Hyperspectral analysis of mangrove foliar chemistry using PLSR and support vector regression

Abstract

Hyperspectral remote sensing enables the large-scale mapping of canopy biochemical properties. This study explored the possibility of retrieving the concentration of nitrogen, phosphorus, potassium, calcium, magnesium, and sodium from mangroves in the Berau Delta, Indonesia. The objectives of the study were to (1) assess the accuracy of foliar chemistry retrieval, (2) compare the performance of models based on support vector regression (SVR), i.e. ϵ-SVR, ν-SVR, and least squares SVR (LS-SVR), to models based on partial least squares regression (PLSR), and (3) investigate which spectral transformations are best suited. The results indicated that nitrogen could be successfully modelled at the landscape level (R² = 0.67, root mean square error (RMSE) = 0.17, normalized RMSE (nRMSE) = 15%), whereas estimations of P, K, Ca, Mg, and Na were less encouraging. The developed nitrogen model was applied over the study area to generate a map of foliar N variation, which can be used for studying ecosystem processes in mangroves. While PLSR attained good results directly using all untransformed bands, the highest accuracy for nitrogen modelling was achieved using a combination of LS-SVR and continuum-removed derivative reflectance. All SVR techniques suffered from multicollinearity when using the full spectrum, and the number of independent variables had to be reduced by singling out the most informative wavelength bands. This was achieved by interpreting and visualizing the structure of the PLSR and SVR models.