Monitoring vegetation growth and exploring the driving force behind it is very important for the study of global climate change and ecological environmental protection. Based on Normalized Difference Vegetation Index (NDVI) data from Moderate-Resolution Imaging Spectroradiometer (MODIS), meteorological and nighttime lights data from 2001 to 2020, this study uses the Theil–Sen slope test, Mann–Kendall significance test, Rescaled Range Analysis and partial correlation analysis to investigate the evolution of NDVI in the Minjiang River Basin, China, from three aspects: the spatial-temporal variation characteristics and future trend prediction of NDVI, the variation of climate and human activities in the basin, and the influences of different driving forces on NDVI. The results show that the average NDVI in the growing season was 0.60 in the Minjiang River Basin in the past twenty years, with a growth rate of 0.002/a. The area with high NDVI growth accounts for 66.02%, mainly distributed in the southeast, the central and the northern low-altitude areas of the basin. Combined with the Hurst index, the NDVI in the Minjiang River Basin exhibits an anti-sustainable tendency, with 63.22% of the area changing from improvement to degradation in the future. Meanwhile, the spatial differentiation of NDVI in the Minjiang River Basin is mainly affected by topography and climate factors, followed by human activities. This study not only provides scientific guidelines for the vegetation restoration, soil and water conservation and sustainable development of the Minjiang River Basin, but also provides a scientific basis for making informed decisions on ecological protection under the impacts of climate change and human activities.

In the context of rapid urbanization development, exploring the driving forces and characteristics of urban agglomerations’ spatial expansion not only enriches the depth and breadth of research on urban agglomerations’ expansion in China, but also has great significance for future urban development planning, rational utilization of land resources, and protection of ecological environment. This study selects nighttime light data to extract the built-up areas of the Chengdu–Chongqing urban agglomeration from 2012 to 2020, and uses urban expansion speed, urban expansion intensity, urban center of gravity migration, compactness, and fractal dimension index, combined with driving force analysis, to explore its development. The results show the following: (1) From 2012 to 2020, the Chengdu–Chongqing urban agglomeration expanded around Chengdu and Chongqing, and the expansion scale of each city first increased and then decreased. (The expansion scale is a comprehensive result obtained by using the formula of urban expansion speed and expansion intensity, indicating the extent of urban expansion.) (2) The expansion direction of the Chengdu–Chongqing urban agglomeration keeps close to Chengdu and Chongqing. The urban spatial form is not stable and begins to focus on urban interior filling. (Urban interior filling refers to the infrastructure construction and content enrichment and renewal within an urban built-up area.) (3) The spatio-temporal expansion of the Chengdu–Chongqing urban agglomeration from 2012 to 2020 is most affected by the levels of economic development and regional investment.

Cropland is a vital resource intricately connected to food security. Currently, the issue of cropland abandonment poses a serious threat to food production and supply, presenting a significant challenge to rural economies and the stability of the food supply chain. The hilly and cloudy regions of southwest China are particularly affected by cropland abandonment, presenting significant challenges in accurately mapping the distribution of abandoned cropland due to fragmentation and heavy cloud pollution. Therefore, this study focuses on Mingshan County, located in Ya’an City, Sichuan Province, China, as the study area. Utilizing Google Earth Engine (GEE) and a random forest algorithm, a method integrating multi-source data from Landsat 8, Sentinel-2, and Sentinel-1 is proposed to extract abandoned cropland spanning from 2018 to 2022. This study analyzes spatial and temporal characteristics, employing the Geodetector with optimal parameters to explore the underlying mechanisms. The findings reveal the following: (1) The method achieves an overall accuracy of land use classification surpassing 88.67%, with a Kappa coefficient exceeding 0.87. Specifically, the accuracy for identifying abandoned cropland reaches 87.00%. (2) From 2018 to 2022, the abandonment rate in Mingshan County fluctuated between 4.58% and 5.77%, averaging 5.03%. The lowest abandonment rate occurred in 2019–2020, while the highest was observed in 2020–2021. (3) Cropland abandonment is influenced by both natural and social factors. Elevation and slope are the main driving factors, alongside factors such as distance to road, town, and residential settlement that all significantly contribute to abandonment trends. These five factors exhibit positive correlation with the abandonment rate, with distance to the river showing relatively weaker explanatory power.

Urbanization-induced ecological problems have affected China’s urban agglomerations since the beginning of rapid economic growth. The InVEST model can be used to study how land use changes affect carbon storage, while land simulation models help project future land use trends and assess the impact of policies on land use, thereby predicting future carbon storage. This study constructs a PLUS-InVEST-MGWR model, corrects carbon storage values in ArcGIS, and thereby analyzes its heterogeneity by MGWR. The economic value of carbon storage is calculated as well. The main findings are as follows: (1) The downward trend of carbon storage in the Chengdu–Chongqing region will continue but slow down to some extent, and only the ecological security scenario can prevent it. (2) In 2015, China’s social cost of carbon (SCC) was CNY 60.83 per ton, with a discount rate of 6.468%, while the economic value of carbon storage (EVCS) in the Chengdu–Chongqing region was CNY 289.516 × 10⁹. (3) Spatial correction of carbon storage is crucial for enhancing the goodness-of-fit and result accuracy of the MGWR model, as the absence of such correction would significantly degrade its performance. The revised InVEST model enables rapid quantification of carbon storage’s spatial heterogeneity.

The research on the spatiotemporal changes and driving factors of ecosystems in rapidly urbanizing regions has always been a topic of widespread concern. As the fourth pole of China’s economic development, the research on the Chengdu-Chongqing region has reference significance for the urbanization process of developing countries such as India, Brazil, and South Africa.The normalized difference vegetation index (NDVI) has been widely applied in studies of plant and ecosystem changes. Based on MODIS NDVI data from 2001 to 2020 and meteorological data of the same period, this study reveals the evolution of NDVI in the Chengdu-Chongqing region from three aspects: the spatiotemporal variation characteristics of NDVI, the prediction of future trends in vegetation coverage, and the response of vegetation to climate change and human activities. During the period of plant growth, the mean NDVI achieved a value of 0.78, and the vegetation coverage rate is increasing year by year. According to the Hurst index, the future NDVI in Chengdu-Chongqing region will tend to decrease, and its trend is opposite to that of the past period of time. The Chengdu-Chongqing region vegetation positively affected by human activities is greater than those negatively affected, and in terms of vegetation degradation, the impact of human activities is greater than climate change.