Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery

Abstract

Scientific and technical challenges remain significant to accurate classification of land cover and forest species as a result of the many spectral and spatial variables influencing surface reflectance, coupled with the constraints imposed by the spectral and spatial characteristics of the remote sensing instrumentation. The use of systematic differences in canopy pigment or chemistry by cover type or by species as a basis for land cover classification has very recently emerged as a potentially new approach. In this study, classification of land cover is investigated, based on chlorophyll content variations as inferred from spectral bands in the red edge reflectance region. This analysis was carried out on data collected with the Compact Airborne Spectrographic Imager (CASI) for a 16 km × 12 km image mosaic over the submodeling grid of the southern study area at the Boreal Ecosystem‐Atmosphere Study (BOREAS). The analysis demonstrates that land cover mapping, based solely on red edge spectral parameters, appears to be feasible, robust, and for some cover classes outperforms other current classification methods. Classification 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 red edge parameter‐based land cover classification showed producer's accuracies which exceeded 68.6% for all classes identified: conifers (however, without an ability to separate wet from dry conifers), mixed stands, fen, and disturbed and regeneration features. The corresponding user's accuracies for these classes ranged between 58 and 66%, with the overall classification accuracies of 61.15% and Kappa coefficient (K) of 0.52. In comparison, the corresponding Kappa coefficients for the cover classification using 16 channel CASI data and for a TM‐based classification, were 0.36 and 0.29, respectively. Results of this study suggest that whereas land cover classification accuracy improvements for the important but illusive fen cover type in the boreal ecosystem are possible using classifications based on red edge parameters, significant uncertainties remain in the estimated aerial extent.