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Proceedings Paper

Removing long-term errors from the AVHRR observation based on Normalized Difference Vegetation Index (NDVI)
Author(s): Md. Z. Rahman; Leonid Roytman; Atiqur Rahman; Felix Kogan; Runa Jesmin
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Paper Abstract

This paper investigates Normalized Difference Vegetation Index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data during 1982-2003. Advanced Very High Resolution Radiometer (AVHRR) weekly data for the five NOAA afternoon satellites for the China dataset is studied, for it includes a wide variety of different ecosystems represented globally. It was found that data for the years 1988, 1992, 1993, 1994, 1995 and 2000 are not stable enough compared to other years because of satellite orbit drift, and AVHRR sensor degradation. It is assumed that data from NOAA-7 (1982, 1983), NOAA-9 (1985, 1986), NOAA-11 (1989, 1990), NOAA-14 (1996, 1997), and NOAA-16 (2001, 2002) to be standard because these satellite's equator crossing time fall within 1330 and 1500, and hence maximizing the value of coefficients. The crux of the proposed correction procedure consists of dividing standard year's data sets into two subsets. The subset 1 (standard data correction sets) is used for correcting unstable years and then corrected data for this years compared with the standard data in the subset 2 (standard data validation sets). In this paper, we apply empirical distribution function (EDF) to correct this deficiency of data for the affected years. We normalize or correct NDVI data by the method of EDF compared with the standard. Using these normalized values, we estimate new NDVI time series which provides NDVI data for these years that match in subset 2 that is used for data validation.

Paper Details

Date Published: 14 October 2008
PDF: 11 pages
Proc. SPIE 7110, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII, 71101G (14 October 2008); doi: 10.1117/12.799900
Show Author Affiliations
Md. Z. Rahman, LaGuardia Community College, CUNY (United States)
Leonid Roytman, City College, CUNY (United States)
Atiqur Rahman, City College, CUNY (United States)
Felix Kogan, National Oceanic and Atmospheric Administration (United States)
Runa Jesmin, King's College London (United Kingdom)

Published in SPIE Proceedings Vol. 7110:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII
Ulrich Michel; Daniel L. Civco; Manfred Ehlers; Hermann J. Kaufmann, Editor(s)

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