2016 Intermountain GIS Conference

In recent years Light Detection And Ranging (LiDAR) has received a great deal of attention in the geospatial and natural resource disciplines. To date, analysts have successfully used LiDAR to measure multiple objects heights and are now learning to relate those heights to forest characteristics such as canopy dimensions, tree basal area, number of trees, and tree diameters. While LiDAR has shown promise in measuring forest characteristics, similar achievements have also been documented using National Agricultural Imagery Program (NAIP) digital aerial imagery in combination with the National Elevation Dataset (NED). Few comparisons, however, have been made between LiDAR based and image and elevation based relationships to estimated forest characteristics. In this study we compare LiDAR, NAIP, and NAIP assisted NED, with respect to their ability to estimate forest characteristic. We use a nested modeling approach, and Akaike’s information criterion (AIC) to evaluate the relative strengths of the comparison datasets. Our finding suggest that there is a high degree of similarity in model fit and the ability to estimate forest characteristic when using LiDAR or NAIP and NAIP with NED.