GLIBERTY-DSAIL coupled model inversion of vegetation LAI in southern mixed forest

doi:10.13474/j.cnki.11-2246.2020.0351

Abstract

Abstract: In order to solve the problem of low leaf area index (LAI) inversion accuracy and little research in the coniferous-broadleaved mixed forest in the southern hilly areas, this paper proposes a GLIBERTY-DSAIL coupling model combined with multiple linear regression inversion LAI method. In this study, the GLIBERTY-DSAIL model selects simulated spectrum and vegetation measured hyperspectral as data sources. Through the correlation analysis, the vegetation index with high correlation about LAI is selected as the inversion factor, a multiple linear regression model is constructed to quantitatively invert vegetation LAI. The paper evaluates the accuracy. The results show that: RVI, DVI, GNDVI, and MSAVI vegetation indexes that are significantly related to LAI are used as inversion factors, combined with the model proposed in this paper to invert LAI, the model prediction coefficient R² is 0.708 6, and the root mean square error RMSE is 0.302 1. The accuracy is higher overall. This combined method can be used to invert vegetation LAI of coniferous-broadleaved mixed forests, and provide new ideas for the study of mixed forest LAI in southern areas.

Key words: GLIBERTY-DSAIL model, multiple linear regression, mixed forest, LAI, inversion

CLC Number:

GUO Yunkai, LIU Jianqin, GUO Yanqing, CAO Xiao, XIE Qiong. GLIBERTY-DSAIL coupled model inversion of vegetation LAI in southern mixed forest[J]. Bulletin of Surveying and Mapping, 2020, 0(11): 39-42,75.

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