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Trend analysis of time-series phenology derived from satellite data

By:
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DOI: 10.1109/AMTRSI.2005.1469863

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Abstract

Remote sensing information has been used in studies of the seasonal dynamics (phenology) of the land surface for the past 15 years. While our understanding of remote sensing phenology is still in development, it is regarded as a key to understanding land surface processes over large areas. Repeat observations from satellite-borne multispectral sensors provide a mechanism to move from plant-specific to regional scale studies of phenology. In addition, we now have sufficient time-series (since 1982 at 8-km resolution covering the globe and since 1989 at 1-km resolution over the conterminous US) to study seasonal and interannual trends from satellite data. Phenology metrics including start of season, end of season, duration of season, and seasonally integrated greenness were derived from 8 km AVHRR data over North America spanning the years 1982-2003. Trend analysis was performed on the annual summaries of the metrics to determine areas with increasing or decreasing trends for the time period under study. Results show only small areas of changing start of season, but the end of season is coming later over well defined areas of New England and SE Canada, principally as a result of land use changes. The total greenness metric is most striking at the shrub/tundra boundary of North America, indicating increasing vegetation vigor or possible vegetation conversion as a result of warming. ?? 2005 IEEE.

Additional Publication Details

Publication type:
Conference Paper
Publication Subtype:
Conference Paper
Title:
Trend analysis of time-series phenology derived from satellite data
ISBN:
0780391187; 9780780391185
DOI:
10.1109/AMTRSI.2005.1469863
Volume
2005
Year Published:
2005
Language:
English
Larger Work Title:
Proceedings of the Third International Workshop on the Analysis of Multi-Temporal Remote Sensing Images 2005
First page:
166
Last page:
168
Number of Pages:
3
Conference Title:
3rd International Workshop on the Analysis of Multi-Temporal Remote Sensing Images 2005
Conference Location:
Biloxi, MS
Conference Date:
16 May 2005 through 18 May 2005