23 October 2018 Quantifying the spatio-temporal variations and impact factors for vegetation coverage in the karst regions of Southwest China using Landsat data and Google Earth engine
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Abstract
This study proposed a remote sensing-based approach to quantify the spatio-temporal patterns of vegetation dynamics and associated impact factors in typical karst regions of Southwest China. Google Earth engine (GEE), the world's most advanced geospatial data cloud computing platform, was employed to construct long time series satellite data set with 30 m resolution, composed of nearly 4,000 Landsat scenes from 1988 to 2016. Image preprocessing was also conducted on the GEE platform. The maximum value composite (MVC) method was used to produce annual maximum normalized difference vegetation index (NDVI) of the study areas. Annual maximum fractional vegetation cover (annFVC) was thus quantitatively estimated based on Dimidiate Pixel Model (DPM). Ordinary least squares (OLS) regression was adopted to identify the spatial patterns of the direction and rate of change in annFVC at a pixel scale. In addition, a terrain niche index (TNI) was used to investigate the influence of topographic factors on vegetation trends. Moreover, the relationships between annFVC and climatic factors were identified using correlation analysis. The results show that annFVC significantly increased at a rate of 0.0032/year in Nandong and 0.0041/year in Xiaojiang watershed for the period 1988-2016. Furthermore, 26.97% and 27.16% of pixels were found to undergo significant increase in terms of annFVC in Nandong and Xiaojiang, respectively. For both Nandong and Xiaojiang, decreasing vegetation trend was curbed with the increase of elevation and slope. Additionally, correlation analysis demonstrated that annFVC was more strongly and positively correlated with temperature than with precipitation in spite of insignificance.
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Jie Pei, Zheng Niu, Li Wang, Ni Huang, and Jianhua Cao "Quantifying the spatio-temporal variations and impact factors for vegetation coverage in the karst regions of Southwest China using Landsat data and Google Earth engine", Proc. SPIE 10780, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications VII, 107800E (23 October 2018); doi: 10.1117/12.2323687; https://doi.org/10.1117/12.2323687
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