The vast majority of theses in this collection are open access and freely available. There are a small number of theses that have access restricted to the WWU campus. For off-campus access to a thesis labeled "Campus Only Access," please log in here with your WWU universal ID, or talk to your librarian about requesting the restricted thesis through interlibrary loan.

Date Permissions Signed

2-24-2021

Date of Award

Winter 2021

Document Type

Masters Thesis

Department or Program Affiliation

Huxley College of the Environment

Degree Name

Master of Science (MS)

Department

Environmental Sciences

First Advisor

Wallin, David O.

Second Advisor

Helfield, James J.

Third Advisor

Maudlin, Michael

Fourth Advisor

Bunn, Andrew Godard

Abstract

This paper addresses two applications of lidar remote sensing: an area-based watershed-scale analysis of forest structure used to prioritize riparian restoration projects for salmon, and an individual-tree-based analysis for tree species classification. Salmon conservation is extremely important in the Pacific Northwest, but restoration efforts have been hampered by insufficient data on riparian stand conditions. I used lidar to map riparian stand structure and composition along the Nooksack River, Washington, and developed a restoration priority model based on six factors: riparian stand conditions, shade potential, cause of riparian impairment, susceptibility to climate change, position in the watershed, and proximity to intact riparian forest. Nine reaches (out of 268 total) were identified as priority targets for riparian restoration. Four of these reaches were on the upper South Fork, two were in the lower South Fork, two were in the lower Middle Fork, and one was in the North Fork near Maple Falls.

At the individual tree level, I compared six different approaches using five different algorithms to sort a discrete-return lidar point cloud into segments representing individual trees. Using these segments, I built models to predict height, diameter, conifer/deciduous classification, and species. Using the best segmentation model, I was able to classify black cottonwood (Populus trichocarpa), Douglas fir (Pseudotsuga menziesii), and red alder (Alnus rubra) with user’s accuracies up to 89%, overall accuracies of 83-92%, and kappa values above 0.6. This was the first landscape-scale study attempting to classify tree species in natural landscapes in the Pacific Northwest.

Type

Text

Keywords

lidar, Nooksack River, riparian, salmon, tree species

Publisher

Western Washington University

OCLC Number

1240261889

Subject – LCSH

Salmon stock management--Washington (State)--Nooksack River Watershed; Riparian restoration--Washington (State)--Nooksack River Watershed; Optical radar--Washington (State)--Nooksack River Watershed

Geographic Coverage

Nooksack River Watershed (Wash.)--Environmental conditions

Format

application/pdf

Genre/Form

masters theses

Language

English

Rights

Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.

Available for download on Tuesday, August 24, 2021

Share

COinS