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Land Use Type Change Detection by Landsat TM and Gaofen-1 Data - A Case Study at Jishui County of Jiangxi Province

  • Zhaopeng ZhangEmail author
  • Zengyuan Li
  • Erxue Chen
  • Xin TianEmail author
Conference paper
Part of the Springer Proceedings in Physics book series (SPPHY, volume 209)

Abstract

Due to natural and anthropic disturbance, the global land use types dramatically changed, especially during the past decades. Based on maximum likelihood classifier, this study applied the Landsat Thematic Mapper 5 (TM) and Chinese domestic high resolution satellite data, Gaofen-1, to investigate the changes of land cover types between 2008 and 2016 at Jishui county of Jiangxi province, the Central China. The validation using the forest field data showed that the overall classifications of 2008 and 2016 are 91.40%, 94.68%, respectively. Based on these two temporal classification maps, we quantitatively analyzed changes of each land use type and its transitions to others. The analysis showed that two types largely changed between the study years, the water area and forest area. The water area increased significantly, which accounted for 1.66% of the total area of the study area, and the total forest coverage rate increased 2.6% from 2008 (62.04%) to 2016 (64.64%).

Keywords

Land use type change GaoFen-1 (GF-1) Landsat Thematic Mapper 5 (TM) 

Notes

Acknowledgements

We thank to China Center for Resources Satellite Data and Application and United States Geological Survey for providing the GF-1 image and the TM images with excellent quality. The authors also would like to thank Jiangxi Forest Inventory and Planning Institute and Jishui County Land Resources Bureau for providing some of the ground true data and the second national land survey land classification data for the validations. This word was funded by the Fundamental Research Funds for the Central Non-profit Research Institution of CAF under Grant CAFYBB2017QC005 and the National Natural Science Foundation of China (21-Y30B05-9001-13/15-1).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of Forest Resources Information TechniqueChinese Academy of ForestryBeijingPeople’s Republic of China
  2. 2.College of GeomaticsXi’an University of Science and TechnologyXi’anPeople’s Republic of China

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