Scholars@Duke
David William Johnston

David William Johnston

Professor of the Practice of Marine Conservation Ecology
Marine Science and Conservation
Suite 3103, Grainger Hall, 9 Circuit Drive, Duke University, Durham, NC 27708

Scholarly Works - Datasets

Data from: Drones reveal spatial patterning of sympatric Alaskan pinniped species and drivers of their local distributions

Dataset · March 31, 2022 This dataset features raw drone imagery, photogrammetric surface models and orthomosaic products, derived terrain rasters, and pinniped locations from a survey of Otter Island, AK, conducted on September 3, 2018. The study that uses these data leverages em ... Full text Cite

Data and scripts from: A Bayesian approach for predicting photogrammetric uncertainty in morphometric measurements derived from UAS

Dataset · November 30, 2020 Increasingly, drone-based photogrammetry has been used to measure size and body condition changes in marine megafauna. A broad range of platforms, sensors, and altimeters are being applied for these purposes, but there is no unified way to predict uncertai ... Full text Cite

Data from: Drones and deep learning produce accurate and efficient monitoring of large-scale seabird colonies

Dataset · September 17, 2020 Population monitoring in some colonial seabirds is often complicated by the large size of colonies, remote locations, and by close inter- and intra-species aggregation. While drones have been successfully used to monitor large inaccessible colonies, the va ... Full text Cite

Data from: A semi-automated method for estimating Adelie penguin colony abundance from a fusion of multispectral and thermal imagery collected with Unoccupied Aircraft Systems

Dataset · September 8, 2020 Monitoring Adelie penguin (Pygoscelis adeliae) populations on the Western Antarctic Peninsula (WAP) provides information about the health of the species and the WAP marine ecosystem itself. In January 2017, surveys of Adelie penguin colonies at Avian Islan ... Full text Cite

Data from: Modeling salt marsh vegetation height using Unoccupied Aircraft Systems and Structure from Motion

Dataset · June 8, 2020 Salt marshes provide important services to coastal ecosystems of the southeastern United States. In many locations, salt marsh habitats are threatened by coastal development and erosion, necessitating large-scale monitoring. Assessing vegetation height acr ... Full text Cite

Data from: Deep learning for coastal resource conservation: automating detection of shellfish reefs

Dataset · November 6, 2019 It is increasingly important to understand the extent and health of coastal natural resources in the face of anthropogenic and climate-driven changes. Coastal ecosystems are difficult to efficiently monitor due to the inability of existing remotely-sensed ... Full text Cite