The University of Massachusetts Amherst
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Classifying environmentally significant urban land uses with satellite imagery

TitleClassifying environmentally significant urban land uses with satellite imagery
Publication TypeJournal Article
Year of Publication2008
AuthorsPark M-H, Stenström MK
JournalJournal for Environmental Management
Start Page181
Date Published01/2008
KeywordsBayesian networks, Land use classification, remote sensing, Satellite imagery, Urban

We investigated Bayesian networks to classify urban land use from satellite imagery. Landsat Enhanced Thematic Mapper Plus (ETM(+)) images were used for the classification in two study areas: (1) Marina del Rey and its vicinity in the Santa Monica Bay Watershed, CA and (2) drainage basins adjacent to the Sweetwater Reservoir in San Diego, CA. Bayesian networks provided 80-95% classification accuracy for urban land use using four different classification systems. The classifications were robust with small training data sets with normal and reduced radiometric resolution. The networks needed only 5% of the total data (i.e., 1500 pixels) for sample size and only 5- or 6-bit information for accurate classification. The network explicitly showed the relationship among variables from its structure and was also capable of utilizing information from non-spectral data. The classification can be used to provide timely and inexpensive land use information over large areas for environmental purposes such as estimating stormwater pollutant loads.