Detection, mapping, and monitoring of wildlife populations can provide significant insight into the health and trajectory of the ecosystems they rely on. In fact, it was not until recently that the benefits of wetland ecosystems were fully understood. Unfortunately, by that point, the United States had removed more than 50% of its native wetlands. The Prairie Pothole Region in North America is the premier breeding location for ducks; responsible for producing more than 50% of the North American ducks annually. The current survey methods for obtaining duck population counts are accomplished primarily using manned flights with observers manually identifying and counting the ducks below with coordinated ground surveys at a subset of these areas to obtain breeding pair estimates. The current industry standard for in situ assessment of nest locations for reproductive effort estimates is known as the “chain drag method”, a manually intensive ground survey technique. However, recent improvements to small unmanned aerial systems (sUAS), coupled with the increased performance of lightweight sensors provide the potential for an alternative survey method. Our objective for this study was to assess the feasibility of utilizing sUAS based thermal longwave infrared (LWIR) imagery for detecting duck nests and ultraviolet (UV) imagery to classify breeding pairs in the Prairie Pothole Region. Our team deployed a DRS Tamarisk 640 LWIR sensor aboard a DJI Matrice 600 hexa-copter at Ducks Unlimited’s Coteau Ranch in Sheridan County, North Dakota, to obtain the thermal imagery. At the ranch, 24 nests were imaged at two altitudes (40m and 80m) during the early morning (04h00-06h00), morning (06h00-08h00), and midday (11h00-13h00). Three main parameters, namely altitude, time of day, and terrain, were varied between flights and the impact that each had on detection accuracy was examined. Each nest image was min-max normalized and contrast enhanced using a high-pass filter, prior to input into the detection algorithm. We determined that the variable with the highest impact on detection accuracies was altitude. We were able to achieve detection accuracies of 58% and 69% for the 80m and 40m flights, respectively. We also determined that flights in the early morning yielded the highest detection accuracies, which was attributed to the increased contrast between the landscape and the nests after the prairie cooled overnight. Additionally, the detection accuracies were lowest during morning flights when the hens might be off the nests on a recess break from incubation. Therefore, we determined that with increases in spatial resolution, the use of sUAS based thermal imagery is feasible for detecting nests across the prairie and that flights should occur early in the morning while the hens are on the nest, in order to maximize detection potential. To assess the feasibility of classifying breeding duck pairs using UV imagery, our team took a preliminary step in simulating UAS reflectance imagery by collecting 260 scans across nine species of upland ducks with a fixed measurement geometry using an OceanOptic’s spectroradiometer. We established baseline accuracies of 83%, 83%, and 76% for classifying age, sex, and species, respectively, by using a random forest (RF) classifier with simulated panchromatic (250-850nm) image sets. When using imagery at narrow UV bands with the same RF classifier, we were able to increase classification accuracies for age and species by 7%. Therefore, we demonstrated the potential for the use of sUAS based imagery as an alternate method for surveying nesting ducks, as well as potential improvements in age and species classification using UV imagery during breeding pair aerial surveys. Next steps should include efforts to extend these findings to airborne sensing systems, toward eventual operational implementation. Such an approach could alleviate environmental impacts associated with in situ surveys, while increasing the scale (scope and exhaustiveness) of surveys.

Library of Congress Subject Headings

Ducks--Remote sensing; Population biology--Remote sensing; Multisensor data fusion; Infrared photography; Ultraviolet spectrometry

Publication Date


Document Type


Student Type


Degree Name

Imaging Science (MS)

Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


Jan van Aardt

Advisor/Committee Member

Carl Salvaggio

Advisor/Committee Member

Susan Ellis-Felege


RIT – Main Campus

Plan Codes