Wil Blouin's LiDAR Portfolio

Goals and Background: This lab practiced the skill of corridor analysis using LiDAR point cloud data from a terrestrial LiDAR scanner (TLS). Other skills that were practiced included projection of point cloud data and extraction of building footprints. The extraction of building footprints included finding building footprint features with Z characteristics that would make the buildings eligible for a letter of map amendment (LOMA). Details on LOMAs can be found here . Methods: Part 1: Projecting an unprojected point cloud This part made simple use of the LP360 tools included in the license for LP360 for Arcmap. All of the LP360 tools available were added to a new toolbox, and the Define LAS File Projection and then Reproject LAS Files tools were used. The projection specified in the readme file for the Algoma,WI LAS dataset was defined, and then the transverse mercator projection was used in the reprojection. In using both of these tools horizontal and vertical coordinate syste...

Goals and Introduction:  This lab demonstrated vegetation metrics modeling in the diverse forest of Eau Claire County. The goal of the lab was to extract various vegetation metrics for the different species of trees found in the project area. This was then used to make hypothetical recommendations to the U.S Forest Service about the carbon sink potential of the forest in the project area. This lab made use of LiDAR point cloud data and the Wiscland 2 land use and land use classification raster. Methods: Part 1: Canopy height modeling Section 1: Canopy surface and ground surface creation This section consisted of generating a 3 foot cell size canopy surface as well as a ground (DTM) surface model which were used in the next section for creation of a canopy height raster. The LiDAR was brought into LP360 and the using the Export LiDAR Data function the models were created. For the DTM single returns were used and for the canopy surface model first returns were used. Secti...

Goals and Introduction: The goal of this lab was to work with topo-bathy LiDAR data to gain general skills. The skills practiced were performing basic QA/QC of topo-bathy LiDAR, generating and conflating shoreline breaklines specially tailored to the topo-bathy LiDAR, and configuring the enforcement of the shoreline behavior for creation of derivatives. A DTM and a hillshade raster file were created. The dataset practiced with was located in the Hiawatha National Forest in Delta County, MI. Methods: Part 1: QA/QC of topo-bathy point cloud classification The NAIP imagery and point cloud data supplied for the lab was examined for inaccurate classification using the profile and 3D windows in LP360's standalone Windows application. Using the QA/QC tool, a feature class was created with features that covered areas that needed to be fixed throughout the LiDAR dataset. Theses areas were then visited and corrected. Many points were classified as ground that should have been classif...