Advances in Earth Science ›› 2020, Vol. 35 ›› Issue (9): 978-989. doi: 10.11867/j.issn.1001-8166.2020.071

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Spatio-temporal Pattern of Remote Sensing Data Sources and Study Areas in Papers on Vegetation Cover Changes in China

Jiamei Wu( ),Qiuzhi Peng( ),Yizhong Huang,Liang Huang   

  1. Faculty of Land Resource Engineering,Kunming University of Science and Technology,Kunming 650093,China
  • Received:2020-05-27 Revised:2020-08-03 Online:2020-09-10 Published:2020-10-28
  • Contact: Qiuzhi Peng E-mail:2793394819@qq.com;pengqiuzhi@kust.edu.cn
  • About author:Wu Jiamei (1994-), female, Kunming City, Yunnan Province, Master student. Research areas include 3S technology and application in land use management. E-mail: 2793394819@qq.com
  • Supported by:
    the National Natural Science Foundation of China “Distribution and change characteristics of construction land on slope gradient in mountainous cities of southern China”(41961039);The Applied Basic Research Programs of Science and Technology Department of Yunnan Province “Change detection of multi-source remote sensing images based on full convolution neural network”(2018FB078)

Jiamei Wu,Qiuzhi Peng,Yizhong Huang,Liang Huang. Spatio-temporal Pattern of Remote Sensing Data Sources and Study Areas in Papers on Vegetation Cover Changes in China[J]. Advances in Earth Science, 2020, 35(9): 978-989.

The study on vegetation cover changes based on remote sensing data sources is an important component and frequent topic of earth surface process research. In order to review and summarize the remote sensing data sources and the spatio-temporal pattern of the study areas for vegetation cover changes in China from the perspective of bibliometrics and GIS spatial presentation, the information of remote sensing data sources and study areas were extracted from 1 021 papers on vegetation. Then the composition of remote sensing data sources and their temporal changes in heat which is termed as study frequency, the spatio-temporal scale characteristics of remote sensing data sources and study areas, and the distributions and change characteristics of hot study regions were analyzed. The results show that the number of papers is increasing gradually, and the frequency of each remote sensing data source is basically consistent with its available timing; the average period length of the remote sensing data used is gradually elongated, and the spatial resolution of remote sensing data is positively correlated with the size of study areas stably; the hot study regions are concentrated in the Loess Plateau area, and the core of the northern arid and semi-arid areas. These results can provide a basic reference for understanding the spatio-temporal pattern and the change trend on the utilization of remote sensing data sources and hot regions for vegetation change studies.

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