Assessment of vegetation productivity on rangelands is needed to assist in timely
decision making with regard to management of the livestock enterprise as well as to
protect the natural resource. Characterization of the vegetation resource over large
landscapes can be time consuming, expensive and almost impossible to do on a near
real-time basis. The overarching goal of this study was to examine available
technologies for implementing near real-time systems to monitor forage biomass
available to livestock on a given landscape. The primary objectives were to examine the
ability of the Climate Prediction Center Morphing Product (CMORPH) and Next
Generation Weather Radar (NEXRAD) rainfall products to detect and estimate rainfall at
semi-arid sites in West Texas, to verify the ability of a simulation model (PHYGROW)
to predict herbaceous biomass at selected sites (patches) in a semi-arid landscape using
NEXRAD rainfall, and to examine the feasibility of using cokriging for integrating
simulation model output and satellite greenness imagery (NDVI) for producing
landscape maps of forage biomass in Mongolia’s Gobi region.
The comparison of the NEXRAD and CMORPH rainfall products to gage
collected rainfall revealed that NEXRAD outperformed the CMORPH rainfall with
lower estimation bias, lower variability, and higher estimation efficiency. When
NEXRAD was used as a driving variable in PHYGROW simulations that were
calibrated using gage measured rainfall, model performance for estimating forage
biomass was generally poor when compared to biomass measurements at the sites. However, when model simulations were calibrated using NEXRAD rainfall,
performance in estimating biomass was substantially better. A suggested reason for the
improved performance was that calibration with NEXRAD adjusted the model for the
general over or underestimation of rainfall by the NEXRAD product. In the Gobi region
of Mongolia, the PHYGROW model performed well in predicting forage biomass except
for overestimations in the Forest Steppe zone. Cross-validation revealed that cokriging
of PHYGROW output with NDVI as a covariate performed well during the majority of
the growing season. Cokriging of simulation model output and NDVI appears to hold
promise for producing landscape maps of forage biomass as part of near real-time forage
monitoring systems.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2827 |
| Date | 15 May 2009 |
| Creators | Angerer, Jay Peter |
| Contributors | Wu, X. Ben |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Dissertation, text |
| Format | electronic, application/pdf, born digital |
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