Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 33, Issue 3, Pages 692-709Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2011.577830
Keywords
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Funding
- National State Key Basic Research Project [2007CB714404]
- Natural Science Foundation of China [40871173]
- Special Grant for Prevention and Treatment of Infectious Diseases [2008ZX10004-012]
- Key Science and Technology R&D Programme of Qinghai Province [2006-6-160-01]
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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.
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