Land cover mapping in an agricultural setting using multiseasonal Thematic Mapper data

A multiseasonal Landsat Thematic Mapper (TM) data set consisting of five image dates from a single year was used to characterize agricultural and related land cover in the Willamette River Basin (WRB) of western Oregon. Image registration was accomplished using an automated ground control point selection program. Radiometric normalization was performed using a semiautomated approach based on the identification of no-change pixels in forest, urban, and water classes. Reference data were developed using existing data sets, including low-level 35-mm color slide photographs, 1:24,000 color airphotos, and ancillary geographic information system (GIS) coverages. Preliminary examination of the data structure included plotting of training set temporal trajectories in spectral space with reference to existing crop calendars. A subsequent stratified, unsupervised classification algorithm, in combination with a geoclimatic rule set and regression analysis, was used to label mapped cells. A map of 20 land cover classes was developed. Classes included agricultural crops and orchards, forest and natural cover types, and urban building densities. An accuracy assessment indicated a final map error of only 26%. The map is now being used to model present and future landscapes for the basin.