中国地理科学 ›› 2017, Vol. 27 ›› Issue (5): 827-835.doi: 10.1007/s11769-017-0894-6

• 论文 • 上一篇    下一篇

Effects of RapidEye Imagery's Red-edge Band and Vegetation Indices on Land Cover Classification in an Arid Region

LI Xianju1, CHEN Gang2, LIU Jingyi3, CHEN Weitao1, CHENG Xinwen4, LIAO Yiwei5   

  1. 1. College of Computer Science and Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China;
    2. College of Marine Science and Technology, China University of Geosciences, Wuhan 430074, China;
    3. Laboratory of Geographic Information and Spatial Analysis, Department of Geography and Planning, Queen's University, Kingston ON K7L3N6, Canada;
    4. Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China;
    5. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • 收稿日期:2016-05-13 修回日期:2016-09-08 出版日期:2017-10-27 发布日期:2017-09-07
  • 通讯作者: CHEN Gang,E-mail:ddwhcg@cug.edu.cn E-mail:ddwhcg@cug.edu.cn
  • 基金资助:

    Under the auspices of Fundamental Research Funds for Central Universities,China University of Geosciences (Wuhan)(No.CUGL150417),National Natural Science Foundation of China (No.41274036,41301026)

Effects of RapidEye Imagery's Red-edge Band and Vegetation Indices on Land Cover Classification in an Arid Region

LI Xianju1, CHEN Gang2, LIU Jingyi3, CHEN Weitao1, CHENG Xinwen4, LIAO Yiwei5   

  1. 1. College of Computer Science and Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan 430074, China;
    2. College of Marine Science and Technology, China University of Geosciences, Wuhan 430074, China;
    3. Laboratory of Geographic Information and Spatial Analysis, Department of Geography and Planning, Queen's University, Kingston ON K7L3N6, Canada;
    4. Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China;
    5. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
  • Received:2016-05-13 Revised:2016-09-08 Online:2017-10-27 Published:2017-09-07
  • Contact: CHEN Gang,E-mail:ddwhcg@cug.edu.cn E-mail:ddwhcg@cug.edu.cn
  • Supported by:

    Under the auspices of Fundamental Research Funds for Central Universities,China University of Geosciences (Wuhan)(No.CUGL150417),National Natural Science Foundation of China (No.41274036,41301026)

摘要:

Land cover classification (LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidEye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidEye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows:1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement (3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.

关键词: arid region, land cover classification, RapidEye, red-edge band, vegetation indices, random forest, Dunhuang Basin

Abstract:

Land cover classification (LCC) in arid regions is of great significance to the assessment, prediction, and management of land desertification. Some studies have shown that the red-edge band of RapidEye images was effective for vegetation identification and could improve LCC accuracy. However, there has been no investigation of the effects of RapidEye images' red-edge band and vegetation indices on LCC in arid regions where there are spectrally similar land covers mixed with very high or low vegetation coverage information and bare land. This study focused on a typical inland arid desert region located in Dunhuang Basin of northwestern China. First, five feature sets including or excluding the red-edge band and vegetation indices were constructed. Then, a land cover classification system involving plant communities was developed. Finally, random forest algorithm-based models with different feature sets were utilized for LCC. The conclusions drawn were as follows:1) the red-edge band showed slight contribution to LCC accuracy; 2) vegetation indices had a significant positive effect on LCC; 3) simultaneous addition of the red-edge band and vegetation indices achieved a significant overall accuracy improvement (3.46% from 86.67%). In general, vegetation indices had larger effect than the red-edge band, and simultaneous addition of them significantly increased the accuracy of LCC in arid regions.

Key words: arid region, land cover classification, RapidEye, red-edge band, vegetation indices, random forest, Dunhuang Basin