This project involved the mapping of forested stands in a GIS for approximately 80,000 acres. Remote sensing products were used such as aerial imagery and LiDAR. The imagery helped to stratify the forest by species types, while the LiDAR provided more precise classifications of density and height classes.
Imagery used in this project was obtained from two sources: 2 meter LiDAR and 1 meter color infrared orthophotography, taking during the winter (both available for free through the state of Ohio’s “Ohio Geographically Referenced Information Program,” http://ogrip.oit.ohio.gov/). Both sets of imagery are from 2006-2008. Color infrared imagery is helpful because it emphasizes spectral reflectance of vegetation and distinguishes it from spectral reflectances of bare ground, water, and infrastructure. Winter imagery is preferable since it allows for easy distinction between pine and hardwood forest types. LiDAR was processed into several rasters including DEM, canopy height, aspect, slope, relative slope position, and normalized canopy surface model.
F4 Tech developed geoprocessing models to take the data from individual pixels to a larger stand-based shapefile. Non-forested and bare areas were eliminated from the model using a canopy height layer, and the remaining forested layer was classified into pine or hardwood based on a supervised classification of winter color-infrared imagery. Using the other rasters for relative slope position, aspect, and canopy height, all layers were combined in an overlay analysis to generate a unique ID and forest type for each pixel. Proximal pixels of the same of similar values were grouped into larger aggregate areas of at least one acre. The data was converted to shapefile format and refined further to generate larger stands.
Forest inventory plots were allocated on four general strata from the mapping. Utilizing SilvAssist Dashboard, efficient allocations and reallocation of plots were enabled since results were reported instantaneously after each day’s work. Two of the strata were much more variable that the other two, so some reallocation was necessary, but provided good results for all four while meeting statistical and plot coverage requirements.
The Division of Wildlife has been able to plan and implement a greater level of management to their lands, increasing wildlife habitat for species such as turkey, deer and the endangered Indiana bat.
Project Status Completed.