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RSS FeedsRemote Sensing, Vol. 13, Pages 4797: Detecting the Turning Points of Grassland Autumn Phenology on the Qinghai-Tibetan Plateau: Spatial Heterogeneity and Controls (Remote Sensing)

 
 

26 november 2021 12:10:11

 
Remote Sensing, Vol. 13, Pages 4797: Detecting the Turning Points of Grassland Autumn Phenology on the Qinghai-Tibetan Plateau: Spatial Heterogeneity and Controls (Remote Sensing)
 


Autumn phenology, commonly represented by the end of season (EOS), is considered to be the most sensitive and crucial productivity indicator of alpine and cold grassland in the Qinghai-Tibetan Plateau. Previous studies typically assumed that the rates of EOS changes remain unchanged over long time periods. However, pixel-scale analysis indicates the existence of turning points and differing EOS change rates before and after these points. The spatial heterogeneity and controls of these turning points remain unclear. In this study, the EOS turning point changes are extracted and their controls are explored by integrating long time-series remote sensing images and piecewise regression methods. The results indicate that the EOS changed over time with a delay rate of 0.08 days/year during 1982–2015. The rates of change are not consistent over different time periods, which clearly highlights the existence of turning points. The results show that temperature contributed most strongly to the EOS changes, followed by precipitation and insolation. Furthermore, the turning points of climate, human activities (e.g., grazing, economic development), and their intersections are found to jointly control the EOS turning points. This study is the first quantitative investigation into the spatial heterogeneity and controls of the EOS turning points on the Qinghai-Tibetan Plateau, and provides important insight into the growth mechanism of alpine and cold grassland.


 
130 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 13, Pages 4796: Landsat Image-Based Retrieval and Analysis of Spatiotemporal Variation of Total Suspended Solid Concentration in Jiaozhou Bay, China (Remote Sensing)
Remote Sensing, Vol. 13, Pages 4801: Initial Results of Modeling and Improvement of BDS-2/GPS Broadcast Ephemeris Satellite Orbit Based on BP and PSO–BP Neural Networks (Remote Sensing)
 
 
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