Abstract
The Qilian Mountain ecosystems play an irreplaceable role in maintaining ecological security in western China. Vegetation, as an important part of the ecosystem, has undergone considerable changes in recent decades in this area, but few studies have focused on the process of vegetation change. A long normalized difference vegetation index (NDVI) time series dataset based on remote sensing is an effective tool to investigate large-scale vegetation change dynamics. The MODerate resolution Imaging Spectroradiometer (MODIS) NDVI dataset has provided very detailed regional to global information on the state of vegetation since 2000. The aim of this study was to explore the spatial-temporal characteristics of abrupt vegetation changes and detect their potential drivers in the Qilian Mountain area using MODIS NDVI data with 1 km resolution from 2000 to 2017. The Breaks for Additive Season and Trend (BFAST) algorithm was adopted to detect vegetation breakpoint change times and magnitudes from satellite observations. Our results indicated that approximately 80.1% of vegetation areas experienced at least one abrupt change from 2000 to 2017, and most of these areas were distributed in the southern and northern parts of the study area, especially the area surrounding Qinghai Lake. The abrupt browning changes were much more widespread than the abrupt greening changes for most years of the study period. Environmental factors and anthropogenic activities mainly drove the abrupt vegetation changes. Long-term overgrazing is likely the main cause of the abrupt browning changes. In addition, our results indicate that national ecological protection policies have achieved positive effects in the study area.
Highlights
Vegetation, as a medium of material cycling, the water cycle, information transfer, and energy flow, is the most critical component of terrestrial ecosystems [1,2,3,4,5,6]
Zhang et al [42] used the Landsat time series normalized difference vegetation index (NDVI) data from 1986 to 2015 to investigate the land use change over the last 30 years, and the results showed that the areas of cropland, forest and grassland declined and the area of Gobi increased, while the extents of grassland fragmentation and desertification increased during the investigated decades
The number of abrupt changes detected in the trend component for each pixel varied from zero to four
Summary
Vegetation, as a medium of material cycling, the water cycle, information transfer, and energy flow, is the most critical component of terrestrial ecosystems [1,2,3,4,5,6]. Within the context of global climate change, it is important to explore vegetation dynamics spatially and temporally at global and regional scales [7]. Through this information, global change scientists, researchers, natural resource managers and policy makers can provide more accurate evaluations and forecasts to inform decisions related to vegetation. Satellite remote sensing technology is a unique and useful method for monitoring vegetation dynamics and environmental changes in a repeatable manner due to its high spatial coverage and long temporal series [8,9]. The first and third situations, which include seasonal and interannual changes, are usually caused by human cultivation or a response to long-term climate variations. To better analyze the driving factors underlying vegetation changes, it is important to detect where and when the vegetation changed, which is hereafter referred to as an abrupt change
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