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Effects of RapidEye imagery’s red-edge band and vegetation indices on land cover classification in an arid region

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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.

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Acknowledgements

We thank Kong Lingfeng, Zhao Duohui, Liu Bo, and Yan Zezhou for helping to conduct the field investigation.

Author information

Authors and Affiliations

  1. College of Computer Science and Hubei Key Laboratory of Intelligent Geo-Information Processing, China University of Geosciences, Wuhan, 430074, China

    Xianju Li & Weitao Chen

  2. College of Marine Science and Technology, China University of Geosciences, Wuhan, 430074, China

    Gang Chen

  3. Laboratory of Geographic Information and Spatial Analysis, Department of Geography and Planning, Queen’s University, Kingston, ON, K7L3N6, Canada

    Jingyi Liu

  4. Faculty of Information Engineering, China University of Geosciences, Wuhan, 430074, China

    Xinwen Cheng

  5. School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yiwei Liao

Authors
  1. Xianju Li
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  2. Gang Chen
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  3. Jingyi Liu
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  4. Weitao Chen
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  5. Xinwen Cheng
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  6. Yiwei Liao
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Corresponding author

Correspondence to Gang Chen.

Additional information

Foundation item: 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)

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Li, X., Chen, G., Liu, J. et al. Effects of RapidEye imagery’s red-edge band and vegetation indices on land cover classification in an arid region. Chin. Geogr. Sci. 27, 827–835 (2017). https://doi.org/10.1007/s11769-017-0894-6

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  • DOI: https://doi.org/10.1007/s11769-017-0894-6

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