Characterizing vegetation structure using waveform LiDAR

Wang, Kunxuan

Permalink

https://hdl.handle.net/2142/93071 Copy

Description

Title

Characterizing vegetation structure using waveform LiDAR

Author(s)

Wang, Kunxuan

Issue Date

2016-07-22

Director of Research (if dissertation) or Advisor (if thesis)

Kumar, Praveen

Department of Study

Civil & Environmental Eng

Discipline

Environ Engr in Civil Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Waveform LiDAR
• Canopy clumping
• Biomass
• Canopy structure

Abstract

The structure of light penetration through the canopy plays an important role in water, carbon, and energy fluxes between the biosphere and the atmosphere. Total foliage and foliage distribution are major aspects of canopy structure that significantly influence light and vegetation interaction. Waveform airborne LiDAR data contains large amounts of vegetation structural information, and is the best tool available for providing detailed physical information for large areas of vegetation. In this thesis, we first provide a complete work flow that extracts and processes waveform LiDAR data for an area of interest. Then we test the feasibility of using waveform LiDAR data to estimate individual tree biomass with limited field samples. We use a voxelization method to generate pseudo-waveforms for individual trees and apply a stepwise regression to find the relationship between pseudo-waveform structural characteristics and biomass estimated by allometric equations using tree survey data. Next, we present a method for describing physical canopy clumping structure for individual trees that provides detailed spatial clumping variations. We utilize the K-means clustering algorithm to extract structure from the large amount of canopy architecture information provided by full-waveform LiDAR. Finally we use representative cluster traits to identify structurally significant clusters.

Graduation Semester

2016-08

Type of Resource

text

Permalink

http://hdl.handle.net/2142/93071

Copyright and License Information

Copyright 2016 Kunxuan Wang

Owning Collections