Peer-Reviewed Articles
641 Canopy Reflectance Related to Marsh Dieback Onset
and Progression in Coastal Louisiana
Elijah Ramsey III and Amina Rangoonwala
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In this study, we extended previous work linking leaf spectral
changes, dieback onset, and progression of Spartina alterniflora
marshes to changes in site-specific canopy reflectance
spectra. First, we obtained canopy reflectance spectra
(approximately 20 m ground resolution) from the marsh sites
occupied during the leaf spectral analyses and from additional
sites exhibiting visual signs of dieback. Subsequently,
the canopy spectra were analyzed at two spectral scales: the
first scale corresponded to whole-spectra sensors, such as the
NASA Earth Observing-1 (EO-1) Hyperion, and the second
scale corresponded to broadband spectral sensors, such as
the EO-1 Advanced Land Imager and the Landsat Enhanced
Thematic Mapper. In the whole-spectra analysis, spectral
indicators were generated from the whole canopy spectra
(about 400 nm to 1,000 nm) by extracting typical dead and
healthy marsh spectra, and subsequently using them to
determine the percent composition of all canopy reflectance
spectra. Percent compositions were then used to classify
canopy spectra at each field site into groups exhibiting
similar levels of dieback progression ranging from relatively
healthy to completely dead. In the broadband reflectance
analysis, blue, green, red, red-edge, and near infrared (NIR)
spectral bands and NIR/green and NIR/red transforms were
extracted from the canopy spectra. Spectral band and band
transform indicators of marsh dieback and progression were
generated by relating them to marsh status indicators derived
from classifications of the 35 mm slides collected at the
same time as the canopy reflectance recordings. The whole
spectra and broadband spectral indicators were both able to
distinguish (a) healthy marsh, (b) live marsh impacted by
dieback, and (c) dead marsh, and they both provided some
discrimination of dieback progression. Whole-spectra resolution
sensors like the EO-1 Hyperion, however, offered an
enhanced ability to categorize dieback progression.
653 Time-Series Analysis of Medium-Resolution, Multisensor
Satellite Data for Identifying Landscape Change
Andrew A. Millward, Joseph M. Piwowar, and
Philip J. Howarth
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The overall goal of this study is to use medium-resolution
satellite imagery to determine recent changes in the landscape
of the coastal zone near Sanya in the Province of Hainan,
China. A search for suitable satellite imagery revealed that the
only way to identify the changes was to use data from three
different sensors acquired over a 12-year time period: a 1987
Landsat 5 Thematic Mapper (TM) image, a 1999 Landsat 7
Enhanced Thematic Mapper Plus (ETM) image, and two SPOT
2 High Resolution Visible (HRV) images acquired in 1991 and
1997. Given that the Landsat and SPOT images have different
spatial resolutions and that the spectral bands cover somewhat
different spectral ranges, the challenge was how to
combine the images in digital format to be able to detect
subtle changes in the landscape. Measures of brightness,
greenness, and the normalized difference vegetation index
(NDVI) were explored using standardized principal components
analysis (PCA). Approximately 38 percent of the scene was
occupied by water, so tests were performed with the water
included and also with the water masked out to remove these
low-variance pixels. Factor loadings and input-band contributions
were used to interpret component images. Results show
that PCA of the visible bands, representing brightness, is the
superior approach for identifying new urban features in the
landscape. For identification of changes to vegetation, the
near-infrared (NIR) bands outperformed NDVI. Selected standardized
PCA images with visible and NIR bands are recommended
for identifying general changes to an urban landscape
using a time-series of imagery acquired by different satellite
sensors. Benefits of using a mask are believed to be dependent
upon study-site characteristics.
665 A Multi-scale Segmentation Approach to Mapping
Seagrass Habitats Using Airborne Digital Camera
Imagery
Richard G. Lathrop, Paul Montesano, and Scott Haag
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The purpose of this study was to map the areal extent and
density of submerged aquatic vegetation, principally the
seagrasses, Zostera marina and Ruppia maritima, as part
of ongoing monitoring for the Barnegat Bay, New Jersey
National Estuary Program. We examine the utility of multiscale
image segmentation/object-oriented image classification
using the eCognition software to map seagrass across our
36,000 ha study area. The multi-scale image segmentation/
object oriented classification approach closely mirrored our
conceptual model of the spatial structure of the seagrass
habitats and successfully extracted the features of ecological
interest. The agreement between the mapped results and the
original field reference was 68 percent (Kappa - 56.5
percent) for the four category map and 83 percent (Kappa
- 63.1 percent) for the presence/absence map; the agreement
between the mapped results and the independent reference
data was 71 percent (Kappa - 43.0 percent) for a simple
presence/absence map. While the aerial digital camera
imagery employed in this study had the advantage of flexible
acquisition, suitable image scale, fast processing return
time, and comparatively low cost, it had inconsistent radiometric
response from image to image. This inconsistency
made it difficult to develop a rule-based classification that
was universally applicable across the 14 individual image
mosaics. However, within the individual scene mosaics,
using the eCognition software in a “manual classification”
mode provided a flexible and time effective approach to
mapping seagrass habitats.
677 Automated Thematic Registration of NOAA,
CoastWatch, and AVHRR Images
Randolph L. Ferguson, Charles Krouse, Marlene Patterson, and
Jonathan A. Hare
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Daily for the United States, the National Oceanic and
Atmospheric Administration (NOAA), CoastWatch Program
produces multiple regional daytime images and sea surface
temperature (SST) images derived from Advanced Very
High Resolution Radiometry (AVHRR). Images have 1.1 km
pixel resolution at nadir, 1 pixel systematic error (rotation,
scale, distortion), but up to 9 pixels translation (offset)
error. New fully-automated registration to a reference map
corrects translation error to mean radial error 1 pixel
suitable for full pixel resolution application in inshore waters
and large estuaries. The approach converts AVHRR Channel 1
and 2 data to a land and water thematic image, finds the
offset to maximize classification accuracy, and repositions the
AVHRR and SST images. Registration was robust to cloud cover
and missing data and was more reliable than manual registration
for cloudy images. Developed for the southeast region of
the U.S., the approach is transportable to other regions.
687 High Spatial Resolution Satellite Imagery, DEM Derivatives,
and Image Segmentation for the Detection of
Mass Wasting Processes
John Barlow, Steven Franklin, and Yvonne Martin
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An automated approach to identifying landslides using a
combination of high-resolution satellite imagery and digital
elevation derivatives is offered as an alternative to aerial
photographic interpretation. Previous research has demonstrated
that per pixel spectral response patterns are ineffective
in discriminating mass movements. This technique
utilizes image segmentation and digital elevation data in
order to identify mass movements based not only on their
reflectance but also on their shape properties and their
geomorphic context. Dividing the classification by process
into debris slides, debris flows, and rock slides makes the
method far more useful than methods that group all mass
movements together. A hierarchical classification scheme
is utilized to eliminate areas that are not of interest and
to identify areas where mass movements are probable. A
supervised classification is then conducted using spectral,
shape, and textural properties to identify failures that were
greater than 1 ha in area. The resulting accuracy was 90
percent for debris slides, 60 percent for debris flows, and
80 percent for rock slides.
693 Fusing Landsat-5 TM Imagery and Shaded Relief Maps
in Tectonic and Geomorphic Mapping: Lesvos Island,
Greece
Nikolaos A. Soulakellis, Irwin D. Novak, Nikolaos Zouros,
Paul Lowman, and Jacob Yates
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The significance of both solar elevation angle and azimuth are
critical elements for examining Earth observation datasets.
Illumination angle is a crucial parameter affecting the appearance
of the topographically related and dependent features.
Therefore, an improved methodology of data fusion for tectonic
and geomorphic mapping is needed to augment the traditional
false color composite analysis. A long-standing problem in
such applications is the bias introduced by illumination
geometry, specifically sun elevation and azimuth. A Landsat-5
image of Lesvos Island, Greece, was combined with digital
elevation models to produce fused images with a wide range of
illumination azimuths and elevation in a GIS environment.
Sixteen combinations of sun elevation angle (using 15° and
30°) paired with azimuth (0° to 360° at 45° increments) were
considered. This new technique compensates for local conditions
such as generally cloudy winters which make it difficult
to obtain images with low sun elevation or images of eroded
landforms with subdued geomorphic expression. The resulting
fused images combine the tonal information and high spatial
resolution of Landsat with the strong topographic rendition
of digital elevation models. Well-known faults, with more
or less significant expression on the surface known from
previous image interpretation and fieldwork, are more easily
identifiable. Shaded relief maps produced by applying the
lower illumination angle in combination with an azimuth
perpendicular to the fault orientation produced the best results.
Additionally, previously unknown linear and circular features,
e.g., calderas, were represented in the low illumination angle
image, independent of its azimuth. Fused images will be
further combined with geologic and seismicity maps to study
problems such as location of the Anatolian Plate’s boundaries
and their nature (sharp or diffuse).
701 Mapping Built-up Areas from Multitemporal Interferometric
SAR Images - A Segment-based Approach
Leena Matikainen, Juha Hyyppä, and Marcus Engdahl
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Automatic mapping of built-up areas from a multitemporal
interferometric ERS-1/2 Tandem dataset was studied. The
image data were segmented into homogeneous regions, and
the regions were classified as built-up areas, forests, and open
areas using their mean intensity and coherence values and
additional contextual information. Compared with a set of
reference points, an overall classification accuracy of 97 percent
was achieved. The classification process was highly
automatic and resulted in homogeneous regions resembling
a map drawn by a human interpreter. The feasibility of the
imagery for dividing built-up areas further into subclasses was
also investigated. The results suggest that low-rise areas, highrise
areas, and industrial areas are difficult to distinguish
from each other. On the other hand, a correlation between the
building density, the proportion of land covered with buildings,
and intensity/coherence in the image data was found. The
dataset thus appeared to be promising for classifying built-up
areas into subclasses according to building density.