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Phillipe A Wernette

Mendenhall Fellow at the USGS Pacific Coastal and Marine Science Center

AREAS OF EXPERTISE

My research seeks to understand how geomorphology and processes across multiple spatial and temporal combine to drive change in a variety of environments, including, but not limited to, coastal cliffs, bluffs, beach-dune systems, and landslides. Ultimately, my work involves utilizing existing data and information in combination with emerging technologies to develop more accurate and comprehensive models of landscape and environmental evolution. I utilize techniques and concepts spanning physical geography and geomorphology, geographic information science (GIS), remote sensing (including structure-from-motion, LiDAR, and hyperspectral imagery), geophysics (GPR, ERT, and seismic), and machine learning. Broadly speaking, my two main research foci are: (1) understanding and modelling the suite of different factors affecting coastal evolution, and (2) developing comprehensive analytical techniques that include quantifying and propagating error in geospatial and temporal analyses. I believe that research should be collaborative and multidisciplinary and will strive to engage with a diverse group of stakeholders and researchers worldwide.

Professional Experience

  • 2020 to present - Research Geologist (Mendenhall Postdoctoral Fellow), U.S. Geological Survey, Pacific Coastal and Marine Science Center

  • 2020 to present - Adjunct Professor, University of Windsor, School of the Environment

  • 2017 to 2020 - Postdoctoral Fellow, University of Windsor, School of the Environment

  • 2017 to 2019 - Conference Chair, University of Windsor, UWill Discover Student Research Conference

  • 2017 - Program Director, Texas A&M University, Aggie Research Programs

  • 2014 to 2016 - Course Instructor, Texas A&M University, Department of Geography

Education and Certifications

  • Ph.D. in Geography - Texas A&M University, 2017

  • M.S. in Geography - Michigan State University, 2012

  • B.S. in Fisheries and Wildlife (major) and Geographic Information Science (minor) - Michigan State University, 2010

Affiliations and Memberships*

  • American Geophysical Union

  • Geological Society of America

Science and Products

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View of beach from roof top, edge of roof in foreground, pier extending from beach out over water in center, park in background.

Remote Sensing Coastal Change

We use remote-sensing technologies—such as aerial photography, satellite imagery, structure-from-motion (SfM) photogrammetry, and lidar (laser-based surveying)—to measure coastal change along U.S. shorelines.
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Natural Hazards Mission Area, Coastal and Marine Hazards and Resources Program, Pacific Coastal and Marine Science Center, Big Sur Landslides
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Remote Sensing Coastal Change

We use remote-sensing technologies—such as aerial photography, satellite imagery, structure-from-motion (SfM) photogrammetry, and lidar (laser-based surveying)—to measure coastal change along U.S. shorelines.
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USGS science for a changing world logo

Coast Train: Massive Library of Labeled Coastal Images to Train Machine Learning for Coastal Hazards and Resources

Scientists who study coastal ecosystems and hazards such as hurricanes, flooding, and cliff failure collect lots of photographs of coastal environments from airplanes and drones. A large area can be surveyed at high resolution and low cost. Additionally, satellites such as Landsat have provided imagery of the Nation’s coastlines every few days for decades. Scientist’s ability to understand coastal...
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Community for Data Integration (CDI)
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Coast Train: Massive Library of Labeled Coastal Images to Train Machine Learning for Coastal Hazards and Resources

Scientists who study coastal ecosystems and hazards such as hurricanes, flooding, and cliff failure collect lots of photographs of coastal environments from airplanes and drones. A large area can be surveyed at high resolution and low cost. Additionally, satellites such as Landsat have provided imagery of the Nation’s coastlines every few days for decades. Scientist’s ability to understand coastal...
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Aerial photogrammetry data and products of the North Carolina coast

This data release presents structure-from-motion (SfM) products derived from aerial imagery collected along the North Carolina coast in response to storm events and the recovery process. U.S. Geological Survey (USGS) researchers use the aerial imagery and products to assess future coastal vulnerability, nesting habitats for wildlife, and provide data for hurricane impact models. This research is p
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Pacific Coastal and Marine Science Center

Aerial Imagery of the North Carolina Coast: 2020-02-08 to 2020-02-09

The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods.
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Coastal and Marine Hazards and Resources Program, St. Petersburg Coastal and Marine Science Center

Aerial Imagery of the North Carolina Coast: 2020-05-08 to 2020-05-09

The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods.
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Coastal and Marine Hazards and Resources Program, St. Petersburg Coastal and Marine Science Center

Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation

Coast Train is a library of images of coastal environments, annotations, and corresponding thematic label masks (or 'label images') collated for the purposes of training and evaluating machine learning (ML), deep learning, and other models for image segmentation. It includes image sets from both geospatial satellite, aerial, and UAV imagery and orthomosaics, as well as non-geospatial oblique and n
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Coastal and Marine Hazards and Resources Program, Community for Data Integration (CDI), Pacific Coastal and Marine Science Center

Aerial Imagery of the North Carolina Coast: 2019-11-26

The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods.
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St. Petersburg Coastal and Marine Science Center

Aerial Imagery of the North Carolina Coast: 2019-10-11

The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods.
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Coastal and Marine Hazards and Resources Program, St. Petersburg Coastal and Marine Science Center

Aerial Imagery of the North Carolina Coast: 2019-09-08 to 2019-09-13, Post-Hurricane Dorian

The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods.
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Coastal and Marine Hazards and Resources Program, St. Petersburg Coastal and Marine Science Center

Aerial Imagery of the North Carolina Coast: 2019-08-30 and 2019-09-02, Pre-Hurricane Dorian

The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods.
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Coastal and Marine Hazards and Resources Program, St. Petersburg Coastal and Marine Science Center

Aerial Photogrammetry Data and Products of the North Carolina coast: 2018-10-06 to 2018-10-08, post-Hurricane Florence

This data release presents structure-from-motion products derived from imagery taken along the North Carolina coast in response to storm events and the recovery process. USGS researchers use the aerial photogrammetry data and products to assess future coastal vulnerability, nesting habitats for wildlife, and provide data for hurricane impact models. This research is part of the Remote Sensing Coas
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Woods Hole Coastal and Marine Science Center

Post-Hurricane Florence Aerial Imagery: Cape Fear to Duck, North Carolina, October 6-8, 2018

The U.S. Geological Survey (USGS) Remote Sensing Coastal Change (RSCC) project collects aerial imagery along coastal swaths, in response to storm events, with optimized endlap/sidelap and precise position information to create high-resolution orthomosaics, three-dimensional (3D) point clouds, and digital elevation/surface models (DEMs/DSMs) using Structure-from-Motion (SfM) photogrammetry methods.
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Coastal and Marine Hazards and Resources Program, St. Petersburg Coastal and Marine Science Center

A 1.2 billion pixel human-labeled dataset for data-driven classification of coastal environments

The world’s coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry
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Daniel Buscombe, Phillipe Alan Wernette, Sharon Fitzpatrick, Jaycee Favela, Evan B. Goldstein, Nicholas Enwright
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Ecosystems Mission Area, Pacific Coastal and Marine Science Center, Wetland and Aquatic Research Center

Sound-side inundation and seaward erosion of a barrier island during hurricane landfall

Barrier islands are especially vulnerable to hurricanes and other large storms, owing to their mobile composition, low elevations, and detachment from the mainland. Conceptual models of barrier-island evolution emphasize ocean-side processes that drive landward migration through overwash, inlet migration, and aeolian transport. In contrast, we found that the impact of Hurricane Dorian (2019) on No
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Christopher R. Sherwood, Andrew C. Ritchie, Jin-Si R. Over, Christine J. Kranenburg, Jonathan Warrick, Jenna A. Brown, Wayne Wright, Alfredo Aretxabaleta, Sara Zeigler, Phillipe Alan Wernette, Daniel D. Buscombe, Christie Hegermiller
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Ecosystems Mission Area, Pacific Coastal and Marine Science Center, Southwest Biological Science Center, St. Petersburg Coastal and Marine Science Center, Woods Hole Coastal and Marine Science Center

Crowd-sourced SfM: Best practices for high resolution monitoring of coastal cliffs and bluffs

Structure from motion (SfM) photogrammetry is an increasingly common technique for measuring landscape change over time by deriving 3D point clouds and surface models from overlapping photographs. Traditional change detection approaches require photos that are geotagged with a differential GPS (DGPS) location, which requires expensive equipment that can limit the ability of communities and researc

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Phillipe Alan Wernette, Ian M. Miller, Andrew C. Ritchie, Jonathan Warrick
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Natural Hazards Mission Area, Coastal and Marine Hazards and Resources Program, Pacific Coastal and Marine Science Center

Human-in-the-Loop segmentation of earth surface imagery

Segmentation, or the classification of pixels (grid cells) in imagery, is ubiquitously applied in the natural sciences. Manual methods are often prohibitively time-consuming, especially those images consisting of small objects and/or significant spatial heterogeneity of colors or textures. Labeling complicated regions of transition that in Earth surface imagery are represented by collections of mi
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Daniel D. Buscombe, Evan B. Goldstein, Christopher R. Sherwood, Cameron S Bodine, Jenna A. Brown, Jaycee Favela, Sharon Fitzpatrick, Christine J. Kranenburg, Jin-Si R. Over, Andrew C. Ritchie, Jonathan Warrick, Phillipe Alan Wernette
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Ecosystems Mission Area, Coastal and Marine Hazards and Resources Program, Pacific Coastal and Marine Science Center, Southwest Biological Science Center, St. Petersburg Coastal and Marine Science Center, Woods Hole Coastal and Marine Science Center

Labeling poststorm coastal imagery for machine learning: Measurement of interrater agreement

Classifying images using supervised machine learning (ML) relies on labeled training data—classes or text descriptions, for example, associated with each image. Data-driven models are only as good as the data used for training, and this points to the importance of high-quality labeled data for developing a ML model that has predictive skill. Labeling data is typically a time-consuming, manual proc
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Evan B. Goldstein, Daniel D. Buscombe, Eli D. Lazarus, Somya Mohanty, Shah N. Rafique, K A Anarde, Andrew D Ashton, Tomas Beuzen, Katherine A. Castagno, Nicholas Cohn, Matthew P. Conlin, Ashley Ellenson, Megan Gillen, Paige A. Hovenga, Jin-Si R. Over, Rose V. Palermo, Katherine Ratlif, Ian R Reeves, Lily H. Sanborn, Jessamin A. Straub, Luke A. Taylor, Elizabeth J. Wallace, Jonathan Warrick, Phillipe Alan Wernette, Hannah E Williams
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Ecosystems Mission Area, Natural Hazards Mission Area, Coastal and Marine Hazards and Resources Program, Pacific Coastal and Marine Science Center, Southwest Biological Science Center, Woods Hole Coastal and Marine Science Center, Hurricane Florence, Hurricane Isaias, Hurricane Michael, Hurricanes

Processing coastal imagery with Agisoft Metashape Professional Edition, version 1.6—Structure from motion workflow documentation

IntroductionStructure from motion (SFM) has become an integral technique in coastal change assessment; the U.S. Geological Survey (USGS) used Agisoft Metashape Professional Edition photogrammetry software to develop a workflow that processes coastline aerial imagery collected in response to storms since Hurricane Florence in 2018. This report details step-by-step instructions to create three-dimen

Authors
Jin-Si R. Over, Andrew C. Ritchie, Christine J. Kranenburg, Jenna A. Brown, Daniel D. Buscombe, Tom Noble, Christopher R. Sherwood, Jonathan A. Warrick, Phillipe A. Wernette
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Ecosystems Mission Area, Natural Hazards Mission Area, Coastal and Marine Hazards and Resources Program, Maryland-Delaware-D.C. Water Science Center, Pacific Coastal and Marine Science Center, Southwest Biological Science Center, St. Petersburg Coastal and Marine Science Center, Woods Hole Coastal and Marine Science Center, Hurricane Florence, Hurricanes

Short communication: evidence for geologic control of rip channels along Prince Edward Island, Canada

Rip currents can move unsuspecting swimmers offshore rapidly and represent a significant risk to beach users worldwide, including along the northern coast of Prince Edward Island (PEI), Canada. Although many rip currents are ephemeral and/or spatially variable in response to changes in the nearshore bar morphology and wave and tidal forcing, it is possible for rip channels to be geologically contr
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Phillipe Alan Wernette, Chris Houser
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Coastal and Marine Hazards and Resources Program, Pacific Coastal and Marine Science Center

A survey of storm-induced seaward-transport features observed during the 2019 and 2020 hurricane seasons

Hurricanes are known to play a critical role in reshaping coastlines, but often only impacts on the open ocean coast are considered, ignoring seaward-directed forces and responses. The identification of subaerial evidence for storm-induced seaward transport is a critical step towards understanding its impact on coastal resiliency. The visual features, found in the National Oceanic and Atmospheric
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Jin-Si R. Over, Jenna A. Brown, Christopher R. Sherwood, Christie Hegermiller, Phillipe Alan Wernette, Andrew C. Ritchie, Jonathan Warrick
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Natural Hazards Mission Area, Coastal and Marine Hazards and Resources Program, Pacific Coastal and Marine Science Center, Woods Hole Coastal and Marine Science Center, Hurricane Delta, Hurricane Dorian, Hurricane Isaias, Hurricane Laura, Hurricanes

Non-USGS Publications**

Wernette, P., C. Houser, A. Evans, and J. Lehner. (2020) Barrier island resiliency and human impacts: Lessons from Hurricane Harvey. Geomorphology 358, 107119.
Wernette, P., J. Lehner, and C. Houser. (2020) What change is ‘real’? A probabilistic approach to accounting for uncertainty in environmental change analysis. Geomorphology, 355, 107083.
Houser, C., J. Lehner, and P. Wernette. (2019) Machine learning analysis of lifeguard flag decisions and recorded rescues. Natural Hazards and Earth System Sciences, 19, 2541-2549.
Houser, C., B. Vlodarchyk, and P. Wernette. (2019) Short Communication: Public Interest in rip currents relative to other natural hazards: Evidence from Google Search data. Natural Hazards, 97, 1395-1405.
Wernette, P., C. Houser, B. Weymer, M.P. Bishop, M. Everett, and B. Reece. (2018) Long-range dependence in framework geology: Asymmetries and implications for barrier island resiliency. Earth Surface Dynamics, 6, 1139-1153.
Weymer, B., M. Everett, P. Wernette, and C. Houser. (2018) Statistical modeling of the long-range-dependent structure of barrier island framework geology and surface geomorphology. Earth Surface Dynamics, 6, 431-450.
Wernette, P., C. Houser, B. Weymer, M.P. Bishop, M. Everett, and B. Reece. (2018) Influence of a spatially complex framework geology on island geomorphology. Marine Geology, 398, 151-162.
Wernette, P., S. Thompson, R. Eyler, H. Taylor, C. Taube, C. Decuir, A. Medlin, and C. Houser. (2018) Defining dunes: Evaluating how dune feature definitions impact dune interpretations from remote sensing. Journal of Coastal Research, 34(6), 1460-1470.
Houser, C., P. Wernette, and B. Weymer. (2018) Scale dependent behavior of the foredune: Implications for barrier island response to storms and sea-level rise. Geomorphology, 303, 362-374.
Houser, C., M.P. Bishop, and P. Wernette. (2017) Short Communication: Multi-scale anisotropy patterns on a barrier island. Geomorphology, 297(15), 153-158.
Wernette, P., A. Shortridge, D. Lusch, and A.F. Arbogast. (2017) Accounting for positional uncertainty in historical shoreline change analysis without ground-reference information. International Journal of Remote Sensing, 38(13), 3906-3922.
Weymer, B., M. Everett, C. Houser, P. Wernette, and P. Barrineau. (2016) Differentiating tidal and groundwater dynamics from barrier island framework geology: Testing the utility of portable multi-frequency EMI profilers. Geophysics, 81(5), E347-E361.
Wernette, P., C. Houser, and M.P. Bishop. (2016) An automated approach for extracting barrier island morphology from digital elevation models. Geomorphology, 262(1), 1-7.
Houser, C., P. Wernette, T. Rentschler, H. Jones, and B. Hammond. (2015) Post-storm beach and dune recovery: Implications for barrier island resilience. Geomorphology, 243, 54-63.
Arbogast, A., M. Luehmann, B. Miller, P. Wernette, K. Adams, J. Waha, G. O’Neil, Y. Tang, J. Boothroyd, C. Babcock, R. Hanson, and A. Young. (2015) Late-Pleistocene paleowinds and aeolian sand mobilization in north-central Lower Michigan. Aeolian Research, 16, 106-116.

**Disclaimer: The views expressed in Non-USGS publications are those of the author and do not represent the views of the USGS, Department of the Interior, or the U.S. Government.

Agisoft Metashape/Photoscan Automated Image Alignment and Error Reduction version 2.0

This repository contains python scripts which automate image alignment and sparse point cloud error reduction in the Agisoft Metashape/Photoscan structure from motion photogrammetry software package using the Agisoft Metashape Python API. The current version of the script (version 2.0) approximates the workflow described in U.S. Geological Survey Open-File Report 2021-1039 (Over et al., 2021). The
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Pacific Coastal and Marine Science Center
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Crowd-Sourcing Coastal Change Data Along Cliffs, Bluffs

Most remote-sensing techniques rely on imagery taken at a specific time and place. What if researchers could crowd-source this data from volunteers...

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Science and Products

*Disclaimer: Listing outside positions with professional scientific organizations on this Staff Profile are for informational purposes only and do not constitute an endorsement of those professional scientific organizations or their activities by the USGS, Department of the Interior, or U.S. Government