A comparison of three methods for estimating the LAI of black locust (Robinia pseudoacacia L.) plantations on the Loess Plateau, China

Abstract

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 r² 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.

Keywords:

• LAI
• texture parameters
• vegetation index
• black locust (Robinia pseudoacacia L.)
• Quickbird image

Acknowledgements

We thank the anonymous reviewers for their constructive and valuable comments, and the editors for their assistance in refining this article. We also thank Wang Cheng from Northwest A&F University for providing valuable suggestions during manuscript revision.