Classifying environmentally significant urban land uses with satellite imagery.
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.
Park
Mi-Hyun
MH
Department of Civil and Environmental Engineering, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, CA 90095-1593, USA. mhpark@seas.ucla.edu
Stenstrom
Michael K
MK
D001499:Bayes Theorem; D002140:California; D002947:Cities; D003132:Commerce; D004777:Environment; D006798:Housing; D007221:Industry; D011998:Recreation; D017215:Satellite Communications; D014186:Transportation; D014867:Water; D014881:Water Supply
D014867:Water