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Land Use/Land Cover
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Linear mixture modeling for quantifying vegetation cover using time series NDVI data
Analysis
Comparing the classified images obtained from multi -temporal Landsat TM, time series NOAA AVHRR and
SPOT VEGETATION monthly composite NDVI data using conventional classification methods with the
faction images extracted using linear mixture modeling, in general, there is a fair agreement between both
for the results obtained using minimum distance method and linear mixture modeling. However, we can
find visually that the fraction images are more detailed, and can reflect the land cover reality better than the
result obtained from the conventional classification method. The advantage of the fraction images is that.they contain physical information, i.e., amount of each component within a pixel.
Based on a simple check on the land cover estimation of the total test area and three selected small areas,
we can find that the area percentage estimation obtained by the conventional method seems not so good.
Although the results obtained from the conventional method are acceptable, the limitation of these
methods is obvious and inevitable. The linear mixture model performs better. Moreover, we can detect land
cover change of the test area more accurately from the fraction images. But as with training areas selection
in other supervised classification methods, endmember collection in linear mixture modeling has a
significant effect on the final fraction images.
Vegetation Fraction of Asia
Endmember Selection
The key to a successful linear mixture modeling is the selection of appropriate endmembers. Endmembers
should define a coherent set of characteristics that are representative of physical components on the
surface, and should also describe the full range of natural variability inherent in the ground. Fortunately, the
development of Global Land Cover Ground Truth (GLCGT) database facilitates endmember selection. The
GLCGT database is developed under the cooperation of many projects and researchers, and will be free
for usage by any researcher. The objective to develop GLCGT database is to realize reliable and
continuously improved land cover ground truth data, and also eliminates duplicated efforts of ground truth
collection among projects. Now, the Regional Land Cover Ground Truth (RLCGT) database of Asia, which
is part of the GLCGT database, has already been completed, and is used for endmember selection in this
study.
Vegetation Cover Mapping of Asia
Time series SPOT VEGETATION data has been provided for the cooperative research of Global Land
Cover 2000 project. Data of three regions, Middle East, Central Asia and Southeast Asia are mosaicked
and cover most of Asia. In the vegetation cover mapping approach, first, water and bare area are extracted
based on the ground truth data collected in RLCGT database of Asia. Then, linear mixture modeling is
applied to the whole scene except in water and bare areas, and area percentage images of forest, farmland
and steppe are obtained. The validation of the resultant fraction images is also performed based on some
local area studies.
Conclusion
This study confirmed the potential of the linear mixture modeling when applied to time series monthly
composite NDVI data for estimation of ground cover proportions at local and continental scales. The mixed
pixel problem is frequently encountered when using coarse spatial resolution remotely sensed data.
However, the possibility of obtaining area percentage estimation directly from time series NDVI data will
undoubtedly lead to a more precise vegetation cover classification and monitoring.
Despite many error sources that are partly due to coarse resolution data characteristics, the area
percentage estimation of the main land cover types of the test area are relatively acceptable compared to
that obtained from multi- temporal Landsat TM data. Fraction images of vegetation cover of Asia need to be
further assessed quantitatively. Inclusion of other land cover components may provide additional
information and possibly more accurate results. Improvement of endmember selection and development of
more sophisticated methods to fully utilize the included information of remotely sensed data should be also
addressed. The research has shown that linear mixture techniques can have great potential for global
studies.
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