Browsing by Subject "NDVI"
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Item Examination of high resolution rainfall products and satellite greenness indices for estimating patch and landscape forage biomass(2009-05-15) Angerer, Jay PeterAssessment 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.Item Investigation of the utility of the vegetation condition index (VCI) as an indicator of drought(2009-05-15) Ganesh, SrinivasanThe relationship between the satellite-based Vegetation Condition Index (VCI) and frequently used agricultural drought indices like Palmer Drought Severity Index, Palmer?s Z-index, Standard Precipitation Index, percent normal and deciles was evaluated using a comparative correlation analysis. These indices were compared at the county level for all 254 Texas counties for the growing-season months (March to August) using monthly data from 1982-1999. The evaluation revealed that the VCI was most strongly correlated with the 6-month SPI and the PDSI. This suggests that the VCI is most similar to drought indices that account for antecedent moisture conditions. There was also significant spatial variability in the magnitude of the correlations between the VCI and the drought indices. The reasons for this variability were explored by utilizing additional data such as irrigation, prevalent landuse/landcover, water table depth, soil moisture levels and soil hydrologic/hydraulic properties. The results demonstrated that mean annual precipitation, soil moisture, landuse/landcover, and depth of the water table accounted for a significant amount of the spatial variability (explaining more than 75% of the variance) in the relationship between the VCI and traditional drought indices.Item Utilizing GRACE TWS, NDVI, and precipitation for drought identification and classification in Texas(2014-08) McCandless, Sarah Elizabeth; Bettadpur, Srinivas Viswanath, 1963-Drought is one of the most widespread and least understood natural phenomena. Many indices using multiple data types have been created, and their success at identifying periods of extreme wetness and dryness has been well documented. In recent years, researchers have begun to assess the potential of total water storage (TWS) anomalies in drought monitoring method- ologies. The Gravity Recovery and Climate Experiment (GRACE) provides temporally and spatially consistent TWS measurements across the globe, and studies have shown GRACE TWS anomalies are suited to identify drought. GRACE TWS is used with MODIS-derived normalized difference veg- etation index (NDVI) and NOAA/NWS precipitation data to create a new drought index, the Merged-dataset Drought Index (MDI). Each dataset corre- lates with a different type of drought, giving robustness to MDI. MDI is based on dataset deviations from a monthly climatology and is objective and easy to calculate. MDI is studied across Texas, which is broken into five dataset- defined sub-regions. Multiple drought events are identified from 2002 - 2014, with the most severe beginning in October 2010. A new drought severity clas- sification scheme is proposed based on MDI, and it is organized to match the current US Drought Monitor Classification Scheme. MDI shows strong correlation with existing drought indices, notably the Palmer Drought Severity Index (PDSI). MDI consistently identifies droughts in different sub-regions of Texas, but shows better performance in regions with large GRACE TWS signals. MDI performance is enhanced through a weighting scheme that relies more on GRACE TWS. Even with this scheme, MDI and PDSI exhibit occasional behavioral differences. Drought analysis using MDI shows for the first time that GRACE data provides information on a sub-regional scale in Texas, an area with low signal amplitudes. Past studies have shown TWS capable of identifying drought, but MDI is the first index to quantitatively use GRACE TWS in a manner consistent with current practices of identifying drought. MDI also establishes a framework for a future, completely remote-sensing based index that can enable temporally and spatially consistent drought identification across the globe. This study is useful as well for establishing a baseline for the necessary spatial resolution required from future geodetic space missions for use in drought identification at smaller scales.