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Proceedings Paper

Estimating fractional vegetation cover of oasis in Tarim Basin, China, using dimidiate fractional cover model
Author(s): Xiaoyong Han; Ling Han
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Paper Abstract

Oasis is an important component of desert ecosystem. This paper employs Landsat Thematic Mapper (TM) multi-spectral data to extract fractional vegetation cover of oasis in Tarim Basin with four methods. The mixture pixel decomposition model based on normalized difference vegetation index(NDVI) is firstly used to estimate fractional vegetation cover(FVC). The results indicated that the method is mainly underestimating the FVC at the low FVC area and overestimating the FVC at high FVC area. Thereafter, a stepwise regression model between 15 Vegetation Indices (VIs) and measured FVC data and a log-linear model have been established through the relation analysis of FVC and NDVI. Trials of these two models showed that they are mainly overestimating the FVC. Finally, a dimidiate fractional cover model was proposed, which is composed of two linear functions. When the NDVI is less than 0.3, the linear function is formed by stress related vegetation index (STVI1) and normal differential water index (NDWI) (R2, 0.764) while the NDVI is greater than 0.3, the linear function is composed of NDVI and perpendicular vegetation index (PVI) (R2, 0.801). The validation of the dimidiate fractional cover model has been tested with the measured data. In the optimal case, the mean error is 0.002 and the RMSE is 0.051, demonstrating that the model can be used in estimating fractional vegetation cover of oasis in Tarim Basin.

Paper Details

Date Published: 9 December 2015
PDF: 8 pages
Proc. SPIE 9808, International Conference on Intelligent Earth Observing and Applications 2015, 98080J (9 December 2015); doi: 10.1117/12.2204868
Show Author Affiliations
Xiaoyong Han, Chang'an Univ. (China)
Ling Han, Chang'an Univ. (China)


Published in SPIE Proceedings Vol. 9808:
International Conference on Intelligent Earth Observing and Applications 2015
Guoqing Zhou; Chuanli Kang, Editor(s)

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