Quantitative inversion of sparse vegetation coverage in desertification area

DOI

10.2991/asei-15.2015.339How to use a DOI?

Keywords

Desertification, Sparse vegetation, Quantitative remote sensing.

Abstract

Based on unmixing model and using the Thematic Mapper (TM) image as well as obtaining end-members by ground spectral measurements, quantitative retrieval of information on sparse vegetation coverage in oasis-desert transitional area in Minqin, Gansu was done. The results showed that a wide band of TM images can be used in extracting sparse vegetation coverage of arid regions. Three components were needed to build unmixing model and there includes light, vegetation - not light and vegetation - bare soil. And the unmixing model has a high correlation with measured vegetation coverage. Methods used to choose the end-members had certain influence on estimating the vegetation coverage. Monte Carlo method recorded 0.64 as coefficient of determination (R2) 3.8 as root mean square error (RMSE) and therefore was more accurate than the root mean square error minimization method.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xuedong Li
AU  - Xuya Zhang
AU  - HongYan Zhang
AU  - Guang bin Yang
PY  - 2015/05
DA  - 2015/05
TI  - Quantitative inversion of sparse vegetation coverage in desertification area
BT  - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation
PB  - Atlantis Press
SP  - 1713
EP  - 1717
SN  - 2352-5401
UR  - https://doi.org/10.2991/asei-15.2015.339
DO  - 10.2991/asei-15.2015.339
ID  - Li2015/05
ER  -