Automatic classification of grassland type in Xinjiang Ili based on spatial interpolation of remote sensing and other data

doi:10.11686/cyxb2017013

Abstract

Abstract: With support of remote sensing and geographic information system technology, the inventory of grassland types in China has advanced rapidly. This study used a range of input data including among others grassland community height, ground cover, aboveground biomass, underground biomass, the surface soil bulk density, soil total carbon content, soil organic carbon content, soil total nitrogen content, and soil total phosphorus content. With the collected data, a spatial interpolation algorithm was applied using a Decision Tree Classifier approach to produce an automated classification and index of grassland type in Xinjiang Ili. The grassland types identified based on these nine input variables, consistently matched grassland survey data collected in the 1980s, and showed the methodology to be reliable This study provides a tool for decision makers involved in managing the utilization of grassland resources and livestock production in the Xinjiang Ili region.

QIAO Yu-Xin, ZHU Hua-Zhong, SHAO Xiao-Ming, ZHONG Hua-Ping, ZHOU Li-Lei, WU Zhao-Wen. Automatic classification of grassland type in Xinjiang Ili based on spatial interpolation of remote sensing and other data[J]. Acta Prataculturae Sinica, 2017, 26(10): 30-45.

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