Subpixel mapping and confusion matrix analysis of plant functional types in peatlands using MESMA applied to AISA Eagle imagery

Abstract

Quantification of the spatial distribution of fractional cover of plant functional types is important for peatland ecosystems analysis. Using a crisp classification to produce a map of the spatial patterns of plant functional types in peatlands is difficult due to their complex and heterogeneous characteristics. We applied multiple endmember spectral mixture analysis (MESMA) on Airborne Imaging Spectrometer for Applications Eagle data to generate fractional covers of peatland plant functional types and further to assess the accuracy of soft classification at the subpixel scale. This was achieved by constructing the spectral library and selecting the optimal endmembers models (two-, three-, and four-endmember MESMA models) to derive the peatland plant functional types (shrub, graminoid, moss, and forbs) fractional covers. The final fractional cover pattern in each case was produced by joining the results from the multiendmembers models as judged by the root mean squared error thresholds to select the best-fit model for each pixel by minimization of pixel-scale fraction errors. Based on the thresholds, the three- and four-endmember MESMA models and their combination were further tested for their performance using an independent in situ dataset of the four-plant functional types. Their accuracy performance was quantified by a subpixel confusion matrix and residual analysis. The results showed that the combined MESMA model was most accurate in deriving the patterns of fractional cover for shrub, graminoid, and moss compared with the performance of the three- and four-endmember MESMA models. However, all models performed poorly in predicting the fractional cover of the sparsely distributed forbs. The results demonstrate the superiority of MESMA models in extracting the patterns of different plant functional types in peatlands as judged by the accuracy of subpixel fractional extraction.