exhibits a higher WUE. This study elucidates the ecological strategies employed by subtropical karst commercial forests to adapt to variations in soil moisture by flexibly adjusting their water absorption depth. The findings provide a scientific basis for the rational allocation of water resources within commercial forests in this region.

As a climate-sensitive critical ecological function area, alpine grassland ecosystems on the Qinghai-Xizang Plateau (QXP) are highly vulnerable to global warming. This study aims to predict the spatiotemporal dynamics of aboveground biomass (AGB) for plant functional groups under future climate scenarios. To assess the response of the AGB to global warming on the QXP, four methods (random forest, multivariate adaptive regression, enhanced regression trees and support vector machine) were employed to evaluate alpine grassland conditions for both the recent past (2018–2021) and the projected future (2021–2100) base on 337 field observations of AGB. The results indicated that from 2018–2021, the average AGB of grassland was 91.80 g/m2, with the AGB that supports grazing being 60.39 g/m2. Specifically, the AGB values for grasses, sedges, legumes, and forbs were 26.03 g/m2, 20.80 g/m2, 4.28 g/m2, and 40.70 g/m2, respectively. The grassland AGB tended to decrease across all the time periods under all the future scenarios, except for SSP5-8.5 during 2060–2080. However, the AGB that supports grazing was projected to increase during the periods 2040–2060, 2060–2080 and 2080–2100 under all future scenarios. With respect to the different plant functional groups, the AGB of the grasslands increased across all the scenarios, whereas the AGB of the sedges, legumes, and forbs decreased in all the situations. In this study, we introduce a novel perspective in which changes in grassland AGB and grazing carrying capacity are not perfectly synchronized under future SSP climate scenarios.

It is urgent and challenging to evaluate the effects of soil conservation (SC) under ecological restoration projects (ERPs) in karst regions. In this study, the surrogate biophysical method was adopted to investigate the spatiotemporal evolution of SC under ERPs in Guizhou Province from 2000 to 2023. We found that SC showed an increasing trend, and SC hotspots were mainly spatially aggregated in southern Guizhou Province (Qianxinan and Qiannan), while its cold spots were concentrated primarily in the western and northern regions. Returning farmland to forest (RFTF) exhibited the strongest SC effectiveness. However, returning farmland to water bodies (RFTW) became increasingly effective in enhancing SC over the study period (0.01-0.04). This indicates that RFTW showed potential for further enhancing SC in karst regions. The dominant factors of SC demonstrated temporal variability, and precipitation, slope, and soil types were the dominant factors of SC in Guizhou Province during the study period.

Climate change threatens carbon sequestration on the Qinghai-Tibet plateau, particularly in the Qilian mountains on its northeastern edge. We combined eddy covariance measurements from five sites at altitudes of 3,000-4,200 m spanning 2011 to 2022 with remote sensing data to evaluate carbon sink dynamics. Mean net ecosystem productivity (NEP) from eddy data were 311.2 g C m-2 year-1, compared with 185.3 g C m-2 year-1 from remote sensing. NEP trends varied with altitude. Above roughly 3,700 m NEP may increase, whereas lower elevations show mixed signals. Overall, gross primary productivity increased faster than ecosystem respiration, producing a net NEP rise of 8.6 ± 4.3 g C m-2 year-2. Upscaled tower estimates indicate a regional carbon uptake of 6.25 × 1010 kg C year-1, while remote sensing estimates give 3.72 × 1010 kg C year-1. This substantial carbon sink could significantly lower the cost of achieving carbon neutrality.

In view of increasing climate pressure and population growth, ensuring food security while progressing towards carbon neutrality has become a key challenge for agricultural development. Although cropping pattern optimization has been widely explored, most existing studies focus on single objectives or static configurations and rarely incorporate long-term cropping dynamics and stakeholder preferences into a unified decision framework, limiting their applicability in agricultural management. This study proposes a hybrid framework that integrates remote sensing data, agricultural systems modeling, life cycle assessment, and multi-objective optimization to identify optimal cropping patterns based on stakeholder preferences. The approach aims to maximize the yield, profitability, and carbon sequestration potential of corn and soybeans while minimizing associated carbon emissions in the typical black soil region of Northeast China. The results show that between 2008 and 2022, both continuous corn cultivation and corn–soybean rotation systems expanded, with continuous corn cultivation accounting for 60–75% of the total cultivated area, whereas continuous soybean cultivation declined steadily. Spatially, most cultivation patterns exhibited a clear northward shift. Overall, the results suggest that continuous corn cultivation can offer the most effective compromise between food production, carbon sequestration, and economic returns, provided that strict measures to reduce emissions are implemented. Among all rotation strategies, the two-year corn and one-year soybean rotation is the most effective in mitigating the adverse effects of continuous cropping while maintaining a balanced food–carbon–profit performance. In contrast, soybean cultivation offers notable environmental benefits but is constrained by relatively low yields and limited economic returns, underscoring the need for targeted optimization measures. This study provides actionable insights for designing sustainable crop patterns that balance agricultural productivity with climate mitigation goals.

Ecosystem stability is essential for maintaining ecosystem structure and function. Using multi-source data from 2000 to 2022, this study quantifies the multidimensional stability of terrestrial ecosystems in China using the ARx model, with validation based on multiple indicators and alternative methods. Stability types are classified in this study, key driving factors are identified using a Random Forest model combined with SHAP analysis, and low-stability risk zones are delineated. The results indicate pronounced spatial heterogeneity and spatiotemporal variation in ecosystem stability across China. Low resilience is mainly observed in northwestern and southwestern China. Drought resistance decreases from the southeast to the northwest, while temperature resistance is relatively low on the Qinghai-Tibet Plateau and in southeastern China. Temporal stability is generally low nationwide. The overall stability index is at a moderate level, showing higher values in the east and south and lower values in the west and north, with a slight increasing trend over time. Stability improves mainly in southern China and parts of eastern North China but declines in the Qinghai-Tibet Plateau, northwestern China, and parts of northeastern and southwestern China. Stability is higher in southern China and mixed forests but lower in northwestern China and desert ecosystems. Moisture conditions, soil nutrients, ecological restoration, and hydrothermal variability are the main drivers. Low-stability risk zones are mainly distributed in the western part of northern China and account for 3.65% of the vegetated area. This study provides scientific support for the precise management of China's ecosystems under global climate change.

Quantifying time-series changes in ecological parameters is essential for assessing the ecological restoration benefits resulting from the construction of photovoltaic power plants and subsequent on-site grazing. This paper quantitatively assessed the ecological restoration effects of photovoltaic power plants in the Gonghe County photovoltaic park, Qinghai Province, using unmanned aerial vehicle imagery, plot-based field survey data, and local grazing-pressure raster datasets. Using spectral indices, texture features, flight altitude, and an XGBoost model, we estimated above-ground biomass (R2 = 0.96; RMSE = 3.92) and analyzed changes in the ecological restoration effects under different grazing-pressure scenarios by jointly evaluating biomass alongside field measurements of soil organic carbon, soil moisture, and biodiversity. The results show that the operation of photovoltaic power plants has a significant restorative effect on ecosystems, but this effect is mainly concentrated in the early to middle stages. The recovery benefits of soil organic carbon and aboveground biomass begin to weaken around the 10th year, while those of soil moisture and biodiversity begin to decline around the 4th year. In addition, moderate grazing does not affect these restorative benefits, whereas continuously increasing grazing pressure shortens their duration. These findings provide an objective basis for assessing the ecological restoration benefits of photovoltaic power plants, and also offer a reference for the adaptive management of grazing in local photovoltaic parks.

The Qinghai-Xizang Plateau (QXP) plays a pivotal role in global biodiversity conservation and climate regulation, but its ecosystem stability is increasingly threatened by climate change and human disturbances. Clarifying the spatial patterns and driving mechanisms of ecosystem stability across the QXP is critical for targeted ecological conservation. By integrating remote sensing data and field surveys at 443 sites, we employed random forest analysis, piecewise structural equation modeling, and linear regression to quantify spatial patterns of ecosystem stability and detrended ecosystem stability and disentangle the direct/indirect effects of geographic, climatic, soil, biotic, and grazing factors. Results showed that ecosystem stability and detrended ecosystem stability of alpine grasslands exhibited significant spatial heterogeneity, with the order of stability being alpine meadow > alpine steppe > alpine desert. The dominant drivers of stability varied among grassland types: plant diversity and mean annual temperature (MAT) were the most critical predictors in alpine deserts; grazing intensity (negative effect) and grazing history (positive effect) dominated stability in alpine meadows; and soil total phosphorus (TP, positive effect) and grazing intensity (negative effect) were the key drivers in alpine steppes. Our findings highlight that the spatial heterogeneity of ecosystem stability on the QXP is fundamentally driven by grassland type-mediated coupling of resource availability, community structure, and soil properties. The divergent driving mechanisms among alpine desert, meadow, and steppe underscore the need for type-specific conservation strategies: prioritizing plant biodiversity protection in alpine deserts, implementing science-based grazing management in alpine meadows, and enhancing soil nutrient conservation in alpine steppes. Our study clarifies the spatial patterns and driving mechanisms of ecosystem stability across different alpine grassland types on the QXP, providing critical scientific support for formulating region-specific ecological conservation strategies.

To accurately quantify the intrinsic absorption efficiency of bamboo leaves to the solar spectrum, we measured the reflectance and transmittance of leaves from 55 bamboo species cultivated at the same site, and developed a mathematical model to calculate the annual cumulative photon absorption of photosynthetically active radiation (PAR) per leaf. The results showed the following: (1) Bamboo leaf optical properties exhibited high instrumental and spatial measurement consistency, with transmittance not significantly fluctuating with changes in incident light intensity or quality. (2) Bamboo leaves exhibited significant spectral selective absorption characteristics, with stronger absorption of blue and red light and weaker absorption of green light; Phyllostachys vivax had the highest mean absorptance per unit area, while Chimonobambusa tumidinoda had the lowest. (3) The annual photon absorption per unit leaf area ranged from 1.83 × 105 to 9.86 × 105 μmol, with Phyllostachys iridescens being the lowest and Chimonobambusa marmorea the highest. The annual photon absorption per single leaf ranged from 1.84 × 106 to 5.13 × 107 μmol, with Indocalamus decorus achieving the highest total absorption due to its largest leaf area (114.9 cm2), while Bambusa multiplex var. riviereorum was the lowest. (4) All tested bamboo species showed consistent seasonal dynamics in photon absorption, with the highest in summer and lowest in winter. Although unit-area absorptance reflects the intrinsic light interception efficiency, leaf morphology has a substantial influence (explaining 99.56% of the variance) in determining total light acquisition per leaf.

The Qilian Mountains, a crucial ecological security barrier and water conservation region in northwestern China, are highly sensitive to climate change. Reliable climate projections are essential for regional environmental management, yet the performance of statistical downscaling methods in this complex mountainous region remains inadequately evaluated. This study evaluates four statistical downscaling methods—Delta Change Method (DCM), Quantile Mapping (QM), Multiple Linear Regression (MLR), and Random Forest (RF)—using station-based observations (1951–2025) and outputs from 34 CMIP6 climate models. Validation results indicate that RF achieved the highest R2 and the lowest RMSE for both temperature and precipitation, with superior stability across models, scenarios, and stations. Based on RF downscaling, future climate projections under SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5 indicate a persistent warming and moderate wetting trend throughout the 21st century. Temperature is projected to increase at rates of 0.03 °C–0.31 °C/decade, while precipitation is projected to increase by approximately 3.2%–8.1% by the end of the century, although inter-model uncertainty remains substantial. Future climate change also exhibits pronounced seasonal and spatial heterogeneity, characterized by winter-dominated warming, reduced summer precipitation but increased autumn–winter precipitation, and strong elevation-dependent responses concentrated in high-elevation areas. The 0 °C isotherm is projected to rise by approximately 100–400 m by the late 21st century. These findings highlight the applicability of RF downscaling for station-scale climate projections in the Qilian Mountains and provide scientific support for regional climate adaptation, water resource management, and ecosystem conservation.

Grazing management significantly influences greenhouse gas (GHG) emissions and the global warming potential (GWP) in grasslands. Yet, a limited understanding of the impact of grazing and grazing exclusion on GHG emissions and GWP in grasslands hinders progress towards grassland ecosystem sustainability and GHG mitigation. We conducted a global meta-analysis of 75 published studies to investigate the effects of grazing and grazing exclusion on methane (CH4), carbon dioxide (CO2), nitrous oxide (N2O), and GWP. Our results revealed that grazing and grazing exclusion significantly increased the CO2 and CH4 emissions, respectively. The responses of GHG emissions and GWP to grazing were regulated by grazing intensity and elevation. We also found that light grazing significantly decreased GWP but heavy grazing increased GWP. Reducing grazing intensity was a simple and effective method through stocking rate adjustment, which promised a large GHG mitigation potential. Our results demonstrated that GHG emissions increased with elevation under grassland grazing, implying that irrational grazing in high-elevation grasslands promoted GHG emissions. In comparison with grazing, only long-term grazing exclusion reduced the GWP, and CH4 emissions enhanced with grazing exclusion duration. However, long-term grazing exclusion may shift economic demand and grazing burden to other areas. Overall, we suggested that regulating the grazing intensity, rather than grazing exclusion, was an effective way to reduce GHG emissions. Our study contributed to the enhancement of sustainable grazing management practices for GHG balance and GWP in global grasslands, and offered a global picture for understanding the changes in GHG emissions and GWP under different grazing management regimes.

The expansion of photovoltaic (PV) plant infrastructure is occurring at a rapid pace; yet, our comprehension of the impacts of PV plants on ecosystem functions in terrestrial environments remains limited. We conducted a meta-analysis to assess the patterns of ecosystem functions in response to land-based solar power development across various terrestrial ecosystems. Results showed that PV plants significantly enhanced biodiversity maintenance and primary production in grassland and desert ecosystems, primarily ascribed to the augmentation of soil moisture. However, PV plants decreased primary production in crop due to a decrease in photosynthetically active radiation. The influence of site selection (including elevation, mean annual temperature and precipitation) on terrestrial ecosystem functions in PV plant installations was found to be statistically significant. Our findings suggest that RR of primary production and soil quality regulation decreased with mean annual temperature but increased with elevation. Overall, we advocate for further future research to assess the ecological impacts of PV plants, aiming to enhance site selection and implement adaptive management measures for PV plant operations.

Due to climate change and human activities, global grasslands were facing different degrees of degradation and various restoration works are commonly used to cope with grassland degradation. However, the restoration effectiveness of different restoration measures in global grasslands was not clear. Here, we comprehensively assessed the response of biodiversity and ecosystem multifunctionality (EMF) to different restoration measures under different aridity index based on 361 papers. Our results showed that aridity index was the key influencing factors for ecological restoration in global grasslands. Grassland restoration significantly improved biodiversity and EMF in all aridity domains expect for biodiversity in hyper arid domain. We found that biodiversity first enhanced then decreased with aridity index, while EMF showed a negative correlation with aridity index. Moreover, biodiversity had positive relationships with the EMF in arid, semi-arid, and dry sub-humid domain, but negative correlation in hyper arid domain. We proposed grazing exclusion, soil inoculation, artificial grassland, and seeded were applicable for restoring degraded grasslands in hyper arid, arid, semi-arid, and dry sub-humid domain, respectively. Analyzing the comprehensive impacts of different restoration measures in different aridity domain is of great significance for improving recovery effectiveness and the realization of sustainable development of grasslands globally.

Ecosystems play a pivotal role in advancing Sustainable Development Goals (SDGs) by providing indispensable and resilient ecosystem services (ESs). However, the limited analysis of spatiotemporal heterogeneity often restricts the recognition of ESs’ roles in attaining SDGs and landscape planning. We selected 183 counties in the Sichuan Province as the study area and mapped 10 SDGs and 7 ESs from 2000 to 2020. We used correlation analysis, principal component analysis, Geographically and Temporally Weighted Regression model, and self-organizing maps to reveal the spatiotemporal heterogeneity of the impacts of the bundle of ESs on the SDGs and to develop spatial planning and management strategies. The results showed that (1) SDGs were improved in all counties, with SDG 1 (No Poverty) and SDG 3 (Good Health and Well-being) exhibiting poor performance. Western Sichuan demonstrated stronger performance in environment-related SDGs in the Sichuan Province, while the Sichuan Basin showed better progress in socio-economic-related SDGs; (2) habitat quality, carbon sequestration, air pollution removal, and soil retention significantly influenced the development of 9 SDGs; (3) supporting, regulating, and provisioning service bundles have persistent and stable spatiotemporal heterogeneity effects on SDG1, SDG8, SDG11, SDG13, and SDG15. These findings substantiate the need for integrated management of multiple ESs and facilitate the regional achievement of SDGs in geographically intricate areas.

Plant transpiration is a fundamental process for maintaining the water cycle, regulating temperature and facilitating nutrient uptake, while also playing a critical role in climate regulation and ecosystem services. However, a significant knowledge gap remains in the understanding of how plant transpiration responds to changes in precipitation patterns within dryland ecosystems. In the present study, the stem sap flow of the phreatophyte xerophytic shrub Tamarix ramosissima, meteorological factors, soil moisture content, and bare soil evaporation were examined to assess the effects of two different rainfall categories (category I: lower mean rainfall amount and duration; category II: higher mean rainfall amount and duration) on stem sap flow dynamics. Our results reveal that the rainfall reduced the stem sap flow by 46.5% and 29.5% compared to the previous days across rainfall category I and II, respectively. The daily and diurnal variation of stem sap flow during the three days following rainfall showed non-significant variation compared to pre-rainfall, regardless of rainfall category. The soil moisture content at depth of 0–40 cm (SMC0-40cm) exhibits a pronounced increase to rainfall events, irrespective of rainfall category, although these events did not significantly increase the soil available moisture content within this depth. Concurrently, the weighing micro-lysimeters utilized in this study revealed that approximately 91.5% of the total precipitation during the experimental period evaporated into the atmosphere. In addition, the daily stem sap flow on the rainfall day and the following three days post rainfall was strongly positively correlated with photosynthetically active radiation, air temperature, and vapor pressure deficit within the two rainfall categories rather than with SMC0-40cm. Together, our findings indicate that the effects of rainfall variability on stem sap flow of T. ramosissima are primarily driven by meteorological factors, independent of the rainfall category. The results of this study provide a valuable insight for assessing species-specific water-use strategies and implementing effective reforestation practices in the future.