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