Constructing lidar DEMs from unclassified point clouds with: + [MCC-LiDAR][2] using the Multiscale Curvature Classification (MCC) algorithm. >MCC-LIDAR is a command-line tool for processing discrete-return LIDAR data in forested environments" (Evans & Hudak, 2007). Workflow illustration (gross cloud --> ground returns classified --> bare-earth DEM): ![enter image description here][3] ---------- Firstly, let's create a hypothetical situation to illustrate the below code example: MCC-LiDAR is installed under the following directory: C:\MCC The lidar cloud (".las" file) is stored in the below directory with the following name: C:\lidar\project\cloud.las The output which are going to be the bare-earth DEM will be stored as the following: C:\lidar\project\dem.asc -------- Now, let's provide an example about how to classify ground returns with the MCC algorithm and create a bare-earth DEM with 1 meter resolution. #MCC syntax: #command #input_file #output_file #output_DEM C:\MCC\bin\mcc-lidar.exe -s 0.5 -t 0.07 C:\lidar\project\cloud.las C:\lidar\project\mcc_ground_cloud.las -c 1 C:\lidar\project\dem.asc Read: [How to Run MCC-LiDAR][4] and also Evans & Hudak (2007) work (see "References" section below), to understand better how the scale (s) parameter and the curvature threshold parameter (t) works. They need to be calibrated to avoid the commission/labeling errors (when a point is classified as belonging to the ground but actually it belongs to vegetation or buildings). See the picture bellow. ![enter image description here][5] For more ground algorithm options see Meng et al. (2010). The MCC-LiDAR uses the interpolation technique of Thin Plate Spline (TPS) to generate the bare-earth DEM from the classified (ground returns) lidar cloud. **References**: Evans, Jeffrey S.; Hudak, Andrew T. 2007. [A multiscale curvature algorithm for classifying discrete return LiDAR in forested environments][6]. IEEE Transactions on Geoscience and Remote Sensing. 45(4): 1029-1038. Meng, X.; Currit, N.; Zhao, K. (2010). [Ground Filtering Algorithms for Airborne LiDAR Data: A Review of Critical Issues][7]. Remote Sensing, 2(3), 833–860. doi:10.3390/rs2030833 [1]:http://forsys.cfr.washington.edu/fusion/fusionlatest.html [2]:http://sourceforge.net/projects/mcclidar/ [3]: https://i.sstatic.net/ocyQ1.jpg [4]: http://sourceforge.net/p/mcclidar/wiki/HowToRun/ [5]: https://i.sstatic.net/qukNi.jpg [6]: http://www.fs.fed.us/rm/pubs_other/rmrs_2007_evans_j001.pdf [7]:http://www.mdpi.com/2072-4292/2/3/833