HARC LAS Point Classification 1 Processed, but unclassified 2 - - PowerPoint PPT Presentation

Lidar 2018 Tree Canopy Density and Height Analysis

Qian Song

HARC

Data: 2014 TCEQ

LAS Point Classification

1 Processed, but unclassified 2 Bare-earth Ground 3 Low Vegetation (0.01m to 1.00m) 4 Medium Vegetation (1.01m to 3.00m) 6 Building 7 Low Point (noise) 9 Water 10 Ignored Ground 14 Culverts 17 Bridge Decks 5 High Vegetation (greater than 3.01m)

Image: https://environment.arlingtonva.us

Tree Canopy Density

Tree Density - Methodology

• 1. Divide the study area into many small equal-sized units (Raster Dataset)
• 2. In each raster cell,

Density = The Number of Above Ground Points The Total Number of Points Tree Canopy Density = The Number of High Vegetation Points The Number of High Vegetation Points + The Ground Points

Tree Density - Workflow

Las Dataset Tree Canopy Density Raster Total Points Raster Above Ground Points Raster Las Point Statistics As Raster (Point Count) Divide Tips

• Convert any resulting NoData cells to 0
• Make sure one raster to be floating point to get floating-point output

Las Dataset Layer (Total) Las Dataset Layer (Above Ground)

Tree Density - Results

Tree Density - Results

Tree Canopy Height

Tree Height - Methodology

Digital Surface Model(DSM): the first return surface First return of the High Vegetation Class Digital Elevation Model(DEM): the bare earth surface Canopy Height = DSM - DEM

Tree Height - Workflow

Las Dataset Las Dataset to Raster DSM DEM Minus Tree Height Las Dataset Layer (Above Ground & First Return)

Tree Height - Results

Tree Height - Results

Houston Advanced Research Center (HARC)

Wrap Up

• Tree Canopy Density and Height Processes and Results
• Other applications

– Flood Inundation Depth and Area

HARCresearch.org

HARC (härk), n.

an independent research hub helping people thrive and nature flourish.

Qian Song

qsong@harcresearch.org 281-364-6085