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The unique geographical conditions in southwestern China lead to a fragile ecological balance and uneven geographical distribution of water resources. This study takes the Aba Tibetan and Qiang Autonomous Prefecture as its research subject, investigating the connection between water resources and the ecological environment in the Aba Tibetan and Qiang Autonomous Prefecture, which is of paramount importance. Given this, the current study constructs the remote sensing ecological index and water footprint for the period spanning from 2000 through 2020, analyses the coordination status of the two by using the coupling coordination degree, and then combines it with a detector to determine the primary drivers impacting the state of the ecological environment. The outcomes reveal that: (1) The ecological environment of the Aba Tibetan and Qiang Autonomous Prefecture gradually improved from 2000 through 2020, and the excellent ecological environment area observed within the study’s scope is primarily focused in the eastern part. The poor ecological environment area is focused mainly in the northwestern part within the study’s scope. (2) The total water footprint of the Aba Tibetan and Qiang Autonomous Prefecture has increased year by year, with agricultural water consumption comprising an immense 78% proportion, dominating the total water footprint. (3) The coupled coordination degree of the remote sensing ecological index and the water footprint shows a rising trend in general, turning from a barely coordinated stage to an intermediate coordinated stage. (4) The geodetector results show that all factor interactions were dual-factor enhancement or nonlinear enhancement, and the WET, NDBSI and NDVI factors contributed the most. The above results can provide important guidance for utilizing water resources and protecting the ecological environment in the Aba Tibetan and Qiang Autonomous Prefecture.

Southwest China is an important carbon sink area in China. It is critical to track and assess how human activity (HA) and climate change (CC) affect plant alterations in order to create effective and sustainable vegetation restoration techniques. This study used MODIS NDVI data, vegetation type data, and meteorological data to examine the regional and temporal variations in the normalized difference vegetation index (NDVI) in Southwest China from 2000 to 2020. Using trend analysis, the study looks at the temporal and geographical variability in the NDVI. Partial correlation analysis was also used to assess the effects of precipitation, extreme climate indicators, and mean temperature on the dynamics of the vegetation. A new residual analysis technique was created to categorize the effects of CC and HA on NDVI changes while taking extreme climate into consideration. The findings showed that the NDVI in Southwest China grew at a rate of 0.02 per decade between 2000 and 2020. According to the annual NDVI, there was a regional rise in around 85.59% of the vegetative areas, with notable increases in 36.34% of these regions. Temperature had a major influence on the northern half of the research region, but precipitation and extreme climate had a notable effect on the southern half. The rates at which climatic variables and human activity contributed to changes in the NDVI were 0.0008/10a and 0.0034/10a, respectively. These rates accounted for 19.1% and 80.9% of the variances, respectively. The findings demonstrate that most areas displayed greater HA-induced NDVI increases, with the exception of the western Sichuan Plateau. This result suggests that when formulating vegetation restoration and conservation strategies, special attention should be paid to the impact of human activities on vegetation to ensure the sustainable development of ecosystems.

Forests in the wildland-urban interface (WUI) are of high value but vulnerable to wildland fires due to abundant fire ignitions and flammable forest fuels. Restoring the post-fire Wildland-Urban Interface (WUI) forest landscape is of utmost importance in order to maintain ecosystem service provision. The near-natural restoration strategy is widely employed in vegetation restoration as it enables the formation of healthy, stable, and diverse artificial mixed forests that resemble natural forests. To evaluate the long-term effects of near-natural restoration on the WUI forest landscape, which are largely unclear, we used a wildfire in 2019 near Shenyang City in northeast China as an example and investigated the post-fire forest dynamics under two different scenarios (i.e., natural succession and near-natural restoration) based on the forest landscape model. The results demonstrated that near-natural restoration can significantly accelerate the restoration process in terms of forest biomass, species biodiversity, and age structure. Under the near-natural restoration scenario, the biomass of the burned area can be quickly restored within 20 years after the fire. At the species level, the biomass and proportion of pioneer tree species such as Pinus tabuliformis and Robinia pseudoacacia decreased under the near-natural restoration scenario, while other species started to increase. Then post-fire near-natural planting accelerated the restoration of forest biodiversity, by 2070, the Shannon–Wiener index was predicted to be 1.49 under natural succession and remained at 2.02 under near-natural restoration. In terms of age structure, near-natural restoration shortens the recovery time of fire trails to mature forests. In summary, near-natural restoration accelerates forest recovery in post-fire WUI areas. Our results highlighted the impact of near-natural restoration on forest conservation to inform post-fire forest planning and management practices.

The aboveground carbon sequestration rate (ACSR) of forests serves as an indicator of their carbon sequestration capacity over time, providing insights into the potential carbon sequestration capacity of forest ecosystems. To explore the long-term Spatiotemporal variation of ACSR in the transitional ecotone of the eastern Tibetan Plateau under climate change scenarios, we utilized a forest landscape model that was parameterized with forest inventory data from the eastern Tibetan Plateau to simulate this ecological function changes. The study found that climate warming had significant effect on forests ACSR in different types of forests. ACSR was significantly reduced (p

China has undergone a long period of rapid urbanization from 2000 to 2020, resulted in huge conversion of arable land into built-up areas. This situation has raised concerns regarding food security, including issues like reduced food supply and challenges to achieving food self-sufficiency, particularly in certain regions and during special period. To address these issues, this study focuses on Xiamen, a rapidly urbanizing coastal city in China, and examined both the food supply and demand. On hand of the supply, this study mainly analyzed the attributes of the food availability, indicators like the food production (FP) of the ecosystem service value (ESV), per capita grain production, imports and exports along with food price were calculated. The findings revealed a significant expansion of built-up areas in Xiamen, mostly at the expense of arable land. The declining arable land coupled with population growth due to rapid urbanization contributed to a decrease in per capita grain production, drop to 6 kg in 2018, which is merely 1% of China’s average. A comparison of per capita grain production across various regions, as well as changes in China’s grain import and export, highlighted a notable discrepancy between urbanization rates and food security in Xiamen. It has become challenging for Xiamen to rely on outside for sustained food import. Therefore, it is imperative for Xiamen to implement a range of measures and policy interventions to ensure a certain level of food self-sufficiency and protect basic arable land.

Many single-land-use simulation models are available to simulate and predict Land Use and Land Cover Change (LUCC). However, few studies have used multiple models to simulate LUCC in the same region. The paper utilizes the CA-Markov model, Land Change Modeler (LCM), and Patch-generating Land Use Simulation model (PLUS) with natural and social driving factors to simulate the LUCC on the Western Sichuan Plateau, using Kappa coefficient, overall accuracy (OA), and Figure of Merit (FoM) to verify the accuracy of the model, and selects a suitable model to predict the LUCC and landscape pattern in the study area from 2020 to 2070. The results are as follows: (1) The LCM has the highest simulation effect, and its Kappa coefficient, OA, and FoM are higher than the other two models. (2) The area of land types other than grassland and wetland will increase from 2020 to 2070. Among them, the grassland area will decrease, but is still most prominent land category in this region. The proportion of wetland areas remains unchanged. The fragmentation degree of forest (F), grassland (GL), shrubland (SL), water bodies (WBs), bare areas (BAs), and permanent ice and snow (PIS) decreases, and the distribution shows a trend of aggregation. The dominance of F and C decreases but still dominates in the landscape. The overall landscape aggregation increased and complexity decreased, and each landscape type’s diversity, evenness, and richness increased, presenting as a more reasonable development. Using multiple models to simulate the LUCC in the same region, and choosing the most suitable local land model is of great significance to scientifically manage and effectively allocate the land resources in the field.

Assessing vegetation phenology is very important for better understanding the impact of climate change on the ecosystem, and many vegetation index datasets from different remote sensors have been used to quantify vegetation phenology from a regional to global perspective. This study mainly analyzes the similarities and differences in phenology derived from GIMMS NDVI3g and MODIS NDVI datasets across different biomes throughout temperate China. We applied three commonly used methods to extract the start and end of the growing season (SOS and EOS) from two datasets between 2000 and 2015, and analyzed the spatio-temporal characteristics and trends of key phenological parameters between these two datasets in temperate China. Results showed that the multi-year mean GIMMS NDVI was higher than MODIS NDVI throughout most of temperate China, and the consistencies between GIMMS NDVI and MODIS NDVI for all biomes in the senescence phase were better than those in the green-up phase. NDVI differences between GIMMS and MODIS resulted in some distinctions between phenology derived from the two datasets. The results of SOS and EOS for three methods also showed wide discrepancies in spatial patterns, especially in SOS. For different biomes, differences of SOS in forests were obviously less than that in shrublands, grasslands-IM, grasslands-QT and meadows, whereas the differences of EOS in forests were relatively greater than that in SOS. Moreover, large differences of phenological trends were found between GIMMS and MODIS datasets from 2000 to 2015 in entire region and different biomes, and it is particularly noteworthy that both SOS and EOS showed a low proportion of the identical significant trends. The results suggested NDVI datasets obtained from GIMMS and MODIS sensors could induce the differences of the inversion of vegetation phenology in some degree due to the differences of instrumental characteristics between these two sensors. These findings highlighted that inter-calibrate datasets derived from different satellite sensors for some biomes (e.g., grasslands) should be needed when analyzing land surface phenology and their trends, and also provided baseline information for choosing different NDVI datasets in subsequent studies on vegetation patterns and dynamics.

Mountain forest ecosystems play an important role in global carbon sequestration and may respond differently to variations in climate. The timely and accurate assessment of the factors (abiotic and biotic) that affect forest types will improve our understanding of the ecological mechanisms underlying forest carbon stock and dynamics. Here, we used linear mixed effect models to assess the impact of abiotic and biotic factors on the rate of net tree C accumulation, growth, and mortality, over nearly four decades in 1248 permanent forest plots, in different forest types along an elevational gradient on the eastern Qinghai-Tibet Plateau in China. We found that the annual rise in net tree C stock ranged from 0.13 to 0.23 Mg C ha ^−1 yr ^−1 , as forest growth increased from 1979 to 2017. The highest rates of growth and mortality were in coniferous broad-leaved mixed forest (CBMF), followed by deciduous broad-leaved forests (BDF), evergreen, deciduous broad-leaved mixed forest (EDBMF), and coniferous forest (CF). Mortality increased in EDBMF and CF during the study period. The most important biotic factors were tree richness and tree density, especially in BDF and CBMF. The most important abiotic factors driving these biological responses were a significant rise in monthly mean temperature (MMT) and a decline in precipitation in the growing season. The decrease in precipitation was negatively correlated with net tree C in CBMF and CF. The increase of MMT was positively correlated with growth and mortality of each forest type, and generally more important than richness and density, and overall had a positive effect on net tree C in CBMF and CF. Overall, we suggest that tree carbon stocks will continue to increase in CBMF and CF in the coming decades due to the warming.