Multi-resolution analysis of MODIS and ASTER satellite data for water classification

Abstract

In Romania there are many areas flooded every year. The estimation of the surfaces covered by water in the post-crisis periods is of real use for the decision makers at all levels. Due to the constraint that high spatial resolution satellite images are low temporal resolution, there exists a need for a reliable method to obtain accurate information from medium resolution data, for example, MODIS satellite images. The overall goal of this paper is to classify MODIS data to get an estimate of water surface area. To develop the classification technique, the strategy was to obtain MODIS and ASTER data acquired at the same time over the same location, and use the ASTER data as "ground truth". For this study, two lakes in the Bihor County of Romania were chosen and MODIS and ASTER data from October 31, 2002 were utilized. The ASTER data were used to create a detailed water mask to be used as ground truth for the MODIS water classification. The percent water image derived from ASTER was superimposed on the MODIS image. A supervised classification for water was performed on the 3-band MODIS image using the feature space algorithm. The water surface area as measured from the MODIS classification was about 16% more than the ASTER ground truth-value. This approach provided useful information concerning the water classification from different resolution data that could help in the estimation of water surface area from MODIS imagery.

Full text

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UNCLASSIFIED/UNLIMITED
UNCLASSIFIED/UNLIMITED
Multi-Resolution Analysis of MODIS and
ASTER Satellite Data for Water Classification
Corina Alecu, Simona Oancea
National Meteorological Administration
97 Soseaua Bucuresti-Ploiesti, 013686, Sector 1, Bucharest
Romania
corina.alecu@meteo.inmh.ro
Emily Bryant
Dartmouth Flood Observatory, Dartmouth College, Hanover NH 03755
USA
ABSTRACT
Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Spaceborne Thermal Emission
and Reflection Radiometer (ASTER) are multi-spectral sensors embarked on the EOS AM-1 (TERRA)
satellite platform. Both sensors operate in different spectral bands, but also with different pixel
resolutions.
The overall goal of this paper is to classify MODIS data to get an estimation of water surface area, very
useful in the post-crisis periods for the decision makers at all levels.
To develop the classification technique, the strategy was to obtain MODIS and ASTER data acquired at
the same time over the same location, and use the ASTER data as “ground truth”. Two lakes in the Bihor
County of Romania were chosen and satellite data from October 31, 2002 were utilized. From the ASTER
data we created a detailed water mask to be used as ground truth for the MODIS water classification. The
percent water image derived from ASTER was superimposed on the MODIS image. A supervised
classification for water was performed on the 3-band MODIS image using the feature space algorithm.
The water surface area as measured from the MODIS classification was about 16% more than the ASTER
ground truth-value. Due to the constraint that high spatial resolution satellite images are low temporal
resolution, there exists a need for a reliable method to obtain accurate information from medium
resolution data.
This approach provided useful information concerning the water classification from different resolution
data that could help in the estimation of water surface area from MODIS imagery.
1.0 INTRODUCTION
Flooding events are quite common in Romania. The estimation of the surfaces covered by water in the
post-crisis periods is of real use for the decision makers at all levels. The classification problem of water
cover surfaces from satellite images was approached in many applications. Even a binary classification of
satellite images from optical domain seems to be simple enough comparing with a multi-class
classification. But there exits many other constrains. The cloud cover in the flood time is important and the
spectral characteristics of water while and after floods are quite different from the clear water and it is
difficult to distinguish. Another difficulty in water surfaces estimation is represented by the ground
Alecu, C.; Oancea, S.; Bryant, E. (2006) Multi-Resolution Analysis of MODIS and ASTER Satellite Data for Water Classification.
In Emerging and Future Technologies for Space Based Operations Support to NATO Military Operations (pp. P2-1 – P2-8).
Meeting Proceedings RTO-MP-RTB-SPSM-001, Poster 2. Neuilly-sur-Seine, France: RTO. Available from:
http://www.rto.nato.int/abstracts.asp.

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Multi-Resolution Analysis of MODIS and ASTER Satellite Data for
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Bucuresti-Ploiesti, Sector 1, 013686 Bucharest Romania
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Multi-Resolution Analysis of MODIS and
ASTER Satellite Data for Water Classification
UNCLASSIFIED/UNLIMITED
resolution of the pixel in the satellite image. In the case of high-resolution sensors (ASTER, SPOT/XS,
LANDSAT-TM, IRS), the water separation is simpler than in the case of medium resolution satellites
(MODIS, NOAA/AVHRR). This is related to the pixel resolution (250-500 m for visible bands for
MODIS, 1.1 km for NOAA/AVHRR images). The water could exist only on a part of the pixel surface but
the signal coming from that pixel indicates water for the entire surface of that pixel. This may result into
an under or over-estimation of the total water surface. Due to the constraint that high spatial resolution
satellite images are low temporal resolution, one needs a reliable method to obtain accurate information
from medium resolution data.
MODIS is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Both sensors are
viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands.
The ASTER instrument is embarked only on the Terra satellite and consists of three separate instrument
subsystems, operating in a different spectral region: the Visible and Near Infrared (VNIR), the Short wave
Infrared (SWIR), and the Thermal Infrared (TIR). In the table 1 are presented some of the spectral bands
of the ASTER and MODIS sensors.
MODIS data have the potential for flood monitoring due to their high time resolution and low cost, with
the constraint that the cloud-free images are quite rare during flood periods. Taking into consideration the
spectral characteristics of the main ground-cover types during floods and satellite signal components, this
paper discusses the comparison between MODIS and ASTER water classification. The methodology was
to approximate the fraction that is water, so we can estimate the on-the-ground surface water area in
MODIS images, on the basis of ASTER data as ground-truth.
Table 1 - The characteristics of ASTER first 9 spectral bands (left) and of MODIS first 7 spectral
bands (right)
Spectral
bands
Spectral Range
Ground
Resolution
1
Band 1: 520 - 600 nm
Nadir looking
2
Band 2: 630 - 690 nm
Nadir looking
3N
Band 3: 760 - 860 nm
Nadir looking
VNIR
3B
Band 3: 760 - 860 nm
Backward looking
15 m
4 Band 4: 1600 - 1700 nm
5 Band 5: 2145 - 2185 nm
6 Band 6: 2185 - 2225 nm
7 Band 7: 2235 - 2285 nm
8 Band 8: 2295 - 2365 nm
SWIR
9 Band 9: 2360 - 2430 nm
500 m
Spectral
bands
Spectral Range
Ground
Resolution
1 620 – 670 nm 250 m
2 841 – 876 nm 250 m
3 459 – 479 nm 500 m
4 545 – 565 nm 500 m
5
1230 – 1250
nm
500 m
6
1628 – 1652
nm
500 m
7
2105 – 2155
nm
500 m
2.0 METHODOLOGY
The task was to compare the water area as determined from the ASTER and MODIS water classifications
for an identical region on the ground. Since the classification has percentage values, one can not just add
up the number of water pixels. The common approach is that each pixel should be multiplied by its percent
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Multi-Resolution Analysis of MODIS and
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water value and then adding them to get the equivalent number of water pixels which can then be
multiplied by the area of a pixel. However, one can also make a comparison by finding the average of the
percent water pixel values to come up with overall percent water for the area.
ASTER and MODIS data are pre-processed
[1], [2]
in order to obtain the water cover surface. Both images
are imported and geo-rectified in the same projection, using ENVI software image processing. Concerning
ASTER image, the Level 1B data imported in ENVI is already projected. VNIR first three bands, at 15 m
resolution, were processed. We used MODIS reflectance data from MOD02 Level 1B data. Even the
spatial resolution of the 1240 nm Shortwave-IR spectral region band is lower (500 m) as visible bands we
preferred to use this too, because of their spectral information valuable in case of sediments present in
water. The MODIS image was corrected of the bow-tie effect, which affects these images. We resized the
SWIR data for matching with the two visible bands and we created a stack with the three bands at 300 m
resolution. The next step was to geo-rectify the data in the Universal Transverse Mercator (UTM)
projection, zone 34, datum WGS84. Because of the errors occurred, we used the geo-referenced ASTER
image for registering the MODIS data.
In order to delineate the water in the ASTER image, the reflectance feature of water at visible green and
absorption feature at NIR were used to map surface water
[3], [4]
. For MODIS image, we tried both a
threshold method
[5]
and a supervised classification for water. This last method was performed on the 3-
band MODIS image using maximum likelihood algorithm in the spectral overlap area and it seemed to
reflect better the water delineation.
The processing algorithm is presented in the figure 1.
High resolution
satellite image
ASTER
Water surface area
MODIS vs. ASTER
Percentage water
Vector water mask
Water mask using
supervised
classification
Water mask using
NDVI values
Geometric corrections using
topographic maps
Subset
Radiometric enhancemen
t
Bow-tie correction
Geometric corrections using
ASTER image
Subset
Radiometric enhancemen
t
Medium resolution
satellite image
MODIS
Figure 1: The methodology to compare water classification from MODIS and ASTER satellite
images.
3.0 DATA
The study area was located in the Bihor County of Romania (fig. 2). Two lakes were selected and data
acquired from TERRA/ASTER (figure 3) and TERRA/MODIS (figure 4) at the site, for October 31, 2002
were chosen.
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We used visible green, visible red, and Near IR bands of ASTER (bands 1, 2, and 3N), and visible red,
Near IR, and Short-Wave (1240 nm wavelength) bands from MODIS (bands 1 and 2 of the 250m
resolution data, and the third of the five 500 m resolution bands). The scenes were geo-rectified to UTM
projection, with pixel size of 15m for ASTER data and 300m for MODIS data. Figures 3 and 4 show geo-
rectified ASTER and MODIS images of the study area. The ASTER image was imported in ENVI image
processing software and rotated with the angle 10.43 degrees in order to co-locate and analyzed with
MODIS image.
22
o
32’
Test area
47
o
07’
Figure 2: The study area located in the north-west of Romania.
Figure 3: ASTER image of study area. Figure 4: MODIS image of study area.
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4.0 RESULTS
The ASTER data were used to create a detailed water mask to be used as ground truth for the MODIS
water classification. From here, a sequence of raster and vector operations comes to compare the two
water classifications. Figure 5 shows a more detailed view of the ASTER image, in the lakes region.
NDVI (Normalized Difference Vegetation Index) was calculated as the fraction between the difference of
the NIR and Red Bands and their sum. ASTER Band 3N and the calculated NDVI (Normalized Difference
Vegetation Index) were used to make the water mask (Figure 6), using a formula: y = -21x + 72 (where x
is NDVI and y is Band 3N). We created an image using this formula and all pixels with the value of 59 or
more were called water.
Figure 7 shows a scatter plot of the ASTER data. The dots in the lower left portion of the plot below the
straight line are classified as water pixels. The formula was applied only to pixels close to the water bodies
as it would not work properly farther away (some pixels would be falsely classified as water).
This raster water mask was vectorized and superimposed for comparison on the MODIS water mask
obtained by MODIS image processing. A supervised classification for water was performed on the 3
bands MODIS image using the feature space algorithm, with maximum likelihood algorithm used in the
spectral overlap area (Figure 8). The “degrade” function in ERDAS Imagine was used on the binary water
versus not water 15 m ASTER mask to estimate the percentage of water in each MODIS pixel classified as
water. Next figure (figure 9) represents the two masks, obtained from ASTER and MODIS data. In the
figure 10 the percent water image derived from ASTER was superimposed on the MODIS image. Finally,
the ASTER and MODIS water delineation were overlaid and we calculated the differences between the
pixels classified as water, both in ASTER and MODIS images.
Figure 5: Detailed ASTER image of study area. Figure 6: Water mask created from ASTER data.
Figure 7: Scatter plot of ASTER data - water pixels are in lower left part of image, below the line.
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Figure 8: Water mask created from MODIS data. Figure 9: MODIS water classification and ASTER-
derived water mask.
Figure 10: Percentage water ground truth (in blue tones) created by "degrading" ASTER water
mask and superimposed on MODIS image and the legend of water pixels.
The water surface area as measured from the MODIS classification was 981.5 hectares, about 16% more
than the ASTER ground truth-value of 847.6 hectares and this difference represents the incorrect
classification of “border” pixels in MODIS image.
5.0 CONCLUSIONS
The overall goal of this paper was to classify MODIS data to get an estimate of water surface area. To
develop the classification technique, the strategy was to obtain MODIS and ASTER data acquired at the
same time over the same location, and use the ASTER data as “ground truth”. Since MODIS pixels are
large compared with many water bodies, it was useful to determine the fraction of a MODIS pixel covered
with water, rather than just binary water versus not-water distinction. This approach gives us useful
information concerning the water classification from different resolution data that could help in the
estimation of water surface area from MODIS imagery. In the future, we plan to use the MODIS
classification as a water mask, and create a percentage of water area for each pixel within the mask, based
on a MODIS band, NDVI, or other band combination.
AKNOWLEDGEMENTS
This research was supported by the National Meteorological Administration (Romania) and Dartmouth
Flood Observatory, Hanover, New Hampshire, as part of the NATO Science for Peace Programme,
Project no. 978016 “Monitoring of Extreme Flood Events in Romania and Hungary Using Earth
Observation (EO) Data”.
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REFERENCES
[1] M. Abrams, S. Hook, and B. Ramachandran, ASTER User Handbook, 135 p., Jet Propulsion
Laboratory, California Institute of Technology, Pasadena, California.
[2] G. Toller, A. Isaacman, MODIS Level1B Product User’s Guide, 61 p., NASA/Goddard Space Flight
Center, Greenbelt, 2003.
[3] Lillesand and Kieffer, Remote Sensing and Image Interpretation, 3
rd
Edition, 750 p., John Willey &
Sons, Inc Publisher, 1994.
[4] Bryant, Emily, “Identifying surface water in ASTER – fractional pixels”, Unpublished document
produced in the Dartmouth Flood Observatory, October 22, 2003.
[5] Putsay, M., “Creating a Water Mask using a Threshold Technique on Multi-spectral MODIS
Images”, Report on Dartmouth Flood Observatory, November 25, 2003.
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