Abstract
Grassland ecosystems in China have experienced degradation caused by natural processes and human activities. Time series segmentation and residual trend analysis (TSS-RESTREND) was applied to grasslands in eastern China. TSS-RESTREND is an extended version of the residual trend (RESTREND) methodology. It considers breakpoint detection to identify pixels with abrupt ecosystem changes which violate the assumptions of RESTREND. With TSS-RESTREND, in Xilingol (111°59′–120°00′E and 42°32′–46°41′E) and Hulunbuir (115°30′–122°E and 47°10′–51°23′N) grassland, 6% and 3% of the area experienced a decrease in greenness between 1984 and 2009, 80% and 73% had no significant change, 5% and 3% increased in greenness, and 9% and 21% were undetermined, respectively. RESTREND may underestimate the greening trend in Xilingol, but both TSS-RESTREND and RESTREND revealed no significant differences in Hulunbuir. The proposed TSS-RESTREND methodology captured both the time and magnitude of vegetation changes.
Highlights
Grasslands are the largest ecosystem in China, covering 393,000 km2, 43% of the country territory [1]
For TSS-RESTREND, 80% and 73% of the pixels were detected as unchanged in Xilingol and Hulunbuir, respectively (Tables 2 and 3). For those pixels considered unchanged with TSS-RESTREND, 80% in Xilingol and 87% in Hulunbuir had decreased based on the Linear Trend Analysis (LTA) results
In areas with low interannual climatic variability, vegetation phenology is relatively stable, which means that breakpoints detected in the growth cycle using breaks for additive seasonal and trend (BFAST) can be attributed to disturbances [18,19] or maximum normalized difference vegetation index (NDVI) regression methods like LTA can acquire abnormal deviation caused by human activities
Summary
Grasslands are the largest ecosystem in China, covering 393,000 km2, 43% of the country territory [1]. In the past forty decades, the grasslands in China have experienced serious land degradation caused by natural process and human activities, including climate change, land use alternations, and socioeconomic transformation [1,2]. Discerning the impacts of these drivers is important for understanding and managing landscapes, for grasslands in arid and semi-arid areas. In these areas, annual precipitation is low and inter-annual variability in rainfall high [3,4]. When conducting vegetation greenness change and land degradation analysis in these areas caused by human activities, the first step is to exclude the impact from climatic variations. Given that land degradation may occur progressively over many decades, it is necessary that measurements be consistent, and combined with large areas influenced [5,8], remote sensing is an appropriate approach
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