Multi‐level Land Cover Mapping of the Twin Cities (Minnesota) Metropolitan Area with Multi‐seasonal Landsat TM/ETM+ Data

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 unsupervised 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%.