Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 35, Issue 1, Pages 171-188Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2013.866289
Keywords
LAI; texture parameters; vegetation index; black locust (Robinia pseudoacaciaL; ); Quickbird image
Funding
- twelfth Five-Year Plan of National Science and Technology in China [2012BAD22B0302]
- programme of Silviculture teaching reform [Z105021003]
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Optical remote sensing is the most widely used method for obtaining leaf area index (LAI) information. However, there is a need for improved processing techniques to increase the accuracy of LAI estimates obtained in this way. This article describes the use of high-resolution optical data from the Quickbird satellite for LAI estimation in the semi-arid region of the Loess Plateau, China. Three different image processing techniques were evaluated: processing based on spectral vegetation indices (SVIs), texture parameters, and combinations of SVIs with textural analyses. Simple linear and nonlinear regression models were developed to describe the relationship between image parameters obtained using these approaches and 52 field measurements of LAI. SVI-based approaches did not yield reliable LAI estimates, accounting for at best 68% of the observed variation in LAI. Texture-based methods were somewhat better, explaining up to 72% of the observed variation. A combination of the two approaches yielded an even better adjusted r(2) value of 0.84. This demonstrates that the accuracy of estimated LAI values based on remote-sensing data can be significantly increased by considering a combination of SVIs and texture parameters.
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