|Nouvellon, Y. - CIRAD, FRANCE|
|Rambal, S. - CEFE, CNRS, FRANCE|
|Lo Seen, D. - CIRAD, FRANCE|
|Lhomme, J. - ORSTOM/CICTUS, MEXICO|
|Begue, A. - CIRAD, FRANCE|
|Chehbouni, A. - ORSTOM/IMADES, MEXICO|
|Kerr, Y. - CESBIO-CNES, FRANCE|
Submitted to: Agricultural and Forest Meteorology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 15, 1999
Publication Date: N/A
Interpretive Summary: In order to properly manage extensive rangeland regions, it is necessary to have regional information on rangeland extent and general health. An approach was developed to combine a grassland growth simulation model with images obtained from orbiting satellite sensors to map rangeland conditions, such as plant density, height, and vigor. By combining the model with regional images, it was possible to obtain both temporal and spatial information about rangeland health. The results were encouraging because the simulated estimates of grassland biomass compared well with measured values at several sites in southeastern Arizona. Ultimately, this coupled modeling/remote sensing approach should result in accurate, timely information on rangeland health that can be used to make intelligent management decisions at the regional scale.
Technical Abstract: A process-based model for semi-arid grassland ecosystems was developed. It is driven by standard daily meteorological data and simulates with a daily time step the seasonal course of root, aboveground green, and dead biomass. Water infiltration and redistribution in the soil, transpiration and evaporation are simulated in a coupled water budget submodel. The main plant processes are photosynthesis, allocations of assimilates between aboveground and belowground compartments, shoots and roots respiration and senescence, and litter fall. Structural parameters of the canopy such as fractional cover and LAI are also simulated. This model was validated in southwest Arizona on a semi-arid grassland site. In spite of simplifications inherent to the process-based modeling approach, this model is useful for elucidating interactions between the shortgrass ecosystem and environmental variables, for interpreting H2O exchange measurements, and for predicting the temporal variation of above- and belowground biomass an the ecosystem carbon budget.