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Proceedings Paper

Multisensor reflectance and vegetation index comparisons of Amazon tropical forest phenology with hyperspectral Hyperion data
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Paper Abstract

Current earth observing satellite sensors have different temporal, spectral and spatial characteristics that present problems in the establishment of long term, time series data records. Vegetation indices (VI's) are commonly used in deriving long term measures of vegetation biophysical properties, which have been shown useful in interannual climate studies and phenology studies. While significant improvements have been made with new sensors, and algorithms, and processing methods, backward compatibility of VI's is desired so that the long term record can extend back and utilize the AVHRR record to 1981. Conversely, any reprocessing of the AVHRR record should consider steps to allow forward compatibility with newer sensors and products. In this study we evaluated the use of sensor-specific enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) data sets, using a time sequence of Hyperion images over Tapajos National Forest in Brazil over the 2001 and 2002 dry seasons. We computed NDVI, EVI, and a 2-band version of EVI (EVI2) for different sensor systems (AVHRR, MODIS, VIIRS, SPOT-VGT, and SeaWiFS) and evaluated their differences and continuity in the characterization of tropical forest phenology. We also analyzed the influence of different atmosphere correction scenarios to assess noise in the phenology signal. Our analyses show that EVI2 maintains the desirable properties of increased sensitivity in high biomass forests across all sensor systems evaluated in this study. We further conclude that EVI2 can be extended to the AVHRR time series record and compliment that current NDVI time series record.

Paper Details

Date Published: 9 October 2007
PDF: 10 pages
Proc. SPIE 6679, Remote Sensing and Modeling of Ecosystems for Sustainability IV, 667906 (9 October 2007); doi: 10.1117/12.734974
Show Author Affiliations
Youngwook Kim, Univ. of Arizona (United States)
Alfredo R. Huete, Univ. of Arizona (United States)
Zhangyan Jiang, Univ. of Arizona (United States)
Tomoaki Miura, Univ. of Hawaii (United States)


Published in SPIE Proceedings Vol. 6679:
Remote Sensing and Modeling of Ecosystems for Sustainability IV
Wei Gao; Susan L. Ustin, Editor(s)

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