Travis Meyer Penn State University

12D

The goal of this project is to assess the qualitative accuracy of ground filtering algorithms applied at varying scales to model mangrove canopy height models using dense lidar point clouds and object-based feature extraction methods. High-resolution imagery and dense lidar point cloud data sets will be used to delineate and extract mangrove and waterbody features as shapefile layers using Object-Based Image Analysis (OBIA) rule sets in eCognition. These layers will later be used to classify water/mangrove points and generate breaklines for hydro-conditioning the surface models within LP360. In LP360, ground filtering algorithms will be applied to the unclassified point data within dense areas of vegetation at 10-meter, 20-meter, and 30-meter filtering grids. The imported mangrove feature shapefile (from eCognition output) will be used to assign the remaining mangrove points to a unique mangrove class using the Classify by Feature Point Cloud Task in LP360.

Canopy Height Models (CHM) will be produced at scales of 1-meter, 2-meter, and 5-meter resolutions by generating Normalized Digital Surface Models (nDSM) within mangrove areas, for a total of 9 different ground filtering and cell size combinations. The 10-meter/1-meter (10/1) CHM will be used as the baseline to assess the accuracy of the other 8 CHMs, as it represents the most accurate underlying data possible. Volumetric differencing will be used to determine how much Above-Ground Biomass (AGB) is lost or “gained” due to interpolation approximations reported in percentage values and volumetric units (cubic meters). The accuracy findings will be documented and detailed both visually and graphically in a final report, along with an accuracy assessment of the OBIA classification process.

10:30 Ground Filtering of Dense Lidar Point Clouds for Modeling Shrub Mangrove Canopies, Travis Meyer