PE&RS January 1997

Cover Image

These images illustrate the mean global Normalized Difference Vegetation Index (NDVI) for the time period of 1982-1993, and its inter-annual variation. They were generated from the NOAA/ NASA Pathfinder AVHRR 8-km Land (PAL) data set (1982-1993).

For the first time-to our knowledge -global inter-annual variations in vegetation dynamics over a 13-year period can be observed. The inter-annual variation is a result of changes in surface veg-etation characteristics due to meteorological fluctuations, atmospheric effects such as volcanic dust, and variations in satellite platforms. This data set was created by NASA's Goddard Space Flight Center and is archived at the Distributed Active Archive Center (DAAC). Land products from this data set are now available free-of-charge to the land science community.

Highlight Article

12 The NOAA/NASA Pathfinder AVHRR 8-Km Land Data Set (Adobe PDF 3.7Mb)
Peter M. Smith, Satya N.V. Kalluri, Steven D. Prince, and Ruth DeFries

Peer Reviewed Articles (Click the linked titles to see the full abstract)

41-49 Aquatic Macrophyte Modeling Using GIS and Logistic Multiple Regression
Sunil Narumalani, John R. Jensen, Shan Burkhalter, John D. Althausen, and Halkard E. Mackey, Jr.

Data derived from the application of the model will provide scientists information on the future spatial growth and distribution of aquatic macrophytes. 

51-58 Forest Ecosystem Modeling in the Russian Far East Using Vegetation and Land-Cover Regions Identified by Classification of GVI
Greg G. Gaston, Peggy M. Bradley, Ted S. Vinson, and Tatayana P. Kilchugina

Qualitative comparison on a pixel-by-pixel basis with detailed topographic maps and other data showed that, in general, despite the widely acknowledged problems with the Global Vegetation Index (GVI), surface conditions were well identified by the GVI classification.  

59-67 Landscape Cover-Type Modeling Using a Multi-Scene Thematic Mapper Mosaic
Collin G. Homer, R. Douglas Ramsey, Thomas C. Edwards, Jr., and Allan Falconer

Thirty-four of 36 cover-type classes were totally or partially identified using digital modeling, with five of 36 classes requiring both digital and analog methods.  

69-77 Obtaining Spatial and Temporal Vegetation Data from Landsat MSS and AVHRR/NOAA Satellite Images for a Hydrologic Model
Zhangshi Yin and T.H. Lee Williams

The research results show that the vegetation data obtained from the satellite imagery are more realistic than those obtained from a crop growth model

79-86 Supervised Classification of Landsat Thematic Mapper Imagery in a Semi-Arid Rangeland by Nonparametric Discriminant Analysis
Steven T. Knick, John T. Rotenberry, and Thomas J. Zarriello

Within major grassland and shrubland groups, 64% accuracy was achieved in separating dominant vegetation classes.

87-93 Using Thematic Mapper Data to Identify Contrasting Soil Plains and Tillage Practices
A.P. van Deventer, A.D. Ward, P.H. Gowda, and J. G. Lyon

The usefulness of space-borne sensors to provide information for purposes of planning, modeling, and environmental impact analysis of agricultural production systems is evaluated.