Cheatgrass die-off in the Great Basin: A comparison of remote sensing detection methods and identification of environments favorable to die-off
type: CreativeWork
description: Invasion by cheatgrass (Bromus tectorum L.) has greatly damaged the rangeland ecosystems of the Western United States. While mechanical and chemical control of cheatgrass is possible, control is costly and often ineffective in the long-term. An improved understanding of cheatgrass die-off, or stand emergence failure, could lead to additional means of control for this widespread invasive. The majority of published research has focused on identifying the cause of die-off, on demonstrating its potential for contributing to restoration success, and on mapping its distribution. At this time, we know little about the regional-scale environments favorable to disease development. While remote sensing methods exist for mapping of cheatgrass die-off, more reliable methods are needed to facilitate both further research and restoration efforts. Here we investigate three methods of using remote sensing to identify patches of die-off across two study areas with distinctly different spectral characteristics: (1) a machine learning method classifying die-off from spectra, (2) a spectral change detection method using threshold values of a vegetation index, and (3) a multiple classifier system (MCS), which identifies die-off by combining the results of the prior two models. Though we hypothesize the MCS approach to be the most reliable, we find the spectral signature classifier to have the most accurate and spatially consistent results. We use die-off mapped by this method to identify spatial and temporal patterns in the occurrence of die-off, finding most patches to be small (<1ha) and ephemeral (lasting 1-2 years). While die-off is widespread, occurring across the majority (>80%) of our study areas at least twice within a 35-year time series, in any given year a variable portion of the landscape will experience die-off (range of 5 to 34% of study area). Next we use a boosted regression tree approach to identify patterns in the environments in which cheatgrass die-off occurs, measur…
abstract: Invasion by cheatgrass (Bromus tectorum L.) has greatly damaged the rangeland ecosystems of the Western United States. While mechanical and chemical control of cheatgrass is possible, control is costly and often ineffective in the long-term. An improved understanding of cheatgrass die-off, or stand emergence failure, could lead to additional means of control for this widespread invasive. The majority of published research has focused on identifying the cause of die-off, on demonstrating its potential for contributing to restoration success, and on mapping its distribution. At this time, we know little about the regional-scale environments favorable to disease development. While remote sensing methods exist for mapping of cheatgrass die-off, more reliable methods are needed to facilitate both further research and restoration efforts. Here we investigate three methods of using remote sensing to identify patches of die-off across two study areas with distinctly different spectral characteristics: (1) a machine learning method classifying die-off from spectra, (2) a spectral change detection method using threshold values of a vegetation index, and (3) a multiple classifier system (MCS), which identifies die-off by combining the results of the prior two models. Though we hypothesize the MCS approach to be the most reliable, we find the spectral signature classifier to have the most accurate and spatially consistent results. We use die-off mapped by this method to identify spatial and temporal patterns in the occurrence of die-off, finding most patches to be small (<1ha) and ephemeral (lasting 1-2 years). While die-off is widespread, occurring across the majority (>80%) of our study areas at least twice within a 35-year time series, in any given year a variable portion of the landscape will experience die-off (range of 5 to 34% of study area). Next we use a boosted regression tree approach to identify patterns in the environments in which cheatgrass die-off occurs, measur…
author: Brehm, Joseph R.
keywords: Boosted regression tree, Bromus tectorum, Cheatgrass, Die-off, Random forest, Remote sensing
date published: 2019
date modified: 2026-07-27T21:45:34Z
provider: University of Nevada - Reno
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Nevada - Reno

PropertyValue
property ID: Handle
value: http://hdl.handle.net/11714/6018