Combining Hyperspectral and Radar Imagery for Mangrove Leaf Area Index Modeling

This study acquired satellite-borne hyperspectral and radar data supplemented with in situ field survey to understand the current biophysical condition of mangrove in Mai Po Ramsar Site of Hong Kong through leaf area index (LAI) modeling by regressing field-measured LAI against vegetation indices (VIs), backscatter, and textural measures. Simple VI regression model revealed that triangular vegetation index (TVI) and modified chlorophyll absorption ratio index (MCARI) had the best relationship (r2 = 0.68) while the commonly-used NDVI had the poorest relationship (r2 = 0.017) with LAI. Poor correlation was found between measured LAI and radar parameters. Results from stepwise multiple regression suggested that TVI combined with GLCM-derived angular second moment formed the best model (r2 = 0.78) and reduced the estimation error (RMSE) to 0.2. This study has demonstrated that single use of radar parameters cannot effectively map mangrove LAI. Nonetheless, spectral and radar data are complementary to enhance the mapping.

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