Many traditional drought assessments are conducted based on climate and hydrologic data. The availability and precision of data limit the spatial and temporal resolution and accuracy of derived drought indices. In this study, Vegetation Condition Index (VCI) and Temperature Condition Index (TCI) were generated from Moderate Resolution Imaging Spectroradiometer (MODIS) products. The VCI was derived from Normalized Difference Vegetation Index (NDVI) that was calculated with near infrared and visible red band reflectance from MOD09Q1. The TCI was derived from land surface temperature (LST) product MOD11A2. The VCI and TCI were then combined with reference to the vegetation coverage information from MOD44B to generate the modified Vegetation Health Index (VHI). The modified VHI was applied to quantify the intensity of drought that took place in Illinois from 2000 to 2012. The results showed that the modified VHI identified the major droughts that occurred in Illinois from 2000 to 2012, especially the extreme one taking place in 2012. Moreover, the modified VHI led to the spatial distributions and temporal trends of drought severity, which were overall similar to those from the U.S. Drought Monitor (USDM) maps, but had more detailed spatial variability and much higher spatial resolution. The modified VHI also differentiated the drought impacts between the vegetated and non-vegetated areas, being a lack of the original VHI. Thus, the modified VHI takes advantage of spatially continuous and timely data from satellites and can be applied to conduct the monitoring and detection of drought intensity at local, regional, and national scales. The modified VHI can effectively synthesize the drought information of LST and NDVI to differentiate the effects of land use and land cover (LULC) types and provide the detailed spatial variability of drought intensity and thus enhance the understanding of relationship between drought condition and LULC types.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:theses-2494 |
| Date | 01 August 2014 |
| Creators | FENG, GUANLING |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses |
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