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Browsing by Author "Remote Sensing and Geospatial Analysis Laboratory, University of Minnesota"

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    Minnesota Land Cover Classification and Impervious Surface Area by Landsat and Lidar: 2013-14 Update
    (2016-08-03) Rampi, Lian P; Knight, Joe F; Bauer, Marvin; jknight@umn.edu; Knight, Joe F; Remote Sensing and Geospatial Analysis Laboratory, University of Minnesota
    This is a 15-meter raster dataset of a land cover and impervious surface classification for 2013-14, level two classification. The classification was created using a combination of multitemporal Landsat 8 data and LiDAR data with Object-based image analysis. By using objects instead of pixels we were able to utilize multispectral data along with spatial and contextual information of objects such as shape, size, texture and LiDAR-derived metrics to distinguish different land cover types. While OBIA has become the standard procedure for classification of high resolution imagery we found that it works equally well with Landsat imagery. For the objects classified as urban or developed, a regression model relating the Landsat greenness variable to percent impervious was developed to estimate and map the percent impervious surface area at the pixel level.