Spacecraft Recovery & Remote Sensing, Volume. 45, Issue 3, 137(2024)
Canopy Closure Inversion of Regional Plantation Based on Li-Strahler Geometric-Optical Model
Forest canopy closure is an important factor in describing forest structure. The traditional remote sensing inversion empirical model of canopy closure has high requirements for ground sample data and has the problem of difficulty in generalizing to large regional scales. In order to solve the above problems, this paper proposes a regional plantation closure inversion method by combining Li-Strahler geometric-optical model and linear spectral decomposition technique. The principle of this method is to use the average tree height and crown radius of different dominant tree species as geometric-optical model input parameters for each subcompartment, based on the extracted abundance of sunlit background endmember through linear spectral decomposition, then the inversion of canopy closure can be achieved. Based on the proposed method, inversion of the canopy closure of regional plantation was carried out in Chifeng and Nanning study areas, the accuracy of the estimated results was then verified by field measured data. The accuracy evaluation results showed that: the root mean square error and the mean absolute percentage error are 0.08 and 9.45% respectively in the plain region, and 0.12 and 14.04% respectively in the mountainous region for Chifeng study area; meanwhile, the root mean square error and the mean absolute percentage error are 0.11 and 8.68% respectively in the plain region, and 0.13 and 13.27% respectively in the mountainous region for Nanning study area; The results above indicate that the inversion method proposed in this paper can effectively improve the inversion accuracy of canopy closure in regional scale plantations, and has important application value.
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Xinwei YANG, Chunxiang CAO, Min XU, Kaimin WANG. Canopy Closure Inversion of Regional Plantation Based on Li-Strahler Geometric-Optical Model[J]. Spacecraft Recovery & Remote Sensing, 2024, 45(3): 137
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Received: Nov. 1, 2023
Accepted: Nov. 1, 2023
Published Online: Oct. 30, 2024
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