Boreal forest mapping at the BOREAS study area using seasonal optical indices sensitive to plant pigment content

Abstract

In this study, forest vegetation classification was investigated based on seasonal variation of pigments as inferred from visible and near-infrared spectral bands. This analysis was carried out on data collected over the southern study area of the Boreal Ecosystem-Atmosphere Study (BOREAS) with the Compact Airborne Spectrographic Imager (casi) in May and July 1994 and with the medium-resolution imaging spectrometer (MERIS) in May and August 2003. Three modified normalized difference vegetation indices (mNDVIs), which are sensitive to relative proportions among pigments and pigment content, and a red-edge spectral parameter, the wavelength at the reflectance minimum (λ0), were used. Accuracy assessments of the derived land cover maps were performed using a forest inventory map provided by the Saskatchewan Environment and Resource Management Forestry Branch Inventory Unit (SERM–FBIU). The forest vegetation classification using seasonal optical indices (mNDVIs and λ0), as derived from the casi data in May and July, shows an overall accuracy of 84% for all cover types identified, namely dry conifer, wet conifer, mixed stands, aspen, fen, and the disturbed and regenerated area. The classification results also demonstrate that classification using reflectance parameters sensitive to pigment absorption features outperforms that using the reflectance itself. In addition, the classification using seasonal information is better than that using information obtained from a single date, and the spatial patterns were consistent with those achieved using multidate MERIS imagery. The forest vegetation classification using seasonal changes in optical indices (mNDVIs and λ0) derived from the MERIS imagery in May and August revealed a reasonably high overall classification accuracy (72%) for all vegetation cover types identified, namely conifer, mixed stands, and fen.