Characterizing Tree Species in Northern Boreal Forests Using Multiple-Endmember Spectral Mixture Analysis and Multi-Temporal Satellite Imagery

Northern boreal forests are characterized by open stands whereby trees, understory background, and shadow are all significant components of the spectral response within a pixels’ spatial footprint. To overcome this mixed pixel problem, accurate spectral characterization of these (endmember) components is necessary for spectral mixture analysis (SMA) to generate forest classifications at the species level. Obtaining these endmember spectra in the field, however, can be difficult or impossible. This study examined whether image endmember spectra can be identified using forest inventory information to derive dominant tree species classifications. This was tested using multiple-endmember SMA (MESMA) and single- and multi-date Landsat imagery of a forested area in the Northwest Territories, Canada. Image classifications (n = 80) were generated based on 20 image-date combinations and four unmixing models. Accuracies of 80% and 82% were achieved for open and medium dense forest stands, respectively using multi-date imagery, which outperformed single-date imagery acquired at peak phenology. The overall accuracy is 72%; lower due to challenges in very open stands. The multi-date MESMA approach was robust for both compositionally pure and mixed stands. The approach merits further investigation, particularly within the context of the increasing availability of regional-scale satellite imagery enabling composite time-series and spectral-temporal image features.