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Time-Series Classification of High-Temporal Resolution AVHRR NDVI Imagery of Mexico
Egbert, Stephen L. Ortega-Huerta, Miguel MartÃnez-Meyer, Enrique Price, Kevin P. Peterson, A. Townsend
Egbert, Stephen L.
Ortega-Huerta, Miguel
MartÃnez-Meyer, Enrique
Price, Kevin P.
Peterson, A. Townsend
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Abstract
Time-series data from wide-field sensors, acquired for
the period of a growing season or longer, capitalize on
phenological changes in vegetation and make it possible to
identify vegetated land cover types in greater detail. Our
objective was to examine the utility of time-series data to
rapidly update maps of vegetation condition and land cover
change in Mexico as an input to biodiversity modeling. We
downloaded AVHRR NDVI 10-day composites from the
USGS EROS Data Center for 1992-1993 and adjusted for
cloud contamination by further aggregating the data. In the
first phase of our analysis, we selected training sites for
various land cover types using a land cover map created by
the Mexican National Institute of Statistics, Geography, and
Informatics (INEGI) as a guide. Since there is a high degree
of spectral variability within many of the vegetated land
cover types, we subjected the spectral response patterns to
cluster analysis. We then used the statistics of the clusters as
training data in a supervised classification. We also
compared unsupervised and univariate decision tree
approaches, but these provided unsatisfactory results. Best
results were achieved with a 19-class map of land use/land
cover employing a supervised approach.
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Date
2000
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IEEE
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Egbert, S. L., M. A. Ortega-Huerta, E. MartÃnez-Meyer, K. P. Price, and A. T. Peterson. 2000. Time-series analysis of high-temporal resolution AVHRR NDVI imagery of Mexico. Proceedings IEEE 2000 International Geoscience and Remote Sensing Symposium (IGARSS) 5:1978-1980. http://dx.doi.org/10.1109/IGARSS.2000.858207