Peer-Reviewed Articles — Focus Issue
273 Deployment and Calibration of Reference Reflectance Tarps for Use with Airborne
Imaging Sensors
M. Susan Moran, Ross B. Bryant, Thomas R. Clarke, and Jiaguo Qi
Abstract
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Chemically treated canvas tarps of large dimension (8 by 8 m) can be
deployed within the field of view of airborne digital sensors to
provide a stable ground reference for converting image digital number
(DN) to surface reflectance factor (;gr). However, the accuracy of
such tarp-based conversion is dependent upon a good knowledge of
tarp ;gr at a variety of solar and view angles (;gu;zs and
;gu;zv), and upon good care and proper deployment of tarps. In this
study, a set of tarps of ;gr ranging from 0.04 to 0.64 were evaluated
to determine the magnitude of error in measured tarp ;gr associated
with variations in ;gu;zs, ;gu;zv, and for reasonable levels of tarp
dirtiness. Results showed that, for operational values of ;gu;zs
and ;gu;zv and for reasonable levels of tarp dirtiness, the variation
of measured tarp ;gr from the factory-designated ;gr could easily
be greater than 50 percent. On the other hand, we found that, if
tarps were deployed correctly and kept clean through careful use
and periodic cleaning, and if tarp ;gr was determined through calibration
equations that account for both ;gu;zs and ;gu;zv, the greatest sources
of error were minimized. General calibration equations were derived
and provided here; these will be useful for applications with tarps
of the same factory-designated ;gr values as those used in this study.
Furthermore, equations were provided to allow calibration coefficients
to be determined from the value of factory-designated ;gr for the
visible and near-infrared spectral bands. The major limitation of
tarps as calibration sources was related to the difficulty associated
with deploying heavy, cumbersome tarps under normal field conditions
characterized by moderate wind, dust, heat, and possibly mud. This
study should provide tarp users with the information necessary to
properly deploy tarps and process results for accurate image interpretation.
287 Fuzzy Image Classification for Continental-Scale Multitemporal NDVI
Series Images Using Invariant Pixels and an Image Stratification Method
Jeong Chang Seong and E. Lynn Usery
Abstract
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The classification of multitemporal image data covering a large area is a challenging
task because of scarce ground-truth data and the phenological variation of
land cover in a study area. This research investigated an invariant pixel
approach with an image stratification method. Using invariant pixels, the
fuzzy image classification technique could be applied to every year with
a satisfactory amount of ground-truth information with the AVHRR NDVI data
covering Asia from 1982 to 1993. Invariant pixels were prepared by subtracting
the growing-season average of the first three years from that of the last
three years. A latitudinal image stratification method was investigated for
minimizing the phenological difference of vegetation along the latitude.
When land-cover information was transferred to other years by referencing
invariant pixels, the classification accuracy of the other years showed only
slight differences. The fuzzy classification results showed the decreases
of forest and cropland areas, but the increases of
openlands such as deserts and rangelands.
295 Mapping Vegetation across Large Geographic Areas: Integration of Remote Sensing
and GIS to Classify Multisource Data
Zhenkui Ma, Melissa M. Hart, and Roland L. Redmond
Abstract
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A two-stage classification process for mapping land cover across large geographic
areas from digital imagery, such as Landsat TM products, is described.
Stage 1 involves a two-pass, unsupervised classification designed to capture
patterns evident in a color composite, followed by a merging of pixels into
raster polygons based on spectral similarity. Stage 2 involves a supervised
classification to label each raster polygon according to cover type (or other
class feature). The second stage takes place in a GIS environment,
after spectral and biophysical attributes are calculated for each polygon
in the image. Classification accuracy is assessed using fuzzy sets, and individual
GIS databases for adjacent images are virtually edge-matched, post-classification,
to create seamless outputs across multiple scenes. We found that, by storing
and analyzing data from separate scenes in separate databases, large geographic
areas can be processed relatively quickly and efficiently.
309 Mapping of Salmon Habitat Parameters Using Airborne Imagery
and Digital Ancillary Data
T.M. Puestow, É.L. Simms, A. Simms, and K. Butler
Abstract
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The successful integration of airborne remotely sensed imagery and digital
ancillary information for the mapping of bottom substrate and channel pattern
as important freshwater habitat parameters for Atlantic salmon was demonstrated.
CASI multispectral image data were collected at wavelengths of 510, 590,
660, and 730 nm. Ancillary information consisted of valley gradient and stream
width. Valley gradient was derived from elevation data contained in a 1:50,000-scale
digital map sheet. Stream width was extracted from the image data. Separate
image classifications were carried out for substrate type and channel pattern
using a hierarchical decision tree algorithm. The final overall classification
accuracies were 73.76 and 64.47 percent, respectively. Substrate type and
channel pattern were combined to create composite maps of spawning habitat
suitability. The resulting stratification of salmon spawning habitats corresponds
well with the findings of earlier investigations.
319 Monitoring Forest Transitions Using Scanned Ground Photographs as a Primary
Data Source
Gary R. Clay and Stuart E. Marsh
Abstract
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Previous studies that have used repeat ground-based photography to document
land-cover or land-use change have generally not assessed canopy transitions
in forest settings. Furthermore, such activities have typically evaluated
change only in a qualitative sense. Our research has investigated procedures
necessary to utilize scanned and digitally processed ground-based photographs
to quantitatively measure the multitemporal spectral response of a forest
canopy in southern Utah. A multi-year photographic inventory was acquired
from surveyed ground positions to document spectral response in a targeted
forest condition. Issues related to normalization, image sampling, and digital
analysis were investigated. The research concluded that highly reproducible
and consistent spectral data can be generated to quantitatively monitor local
canopy conditions, and to document the impact of natural or human-induced
disturbance.
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