Title:

Digital surface, terrain, and canopy height models for a portion of the Black Hills Experimental Forest in 2002

Author(s):

Hudak, Andrew T.; Liebermann, Robert J.; Moreira, Eder P.; Rowell, Eric

Publication Year:

2013

How to Cite:

These data were collected using funding from the South Dakota School of Mines and Technology and the U.S. Government, and can be used without additional permissions or fees. If you use these data in a publication, presentation, or other research product please use the following citation:

Hudak, Andrew T.; Liebermann, Robert J.; Moreira, Eder P.; Rowell, Eric. 2013. Digital surface, terrain, and canopy height models for a portion of the Black Hills Experimental Forest in 2002. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. https://doi.org/10.2737/RDS-2013-0002

Abstract:

The data publication contains 1 meter raster data sets for three different digital elevation models (DEM) for a portion of the Black Hills Experimental Forest in South Dakota in 2002. The first is a digital terrain model (DTM), which is the ground surface with all vegetation and human-made structures removed. The second is a digital surface model (DSM), which includes all vegetation and human-made structures. The last is a canopy height model (CHM), which is the difference between the DSM and the DTM surfaces.

Keywords:

LiDAR; digital elevation model; digital terrain model; digital surface model; canopy height model; elevation data; topography; land cover; high-resolution; vegetation; elevation; Ecology, Ecosystems, & Environment; Landscape ecology; Natural Resource Management & Use; Landscape management; South Dakota; Black Hills Experimental Forest; Black Hills National Forest

Related publications:

• Rowell, Eric; Seielstad, Carl; Vierling, Lee; Queen, Lloyd; Shepperd, Wayne. 200612. Using Laser Altimetry-based Segmentation to Refine Automated Tree Identification in Managed Forests of the Black Hills, South Dakota. Photogrammetric Engineering & Remote Sensing. 72(12): 1379-1388. https://www.asprs.org/Photogrammetric-Engineering-and-Remote-Sensing/PE-RS-Journals.html

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