ERA

Download the full-sized PDF of Assessing diversity of prairie plants using remote sensingDownload the full-sized PDF

Analytics

Share

Permanent link (DOI): https://doi.org/10.7939/R32805C5N

Download

Export to: EndNote  |  Zotero  |  Mendeley

Communities

This file is in the following communities:

Graduate Studies and Research, Faculty of

Collections

This file is in the following collections:

Theses and Dissertations

Assessing diversity of prairie plants using remote sensing Open Access

Descriptions

Other title
Subject/Keyword
biodiversity
grassland
remote sensing
imaging spectroscopy
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Wang,Ran
Supervisor and department
Gamon, John (Earth and Atmospheric Sciences and Biological Sciences)
Examining committee member and department
Macdonald, Ellen (Renewable Resources)
Reyes, Alberto (Earth and Atmospheric Sciences)
Cahill, James (Biological Sciences)
Epstein, Howard (University of Virginia)
Department
Department of Earth and Atmospheric Sciences
Specialization

Date accepted
2017-06-08T13:19:34Z
Graduation date
2017-11:Fall 2017
Degree
Doctor of Philosophy
Degree level
Doctoral
Abstract
Biodiversity loss endangers ecosystem services and is considered as a global change that may generate unacceptable environmental consequences on the Earth system. Global biodiversity observations are needed to provide a deep understanding of the biodiversity - ecosystem services relationship and conserve the Earth’s biodiversity. Traditionally, in situ biodiversity monitoring is limited in time and space and is usually a costly and time-consuming enterprise. Remote sensing can provide data over a large area in a consistent, objective manner and has been used to detect plant biodiversity in a range of ecosystems based on the varying spectral properties of different species or functional groups. Studies estimating biodiversity using remote sensing can be generally categorized into three types: estimating biodiversity indirectly with habitat mapping; mapping distribution of individuals as a basis for assessing community composition and diversity; and assessing species richness directly from patterns of spectral variation to yield α-diversity. However, key questions remain: 1) can the diversity-productivity relationship be assessed using remote sensing? 2) what drives the variation of optical signal among species or functional groups? and 3) what is the appropriate spectral and spatial scale for biodiversity detection using remote sensing? To answer these questions, a series studies were accomplished at Cedar Creek Ecosystem Science Reserve, Minnesota, where the biodiversity manipulation of prairie plants provided a proper diversity gradient. First, the productivity-biodiversity relationship was tested from a remote sensing perspective. Results indicated that NDVI and biodiversity were positively related, and that the NDVI-biodiversity relationship varied slightly across the growing season and was affected by other factors including canopy structure, short-term water stress, and shifting flowering patterns. Second, proximal remote sensing revealed rapid information loss with increasing pixel size. The best resolution to detect α diversity using spectral diversity at this prairie ecosystem was at a size close to a typical herbaceous plant leaf or single canopy. Furthermore, results from a combination of field spectral measurements and a modeling framework indicated that both species richness and evenness influenced spectral diversity metrics. Species identities also showed substantial effects on spectral diversity metrics at the fine scale. Background (e.g., soil) effects on spectral diversity varied with metrics: spectral diversity metrics based on information theory were sensitive to the background, while background had no effects on classification-based indices at this fine scale. Using full range spectra (400 – 2500nm) slightly increased the species separability over using visible-NIR wavelength only. Additionally, the primary spectral diversity metric, the coefficient of variation of spectral reflectance in space, was also tested in a prairie ecosystem in Southern Alberta to detect the biodiversity in a natural landscape. Overall, the plant optical signal was influenced by both leaf traits and canopy structure, and the ability to use spectral diversity metrics in biodiversity estimation depended on the species richness, evenness, composition, associated spectral properties, sensor characteristics, and the particular spectral diversity metrics selected. This project provides a critical foundation for assessing biodiversity using imaging spectrometry and these findings can be used to guide regional studies of biodiversity estimation using high spatial and spectral resolution remote sensing.
Language
English
DOI
doi:10.7939/R32805C5N
Rights
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
Citation for previous publication
Ran Wang, John A. Gamon, Rebecca Montgomery, Philip A. Townsend, Arthur I. Zygielbaum, Keren Bitan, David Tilman and Jeannine Cavender-Bares. 2016. “Seasonal variation in the NDVI–species richness relationship in a prairie grassland experiment (Cedar Creek)”. Remote Sensing. 8: 128.Ran Wang, John A. Gamon, Craig A. Emmerton, Haitao Li, Enrica Nestola, Gilberto Z. Pastorello and Olaf Menzer. 2016. “Integrated analysis of productivity and biodiversity in a southern Alberta prairie”. Remote Sensing. 8: 214.Ran Wang, John A. Gamon, Jeannine Cavender-Bares, Philip A. Townsend, Arthur I. Zygielbaum. “The spatial sensitivity of the spectral diversity-biodiversity relationship: an experimental test in a prairie grassland”. – Ecological Applications (in press).

File Details

Date Uploaded
Date Modified
2017-06-08T19:57:25.091+00:00
Audit Status
Audits have not yet been run on this file.
Characterization
File format: pdf (PDF/X)
Mime type: application/pdf
File size: 9814811
Last modified: 2017:11:08 16:29:44-07:00
Filename: Wang_Ran_201706_PhD.pdf
Original checksum: bcd47a391427715289ada004c853114e
Activity of users you follow
User Activity Date