Austin M. Jensen

Band-reconfigurable Multi-UAV-based Cooperative Remote Sensing for Real-time Water Management and Distributed Irrigation Control

Abstract This paper presents an overview of ongoing research on small unmanned autonomous vehicles (UAVs) for cooperative remote sensing for real-time water management and irrigation control. Small UAVs can carry embedded cameras with different wavelength bands, which are low-cost but have high spatial-resolution. These imagers mounted on UAVs can form a camera array to perform multispectral imaging with reconfigurable bands dependent on mission. Development of essential subsystems, such as the UAV platforms, embedded multispectral imagers, and image stitching and registration, is introduced together with real UAV flight test results of one typical example mission.

A Survey and Categorization of Small Low-Cost Unmanned Aerial Vehicle System Identification

Remote sensing has traditionally be done with satellites and manned aircraft. While these methods can yield useful scientific data, satellites and manned aircraft have limitations in data frequency, process time, and real time re-tasking. Small low-cost unmanned aerial vehicles (UAVs) can bridge the gap for personal remote sensing for scientific data. Precision aerial imagery and sensor data requires an accurate dynamics model of the vehicle for controller development. One method of developing a dynamics model is system identification (system ID). The purpose of this paper is to provide a survey and categorization of current methods and applications of system ID for small low-cost UAVs. This paper also provides background information on the process of system ID with in-depth discussion on practical implementation for UAVs. This survey divides the summaries of system ID research into five UAV groups: helicopter, fixed-wing, multirotor, flapping-wing, and lighter-than-air. The research literature is tabulated into five corresponding UAV groups for further research.

Low-Cost Multispectral Aerial Imaging using Autonomous Runway-Free Small Flying Wing Vehicles

Aerial imaging has become very important to areas like remote sensing and surveying. However, it has remained expensive and difficult to obtain with high temporal and spatial resolutions. This paper presents a method to retrieve georeferenced aerial images by using a small UAV (unmanned aerial vehicle). Obtaining aerial images this way is inexpensive, easy-to-use and allows for high temporal and spatial resolutions. New and difficult problems are introduced by the small image footprint and the inherent errors from an inexpensive compact inertial measurement unit (IMU). The small image footprint prevents us from using the features from the images to help negate the errors from the IMU, which is done in conventional methods. Our method includes: using the data from the IMU to georeference the images, projecting the images on the earth and using a man-in-the-loop approach to minimize the error from the IMU. Sample results from our working system are presented for illustration.

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.

AggieAir — a low-cost autonomous multispectral remote sensing platform: New developments and applications

Data acquired by aircraft, satellites and other sources of remote sensing has become very important for many applications. Even though current platforms for remote sensing have proved to be robust, they can also be expensive, have low spatial and temporal resolution, with a long turnover time. At Utah State University (USU), there is an ongoing project to develop a new small, low-cost, high resolution, multispectral remote sensing platform which is completely autonomous, easy to use and has a fast turnover time. Many new developments have been added to AggieAir which have improved the flight performance and flexibility, increased the flight time and payload capacity. Furthermore, these developments have made it possible to carry an imaging system with more quality and resolution. With these new developments, AggieAir has begun work with many projects from areas in agriculture, riparian habitat mapping, highway and road surveying and fish tracking. Development on AggieAir continues with future plans with a thermal inferred camera, an in house iner-tial measurement unit (IMU) and better navigation to handle higher winds.

Using a multispectral autonomous unmanned aerial remote sensing platform (AggieAir) for riparian and wetlands applications

An autonomous, unmanned, aerial, remote sensing platform called AggieAir™ has been developed at Utah State University (USU) to produce multispectral aerial imagery. Its independence of a runway, low cost, and rapid turn-around time for imagery make it an efficient platform for applications in riparian areas and in wetlands management. Using third-party software, the imagery from AggieAir can be stitched together into mosaics, georeferenced, and used to classify vegetation and map riparian systems, substrates and fish habitat for hydraulic modeling, river morphology and restoration monitoring. Likewise, the multispectral mosaics can be used to monitor changes in meso-scale aquatic habitat features and invasive/native plant species, as well as delineate different types of wetlands for wetlands management. This paper introduces AggieAir and highlights some of the projects in riparian and wetlands applications in which it has been involved. AggieAir has also been involved with agricultural and biofuel applications.

Thermal remote sensing with an autonomous unmanned aerial remote sensing platform for surface stream temperatures

Stream temperature is important for understanding the environment within a stream. Many collect these data at discrete locations over specific time periods using temperature sensors, however it is becoming more common to gather thermal imagery to have a spatially distributed understanding. The utility of these data can be limited due to cost, and spatial and temporal resolutions. This paper presents a platform for low-cost high-resolution thermal imagery using an unmanned aerial vehicle (AggieAir1). AggieAir can be used to acquire visual, near-infrared (NIR), and thermal imagery at high spatial and temporal resolutions at a much lower cost when compared to conventional sources of remote sensing. In this application, AggieAir is used to collect visual, NIR, and thermal imagery for a stream in northern Utah. Details about the payload and postprocessing methods are presented. The resulting imagery was used to clip the thermal mosaic to only include pixels associated with the stream. This thermal image provided 30cm×30cm resolution stream temperatures. Finally, a simple method of adjusting the images to observed temperatures is proposed to provide better information regarding absolute stream temperatures which are key in understanding the health of the aquatic ecosystem.

Use of high-resolution multispectral imagery acquired with an autonomous unmanned aerial vehicle to quantify the spread of an invasive wetlands species

Management of wetlands resources often requires assessment of changes in wetland vegetation over time. Accurate tracking of the expansion or retraction of invasive plant species is especially critical for natural resource managers who must make decisions on the deployment of effective control measures. Many available remote sensing strategies to quantify the location of invasive plant species are either too expensive to deploy on a regular basis or lack sufficient geographic or temporal resolution to be of use to resources managers. This paper presents the results of the use of a new unmanned aerial vehicle platform, called AggieAir™, and a new classification algorithm to track the spread of an invasive grass species, Phragmites australis, in a large and important wetland in northern Utah. The combination of high resolution multi-spectral images (in space and time) and the classification algorithm based on advances in statistical learning theory produce quantitative land cover descriptions that identify Phragmites locations with an accuracy of 95 percent. The combination of these two tools provides wetlands managers with new and potentially valuable methods to quantify the spread of Phragmites and to evaluate the efficacy of their attempts to control it.

AggieAir: Towards low-cost cooperative multispectral remote sensing using small unmanned aircraft systems

This chapter focuses on using small low-cost unmanned aircraft systems (UAS) for remote sensing of meteorological and related conditions over agricultural fields or environmentally important land areas. Small UAS, including unmanned aerial vehicle (UAV) and ground devices, have many advantages in remote sensing applications over traditional aircraftor satellite-based platforms or ground-based probes for many applications. This is because small UAVs are easy tomanipulate, cheap tomaintain, and remove the need for human pilots to perform tedious or dangerous jobs. Multiple small UAVs can be flown in a group and complete challenging tasks such as real-time mapping of large-scale agriculture areas. The purpose of remote sensing is to acquire information about the Earth’s surface without coming into contact with it. One objective of remote sensing is to characterize the electromagnetic radiation emitted by objects (James, 2006). Typical divisions of the electromagnetic spectrum include the visible light band (380− 720nm), near infrared (NIR) band (0.72− 1.30μm), and mid-infrared (MIR) band (1.30− 3.00μm). Band-reconfigurable imagers can generate several images from different bands ranging from visible spectra to infra-red or thermal based for various applications. The advantage of an ability to examine different bands is that different combinations of spectral bands can have different purposes. For example, the combination of red-infrared can be used to detect vegetation and camouflage and the combination of red slope can be used to estimate the percent of vegetation cover (Johnson et al., 2004). Different bands of images acquired remotely through UAS could be used in scenarios like water management and irrigation control. In fact, it is difficult to sense and estimate the state of water systems because most water systems are large-scale and need monitoring of many factors including the quality, quantity, and location of water, soil and vegetations. For the mission of accurate sensing of a water system, ground probe stations are expensive to build and can only provide data with very limited sensing range (at specific positions and second level temporal resolution). Satellite photos can cover a large area, but have a low resolution and a slow update rate (30-250 meter or lower spatial resolution and week level temporal resolution). Small UAVs cost less money but can provide more accurate information (meter or centimeter spatial

Tracking tagged fish with swarming Unmanned Aerial Vehicles using fractional order potential fields and Kalman filtering

Tracking fish using implanted radio transmitters is an important part of studying and preserving native fish species. However, conventional methods for locating the fish after they are tagged can be time consuming and costly. Unmanned Aerial Vehicles (UAV)s have been used in general radio localization applications and can possibly be used to locate fish quickly and effectively. However, the methods developed for multi-UAV navigation and transmitter localization are complex and might not work well for practical and routine use. This work focuses on developing simple methods for multi-UAV navigation and transmitter localization. Swarm-like navigation methods (using potential fields) are used for multi-UAV navigation, and a simple Kalman Filter is used to estimate the location of the transmitter. Simulations are presented using one, two and three UAVs. The simulation results show the success with locating the transmitter with two or three UAVs. In addition, coordination between the UAVs is successful using the simple rules of their virtual magnetic fields. A clustering behavior is observed and contributes to the success of the localization.