Land cover classification of the Lake of the Woods/Rainy River Basin by object-based image analysis of Landsat and lidar data

Abstract

ABSTRACTOlmanson LG, Bauer ME. 2017. Land cover classification of the Lake of the Woods/Rainy River Basin by object-based image analysis of Landsat and lidar data. Lake Reserv Manage. 33:335–346.The recent availability of lidar data throughout Minnesota, USA has opened up many opportunities for improved land cover classification and mapping. To integrate spectral and spatial information from Landsat imagery and lidar point cloud and topographic metrics, we utilized object-based image analysis (OBIA) with random forest classification. By classifying objects instead of pixels, we were able to use 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. These methods were used to create land cover maps and land cover change maps for the ∼1990 and ∼2010 time periods of the Lake of the Woods/Rainy River Basin for use as inputs to hydrologic models and analyses of land cover and land cover change. The overall accuracy for the general level 1 classification was over 95% and over 90% for the more detailed level 2 classification. The basin is dominated by forests, wetlands, and lakes that comprise 96.3% of the basin. Developed areas had a slight increase of 2650 ha (2.9%) from 1990 to 2010 at the basin level. The primary changes were due to forest disturbance from harvesting and fire and regeneration of the forest in disturbed areas. While areas where forests have been disturbed changed between the time periods, there was also an increase of forest disturbance to 6.5% of the basin in 2010 from 5.2% in 1990. There were no changes detected from 1990 to 2010 for 88% of the basin.