Dissertation/Thesis Abstract

Remote sensing of vegetation structure using computer vision
by Dandois, Jonathan P., Ph.D., University of Maryland, Baltimore County, 2014, 266; 3637314
Abstract (Summary)

High-spatial resolution measurements of vegetation structure are needed for improving understanding of ecosystem carbon, water and nutrient dynamics, the response of ecosystems to a changing climate, and for biodiversity mapping and conservation, among many research areas. Our ability to make such measurements has been greatly enhanced by continuing developments in remote sensing technology—allowing researchers the ability to measure numerous forest traits at varying spatial and temporal scales and over large spatial extents with minimal to no field work, which is costly for large spatial areas or logistically difficult in some locations. Despite these advances, there remain several research challenges related to the methods by which three-dimensional (3D) and spectral datasets are joined (remote sensing fusion) and the availability and portability of systems for frequent data collections at small scale sampling locations. Recent advances in the areas of computer vision structure from motion (SFM) and consumer unmanned aerial systems (UAS) offer the potential to address these challenges by enabling repeatable measurements of vegetation structural and spectral traits at the scale of individual trees. However, the potential advances offered by computer vision remote sensing also present unique challenges and questions that need to be addressed before this approach can be used to improve understanding of forest ecosystems. For computer vision remote sensing to be a valuable tool for studying forests, bounding information about the characteristics of the data produced by the system will help researchers understand and interpret results in the context of the forest being studied and of other remote sensing techniques. This research advances understanding of how forest canopy and tree 3D structure and color are accurately measured by a relatively low-cost and portable computer vision personal remote sensing system: 'Ecosynth'. Recommendations are made for optimal conditions under which forest structure measurements should be obtained with UAS-SFM remote sensing. Ultimately remote sensing of vegetation by computer vision offers the potential to provide an 'ecologist's eye view', capturing not only canopy 3D and spectral properties, but also seeing the trees in the forest and the leaves on the trees.

Indexing (document details)
Advisor: Ellis, Erle C.
Commitee: Baker, Matthew, Olano, Marc, Parker, Geoffrey G., Tang, Junmei
School: University of Maryland, Baltimore County
Department: Geography and Environmental Systems
School Location: United States -- Maryland
Source: DAI-B 76/02(E), Dissertation Abstracts International
Source Type: DISSERTATION
Subjects: Ecology, Remote sensing, Computer science
Keywords: Canopy structure, Computer vision, Ecosynth, Phenology, Structure from motion, UAS
Publication Number: 3637314
ISBN: 9781321200683
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