Large diameter trees are important to a wide variety of wildlife, including many species that are rare or endangered, such as the California Spotted Owl. LiDAR has been successfully utilized to identify the density of large trees, either by segmenting the LiDAR point cloud by individual trees, or by complex statistical models built on a suite of sometimes abstract metrics extracted from the LiDAR point cloud. Neither of these methods is easily accessible for land managers, and much LiDAR data available is being underutilized due to the steep learning curve of advanced processing.
This study seeks to derive a simple, yet effective method for estimating the density of large-stemmed trees from the LiDAR canopy height model, which is often delivered with the LiDAR and is easy to process by personnel trained in GIS, but with no specific LiDAR training. This method will then be used to quantify large tree density around known California Spotted Owl nest sites.
+ 225 square km of LiDAR in Meadow Valley, CA; 150 km northwest of Lake Tahoe .
+ average point density: 4.68 pts/m^2
1) merge and retile the original dataset with buffers [lastile]
2) height-normalize tiles on many cores in parallel [lasheight]
3) calculate a suite of 24 metrics for each of 143 plots x 3 plot sizes per plot [lascanopy]
a)16 standard metrics
b) 8 classes of relative percent cover across vertical height bins, as described in Kramer et al. (2014)
4) calculate a Canopy Height Model based on the methods of Khosravipour et al. (2014) with the workflow described here and compare it to a FUSION-derived CHM [las2dem, lasthin, lasgrid]
Khosravipour, A., Skidmore, A.K., Isenburg, M., Wang, T.J., Hussin, Y.A., 2014. Generating pit-free Canopy Height Models from Airborne LiDAR. PE&RS = Photogrammetric Engineering and Remote Sensing 80, 863-872.
Kramer, H.A., Collins, B., Kelly, M., Stephens, S., 2014. Quantifying Ladder Fuels: A New Approach Using LiDAR. Forests 5(6), 1432–1453.