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

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Procedures for Correcting Digital Camera Imagery Acquired by the AggieAir Remote Sensing Platform

Developments in sensor technologies have made consumer-grade digital cameras one of the more recent tools in remote sensing applications. Consumer-grade digital cameras have been the imaging sensor of choice by researchers due to their small size, light weight, limited power requirements, and their potential to store hundreds of images (Hardin 2011). Several studies have focused on the use of digital cameras and their efficacy in remote sensing applications. For satellite and airborne multispectral imaging systems, there is a well established radiometric processing approach. However, radiometric processing lines for digital cameras are currently being researched. The goal of this report is to describe an absolute method of radiometric normalization that converts digital numbers output by the camera to reflectance values that can be used for remote sensing applications. This process is used at the AggieAir Flying Circus (AAFC), a service center at the Utah Water Research Laboratory at Utah State University. The AAFC is a research unit that specializes in the acquisition, processing, and interpretation of aerial imagery obtained with the AggieAirTM platform. AggieAir is an autonomous, unmanned aerial vehicle system that captures multi-temporal and multispectral high resolution imagery for the production of orthorectified mosaics. The procedure used by the AAFC is based on methods adapted from Miura and Huete (2009), Crowther (1992) and Neale and Crowther (1994) for imagery acquired with Canon PowerShot SX100 cameras. Absolute normalization requires ground measurements at the time the imagery is acquired. In this study, a barium sulfate reflectance panel with absolute reflectance is used. The procedure was demonstrated using imagery captured from a wetland near Pleasant Grove, Utah, that is managed by the Utah Department of Transportation

Multispectral Remote Sensing of the Earth and Environment Using KHawk Unmanned Aircraft Systems

This thesis focuses on the development and testing of the KHawk multispectral remote sensing system for environmental and agricultural applications. KHawk Unmanned Aircraft System (UAS), a small and low-cost remote sensing platform, is used as the test bed for aerial video acquisition. An efficient image geotagging and photogrammetric procedure for aerial map generation is described, followed by a comprehensive error analysis on the generated maps. The developed procedure is also used for generation of multispectral aerial maps including red, near infrared (NIR) and colored infrared (CIR) maps. A robust Normalized Difference Vegetation index (NDVI) calibration procedure is proposed and validated by ground tests and KHawk flight test. Finally, the generated aerial maps and their corresponding Digital Elevation Models (DEMs) are used for typical application scenarios including prescribed fire monitoring, initial fire line estimation, and tree health monitoring

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

Cyber-Physical Systems Enabled By Unmanned Aerial System-Based Personal Remote Sensing: Data Mission Quality-Centric Design Architectures

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

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

Multispectral Remote Sensing and Spatiotemporal Mapping of the Environment and Natural Disasters Using Small UAS

This dissertation focuses on development of new methods for multispectral remote sensing, measurement, and mapping of the environment and natural disasters using small Unmanned Aircraft Systems (UAS). Small UAS equipped with multispectral cameras such as true color (RGB), near infrared (NIR), and thermal can gather important information about the environment before, during, and after a disaster without risking pilots or operators. Additionally, small UAS are generally inexpensive, easy to handle, and can detect features at small spatiotemporal scales that are not visible in manned aircraft or satellite imagery. Four important problems in UAS remote sensing and disaster data representation are focused in this dissertation. First, key considerations for the development of UAS disaster sensing systems are provided, followed by detailed descriptions of the KHawk system and representative environment and disaster data sets. Second, a new method is proposed and demonstrated for accurate mapping and measurement of grass fire evolution using multitemporal thermal orthomosaics collected by a fixed-wing UAS flying at low altitudes. Third, a low-cost and effective solution is further developed for spatiotemporal representation and measurement of grass fire evolution using time-labeled UAS NIR orthomosaics and a novel Intensity Variance Thresholding (IVT) method is proposed for grass fire front extraction to support fire spread metrics measurement of fire front location and rate of spread (ROS). A UAS grass fire observation data set is also presented including thermal and NIR orthomosaics and supporting weather and fuel data. Fourth, a new Satellite-based Cross Calibration (SCC) method is proposed for surface reflectance estimation of UAS images in digital numbers (DN) using free and open calibrated satellite reflectance data. This also serves as a solid foundation for data-enabled multiscale remote sensing and large scale environmental observations. Finally, the main conclusions and future research considerations are summarized

Determining estuarine seagrass density measures from low altitude multispectral imagery flown by remotely piloted aircraft

Seagrass is the subject of significant conservation research. Seagrass is ecologically important and of significant value to human interests. Many seagrass species are thought to be in decline. Degradation of seagrass populations are linked to anthropogenic environmental issues. Effective management requires robust monitoring that is affordable at large scale. Remote sensing methods using satellite and aircraft imagery enable mapping of seagrass populations at landscape scale. Aerial monitoring of a seagrass population can require imagery of high spatial and/or spectral resolution for successful feature extraction across all levels of seagrass density. Remotely piloted aircraft (RPA) can operate close to the ground under precise flight control enabling repeated surveys in high detail with accurate revisit-positioning. This study evaluates a method for assessing intertidal estuarine seagrass (Zostera muelleri) presence/absence and coverage density using multispectral imagery collected by a remotely piloted aircraft (RPA) flying at 30 m above the estuary surface (2.7 cm ground sampling distance). The research was conducted at Wharekawa Harbour on the eastern coast of the Coromandel Peninsula, North Island, New Zealand. Differential drainage of residual ebb waters from the surface of an estuary at low tide creates a mosaic of drying sediment, draining surface and static shallow pooling that has potential to interfere with spectral observations. The field surveys demonstrated that despite minor shifts in the spectral coordinates of seagrass and other surface material, there was no apparent difference in image classification outcome from the time of bulk tidal water clearance to the time of returning tidal flood. For the survey specification tested, classification accuracy increased with decreasing segmentation scale. Pixel-based image analysis (PBIA) achieved higher classification accuracy than object-based image analysis (OBIA) assessed at a range of segmentation scales. Contaminating objects such as shells and detritus can become aggregated within polygon objects when OBIA is applied but remain as isolated objects under PBIA at this image resolution. There was clear separability of spectra for seagrass and sediment, but shell and detritus confounded the classification of seagrass density in some situations. High density seagrass was distinct from sediment, but classification error arose for sparse seagrass. Three classifiers (linear discriminant analysis, support vector machine and random forest) and three feature selection options (no selection, collinearity reduction and recursive feature elimination) were assessed for effect on classification performance. The random forest classifier yielded the highest classification accuracy, with no accuracy benefit gained from collinearity reduction or recursive feature elimination. Spectral vegetation indices and texture layers substantially improved classification accuracy. Object geometry made a negligible contribution to classification accuracy using mean-shift segmentation at this image-scale. The method achieved classification of seagrass density with up to 84% accuracy on a three-tier end-member class scale (low, medium, and high density) when using training data formed using visual interpretation of ground reference photography, and up to 93% accuracy using precisely measured seagrass leaf-area. Visual interpretation agreed with precisely measured seagrass leaf area 88% of the time with some misattribution at mid-density. Visual interpretation was substantially faster to apply than measuring the leaf area. A decile class scale for seagrass density correlated with actual leaf area measures more than the three-tier scale, however, was less accurate for absolute class attribution. The research demonstrates that seagrass feature extraction from RPA-flown imagery is a feasible and repeatable option for seagrass population monitoring and environmental reporting. Further calibration is required for whole- and multi-estuary application