Abstract:
The MODIS sensor to be launched on tho EOS-AM platform will be the most important sensor for global vegetation mapping. Among the programmatic goals for the MODIS sensor are to assess and track changes in land use/landcover, leaf area index (LAI), and net primary productivity (NPP). For these products to be used in global models, they must be rigorously validated with site-specific data products. This article presents a review of some of the problems facing a regional- to global-scale validation effort and presents strategies for coordinating the land-cover classification process across multiple sites. We suggest the Enhanced Thematic Mapper (ETM+) as the source of remotely sensed data for validation, and that the IGBP 17-class land-cover classification system be used to provide a link between more complex site-specific systems and global-scale data products.. We further recommend that the best site-specific land-cover classifications be obtained using whatever ancillary data are found to be useful as a basis for validation. In addition, we propose ways in which ambiguities in translation of classes, from specific to general systems may be identified. Finally, we stress that even though standardization of methodology among sites may not be appropriate to the goal of obtaining the best possible land-cover prodnets, there should be standardization of error analysis and metadata reporting. (C) Elsevier Science Inc., 1999.

KeyWords Plus:
SURFACE PARAMETERIZATION SIB2, LAND-COVER, VEGETATION CLASSIFICATION, ATMOSPHERIC GCMS, SATELLITE DATA, CARBON, ACCURACY, MODEL, FORMULATION, ALGORITHMS

Addresses:
Thomlinson JR, Univ Puerto Rico, Inst Trop Ecosyst Studies, POB 363682, San Juan, PR 00936 USA
Univ Puerto Rico, Inst Trop Ecosyst Studies, San Juan, PR 00936 USA
Univ Minnesota, Dept Forest Resources, St Paul, MN 55108 USA
USDA, Forest Serv, Forestry Sci Lab, Corvallis, OR USA

Publisher:
ELSEVIER SCIENCE INC, 655 AVENUE OF THE AMERICAS, NEW YORK, NY 10010 USA

IDS Number:
241LJ

ISSN:
0034-4257