Evaluation of Various Spectral Inputs for Estimation of Forest Biochemical and Structural Properties From Airborne Imaging Spectroscopy Data

Abstract

ABSTRACT:In this study we evaluated various spectral inputs for retrieval of forest chlorophyll content (Cab) and leaf area index (LAI) from high spectral and spatial resolution airborne imaging spectroscopy data collected for two forest study sites in the Czech Republic (beech forest at Štítná nad Vláří and spruce forest at Bílý Kříž). The retrieval algorithm was based on a machine learning methodsupport vector regression (SVR). Performance of the four spectral inputs used to train SVR was evaluated: a) all available hyperspectral bands, b) continuum removal (CR) 645 -710 nm, c) CR 705 -780 nm, and d) CR 680 -800 nm. Spectral inputs and corresponding SVR models were first assessed at the level of spectral databases simulated by combined leaf-canopy radiative transfer models PROSPECT and DART. At this stage, SVR models using all spectral inputs provided good performance (RMSE for Cab < 10 µg cm -2 and for LAI < 1.5), with consistently better performance for beech over spruce site. Since application of trained SVRs on airborne hyperspectral images of the spruce site produced unacceptably overestimated values, only the beech site results were analysed. The best performance for the Cab estimation was found for CR bands in range of 645 -710 nm, whereas CR bands in range of 680 -800 nm were the most suitable for LAI retrieval. The CR transformation reduced the across-track bidirectional reflectance effect present in airborne images due to large sensor field of view.

Quantifying Vegetation Biophysical Variables from Imaging Spectroscopy Data: A Review on Retrieval Methods

“…For instance, leaf chlorophyll content was estimated based on a PROSAIL-SVR model and applied to imaging spectroscopy (Preidl and Doktor, 2011). An analogous concept was applied for a SVR that was trained by PROSPECT-DART simulations in combination with continuum removal transformations, with the purpose of quantifying forest biochemical and structural properties (Homolová et al, 2016).…”