{ U.S. Department of the Interior U.S. Geological Survey Frank Velasquez / Kristina Yamamoto GIS in the Rockies 24 September 2015 Point Spacing and Pixel Size

Agenda  Objective  Literature  Alpha Table  Concept of Operations  Study Areas  Point Clouds  Process Flow  RMSE Results  Charts  Discussions

Objective Attempt to fill in the literature surrounding the optimal point space (DEMpost space) For a given nominal point space (nps) distance (or point density) of a lidar point cloud, what is the optimal grid resolution without overly interpolating pixels or losing data quality i.e. What is the scalar to apply to nps to achieve optimal DEMpost spacing α = nps ÷ DEMpost Empirical research problem Experimental design is complete Execution of the designed tests are (mostly) complete Analysis of results is ongoing

Literature Rees and Arnold (2007) created a 2m raster grid from a 0.8 m nps Hopkinson et al. (2009) had varying nps between 1 and 4m and created two DEMs; one at 5m and one at 25m Perroy et al. (2010) created a raster grid at resolution equal to the nps Gonzalez et al. (2010) suggest creating a grid of 2m or 5m from a 1m nominal point spacing Jones et al. (2010) used a 2m resolution grid from a 1.6m nps Dong et al. (2010) suggested a raster resolution of one-third to one-fifth the nps; i.e. of 3 to 5m, raster resolution would be 1m Long et al. (2011) contradicts (or inverted) this suggesting that for spacing you would construct a 3m resolution grid. Keith Clarke (UCSB) suggested a DEMpost of 2 times the min, max, mean, or median of the nearest neighbor interpoint spacing

Alpha Table Alpha Table * Postgres/PostGIS algorithm developed by Mike Gleason of NREL to calculate nearest neighbor All other alphas were derived from the literature (α = nps ÷ DEMpost)

Concept of Operations Raw point cloud Filter ground points Reclassify ~5% of points for control group Generate DEM Ground points Generate shapefile of control points Shapefile DEM Compare DEM to shapefile, calculate Δz Reclassified point cloud Output csv & generate charts

Great Smoky Mtn. Study Area

Grand Canyon Study Area

Great Smoky Mtn. Point Clouds All Points Ground Points Control Points 5,614,743879,891 46,210 (5.25%)

Grand Canyon Point Clouds All Points Ground Points Control Points 37,975,51712,003, ,699 (4.65%)

Process Flow LP360 to filter ground points from raw point cloud (Kim Mantey) lasthin to filter/reclassify ~5% control points ArcMap to create las dataset, calculate point statistics, verify reclassified point % las2shp to generate shapefile of control points Global Mapper to generate GeoTIFF DEM at various grid resolutions ArcMap to verify DEM grid size Global Mapper to compare DEM pixel z values to control group z values  Select all points in shapefile  Rename “elevation” attribute to “point elevation”  Add coordinates attributes to control points  Apply elevation attributes from terrain layer (DEM) to control points  Verify points now have two elevation attributes  Calculate elevation delta  Create a new attribute (“elevation delta”)  Subtract “elevation” from “point elevation”  Generate statistics report (included slope attributes for possible future analysis)  Calculate RMSE and generate charts

Great Smoky Mtn. Great Smoky Mtn. NPS.69 Gonzalez 7b alpha same as Hopkinson 5a Jones 8 alpha same as Hopkinson 5c * Gleason algorithm nearest neighbor results: Min: m Mean: m Max: m Median: m

Grand Canyon Grand Canyon NPS.317 (Reported point density 9.97m 2 ) Nyquist-Shannon and Clarke inter-point spacing data unavailable at this time Gonzalez 7b alpha same as Hopkinson 5a Jones 8 alpha same as Hopkinson 5c

Great Smoky Mtn. Alpha / RMSE

Great Smoky Mtn. DEM Post / RMSE

Grand Canyon Alpha / RMSE

Grand Canyon DEM Post / RMSE

{ U.S. Department of the Interior U.S. Geological Survey Frank Velasquez / Kristina Yamamoto GIS in the Rockies 24 September 2015 Point Spacing and Pixel Size Discussions