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Estimation of Vegetation Coverage in Semi-arid Sandy Land Based on Multivariate Statistical Modeling Using Remote Sensing Data

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Abstract

The estimation of vegetation coverage is essential in the monitoring and management of arid and semi-arid sandy lands. But how to estimate vegetation coverage and monitor the environmental change at global and regional scales still remains to be further studied. Here, combined with field vegetation survey, multispectral remote sensing data were used to estimate coverage based on theoretical statistical modeling. First, the remote sensing data were processed and several groups of spectral variables were selected/proposed and calculated, and then statistically correlated to measured vegetation coverage. Both the single- and multiple-variable-based models were established and further analyzed. Among all single-variable-based models, that is based on Normalized Difference Vegetation Index showed the highest R (0.900) and R 2 (0.810) as well as lowest standard estimate error (0.128024). Since the multiple-variable-based model using multiple stepwise regression analysis behaved much better, it was determined as the optimal model for local coverage estimation. Finally, the estimation was conducted based on the optimal model and the result was cross-validated. The coefficient of determination used for validation was 0.867 with a root-mean-squared error (RMSE) of 0.101. The large-scale estimation of vegetation coverage using statistical modeling based on remote sensing data can be helpful for the monitoring and controlling of desertification in arid and semi-arid regions. It could serve for regional ecological management which is of great significance.

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Acknowledgments

The authors are grateful to China Centre for Resources Satellite Data and Application (CRESDA) for providing the HJ-1 data. Thanks to professor Mohamed Musbah for providing language editing on the manuscript. The authors also thank all people that have given help on the field survey.

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Authors and Affiliations

  1. Biosphere Informatics Laboratory, Department of Social Informatics, Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan

    Wei Chen, Tetsuro Sakai, Kazuyuki Moriya & Lina Koyama

  2. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing, 100101, China

    Chunxiang Cao

Authors
  1. Wei Chen
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  2. Tetsuro Sakai
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  3. Kazuyuki Moriya
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  4. Lina Koyama
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Correspondence to Wei Chen.

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Chen, W., Sakai, T., Moriya, K. et al. Estimation of Vegetation Coverage in Semi-arid Sandy Land Based on Multivariate Statistical Modeling Using Remote Sensing Data. Environ Model Assess 18, 547–558 (2013). https://doi.org/10.1007/s10666-013-9359-1

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  • DOI: https://doi.org/10.1007/s10666-013-9359-1

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