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thesis mapping – marc holland 2003 - RWTH Aachen
Geology of Kilauea caldera and Ka’u desert
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A
remote
sensing based thesis mapping was done in coorperation with the University of
Hawaii
at Manoa in the winter 2002/2003. As part of a combined diploma
thesis/mapping
project a variety of different data sets were used to map a 255
km²
area in 1:25.000 on the Big Island/Hawaii.
The excellent
outcrop
conditions in the proximity of the Kilauea caldera and Ka’u desert
provided
high quality data sets analysed prior to a 4 week lasting fieldwork
period.
The processing
of
the raw data with the software package ENVI at the University of
Hawaii
at Manoa delivered preliminary maps that were checked and enhanced
during
the fieldwork.This page gives a short overview over the data sets and
the
mapping.
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Data sets
The data sets of the area are of varying quality and age
–
mostly acquired during the 1980’s. Most sets needed to be processed and
georeferenced.
The content of the imagery holds data of different wavelengths offering
sufficient
insight in the repertoire of surface units.
The data set shown above is a USGS DEM file (Digital
Elevation Model). The data represents the
elevation
of the terrain. Acquisition of such data is commonly done by digitizing
topographic
maps or by the processing of radar. The visual information of the
displayed upper
image is limited but the file can be used to create contour lines or to
create
a 3D impression of the area. (Untreated DEM image, 17×15 km).
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A mathematical operation on the DEM determines the slope
of
the terrain along a line of view and creates a synthetic shaded
relief
image. The upper image (with superimposed red contour lines) now
highlights
the morphology of the area with e.g. the caldera (NE) and the flanks of
Mauna
Loa (NW). Even the contours of major lava flows are now visible (Shaded
relief
image, 17×15 km, illumination 315/25,
contour
interval is 100 m).
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The SPOT data is a satellite data set. SPOT's
panchromatic
band (black and white) gives a resolution of 10 m/pixel. Since
the
data is acquired from extreme height, limited distortion is expected.
Georeferenced
on the DEM the spot image was used as base image for the mapping, since
it
covers the entire mapping area. Visible on the upper image are younger,
dark
appearing lava flows as well as dark streaks, which are faults (Koa’e
fault
zone). (SPOT image 17×15 km,
georeferenced,
panchromatic band, manually stretched)
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The Landsat TM data set has 7 bands sampling the
visual
spectrum and the near infrared. It is capable of displaying information
familiar
to the human vision and information important to verify plant growth.
The
poor spacial resolution of 30 m/pixel limits the use of this data set
for
the mapping although it was commonly used for areas that are missing on
the
other data sets. (Landsat TM image, 17×15
km, georeferenced, bands 2,5,7 as RGB)
The bands 3
and
4 of the Landsat data allow statements about the vegetation. The NDVI
is a normalized index of the density and health of plants derived from
the
bands 3 and 4. The upper image shows the NDVI (on a scale from 0-100%)
in
the mapping area. Bright white values represent moderately to extreme
vegetation
cover, whereas black areas represent vegetation free areas. Note that
the
black area of the Ka’u desert is leeward (SW) of the Kilauea caldera.
The
sulphuric fumes of the crater limit plant growth downwind creating a
chemical
desert. The lava flows of Mauna Loa (NW) and younger craters (Kilaua
Iki
in the NE) are visible as well. (Indexed NDVI image, 17×15
km)
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The TIMS
data (Thermal Infrared Multi Spectrometer) is acquired by an airborne
system.
The trajectory of the plane as well as its varying elevation above the
ground
results in distorted image files that need to be georeferenced
carefully.
The swaths (image coverage, see upper image) do not cover the outer
parts
of the mapping area but hold the most interesting central part.
TIMS samples data in the thermal infrared in 6 bands. It responds to
the
thermal properties of the rocks and shows similar results for the
predominantly
basaltic surface.
The brightness differences in the two swaths are a result of the
acquisition
and reflect thermal differences due to calibration and differences in
the
daytime (TIMS image, 17×15 km, georeferenced, Bands
1,2,3
as RGB, manually stretched).
The thermal
properties
of the Basalts are very similar and highly correlated. This is
documented
by the low contrast, and dull colors on the pervious TIMS image. To
stress
the differences in highly correlated data, a principle component
analysis
can be applied. This mathematical operation recalculates the data to
maximize
the visual contrast to the processor. The upper image shows such an
analysis.
Differences within individual flow units are now highlighted.
(Principle
Component TIMS, 17×15 km, georeferenced, PC
1,2,3
as RGB, manually stretched)
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Another airborne data set is the AIRSAR set (SAR
=
synthetic aperture radar). The data set uses radar wavelengths to
determine
the surface roughness. The sensor uses three different wavelengths (68
cm,
24 cm and 5.6 cm) to give a measure for the roughness of the surface in
those
scales. The three bands displayed as an RGB image allow distinguishing
different
surface textures. White areas are rough in all wavelengths and
represent
either vegetation or rugged a’a lava. Blue areas are smooth in the
large
scales and rough on the 5.6 cm scale. This represents roughness created
by
small chunks (e.g. pyroclastics) (patched ARISAR image, 17×15 km, georeferenced, P-,
L-,
C-band as RGB).
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The major advantage of the data sets is its variety in
different
wavelengths. This enables good estimates of the surface units.
Unfortunately
the data sets were poorly georeferenced and distorted to a high extent.
The
poor spatial coverage as well as the differences in the important
TIMS
swaths required combining all data sets to provide a proper view on the
surface
units. However some units are not uniquely represented by the data.
A four week lasting fieldwork period was carried out to verify the
results
of the preliminary data processing. Small scaled observations and data
retrieval
for the thesis work were done as well. The lessons learned in the field
allowed
creating a high detail surface map of the area highlighting different
surface
units, relative ages and the relative roughness.
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(final map, click here to enlarge)
This work was carried out with the support and help of
Janos
L. Urai (GED/RWTH), Stephen Martel (GG/SOEST) and Scott Rowland
(HIGP/SOEST).
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