Obtaining the Thermal Structure of Lakes from the Air

The significance of thermal heterogeneities in small surface water bodies as drivers of mixing and for habitat provision is increasingly recognized, yet obtaining three-dimensionally-resolved observations of the thermal structure of lakes and rivers remains challenging. Remote observations of water temperature from aerial platforms are attractive: such platforms do not require shoreline access; they can be quickly and easily deployed and redeployed to facilitate repeated sampling and can rapidly move between target locations, allowing multiple measurements to be made during a single flight. However, they are also subject to well-known limitations, including payload, operability and a tradeoff between the extent and density over which measurements can be made within restricted flight times. This paper introduces a novel aerial thermal sensing platform that lowers a temperature sensor into the water to record temperature measurements throughout a shallow water column and presents results from initial field experiments comparing in situ temperature observations to those made from the UAS platform. These experiments show that with minor improvements, UASs have the potential to enable high-resolution 3D thermal mapping of a ~1-ha lake in 2–3 flights (circa 2 h), sufficient to resolve diurnal variations. This paper identifies operational constraints and key areas for further development, including the need for the integration of a faster temperature sensor with the aerial vehicle and better control of the sensor depth, especially when near the water surface

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

The future of Earth observation in hydrology

In just the past 5 years, the field of Earth observation has progressed beyond the offerings of conventional space-agency-based platforms to include a plethora of sensing opportunities afforded by CubeSats, unmanned aerial vehicles (UAVs), and smartphone technologies that are being embraced by both for-profit companies and individual researchers. Over the previous decades, space agency efforts have brought forth well-known and immensely useful satellites such as the Landsat series and the Gravity Research and Climate Experiment (GRACE) system, with costs typically of the order of 1 billion dollars per satellite and with concept-to-launch timelines of the order of 2 decades (for new missions). More recently, the proliferation of smart-phones has helped to miniaturize sensors and energy requirements, facilitating advances in the use of CubeSats that can be launched by the dozens, while providing ultra-high (3-5 m) resolution sensing of the Earth on a daily basis. Start-up companies that did not exist a decade ago now operate more satellites in orbit than any space agency, and at costs that are a mere fraction of traditional satellite missions. With these advances come new space-borne measurements, such as real-time high-definition video for tracking air pollution, storm-cell development, flood propagation, precipitation monitoring, or even for constructing digital surfaces using structure-from-motion techniques. Closer to the surface, measurements from small unmanned drones and tethered balloons have mapped snow depths, floods, and estimated evaporation at sub-metre resolutions, pushing back on spatio-temporal constraints and delivering new process insights. At ground level, precipitation has been measured using signal attenuation between antennae mounted on cell phone towers, while the proliferation of mobile devices has enabled citizen scientists to catalogue photos of environmental conditions, estimate daily average temperatures from battery state, and sense other hydrologically important variables such as channel depths using commercially available wireless devices. Global internet access is being pursued via high-altitude balloons, solar planes, and hundreds of planned satellite launches, providing a means to exploit the "internet of things" as an entirely new measurement domain. Such global access will enable real-time collection of data from billions of smartphones or from remote research platforms. This future will produce petabytes of data that can only be accessed via cloud storage and will require new analytical approaches to interpret. The extent to which today's hydrologic models can usefully ingest such massive data volumes is unclear. Nor is it clear whether this deluge of data will be usefully exploited, either because the measurements are superfluous, inconsistent, not accurate enough, or simply because we lack the capacity to process and analyse them. What is apparent is that the tools and techniques afforded by this array of novel and game-changing sensing platforms present our community with a unique opportunity to develop new insights that advance fundamental aspects of the hydrological sciences. To accomplish this will require more than just an application of the technology: in some cases, it will demand a radical rethink on how we utilize and exploit these new observing systems

UAVs for the Environmental Sciences

This book gives an overview of the usage of UAVs in environmental sciences covering technical basics, data acquisition with different sensors, data processing schemes and illustrating various examples of application

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

In less than two decades, UASs (unmanned aerial systems) have revolutionized the field of hydrology, bridging the gap between traditional satellite observations and ground-based measurements and allowing the limitations of manned aircraft to be overcome. With unparalleled spatial and temporal resolutions and product-tailoring possibilities, UAS are contributing to the acquisition of large volumes of data on water bodies, submerged parameters and their interactions in different hydrological contexts and in inaccessible or hazardous locations. This paper provides a comprehensive review of 122 works on the applications of UASs in surface water and groundwater research with a purpose-oriented approach. Concretely, the review addresses: (i) the current applications of UAS in surface and groundwater studies, (ii) the type of platforms and sensors mainly used in these tasks, (iii) types of products generated from UAS-borne data, (iv) the associated advantages and limitations, and (v) knowledge gaps and future prospects of UASs application in hydrology. The first aim of this review is to serve as a reference or introductory document for all researchers and water managers who are interested in embracing this novel technology. The second aim is to unify in a single document all the possibilities, potential approaches and results obtained by different authors through the implementation of UASs

Assessing the potential of drone-based thermal infrared imagery for quantifying river temperature heterogeneity

© 2019 Crown copyright. Hydrological Processes © 2019 John Wiley & Sons, Ltd. Climate change is altering river temperature regimes, modifying the dynamics of temperature-sensitive fishes. The ability to map river temperature is therefore important for understanding the impacts of future warming. Thermal infrared (TIR) remote sensing has proven effective for river temperature mapping, but TIR surveys of rivers remain expensive. Recent drone-based TIR systems present a potential solution to this problem. However, information regarding the utility of these miniaturised systems for surveying rivers is limited. Here, we present the results of several drone-based TIR surveys conducted with a view to understanding their suitability for characterising river temperature heterogeneity. We find that drone-based TIR data are able to clearly reveal the location and extent of discrete thermal inputs to rivers, but thermal imagery suffers from temperature drift-induced bias, which prevents the extraction of accurate temperature data. Statistical analysis of the causes of this drift reveals that drone flight characteristics and environmental conditions at the time of acquisition explain ~66% of the variance in TIR sensor drift. These results shed important light on the factors influencing drone-based TIR data quality and suggest that further technological development is required to enable the extraction of robust river temperature data. Nonetheless, this technology represents a promising approach for augmenting in situ sensor capabilities and improved quantification of advective inputs to rivers at intermediate spatial scales between point measurements and “conventional” airborne or satellite remote sensing

Design, Testing and Evaluation of Robotic Mechanisms and Systems for Environmental Monitoring and Interaction

Unmanned Aerial Vehicles (UAVs) have significantly lowered the cost of remote aerial data collection. The next generation of UAVs, however, will transform the way that scientists and practitioners interact with the environment. In this thesis, we address the challenges of flying low over water to collect water samples and temperature data. We also develop a system that allows UAVs to ignite prescribed fires. Specifically, this thesis contributes a new peristaltic pump designed for use on a UAV for collecting water samples from up to 3m depth and capable of pumping over 6m above the water. Next, temperature sensors and their deployment on UAVs, which have successfully created a 3D thermal structure map of a lake, contributes to mobile sensors. A sub-surface sampler, the “Waterbug” which can sample from 10m deep and vary buoyancy for longer in-situ analysis contributes to robotics and mobile sensors. Finally, we designed and built an Unmanned Aerial System for Fire Fighting (UAS-FF), which successfully ignited over 150 acres of prescribed fire during two field tests and is the first autonomous robot system for this application. Advisers: Carrick Detweiler and Carl Nelso

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

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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