Particulate matter expelled from tunnel-ventilated animal feeding operations (AFOs) is known to transport malodorous compounds. As a mitigation strategy, vegetative environmental buffers (VEBs) are often installed surrounding AFOs in order to capture particulates and induce lofting and dispersion. Many farmers are or are interested in implementing VEBs, yet research supporting their efficacy remains sparse. Currently, point measurements, often combined with models, are the primary means by which emission rates from AFOs and VEB performance has been investigated. The existing techniques lack spatial resolution and fail to assign the observed particulate reduction to capture, lofting, or dispersion. In recent years, lidar has emerged as a suitable partner to point measurements in agricultural research. Lidar is regarded for its ability to capture entire plume extents in near real time. Here, a technique is presented for estimating the capture efficiency of a VEB using lidar. An experiment was conducted in which dust was released upwind of a VEB at a known rate, and the emission rate downwind of the VEB was estimated using an elastic scanning lidar. Instantaneous lidar scans showed periodic lofting well above the VEB, but when scans were averaged over several hours, the plumes appeared Gaussian. The experiment revealed a capture efficiency ranging from 21-74β, depending on the time of day. The methodology presented herein addresses deficiencies in the existing techniques discussed above, and the results presented add to the lacking body of research documenting VEB capture efficiency.

Medusahead is a harmful weed that is invading public lands in the West. The invasion is a serious concern to the public because it can reduce forage for livestock and wildlife, increase fire frequency, alter important ecosystem cycles (like water), reduce recreational activities, and produce landscapes that are aesthetically unpleasing. Invasions can drive up costs that generally require taxpayer’s dollars. Medusahead seedlings typically spread to new areas by attaching itself to passing objects (e.g. vehicles, animals, clothing) where it can quickly begin to affect plants communities. To be effective, management plans need to be sustainable, informed, and considerate to invasion levels across large landscapes. Ecological remote sensing analysis is a method that uses airborne imagery, taken from drones, aircrafts, or satellites, to gather information about ecological systems. This Thesis strived to use remote sensing techniques to identify medusahead in the landscape and its changes through time. This was done for an extensive area of rangelands in the Channel Scabland region of eastern ashington. This Thesis provided results that would benefit land managers that include: 1) a dispersal map of medusahead, 2) a time line of medusahead cover through time, 3) “high risk’ dispersal areas, 4) climatic factors showing an influence on the time line of medusahead, 5) a strategy map that can be utilized by land managers to direct management needs. This Thesis shows how remote sensing applications can be used to detect medusahead in the landscape and understand its invasiveness through time. This information can help create sustainable and effective management plans so land managers can continue to protect and improve western public lands threatened by the invasion of medusahead.

Remote sensing helps many applications like precision irrigation, habitat mapping, and traffic monitoring. However, due to shortcomings of current remote sensing platforms - like high cost, low spatial, and temporal resolution - many applications do not have access to useful remote sensing data. A team at the Center for Self-Organizing and Intelligent Systems (CSOIS) together with the Utah Water Research Laboratory (UWRL) at Utah State University has been developing a new remote sensing platform to deal with these shortcomings in order to give more applications access to remote sensing data. This platform (AggieAir) is low cost, fully autonomous, easy to use, independent of a runway, has a fast turnover time, and a high spatial resolution. A program called the Geospatial Real-Time Aerial Image Display (gRAID) has also been developed to process the images taken from AggieAir. gRAID is able to correct the camera lens distortion, georeference, and display the images on a 3D globe, and export them in a conventional Geographic Information System (GIS) format for further processing. AggieAir and gRAID prove to be innovative and useful tools for remote sensing.

Irrigation systems are designed to deliver water to crops, but their efficacy varies widely due to operational decisions, weather variability, and water availability. The operation of an irrigation system is studied in this dissertation to determine irrigation performance and potential for improvement.Satellite remote-sensing was used to determine inter-annual variability in crop evapotranspiration and link it with weather patterns and operational decisions. A decade was studied to include several dry, wet and average years of snowfall. It was found that the irrigation district has the capacity to buffer a dry year, but crop evapotranspiration patterns indicated that the buffer capacity of the irrigation district is limited in a second dry year. Studying the current operations of an irrigation system also requires an analysis of the spatial variability within the system to identify potential areas for improvement. Achieving such information is challenging due to the spatial heterogeneity between farm fields. The Ador irrigation system simulation model is used in this study with satellite remote sensing data, which were combined in the calibration and validation process to ease the re-adjustment of management parameters. This approach provides a cost-effective and innovative method for model simulation when field observations are limited. Alternative water delivery scenarios were simulated with the Ador irrigation system simulation model to quantify changes in the water balance, irrigation performance, and water productivity. Results for implementing a minimal irrigation time indicated that irrigation events occurred with a higher frequency and reduced crop water stress. Water productivity for the irrigation district increased substantially in this scenario, whilst district water savings were achieved by diverting less irrigation water. Advantages are only achieved if farmers collectively make the decision to change. A water accounting analysis is required to examine if water savings are achieved at basin scale. There is a potential for the rebound effect to occur, which suggests that an increase of water efficiency causes the increase of water consumption. Simulation results indicated that if the efficiency is increased through improvements of the water delivery, the water consumption increased. Water savings achieved by reducing irrigation diversions did not compensate for the decrease in drainage that downstream users depend on.

This dissertation focuses on how to design and employ networked unmanned vehicles for remote sensing and distributed control purposes in the current information-rich world. The target scenarios are environmental or agricultural applications such as river/reservoir surveillance, wind profiling measurement, and monitoring/control of chemical leaks, etc. AggieAir, a small and low-cost unmanned aircraft system, is designed based on the remote sensing requirements from environmental monitoring missions. The state estimation problem and the advanced lateral flight controller design problem are further attacked focusing on the small unmanned aerial vehicle (UAV) platform. Then the UAV-based remote sensing problem is focused with further flight test results. Given the measurements from unmanned vehicles, the actuation algorithms are needed for missions like the diffusion control. A consensus-based central Voronoi tessellation (CVT) algorithm is proposed for better control of the diffusion process. Finally, the dissertation conclusion and some new research suggestions are presented.

Monitoring the dynamic thermal activity in Yellowstone National Park is required by the United States Congress. The continuous monitoring is important to maintain the safety of the visitors and park service personnel, plan and relocate infrastructure, and study potential impact from nearby geothermal development including oil and gas industry. This dissertation is part of a study initiated in the early 2000s to monitor the thermal activity of dynamic areas within the Park, using airborne remote sensing imagery. This study was focused in Norris Geyser Basin, the hottest geyser basin in the park, located near the northwestern rim of the Yellowstone’s caldera. The study is considered the first long-term comprehensive airborne remote sensing study in the basin which took place between August 2008 and October 2013. In this study, at least one 1-meter resolution thermal infrared image and three-band images (multispectral) were acquired and used to estimate year-to-year changes in radiant temperature, radiant flux, and radiant power from the thermal source in Norris. Presence of residual radiant flux in the ground from absorbed solar radiation and atmospheric longwave radiation was the main challenge to compere year-to-year changes in the thermal activity. This residual flux is included in the total radiant flux calculated through the remote sensing images which gives false estimates of the flux generated from the underling thermal source. Two methods were suggested in Chapters 2 and 4 of this dissertation to estimate the residual radiant flux. A method was developed in Chapter 2 to estimate the residual radiant flux in a bare ground area covered with hydrothermal siliceous sinter deposit. The method compared ground-based measurements with high spatial resolution airborne remote sensing measurements to estimate the residual radiant flux. In Chapter 4, a method was developed to estimate the residual radiant flux in the six surface classes in Norris, including bare ground, bare ground with siliceous sinter deposit, lakes and pools, river, forest, and grass. The assumptions and implications of each method were discussed to suggest a reliable method to estimate the geothermal radiant flux after subtracting the absorbed residual radiant flux. Chapter 3 provides an analysis of the four components of heat flux in the ground surface, including conduction of sensible heat, convection of sensible heat by liquid water and water vapor, and convection of latent heat by water vapor. The main purpose from the analysis was to assess the hypothesis that the convection and latent heat flux are negligible which therefore supported the results obtained from the analysis in Chapters 2 and 4.

This thesis presents work done towards a Personal Remote Sensing (PRS) system: small Unmanned Aerial Vehicles (UAVs) with electronic, control, and sensing subsystems. Based on papers presented to conferences (AutoTestCon2008 and MESA2009), as well as other work on PRS, multiple levels of engineering are detailed: complex multi-UAV data flow; attitude estimation filters; real-time microprocessor functionality; and small, mobile power systems. Wherever possible, Open-Source tools and designs have been used, modified, or studied, providing excellent cost to performance ratios in most cases. First, the overall PRS UAV architecture, AggieAir, is presented with a motivating examples (GhostEye and EagleEye camera payloads). Then, AggieNav, an inertial navigation system for small UAVs, is detailed, along with information about a Kalman filter for estimation of UAV navigation, position, and attitude. Finally the Spatial Environment Autonomous Logger (SEAL), a general-purpose wireless datalogger for small UAV applications, is presented, with application examples with and without small UAVs. This work represents designs based on two years of organic small UAV system growth, and provides integrated solutions to many problems of small UAV communication, sensing, and control.

Temperatures strongly affect physical, chemical, and biological processes in rivers and streams. The processes that influence river temperatures are known across most geographic regions, but the relative importance varies significantly. Little is known about what controls water temperature Arctic rivers, limiting our ability to understand the impacts of climate change. This dissertation addresses this knowledge gap by incorporating field measurements with river temperature modeling to estimate the relative importance of key factors that affect Arctic river temperatures. Results indicate that shortwave radiation (e.g., sunlight) and net longwave radiation are significant throughout an Arctic watershed in all flow conditions. In areas where the river is smaller, however, exchange of water with the riverbed and inputs of water from the landscape become significant under low-flow and high-flow conditions, respectively. Additional field observations and modeling were used to quantify the water and heat exchanges between the river and the riverbed. These heat exchanges were found to cool the river and reduce the daily range of temperatures. To better estimate the flow of water from the landscape to the river, a new method for estimating river flow was developed using high-resolution aerial imagery. This method allows us to estimate river flow without depending on field measurements, and presents a potential solution to barriers in performing river temperature modeling in other parts of the Arctic.

In the coming 20 years, unmanned aerial data collection will be of great importance to many sectors of civilian life. Of these systems, Personal Remote Sensing (PRS) Small Unmanned Aerial Systems (sUASs), which are designed for scientic data collection, will need special attention due to their low cost and high value for farming, scientic, and search-andrescue uses, among countless others. Cyber-Physical Systems (CPSs: large-scale, pervasive automated systems that tightly couple sensing and actuation through technology and the environment) can use sUASs as sensors and actuators, leading to even greater possibilities for benet from sUASs. However, this nascent robotic technology presents as many problems as possibilities due to the challenges surrounding the abilities of these systems to perform safely and eectively for personal, academic, and business use. For these systems, whose missions are dened by the data they are sent to collect, safe and reliable mission quality is of highest importance. Much like the dawning of civil manned aviation, civilian sUAS ights demand privacy, accountability, and other ethical factors for societal integration, while safety of the civilian National Airspace (NAS) is always of utmost importance. While the growing popularity of this technology will drive a great effort to integrate sUASs into the NAS, the only long-term solution to this integration problem is one of proper architecture. In this research, a set of architectural requirements for this integration is presented: the Architecture for Ethical Aerial Information Sensing or AERIS. AERIS provides a cohesive set of requirements for any architecture or set of architectures designed for safe, ethical, accurate aerial data collection. In addition to an overview and showcase of possibilities for sUAS-enabled CPSs, specific examples of AERIS-compatible sUAS architectures using various aerospace design methods are shown. Technical contributions include specic improvements to sUAS payload architecture and control software, inertial navigation and complementary lters, and online energy and health state estimation for lithium-polymer batteries in sUAS missions. Several existing sUASs are proled for their ability to comply with AERIS, and the possibilities of AERIS data-driven missions overall is addressed.

Detection of chemical plumes in hyperspectral data is a problem having solutions that focus on spectral information. These solutions neglect the presence of the spatial information in the scene. The spatial information is exploited in this work by assignment of prior probabilities to neighborhood configurations of signal presence or absence. These probabilities are leveraged in a total probability approach to testing for signal presence in a pixel of interest. The two new algorithms developed are named spatial information detection enhancement (SIDE) and bolt-on SIDE (B-SIDE). The results are explored in comparison to the clutter matched filter (CMF), a standard spectral technique, and to several supervised machine learning techniques. The results show a great improvement of SIDE over these other techniques, in some cases showing the poorest performance of the SIDE filter being much better than the CMF at its best.