DocumentCode
640717
Title
Inconsistent NDVI trends from AVHRR, MODIS, and SPOT sensors in the Tibetan Plateau
Author
Zhen Yu ; Jingxin Wang ; Shirong Liu ; Pengsen Sun ; Weiguo Liu
Author_Institution
Div. of Forestry & Natural Resources, West Virginia Univ., Morgantown, WV, USA
fYear
2013
fDate
12-16 Aug. 2013
Firstpage
97
Lastpage
101
Abstract
The Normalized Difference Vegetation Index (NDVI) from the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA - AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Systèm Pour l´Observation de la Terre-VEGETATION (SPOT-VEG) have been widely used in numerous local to global scale modeling and analysis. In this study, we evaluated the accuracy of the GIMMS NDVI and examined the potential errors of the NDVI data in the Tibetan Plateau. We found that the spring AVHRR NDVI decreased from late of 1990s, while SPOT and MODIS NDVI slightly increased during the same period. The AVHRR NDVI exhibit both a negatively and significantly correlation with water vapor (or precipitable water), suggesting AVHRR NDVI was affected by the atmospheric water vapor variation.
Keywords
atmospheric humidity; remote sensing; vegetation; AVHRR sensor; Advanced Very High Resolution Radiometer; MODIS sensor; Moderate Resolution Imaging Spectroradiometer; NDVI data; National Oceanic and Atmospheric Administration; Normalized Difference Vegetation Index; SPOT sensor; System Pour l´Observation de la Terre-VEGETATION; Tibetan Plateau; atmospheric water vapor variation; inconsistent NDVI trends; local-to-global scale analysis; local-to-global scale modeling; Correlation; MODIS; Market research; Remote sensing; Sensors; Springs; Vegetation mapping; AVHRR; Climate Change; MODIS; NDVI; SPOT; Tibetan Plateau;
fLanguage
English
Publisher
ieee
Conference_Titel
Agro-Geoinformatics (Agro-Geoinformatics), 2013 Second International Conference on
Conference_Location
Fairfax, VA
Type
conf
DOI
10.1109/Argo-Geoinformatics.2013.6621887
Filename
6621887
Link To Document