Spatiotemporal Variations in Fractional Vegetation Cover and Their Responses to Climatic Changes on the Qinghai–Tibet Plateau

Abstract

The alpine vegetation of the Qinghai–Tibet Plateau (QTP) is extremely vulnerable and sensitive to climatic fluctuations, making it an ideal area to study the potential impacts of climate on vegetation dynamics. Fractional vegetation cover (FVC) is regarded as one of the key indicators in monitoring semiarid and arid ecosystems due to its sensitive responses to vegetation behavior under climatic changes. Although many studies have analyzed the responses of vegetation on the QTP to climatic change, limited information is available on the influence of climatic variables on FVC changes in this area. In this study, we used satellite images and meteorological data to investigate the spatiotemporal variations of FVC during the growing season (FVCGS) during 1998–2018 and evaluated the responses to changes in climatic variables. Results showed that FVCGS displayed an overall fluctuating rise of 0.01/10 a (p < 0.01) over the study period. The FVCGS variation was spatially heterogeneous, with a general trend of greening in the northern and browning in the southern QTP. Obvious correlations were observed between the average FVC, average temperature, and total precipitation of the growing season, with precipitation being the primary controlling factor for vegetation growth. Some regions in the northwestern and northeastern QTP showed greening trends due to the positive influence of precipitation. Some areas in the southwestern QTP experienced browning trends due to water shortages caused, probably, by the weakening of the Indian monsoon. Browning in the southeastern parts was likely caused by drought and permafrost degradation resulting from high temperature. The inconsistent trend of vegetation change on the QTP is relatively high considering the continuous warming and changing atmospheric circulation patterns. FVC in most regions of the QTP has 0–1 month temporal responses to precipitation and temperature. Moreover, the one-month lagged effects of temperature and precipitation had a greater influence on steppe and desert vegetation than on other vegetation types. This research provides new perspectives for understanding the QTP vegetation response to climatic changes and a basis for making reasonable vegetation conservation and management policies.

The Impact of Groundwater Burial Depth on the Vegetation of the Dariyabui Oasis in the Central Desert

“…However, remote sensing images are affected by the atmosphere, surface complexity, and soil when obtaining spectral information, and scholars often investigate the study area in the field and determine confidence intervals by combining various factors. For example, confidence values differ, with cumulative likelihoods of NDVI purely bare ground taken as values of 0, 0.5%, 1%, 2%, and 5%, and cumulative likelihoods of NDVI purely vegetation taken as values of 100%, 99.5%, 99%, 98%, and 95% [2,[49][50][51][52][53]. The study area was an arid desert hinterland with a single type of surface vegetation.…”

Effects of hydrothermal factors and human activities on the vegetation coverage of the Qinghai-Tibet Plateau

“…Han et al . identified a significant correlation between average temperature, total precipitation, and vegetation coverage during the growing season on the QTP, with precipitation being the main controlling factor for vegetation growth 16 . Huang et al .…”

An advanced TSMK-FVC approach combined with Landsat 5/8 imagery for assessing the long-term effects of terrain and climate on vegetation growth

“…It can be utilized to track the development of vegetation cover ( Marsett et al., 2006 ; Lehnert et al., 2015 ). Therefore, FVC can serve as an effective indicator of the vegetation assessment, reflecting the dynamic changes of vegetation affected by various elements such as climate shift, land cover variation, and environmental projects ( Geng et al., 2022 ; Han et al., 2023 ). On the other hand, long time series-based FVC allows for the analysis of vegetation cover changes in the study area and require the integration of multiple change monitoring methods ( Zhu et al., 2021 ; Fu et al., 2023 ).…”