LANDSAT data is seen as providing essential up-to-date resource information for the planning process. LANDSAT data of Mindoro Island in the Philippines was processed to provide thematic maps showing patterns of agriculture, forest cover, terrain, wetlands and water turbidity. A hybrid approach using both supervised and unsupervised classification techniques resulted in 30 different scene classes which were subsequently color-coded and mapped at a scale of 1:250,000. In addition, intensive image analysis is being carried out in evaluating the images. The images, maps, and aerial statistics are being used to provide data to seven technical departments in planning the economic development of Mindoro. Multispectral aircraft imagery was collected to compliment the application of LANDSAT data and validate the classification results

Fernandez, J. C.

Wagner, T. W.

English

NASA Technical Reports Server

N78-14583
APPLICATIONS OF LANDSAT DATA TO THE
INTEGRATED ECONOMIC DEVELOPMENT OF MINDORO, PHILIPPINES
T. W. Wagner
Environmental Research Institute of Michigan
Ann Arbor, Michigan
J. C. Fernandez
Bureau of Mines
Manila, Philippines
ABSTRACT
Landsat data is seen as providing essential up-to-date
resource information for the planning process. As part of
a USAID-funded grant, Landsat data of Mindoro Island in
the Philippines was processed to provide thematic maps
showing patterns of agriculture, forest cover, terrain,
wetlands and water turbidity. A hybrid approach using
both supervised and unsupervised classification
techniques resulted in 30 different scene classes which
were subsequently color-coded and mapped at a scale of
1:250,000. In addition, intensive image analysis is
being carried out by the various Philippine government
agencies in evaluating the images.
The images, maps, and aerial statistics are being
used to provide data to seven technical departments in
planning the economic development of Mindoro. Multi-
spectral aircraft imagery has been collected to
compliment the application of Landsat data and validate
the classification results.
INTRODUCTION
The purpose of USAID's Remote Sensing Grant Program is to support estab-
lishment of an indigenous capability in developing countries to use appropriate
remote sensing technology. In instituting the Grant Program AID recognized
that developing-country planners and scientists are frequently in the best
position to determine their country's priority information needs and in what
way remote sensing is likely to contribute to national development.
The Bureau of Mines of the Philippines' Department of Natural Resources
submitted a proposal to USAID to use remote sensing data in connection with
large-scale integrated economic development program for the Island of Mindoro.
This proposal was competitively selected and subsequently approved for funding
in late 1975. In 1976 the Pnilippines grantee received an award of $20,000
and technical assistance from the Environmental Research Institute of Michigan
(ERIM), AID's contractor for administering these grants.
As a small part of the Philippines' Grant project ERIM processed Landsat
data in preparation of tnree thematic image-maps of Mindoro. Herein is a
brief description of that effort in support of the Mindoro project.
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https://ntrs.nasa.gov/search.jsp?R=19780006640 2020-03-22T05:43:27+00:00Z
The purpose of producing thematic image-maps was to determine the nature
and accuracy of the information available from Landsat data of the Philippines.
While it is recognized that manual image analysis techniques provide highly
useful land cover and water quality data, computer compatible tapes may offer
the greatest opportunity for systematic and quantitative analysis. This task
was intended to test the use of digital Landsat data for large-area development
planning.
MINDORO ISLAND
Mindoro is the seventh largest island in the Philippines and comprises
10,347 square kilometers (3,995 square miles) of hilly or mountainous terrain
and narrow coastal lowlands. It is located due south of the Island of Luzon
and 123 kilometers from Manila. Mindoro has a population estimated at 300,000
people, many of whom belong to tribal groups with fairly primitive methods of
fishing and snifting agriculture. Development has lagged on this island owing
to the relatively small proportions of good level agricultural land in relation
to the extensive areas of forested mountain slopes. However, it is clear that
witi. comprehensive resource development Mindoro could become more prosperous.
It is rich in primary forests, aquatic resources, and possibly economic
minerals. Forestry, fishing, mining, and agriculture are economic activities
which could benefit from a more accurate knowledge of Mindoro's resources.
Mindoro is positioned such that the entire island is usually recorded on a
single Landsat image frame (see Figure 1). The first satellite picture of the
island was obtained in the fall of 1972 and subsequently the island has been
imaged more than a dozen times on successive passes of Landsat. Cloud cover is
always a problem in viewing Mindoro because the northwest-southeast trending
mountain ranges are perpendicular to the directions of the moisture carrying
monsoons. Clouds usually cover either the northeastern half or the south-
western half of the island. Only occasionally, during the inter-monsoon period,
is Mindoro largely free of cloud cover. Landsat I data collected on December 23,
1972 showed relatively few clouds; these data were used for machine-processing.
PROCESSING PROCEDURES
The objective of the macnine-processing was to produce thematic maps and
areal statistics which would assist in integrated development planning.
Specifically, different terrain and cover classes were selected and mapped at a
scale of 1:250,000. It was felt that the available satellite information could
be lumped into five general classes on the basis of spectral characteristics:
(1) forests (including primary and secondary), (2) agricultural lands, (3) natural
aquatic vegetation (swamps and marshes), (4) geologic structure and drainage
patterns, and (5) coastal water quality (primarily turbidity). On the maps
produced, forests and aquatic vegetation were shown on the same map, and
geologic structure and water quality were combined -- resulting in a total of
three maps.
The ERIM Multivariate Interactive Digital Analysis System (MIDAS) was
used for this task. The MIDAS special-purpose computer provides an inter-
active, user-oriented capability for producing thematic maps derived from
Landsat multispectral data. In this machine, the parallel digital implementa-
tion capabilities of the processor are combined with a mini-computer to achieve
near-real-time operation coupled with multiple preprocessing functions and color
displays. The data classifier is designed to perform a one pass maximum-likeli-
hood decision with a priori probabilities, assuming multi-model Gaussian
multivariate spectral distributions. The output of this classification process
is a geometrically-rectified color-coded hard-copy image-map. These maps may
be produced at any convenient scales; 1:250,000 for this large-area project.
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POOR QUALITY
FIGURE 1. Landsat Image of Mindoro Island (Band 5)
.00 7.00 14.00 21.00
Band 5
28.00 35.00
FIGURE 2. SIGNATURE VALUES IN LANDSAT BAND 5 AND BAND 7.
ELLIPSES REPRESENT ONE STANDARD DEVIATION FROM MEAN VALUES.
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SIGNATURE SELECTION
Spectral signature selection for classifying Landsat MSS data is the key
to successful processing results. Therefore, careful attention was given to
data analysis directed at appropriate signature selection.
Utilizing the forestry, soil, crop, wetland, and bathymetric information
supplied by the Bureau of Mines, some 43 different signature locations
("training sets") were selected. The spectral characteristics of such features
as primary forest, secondary forest, beach, stream courses, rice, marshland,
coconuts, other crops, and turbid and clear water as nearly as could be
identified were obtained in all four Landsat bands. These were described by
their mean values and covariances in each band. Subsequently, these character-
istics were plotted as one-standard deviation ellipses for two band combinations.
One of these is shown in Figures 2 (Band 5 vs Band 7). As can be seen,
considerable overlapping occurs between ellipses, indicating that there is a
spectral continuum associated with the data and that it is not possible to
selectively eliminate all overlapping and consequent probable misclassification
of features. (Other band combinations showed greater overlapping.)
In addition to the training-set selection (for supervised classification),
data clustering, for unsupervised classification, was also performed. The
clustering procedures allow an even, non-overlapping arrangement of signatures
(see Figure 3) but considerable uncertainty concerning signature identification.
Finally, spectral signatures selected for data classification comprised a
combination of subsets of both the known training set signatures and the cluster
signatures. Training-set signatures of known scene classes were used where
possible, but for spectral conditions not covered by some selected training
set, cluster signatures were used to "fill-in". Twenty-nine different signatures
were subsequently classified.
CLASSIFICATION RESULTS
The twenty-nine signatures selected were thought to represent almost the
entire spectral range of the Mindoro data. Only 1.47» of the data was
subsequently left "unclassified" on the basis of a maximum likelihood ratio
decision rule implemented by the computer.
Essentially the computer compared the recorded spectral characteristics
of each and every picture element (pixel) and determined which of the twenty-
nine signatures, if any, is most similar. Therefore, each of the pixels were
assigned to one of twenty-nine different classes or an "unclassified" class
if it was very different. In doing so, the computer tabulated the number of
pixels identified in each class and the total area represented by each.
Forty percent of the total area was classified (16,370 sq km) as water
and 3.7% was cloud and cloud shadow. Of the land area classified 217. (2,043
sq km.) is forest, 38% (3,750 sq km) is cropland or aquatic vegetation.
Some misclassification occurred due to the overlapping spectral characteristics
of certain features -- for example, secondary forest appeared similar to
certain types of cropland and lowland rice appears similar to wetland.
MAP GENERATION
In addition to the statistics generated by the data classification
procedure, the final product of this task was the production of three thematic
maps wherein different terrain classes and combinations of classes were color-
coded. It is not po'.iible to clearly display 29 different colors, representing
29 different terrain Classes at a scale of 1:250,000. In general, the eye is
able to discern about 10 different colors when they are complexly interwoven
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B
rt
cr
oO
~0
QO0 O
O O
7.00 14.00 21.00
Band 5
28.00 35.OO
FIGURE 3. UNSUPERVISED CLUSTER VALUES IN LANDSAT BAND 5 AND BAND 1.
ELLIPSES REPRESENT ONE STANDARD DEVIATION FROM MEAN VALUES.
FIGURE 4. ERIM MIDAS CLASSIFICATION MAP SHOWING TOPOGRAPHIC STRUCTURE.
1379
in a detailed map. Using the 29 different classes, colors were selected to show
spatial patterns associated with forests, topography and water quality, and crop-
land and wetland on separate image-maps. While the agriculture and forest maps
were conventionally represented, the image map showing topography was somewhat
unique. Signatures for north and west facing slopes were colored violet in
contrast to the south and east slopes which were gray (see Figure 4). Solar
illumination differences resulted in different signatures for these slopes (the
sun elevation was 42° and azimuth, 139°).
MAP EVALUATION
Using the ground truth information supplied by the Bureau of Mines some
preliminary comparisons with the MIDAS map results showed the Landsat data to
provide greater spatial detail than is possible to obtain using simple manual
techniques. The results also demonstrate that the illumination of slopes
varied greatly with orientation and that several signatures were required to
map the same features. For example, primary forests on northern and western
slopes appeared quite different from similar forests on eastern and southern
slopes. Some areas indicated on the ground truth maps as primary forest did
not appear on the Landsat forestry map and need to be investigated further.
Similarly extensive agricultural areas show complex spectral patterns indicating
a variety of crop and cultivation conditions. Aerial photography and ground
observations are being used to evaluate the accuracy of specific classifications.
CONCLUSION
The Republic of the Philippines is an archipelago extending for 1,100 miles
and is composed of 7,100 islands and islets. With its burgeoning population
of 46 million people and increasing need to develop its resources, the Philippines
is looking, in this and other projects, to the use of Landsat data for resource
information required for sound development planning. This space-acquired
information can then be combined with other information (such as aircraft
and/or ground survey data) through a planning or management system. Ultimately
this information will be supplied to a decision-making institution which can
use the information for development planning purposes.
ACKNOWLEDGEMENTS
The authors wish to acknowledge Mr. Carlos Teodoro of the Bureau of Mines
in pursuing this project and Dr. Merrill Conitz, the USAID Technical Monitor.
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Applications of LANDSAT data to the integrated economic development of Mindoro, Phillipines

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