gRAID: A Geospatial Real-Time Aerial Image Display for a Low-Cost Autonomous Multispectral Remote Sensing Platform (AggieAir)

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

Third Annual Report of the Farm Management Service for Farmers in Soil Conservation Demonstration Areas for the year 1937 (April 1937 to March 1938)

Farm Management,

The Effect of Respirator Wear on Blood Lactate During Maximal Exertion

The impact of a filtering half-face respirator and a half-face supplied air respirator use on blood lactate production was assessed during maximal exertion to determine if anaerobic strain increased compared to no respirator use. Twenty-eight participants performed a 30 second cycling Wingate anaerobic test (WAnT) wearing a half-face respirator. Blood lactate production was measured to evaluate if there was an increase in anaerobic strain from wearing a tight fitting half-face respirator compared to wearing no respirator. A supplied air respirator WAnT was then performed using 18 participants from the first experiment to evaluate if supplied air decreased anaerobic strain. Data from both experiments were compared to evaluate differences in the physiological effects due to respirator use during maximal exertion. A survey was administered following the second WAnT experiment to measure the participants\u27 perception of acceptability and impact of supplied air respirator use in workplace. The blood lactate levels measured directly after the WAnT yielded lower overall mean values during the half-mask respirator trial (12.1 mmollL) and supplied air respirator trial (12.2 mmollL) than the no respirator trial (13.1 mmoI/L). However, differences in blood lactate levels were not statistically significant (p =0.597). Participants reported an average acceptability of 92.3% to wearing the supplied air respirator while performing light work. However, the average acceptability decreased as the exertion increased to moderate (78.8%) and heavy (46.6%) workloads. The supplied air respirator used provided no significant reduction in anaerobic strain within this study group compared to either the filtering half-face respirator or the no respirator condition. However, there were differences in physiological effects of respirators on each gender identified in this study. Further assessment of the anaerobic impact of respirators on each gender should be conducted

Innovative Payloads for Small Unmanned Aerial System-Based Personal Remote Sensing and Applications

Remote sensing enables the acquisition of large amounts of data, over a small period of time, in support of many ecological applications (i.e. precision agriculture, vegetation mapping, etc.) commonly from satellite or manned aircraft platforms. This dissertation focuses on using small unmanned aerial systems (UAS) as a remote sensing platform to collect aerial imagery from commercial-grade cameras and as a radio localization platform to track radio-tagged sh. The small, low-cost nature of small UAS enables remotely sensed data to be captured at a lower cost, higher spatial and temporal resolution, and in a more timely manner than conventional platforms. However, these same attributes limit the types of cameras and sensors that can be used on small UAS and introduce challenges in calibrating the imagery and converting it into actionable information for end users. A major contribution of this dissertation addresses this issue and includes a complete description on how to calibrate imagery from commercial-grade visual, near-infrared, and thermal cameras. This includes the presentation of novel surface temperature sampling methods, which can be used during the ight, to help calibrate thermal imagery. Landsat imagery is used to help evaluate these methods for accuracy; one of the methods performs very well and is logistically feasible for regular use. Another major contribution of this dissertation includes novel, simple methods to estimate the location of radio-tagged fish using multiple unmanned aircraft (UA). A simulation is created to test these methods, and Monte Carlo analysis is used to predict their performance in real-world scenarios. This analysis shows that the methods are able to locate the radio-tagged fish with good accuracy. When multiple UAs are used, the accuracy does not improve; however the fish is located much quicker than when one UA is used

AggieAir: Towards Low-cost Cooperative Multispectral Remote Sensing Using Small Unmanned Aircraft Systems

Non

Assessment of Surface Soil Moisture Using High-Resolution Multi-Spectral Imagery and Artificial Neural Networks

Many crop production management decisions can be informed using data from high-resolution aerial images that provide information about crop health as influenced by soil fertility and moisture. Surface soil moisture is a key component of soil water balance, which addresses water and energy exchanges at the surface/atmosphere interface; however, high-resolution remotely sensed data is rarely used to acquire soil moisture values. In this study, an artificial neural network (ANN) model was developed to quantify the effectiveness of using spectral images to estimate surface soil moisture. The model produces acceptable estimations of surface soil moisture (root mean square error (RMSE) = 2.0, mean absolute error (MAE) = 1.8, coefficient of correlation (r) = 0.88, coefficient of performance (e) = 0.75 and coefficient of determination (R2) = 0.77) by combining field measurements with inexpensive and readily available remotely sensed inputs. The spatial data (visual spectrum, near infrared, infrared/thermal) are produced by the AggieAir™ platform, which includes an unmanned aerial vehicle (UAV) that enables users to gather aerial imagery at a low price and high spatial and temporal resolutions. This study reports the development of an ANN model that translates AggieAir™ imagery into estimates of surface soil moisture for a large field irrigated by a center pivot sprinkler system

Topsoil Moisture Estimation for Precision Agriculture Using Unmanned Aerial Vehicle Multispectral Imagery

There is an increasing trend in crop production management decisions in precision agriculture based on observation of high resolution aerial images from unmanned aerial vehicles (UAV). Nevertheless, there are still limitations in terms of relating the spectral imagery information to the agricultural targets. AggieAir™ is a small, autonomous unmanned aircraft which carries multispectral cameras to capture aerial imagery during pre-programmed flights. AggieAir enables users to gather imagery at greater spatial and temporal resolution than most manned aircraft and satellite sources. The platform has been successfully used in support of a wide variety of water and natural resources management areas. This paper presents results of an on-going research in the application of the imagery from AggieAir in the remote sensing of top soil moisture estimations for a large field served by a center pivot sprinkler irrigation system