Abstract
Land cover maps, especially vegetation maps, are of increasing interest and use to resource agencies. This paper describes a three-stage hybrid classification method for regional-scale multi-level land cover mapping. The first stage involves an unsnpervised classification and stratification. The second stage includes supervised classification of forest types, rule-based clustering of non-forested vegetation, and estimation of percent impervious area with a regression model. The third stage is final map generation and post processing. Landsat TM/ETM+ images of three (spring, summer, fall) dates were used to classify land cover of the seven-county Twin Cities Metropolitan Area of Minnesota into three levels of the modified Minnesota Land Cover Classification System. The overall accuracies for Level-1 and Level-2 classes were 95% and 89%, respectively, and the agreement between the estimation of percent impervious surface in Level-3 classification and the measurements from digital ortho photographs was 96%.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 5-13 |
| Number of pages | 9 |
| Journal | Geocarto International |
| Volume | 20 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2005 |
Bibliographical note
Funding Information:This research was supported by Minnesota Metropolitan Council (contract SG-01-69) and field data were provided by Minnesota Department of Natural Resource. Additional support was provided by the University of Minnesota, Agricultural Experiment Station, project 42-037.
