Abstract
Remote sensing information has been widely used to
monitor vegetation condition and variations in a variety of
ecosystems, including shrublands. Careful application of
remotely sensed imagery can provide additional spatially
explicit, continuous, and extensive data on the composition
and condition of shrubland ecosystems. Historically, the most
widely available remote sensing information has been collected
by Landsat, which has offered large spatial coverage
and moderate spatial resolution data globally for nearly three
decades. Such medium-resolution satellite remote sensing
information can quantify the distribution and variation of
terrestrial ecosystems. Landsat imagery has been frequently
used with other high-resolution remote sensing data to classify
sagebrush components and quantify their spatial distributions
(Ramsey and others, 2004; Seefeldt and Booth, 2004; Stow
and others, 2008; Underwood and others, 2007). Modeling
algorithms have been developed to use field measurements
and satellite remote sensing data to quantify the extent and
evaluate the quality of shrub ecosystem components in large
geographic areas (Homer and others, 2009). The percent cover
of sagebrush ecosystem components, including bare-ground,
herbaceous, litter, sagebrush, and shrub, have been quantified
for entire western states (Homer and others, 2012). Furthermore,
research has demonstrated the use of current measurements
with historical archives of Landsat imagery to quantify
the variations of these components for the last two decades
(Xian and others, 2012).
The modeling method used to quantify the extent and
spatial distribution of sagebrush components over a large area
also has required considerable amounts of training data to
meet targeted accuracy requirements. These training data have
maintained product accuracy by ensuring that they are derived from good quality field measurements collected during appropriate
ecosystem phenology and subsequently maximized by
extrapolation on high-resolution remote sensing data (Homer
and others, 2012). This method has proven its utility; however,
to develop these products across even larger areas will require
additional cost efficiencies to ensure that an adequate product
can be developed for the lowest cost possible. Given the vast
geographic extent of shrubland ecosystems in the western
United States, identifying cost efficiencies with optimal training
data development and subsequent application to medium
resolution satellite imagery provide the most likely areas for
methodological efficiency gains.
The primary objective of this research was to conduct
a series of sensitivity tests to evaluate the most optimal
and practical way to develop Landsat scale information for
estimating the extent and distribution of sagebrush ecosystem
components over large areas in the conterminous United
States. An existing dataset of sagebrush components developed
from extensive field measurements, high-resolution
satellite imagery, and medium resolution Landsat imagery in
Wyoming was used as the reference database (Homer and others,
2012). Statistical analysis was performed to analyze the
relation between the accuracy of sagebrush components and
the amount and distribution of training data on Landsat scenes
needed to obtain accurate predictions.
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First posted December 12, 2012
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