Retrieval of forest canopy attributes based on a geometric-optical model using airborne LiDAR and optical remote-sensing data

Abstract

In the retrieval of forest canopy attributes using a geometric-optical model, the spectral scene reflectance of each component should be known as prior knowledge. Generally, these reflectances were acquired by a foregone survey using an analytical spectral device. This article purposed to retrieve the forest structure parameters using light detection and ranging (LiDAR) data, and used a linear spectrum decomposition model to determine the reflectances of the spectral scene components, which are regarded as prior knowledge in the retrieval of forest canopy cover and effective plant area index (PAI_e) using a simplified Li–Strahler geometric-optical model based on a Satellites Pour l'Observation de la Terre 5 (SPOT-5) high-resolution geometry (HRG) image. The airborne LiDAR data are first used to retrieve the forest structure parameters and then the proportion of the SPOT pixel not covered by crown or shadow K_g of each pixel in the sample was calculated, which was used to extract the reflectances of the spectral scene components by a linear spectrum decomposition model. Finally, the forest canopy cover and PAI_e are retrieved by the geometric-optical model. As the acquired time of SPOT-5 image and measured data has a discrepancy of about 2 months, the retrieved result of forest canopy cover needs a further validation. The relatively high value of R ² between the retrieval result of PAI_e and the measurements indicates the efficiency of our methods.

Acknowledgements

This article has been supported by the National State Key Basic Research Project (Grant No. 2007CB714404); the Natural Science Foundation of China (Grant No. 40871173); the Special Grant for Prevention and Treatment of Infectious Diseases (2008ZX10004-012); and the Key Science and Technology R&D Programme of Qinghai Province (Grant No. 2006-6-160-01). We thank the Japan Aerospace Exploration Agency (JAXA) for providing the ALOS AVNIR-2 data. We are grateful to Prof. Ramesh P. Singh for his fruitful suggestions in improving the language of this article. We also thank all the people who helped in the preparation of this article.