EXPLORING THE POTENTIAL OF GIS AND REMOTE SENSING TECHNIQUES TO STUDY THE IMPACTS OF DROUGHT ON PLANT-MOISTURE CONTENT IN HAMILTON COUNTY, TENNESSEE

Droughts are prolonged periods of abnormally low rainfall, which are influential environmental hazards that can contribute to decreased crop yields and increased wildfire risk in the affected areas. From June to December 2016, the southeastern United States experienced an “Extreme Drought” (D3) as classified by the U.S. Drought Monitor. The southern portion of Hamilton County, TN was one of the most intensely affected areas, but despite this interpretation of the drought occurrence, there is no available data to determine the relationship between drought and related stress on vegetation caused by moisture deficiency. Remote sensing and digital image processing techniques have been used for mapping vegetation and estimating land surface temperature (LST) for many years. This study combines remotely sensed vegetation and LST data to determine the areas affected by drought in the study site.

The Normalized Difference Vegetation Index (NDVI) and LST images were derived from Landsat 8 OLI and TIRS sensors, representing the presence of vegetation and land surface temperature, respectively in three selected sites in the Chattanooga area for April 16, 2016 (pre-drought) and October 25, 2016 (peak-drought). An unsupervised classification scheme was applied after processing the imagery to create a 15-class thematic image coupled with a corresponding feature space image. The obtained thematic and feature space images were compared with the scatter plot of a Vegetation Index (VI)-LST trapezoidal model to determine three different classes that represent normal, moderate to low, and low vegetation moisture conditions in the study sites. The resultant images for each timeframe are in general agreement with the established drought timeline and illustrated a drastic decrease in moisture content and vegetation health with the exception of Site 1. For future study, it is suggested to acquire more images from pre-drought seasons in order to assess the accuracy of the proposed study.