In recent decades, the vegetation of the Sanjiangyuan region has undergone a series of changes under the influence of climate change, and ecological restoration projects have been implemented. In this paper, we analyze the spatiotemporal dynamics of vegetation in this region using the satellite-retrieved normalized difference vegetation index (NDVI) from the global inventory modeling and mapping studies (GIMMS) and moderate resolution imaging and spectroradiometer (MODIS) datasets during the past 34 years. Specifically, the characteristics of vegetation changes were analyzed according to the stage of implementation of different ecological engineering programs. The results are as follows. (1) The vegetation in 65.6% of the study area exhibited an upward trend, and in 53.0% of the area, it displayed a large increase, which was mainly distributed in the eastern part of the study area. (2) The vegetation NDVI increased to differing degrees during stages of ecological engineering. (3) The NDVI in the western part of the Sanjiangyuan region is mainly affected by temperature, while in the northeastern part, the NDVI is affected more by precipitation. In the southern part, however, vegetation growth is affected neither by temperature nor by precipitation. On the whole region, vegetation growing is more affected by temperature than by precipitation. (4) The impacts of human activities on vegetation change are both positive and negative. In recent years, ecological engineering projects have had a positive impact on vegetation growth. This study can help us to correctly understand the impact of climate change on vegetation growth, so as to provide a scientific basis for the evaluation of regional ecological engineering effectiveness and the formulation of ecological protection policies.

As the population has increased and the economy has developed in the Qaidam Basin, the demand for food and energy in the basin has increased, and the contradiction between economic development and ecological protection is gradually becoming prominent. In this study, the eco-environmental quality of the Qaidam Basin from 1986 to 2019 was evaluated and analyzed based on the Modified Remote Sensing Ecological Index (MRSEI) retrieved by the Google Earth Engine (GEE) and meteorological and socioeconomic auxiliary data. The results show that (1) the Qaidam Basin had a lower overall level of eco-environmental quality, with higher eco-environmental quality in the southeastern part of the basin and lower eco-environmental quality in the central and northwestern parts of the basin. (2) During the period of 1986 to 2019, the eco-environmental quality of the Qaidam Basin started to reverse in 2003; it decreased first and then increased, and the overall performance showed an upward trend over the past 34 years. The most obvious changes were in the northwestern, northeastern, southwestern and central parts of the basin. The eco-environmental quality continued to decline in the northwestern and rise in the northeastern and southwestern regions, and in the central part, it decreased first and then plateaued. (3) The eco-environmental quality of the Qaidam Basin was affected by both natural and human factors. From 1986 to 2019, the “warm and wet” climate in the basin promoted the growth of vegetation. Furthermore, the optimization of industrial structures alleviated the pressure of agriculture and livestock and jointly improved the ecological environment in the Qaidam Basin.

The normalized difference vegetation index (NDVI) is a powerful tool for understanding past vegetation, monitoring the current state, and predicting its future. Due to technological and budget limitations, the existing global NDVI time-series data cannot simultaneously meet the needs of high spatial and temporal resolution. This study proposes a high spatiotemporal resolution NDVI fusion model based on histogram clustering (NDVI_FMHC), which uses a new spatiotemporal fusion framework to predict phenological and shape changes. Meanwhile, this model also uses four strategies to reduce error, including the construction of an overdetermined linear mixed model, multiscale prediction, residual distribution, and Gaussian filtering. Five groups of real MODIS_NDVI and Landsat_NDVI datasets were used to verify the predictive performance of the NDVI_FMHC. The results indicate that NDVI_FMHC has higher accuracy and robustness in forest areas (r = 0.9488 and ADD = 0.0229) and cultivated land areas (r = 0.9493 and ADD = 0.0605), while the prediction effect is relatively weak in areas subject to shape changes, such as flooded areas (r = 0.8450 and ADD = 0.0968), urban areas (r = 0.8855 and ADD = 0.0756), and fire areas (r = 0.8417 and ADD = 0.0749). Compared with ESTARFM, NDVI_LMGM, and FSDAF, NDVI_FMHC has the highest prediction accuracy, the best spatial detail retention, and the strongest ability to capture shape changes. Therefore, the NDVI_FMHC can obtain NDVI time-series data with high spatiotemporal resolution, which can be used to realize long-term land surface dynamic process research in a complex environment.