Global Inventory Modeling and Mapping Studies (GIMMS)

Data Access

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Overview

The GIMMS (Global Inventory Modeling and Mapping Studies) data set is a normalized difference vegetation index (NDVI) product available for a 25 year period spanning from 1981 to 2006. The data set is derived from imagery obtained from the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the NOAA satellite series 7, 9, 11, 14, 16 and 17. This is an NDVI dataset that has been corrected for calibration, view geometry, volcanic aerosols, and other effects not related to vegetation change.

Code Values

Value	Label
0.0 - 1.0	NDVI
-0.1	Water
-0.05	Null

How to Cite This Data Set

Citation Format: Author (Publication Date), Collection Name, Product Name, Processing Level, Publisher, Publisher Location, Product Coverage Date.

Citation Parameters Example

Author: Tucker, C.J., J.E. Pinzon, M.E. Brown
Publication Date: 2004
Collection Name: Global Inventory Modeling and Mapping Studies
Product Name: (specify image name from metadata or naming convention)
Processing Level: 2.0
Publisher: Global Land Cover Facility, University of Maryland
Publisher Location: College Park, Maryland
Product Coverage Date: {specify from metadata or naming convention}

Full Example Citation: Tucker, C.J., J.E. Pinzon, and M.E. Brown (2004), Global Inventory Modeling and Mapping Studies, NA94apr15b.n11-VIg, 2.0, Global Land Cover Facility, University of Maryland, College Park, Maryland, 04/15/1994.

Associated Peer-Reviewed Publication (cite these publications whenever the data are used):
Pinzon, J., Brown, M.E. and Tucker, C.J., 2005. Satellite time series correction of orbital drift artifacts using empirical mode decomposition. In: N. Huang (Editor), Hilbert-Huang Transform: Introduction and Applications, pp. 167-186.

Tucker, C.J., J. E. Pinzon, M. E. Brown, D. Slayback, E. W. Pak, R. Mahoney, E. Vermote and N. El Saleous (2005), An Extended AVHRR 8-km NDVI Data Set Compatible with MODIS and SPOT Vegetation NDVI Data. International Journal of Remote Sensing, Vol 26:20, pp 4485-5598.

Intellectual Property Rights

University of Maryland; Department of Geography; use is free to all if acknowledgement is made. UMD holds ultimate copyright.

Source

As a courtesy, please credit the Global Land Cover Facility (GLCF) as the source for this data set in some manner. Our suggestion: Source for this data set was the Global Land Cover Facility, www.landcover.org.