Browsing by Author "Whitehurst, Daniel Scott"

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    Aerial Cadastral and Flood Assessment for Disaster Risk Management in Appalachia
    Whitehurst, Daniel Scott (Virginia Tech, 2025-01-30)
    As natural disasters have continued to become more prevalent in recent years, the need for effective disaster management efforts has become even more critical. Flooding is an extremely common natural disaster which can cause significant damage to homes and other property. Using low-cost drones, 3D cadastre models can be created and combined with flood models to quantify individual building risk before, during, and after flood events. As severe flooding devastated areas nearby to Virginia Tech, the need for accurate flood risk quantification became evident. In this work, we focused on the Appalachian area of the United States for flood modeling. The unique terrain of this area coupled with increasing major weather events has lead to devastating flooding in the area. In particular, we focused on an area in Southwest Virginia, Hurley, due to a devastating flood event in 2021 as well as its proximity to Virginia Tech. Digital Elevation Models from before the flood and available weather data are used to perform simulations of the flood event using HEC-RAS software. These were validated with measured water height values and found to be very accurate, with errors as low as 2 percent. After this, simulations are performed using the Digital Elevation Models created from drone imagery collected after the flood, and we found that a similar rainfall event on the new terrain would cause even worse flooding, with water depths between 29% and 105% higher. Simulations like these could be used to guide recovery efforts as well as aid response efforts for any future events. After this, a major flood event in 2022 shifted our focus to an area in Eastern Kentucky. The terrain in this area has been affected by significant surface coal mining, which became a focus due to the limited amount of research into the impacts of surface coal mining on flooding. Through the digitization of historical topographic maps, pre-mining terrain and land cover is compared to the current landscape with respect to runoff and flood potential. Additionally, multiple mine reclamation methods, including the regrowth of forest, grassland, or shrubland, were looked at to reduce the risk of major flooding in the future after mining has been completed. SWAT simulations showed a significant increase, as large as high as 55.8 percent, in surface runoff from the coal mining in the area. HEC-RAS simulations showed localized increases in flooding resulting from mine lands, with some areas seeing an increase of over 2 feet of water depth. Mine reclamation methods show the potential to reduce the amount of surface runoff, by as much 1 foot of water depth, although these ideal scenarios still do not reach pre-mined levels. While the impact which surface mining has had on the environment can not be fully reversed, significant improvements can be made to prevent future flooding in these areas. After these flood case studies, the water depth modeling is combined with high-resolution cadastre data to produce accurate flood risk assessments for the community and property level.
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    Techniques for Processing Airborne Imagery for Multimodal Crop Health Monitoring and Early Insect Detection
    Whitehurst, Daniel Scott (Virginia Tech, 2016-09-27)
    During their growth, crops may experience a variety of health issues, which often lead to a reduction in crop yield. In order to avoid financial loss and sustain crop survival, it is imperative for farmers to detect and treat crop health issues. Interest in the use of unmanned aerial vehicles (UAVs) for precision agriculture has continued to grow as the cost of these platforms and sensing payloads has decreased. The increase in availability of this technology may enable farmers to scout their fields and react to issues more quickly and inexpensively than current satellite and other airborne methods. In the work of this thesis, methods have been developed for applications of UAV remote sensing using visible spectrum and multispectral imagery. An algorithm has been developed to work on a server for the remote processing of images acquired of a crop field with a UAV. This algorithm first enhances the images to adjust the contrast and then classifies areas of the image based upon the vigor and greenness of the crop. The classification is performed using a support vector machine with a Gaussian kernel, which achieved a classification accuracy of 86.4%. Additionally, an analysis of multispectral imagery was performed to determine indices which correlate with the health of corn crops. Through this process, a method for correcting hyperspectral images for lighting issues was developed. The Normalized Difference Vegetation Index values did not show a significant correlation with the health, but several indices were created from the hyperspectral data. Optimal correlation was achieved by using the reflectance values for 740 nm and 760 nm wavelengths, which produced a correlation coefficient of 0.84 with the yield of corn. In addition to this, two algorithms were created to detect stink bugs on crops with aerial visible spectrum images. The first method used a superpixel segmentation approach and achieved a recognition rate of 93.9%, although the processing time was high. The second method used an approach based upon texture and color and achieved a recognition rate of 95.2% while improving upon the processing speed of the first method. While both methods achieved similar accuracy, the superpixel approach allows for detection from higher altitudes, but this comes at the cost of extra processing time.