|Moran, Mary - Susan|
|Heilman, Philip - Phil|
|Lo Seen, D|
Submitted to: Intnl Conference On Geospatial Information In Agriculture And Forestry
Publication Type: Proceedings
Publication Acceptance Date: 10/7/1999
Publication Date: N/A
Citation: N/A Interpretive Summary: Range managers are interested in tools that predict plant growth and soil moisture over their ranch. This information is useful for determining stocking rate and for suggesting management policies that prevent soil erosion and maintain high levels of grassland productivity. Plant growth models have the ability to produce this information. Their operational application over large scales, however, is often hampered by unknown model parameters (e.g. soil nutient status), at these scales. The objective of this paper is to describe a method that uses satellite images to apply a plant growth model over a semi-arid grassland watershed in southeastern Arizona. An assessment of the model over a ten-year simulation period demonstrated that after incorporation of satellite information, the model was able to predict the plant growth accurately. These promising results show that this approach, which combines satellite images and plant growth modeling, could provide accurate maps of soil moisture and plant production for day-to-day grassland management. This represents a significant step toward the development of effective tools for ranchers, government agencies, and consultants for rangeland management.
Technical Abstract: This study investigates the use of high-spatial, low-temporal scale visible remote sensing data for calibration of a Soil-Vegetation- Atmosphere-Transfer (SVAT) model for semi-arid perennial grasslands. The SVAT model is driven by meteorological data and simulates plant growth and water budget on a daily time step. The model was combined with a canopy reflectance model to simulate shortwave radiometric temporal profiles. Landsat Thematic Mapper (TM) images obtained during a series of ten consecutive years were used to refine the model to work on a spatially-distributed basis over a semi-arid grassland watershed. Continuous simulations were used to estimate two spatially-variable initial conditions and model parameters through a calibration procedure which minimized the difference between the surface reflectance simulated by the model and measured by the TM sensor. Accuracy of model products such as daily above-ground biomass and soil moisture was assessed by comparison with field measurements. The promising results suggest that this approach could provide spatially-distributed information about vegetation and soil conditions for day-to-day grassland management.