Nicholas Etienne Clinton
Nick Clinton is on the Google Earth Engine developer relations team. He received a bachelors, masters and PhD from the department of Environmental Science, Policy and Management at UC Berkeley. From 2008-2011, Nick worked in the Airborne Sensor Facility of NASA Ames Research Center, producing science quality calibrated imagery and supporting sensor maintenance for thermal, multispectral and hyperspectral imagers. From 2012-2015, he was on the faculty of the Center for Earth System Science at Tsinghua University, in Beijing, China. He joined Google in 2015. His research interests include machine learning on geospatial imagery, information extraction from spaceborne and airborne sensors, statistical modeling of Earth surface processes.
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EEAGER: A neural network model for finding beaver complexes in satellite and aerial imagery
Emily Fairfax
Steffi Maiman
Aman Shaikh
William W. Macfarlane
Joseph M. Wheaton
Dan Ackerstein
Eddie Corwin
JGR Biosciences, vol. 128 (2023), N/A
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Beavers are ecosystem engineers that create and maintain riparian wetland ecosystems in a variety of ecologic, climatic, and physical settings. Despite the large-scale implications of ongoing beaver conservation and range expansion, relatively few landscape-scale studies have been conducted, due in part to the significant time required to manually locate beaver dams at scale. To address this need, we developed EEAGER—an image recognition machine learning model that detects beaver complexes in aerial and satellite imagery. We developed the model in the western United States using 13,344 known beaver dam locations and 56,728 nearby locations without beaver dams. Performance assessment was performed in twelve held out evaluation polygons of known beaver occupancy but previously unmapped dam locations. These polygons represented regions similar to the training data as well as more novel landscape settings. Our model performed well overall (accuracy = 98.5%, recall = 63.03%, precision = 25.83%) in these areas, with stronger performance in regions similar to where the model had been trained. We favored recall over precision, which results in a more complete catalog of beaver dams found but also a higher incidence of false positives to be manually removed during quality control. These results have far-reaching implications for monitoring of beaver-based river restoration, as well as potential applications detecting other complex landforms.
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The global distribution and trajectory of tidal flats
Nicholas J. Murray
Stuart R. Phinn
Renata Ferrari
Renee Johnston
Mitchell B. Lyons
David Thau
Richard A. Fuller
Nature, vol. 565 (2019), pp. 222-225
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Increasing human populations around the global coastline have caused extensive loss, degradation and fragmentation of coastal ecosystems, threatening the delivery of important ecosystem services. As a result, alarming losses of mangrove, coral reef, seagrass, kelp forest and coastal marsh ecosystems have occurred. However, owing to the difficulty of mapping intertidal areas globally, the distribution and status of tidal flats—one of the most extensive coastal ecosystems—remain unknown. Here we present an analysis of over 700,000 satellite images that maps the global extent of and change in tidal flats over the course of 33 years (1984–2016). We find that tidal flats, defined as sand, rock or mud flats that undergo regular tidal inundation, occupy at least 127,921 km² (124,286–131,821 km², 95% confidence interval). About 70% of the global extent of tidal flats is found in three continents (Asia (44% of total), North America (15.5% of total) and South America (11% of total)), with 49.2% being concentrated in just eight countries (Indonesia, China, Australia, the United States, Canada, India, Brazil and Myanmar). For regions with sufficient data to develop a consistent multi-decadal time series—which included East Asia, the Middle East and North America—we estimate that 16.02% (15.62–16.47%, 95% confidence interval) of tidal flats were lost between 1984 and 2016. Extensive degradation from coastal development, reduced sediment delivery from major rivers, sinking of riverine deltas increased coastal erosion and sea-level rise signal a continuing negative trajectory for tidal flat ecosystems around the world. Our high-spatial-resolution dataset delivers global maps of tidal flats, which substantially advances our understanding of the distribution, trajectory and status of these poorly known coastal ecosystems.
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