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Abstract
Land cover/land use (LCU) classifications serve as important decision support products for researchers and land managers. The LCU classifications produced by the U.S. Geological Survey’s Upper Midwest Environmental Sciences Center (UMESC) include canopy height estimates that are assigned through manual aerial photography interpretation techniques. In an effort to improve upon these techniques, this project investigated the use of high-density lidar data for the Upper Mississippi River System to determine canopy height. An ArcGIS tool was developed to automatically derive height modifier information based on the extent of land cover features for forest classes. The measurement of canopy height included a calculation of the average height from lidar point cloud data as well as the inclusion of a local maximum filter to identify individual tree canopies. Results were compared to original manually interpreted height modifiers and to field survey data from U.S. Forest Service Forest Inventory and Analysis plots. This project demonstrated the effectiveness of utilizing lidar data to more efficiently assign height modifier attributes to LCU classifications produced by the UMESC.
Study Area
| Publication type | Thesis |
|---|---|
| Publication Subtype | Thesis |
| Title | Automated lidar-derived canopy height estimates for the Upper Mississippi River System |
| Year Published | 2015 |
| Language | English |
| Publisher | University of Redlands |
| Contributing office(s) | Upper Midwest Environmental Sciences Center |
| Description | xviI, 70 p. |
| Country | United States |
| State | Minnesota, Wisconsin |
| Other Geospatial | Upper Mississippi River |
| Google Analytic Metrics | Metrics page |