Date of Award
Master of Science
Geography and Environmental Resources
Unmanned Aerial Vehicles (UAVs) can be used as a cost-effective alternative to map military training induced vegetation disturbances and monitor their dynamics because of high spatial resolution images provided at acceptable cost, great accuracy and flexibility of time to collect the images. This study aims to develop a method to map vegetation cover change from different military training activities using UAV imagery acquired at two different dates (before and after the military training). Three flight boxes located in the Fort Riley (FR) were selected as the study area. Vegetation cover data was collected in the field using Daubenmire frame for 1m, 5m and 10m sample plots. The UAV imagery was resampled to the spatial resolutions that match the plot sizes. The UAV images were processed for their geometric and radiometric calibrations and quality control. Eight vegetation indices (VIs) were calculated from UAV imagery and step-wise regression was used to find the final model for each boxes. The results suggested that the UAV images can be used to map vegetation cover and disturbance caused by military training activities. Moreover, it was found that separately modelling the military training induced vegetation disturbances for the training boxes led to greater accuracy than modelling the vegetation disturbances by pooling the data together. The accuracy of modelling was also higher before the training than that after the training because the training activities led to higher spatial variation of vegetation cover. In addition, the 5 m by 5 m spatial resolution images were more capable in capturing spatial variation of the vegetation disturbances than those at 10 m by 10 m spatial resolution, which implied that the 5 m by 5 m plots should be utilized for field data collection. Finally, compared with the original UAV image bands, the VIs improved the correlation with vegetation cover, and the Red Edge Modified Simple Ratio (REMSR), Normalized Difference Vegetation Index (NDVI) and Green Normalized Difference Vegetation Index (GNDVI) were more frequently selected in the final models than other vegetation indices (VIs). Overall, this study enhanced the understanding of using UAV images to map vegetation cover change from different military training activities
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