Spectral Patterns of Pixels and Objects of the Forest Phytophysiognomies in the Anauá National Forest, Roraima State, Brazil

Forest phytophysiognomies have specific spatial patterns that can be mapped or translated into spectral patterns of vegetation. Regions of spectral similarity can be classified by reference to color, tonality or intensity of brightness, reflectance, texture, size, shape, neighborhood influence, etc. We evaluated the power of accuracy of supervised classification algorithms via per-pixel (maximum likelihood) and geographic object-based image analysis (GEOBIA) for distinguishing spectral patterns of the vegetation in the northern Brazilian Amazon. A total of 280 training samples (70%) and 120 validation samples (30%) of each of the 11 vegetation cover and land-use classes (N = 4400) were classified based on differences in their visible (RGB), near-infrared (NIR), and medium infrared (SWIR 1 or MIR) Landsat 8 (OLI) bands. Classification by pixels achieved a greater accuracy (Kappa = 0.75%) than GEOBIA (Kappa = 0.72%). GEOBIA, however, offers a greater plasticity and the possibility of calibrating the spectral rules associated with vegetation indices and spatial parameters. We conclude that both methods enabled precision spectral separations (0.45–1.65 μm), contributing to the distinctions between forest phytophysiognomies and land uses—strategic factors in the planning and management of natural resources in protected areas in the Amazon region.