High-resolution Land Cover and Impervious Surface Classifications in the Twin Cities Metropolitan Area with NAIP Imagery

Abstract

High-resolution land-use and land-cover (lulc) and impervious surface maps were traditionally created by time-consuming field surveys or aerial photo interpretation and digitizing methods. In the United States, the recent availability of 1 m digital color orthoimagery through the National Agriculture Imagery Program (naip) offers an opportunity to update land cover maps for areas of interest. In this study, an object-based feature extraction and a regression tree technique were applied to the NAIP imagery for the Twin Cities Metropolitan Areas (tcma) of Minnesota. An overall accuracy of 74 percent and 95 percent was achieved for the general LULC classification and the impervious surface map, respectively. It was found that both techniques could be used to extract impervious surfaces from the NAIP imagery with relatively high accuracy. The LULC classification results were compared to the 2006 National Land Cover Database (nlcd) and another Landsat-based 2006 classification. Implications for future classification studies were also discussed.