Sub-pixel mapping of urban land cover using multiple endmember spectral mixture analysis: Manaus, Brazil
The spatial and spectral variability of urban environments present fundamental challenges to deriving accurate remote sensing products forurban areas. Multiple endmember spectral mixture analysis (MESMA) is a technique that potentially addresses both challenges. MESMA modelsspectra as the linear sum of spectrally pure endmembers that vary on a per-pixel basis. Spatial variability is addressed by mapping sub-pixelcomponents of land cover as a combination of endmembers. Spectral variability is addressed by allowing the number and type of endmembers tovary from pixel to pixel. This paper presents an application of MESMA to map the physical components of urban land cover for the city ofManaus, Brazil, using Landsat Enhanced Thematic Mapper (ETM+) imagery.We present a methodology to build a regionally specific spectral library of urban materials based on generalized categories of urban land-covercomponents: vegetation, impervious surfaces, soil, and water. Using this library, we applied MESMA to generate a total of 1137 two-, three-, andfour-endmember models for each pixel; the model with the lowest root-mean-squared (RMS) error and lowest complexity was selected on a perpixelbasis. Almost 97% of the pixels within the image were modeled within the 2.5% RMS error constraint. The modeled fractions were used togenerate continuous maps of the per-pixel abundance of each generalized land-cover component. We provide an example to demonstrate that landcovercomponents have the potential to characterize trajectories of physical landscape change as urban neighborhoods develop through time.Accuracy of land-cover fractions was assessed using high-resolution, geocoded images mosaicked from digital aerial videography. Modeledvegetation and impervious fractions corresponded well with the reference fractions. Modeled soil fractions did not correspond as closely with thereference fractions, in part due to limitations of the reference data. This work demonstrates the potential of moderate-resolution, multispectralimagery to map and monitor the evolution of the physical urban environment.