ProQuest
Abstract/Details

A Bird’s Eye View of Stream Ecology: Evaluating Stream Condition and Restoration Impacts Using Drones, Structure-From-Motion Photogrammetry, and Machine Learning Methods

Evans, Alexandra Danielle.   University of New Hampshire ProQuest Dissertation & Theses,  2021. 28318834.

Abstract (summary)

Administrative, technical, and financial barriers often prevent sufficient collection of data for stream restoration project evaluation (Roni & Beechie, 2012; Bernhardt et al. 2005; NRC 1992). This lack of evaluation and understanding of restoration impacts can lead to the same misinformed strategies being repeated across restoration sites (Sommerville & Pruitt, 2004). This is particularly relevant for dam management, as the river ecosystem response of dam management strategies, like removal, are not fully understood due to minimal pre-/post-removal studies (Foley et al. 2017; Hart et al. 2002; Poff & Hart, 2002). Stream restoration practitioners need ecological assessment approaches that are affordable, repeatable, objective, and logistically feasible to develop science-based restoration techniques. This dissertation demonstrates how ecological evaluation workflows that use consumer-grade drone imagery coupled with structure-from-motion (SfM) photogrammetry provide an affordable, feasible, adaptable, and flexible solution to many of the challenges facing stream restoration, improving the frequency, accuracy, precision, and coverage of evaluations and improving our knowledge of ecological impacts from restoration practices. The first chapter explores how illustrative drone products (videos, orthomosaics, and 3D models), made using a simple structure-from-motion photogrammetry workflow, can be coupled with a visual stream ecological assessment protocol to provide a remote visual evaluation and ecological condition archive approach. The second chapter explores the intersection of drones, close-range remote sensing, and data science. The work demonstrates how hybrid feature sets derived from the drone RGB orthomosaics and digital surface models can be used to accurately map and quantify riparian vegetation structure via machine learning algorithms for conventional classification tasks that focus on classifying a single site, providing sufficiently accurate results with workflows amenable to use by restoration practitioners. The third chapter demonstrates how drone workflows for mapping and quantifying riparian vegetation structure as well as erosion and deposition throughout fluvial environments can be used to evaluate and monitor changes pre-/post-dam removal, using the Sawyer Mill dam removal project in Dover, NH, USA as a case study. The limitations and efficacies of the drone approaches vs. conventional ecological approaches are compared, and the study demonstrates how the drone approaches leverage the drone’s aerial perspective to provide holistic ecological evaluation data at a landscape scale.

Indexing (details)


Business indexing term
Subject
Environmental science;
Remote sensing;
Geographic information science;
Software;
Collaboration;
Photogrammetry;
Success;
Trends;
Topography;
Project evaluation;
Dissertations & theses;
Sediments;
Illustrations;
Research & development--R&D;
Engineers;
Case studies;
Vegetation;
Creeks & streams;
Geomorphology;
River ecology;
Classification;
Archives & records;
Data collection;
Geographic information systems;
Drones;
Algorithms;
Subjectivity
Classification
0768: Environmental science
0370: Geographic information science
0799: Remote sensing
0484: Geomorphology
Identifier / keyword
Dam removal; Drone; Restoration; River; Stream; Structure-from-motion
Title
A Bird’s Eye View of Stream Ecology: Evaluating Stream Condition and Restoration Impacts Using Drones, Structure-From-Motion Photogrammetry, and Machine Learning Methods
Author
Evans, Alexandra Danielle  VIAFID ORCID Logo 
Number of pages
549
Publication year
2021
Degree date
2021
School code
0141
Source
DAI-B 82/12(E), Dissertation Abstracts International
ISBN
9798516963766
Advisor
Gardner, Kevin
Committee member
Gold, Arthur; Palace, Michael; Still, Brett; Wollheim, Wilfred
University/institution
University of New Hampshire
Department
Earth and Environmental Sciences
University location
United States -- New Hampshire
Degree
Ph.D.
Source type
Dissertation or Thesis
Language
English
Document type
Dissertation/Thesis
Dissertation/thesis number
28318834
ProQuest document ID
2556389563
Copyright
Database copyright ProQuest LLC; ProQuest does not claim copyright in the individual underlying works.
Document URL
https://www.proquest.com/pqdtglobal/docview/2556389563/F0DE2828BA0A4922PQ