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

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

Estimation of High-Resolution Evapotranspiration in Heterogeneous Environments Using Drone-Based Remote Sensing

Evapotranspiration (ET) is a key element of hydrological cycle analysis, irrigation demand, and for better allocation of water resources in the ecosystem. For successful water resources management activities, precise estimate of ET is necessary. Although several attempts have been made to achieve that, variation in temporal and spatial scales constitutes a major challenge, particularly in heterogeneous canopy environments such as vineyards, orchards, and natural areas. The advent of remote sensing information from different platforms, particularly the small unmanned aerial systems (sUAS) technology with lightweight sensors allows users to capture high-resolution data faster than traditional methods, described as “flexible in timing”. In this study, the Two Source Energy Balance Model (TSEB) along with high-resolution data from sUAS were used to bridge the gap in ET issues related to spatial and temporal scales. Over homogeneous vegetation surfaces, relatively low spatial resolution information derived from Landsat (e.g., 30 m) might be appropriate for ET estimate, which can capture differences between fields. However, in agricultural landscapes with presence of vegetation rows and interrows, the homogeneity is less likely to be met and the ideal conditions may be difficult to identify. For most agricultural settings, row spacing can vary within a field (vineyards and orchards), making the agricultural landscape less homogenous. This leads to a key question related to how the contextual spatial domain/model grid size could influence the estimation of surface fluxes in canopy environments such as vineyards. Furthermore, temporal upscaling of instantaneous ET at daily or longer time scales is of great practical importance in managing water resources. While remote sensing-based ET models are promising tools to estimate instantaneous ET, additional models are needed to scale up the estimated or modeled instantaneous ET to daily values. Reliable and precise daily ET (ETd) estimation is essential for growers and water resources managers to understand the diurnal and seasonal variation in ET. In response to this issue, different existing extrapolation/upscaling daily ET (ETd) models were assessed using eddy covariance (EC) and sUAS measurements. On the other hand, ET estimation over semi-arid naturally vegetated regions becomes an issue due to high heterogeneity in such environments where vegetation tends to be randomly distributed over the land surface. This reflects the conditions of natural vegetation in river corridors. While significant efforts were made to estimate ET at agricultural landscapes, accurate spatial information of ET over riparian ecosystems is still challenging due to various species associated with variable amounts of bare soil and surface water. To achieve this, the TSEB model with high-resolution remote sensing data from sUAS were used to characterize the spatial heterogeneity and calculate the ET over a natural environment that features arid climate and various vegetation types at the San Rafael River corridor

Using uncrewed aerial vehicles for identifying the extent of invasive phragmites australis in treatment areas enrolled in an adaptive management program

Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the Phragmites and other cover types present at each of the sites and calculate the percent cover of Phragmites, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live Phragmites, Dead Phragmites, Other Vegetation, Other Non-vegetation). The Live Phragmites class had a mean user’s accuracy of 90.3% and a mean producer’s accuracy of 90.1%, and the Dead Phragmites class had a mean user’s accuracy of 76.5% and a mean producer’s accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live Phragmites at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of Phragmites at management sites

DETERMINING TIDAL CHARACTERISTICS IN A RESTORED TIDAL WETLAND USING UNMANNED AERIAL VEHICLES AND DERIVED DATA

Unmanned aerial vehicle (UAV) technology was used to determine tidal extent in Kimages Creek, a restored tidal wetland located in Charles City County, Virginia. A Sensefly eBee Real-Time Kinematic UAV equipped with the Sensor Optimized for Drone Applications (SODA) camera (20-megapixel RGB sensor) was flown during a single high and low tide event in Summer 2017. Collectively, over 1,300 images were captured and processed using Pix4D. Horizontal and vertical accuracy of models created using ground control points (GCP) ranged from 0.176 m to 0.363 m. The high tide elevation model was subtracted from the low tide using the ArcMap 10.5.1 raster calculator. The positive difference was displayed to show the portion of high tide that was above the low tide. These results show that UAVs offer numerous spatial and temporal advantages, but further research is needed to determine the best method of GCP placement in areas of similar forest structure

Remote Sensing for Management of Invasive Plants in Great Lakes Coastal Wetlands

Great Lakes coastal wetlands are some of the most crucial ecosystems to biodiversity in the Great Lakes Basin, yet suffer increasing degradation due to invasive plants. Wetland plant invasions can be controlled in their initial stages, but early detection of invasive plants using field surveys are often untenable due to budget constraints. Remote sensing techniques offer solutions to management objectives during the early stages of invasion on a landscape scale due to their ability to cheaply create spatially explicit information about plant distributions. Some invasive plants, such as Typha x. glauca, are conspicuous on a large scale, and can be mapped to their current extent using new satellite and modeling techniques. Inconspicuous invasive plants however, such as Hydrocharis morsus-ranae, may be undetectable by remote sensing sources and require predictive strategies. In this thesis I explored the use of remote sensing in the management of a conspicuous and inconspicuous invasive wetland plants in the St. Mary’s River, MI. I successfully classified the current extent of conspicuous Typha x. glauca and other wetland vegetation types to provide spatially explicit maps for early detection and management and examined methods that can be adapted for use in emergent wetlands worldwide. The habitat suitability of inconspicuous Hydrocharis morsus-ranae was also determined using novel fine-scale habitat covariates determined from lidar and radar. Habitat covariates derived from these sources should see wider use in species distribution modeling, particularly with wetland plants, to create better predictions of invasive plant expansions. Implementation of new and upcoming remote sensing data sources and methods will allow for better invasive wetland plant management at greater spatial and temporal scales than field studies alone

An Automated Framework to Identify Lost and Restorable Wetlands in the Prairie Pothole Region

Abstract While progress has been made in automating wetland identification, identifying lost and restorable wetlands remains a challenge. A suite of automated methods was developed and applied to the Nose Creek watershed near Calgary, Alberta to establish a historical wetland inventory and the proportion of permanently versus temporarily lost wetlands. A power-law function of wetland area vs. wetland frequency using wetlands derived from the fusion of a high resolution digital elevation model and near-infrared data identified permanent loss of 11.0% by number and 0.6% by area. The difference between historical and existing wetlands was used to estimate a further temporary loss of 61.1% by number and 78.3% by area. Historical wetlands lost to ditch drainage are easily restored by ditch plugging. Therefore, an algorithm was created using digital terrain analysis that distinguished drainage ditches intersecting wetlands using surface curvature. The 1,588 ditch-drained wetlands identified represent a potential recovery of 11.7% of the temporary loss by number and 12.5% by area. Automated techniques to estimate wetland loss and identify priority wetlands for restoration provide powerful tools for wetland management