Multi-Modal and Multi-Temporal Remote Sensing for Infrastructure and Heritage Monitoring at the Birnirk Mounds, Utqiaġvik, Alaska (2023–2025)
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2025
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| _version_ | 1866901492502364160 |
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| author | Rowell, Steven Leggett, Hollie Kuhlke, Olaf |
| author_facet | Rowell, Steven Leggett, Hollie Kuhlke, Olaf |
| contents | <p><strong>Abstract</strong></p> <p>This presentation details a multi-year (2023-2025) monitoring project at the Birnirk National Historic Landmark in Utqiaġvik, Alaska, an area characterized by ice-rich coastal bluffs and significant cultural heritage features currently at risk from rapid permafrost thaw and coastal erosion. Utilizing a multi-modal remote sensing approach, the study integrates aerial LiDAR (RESEPI XT-32M2X), terrestrial LiDAR (Matterport Pro3), aerial photogrammetry (Structure from Motion), and long-wave infrared (LWIR) thermal imaging.</p> <p>The methodology focuses on creating high-resolution 3D datasets to quantify vertical change and shoreline retreat through Difference of DEMs (DoD) and M3C2 point-cloud distance analysis. A key finding highlights the utility of LWIR as a qualitative cueing layer to identify subsurface drainage and ice-wedge collapse pathways before physical failure is visible. Furthermore, the project explores the use of "GeoAI" (ML-assisted geospatial analysis) for early-warning risk targeting of heritage sites and community infrastructure. Data products, including tiled point clouds and time-series rasters, are being integrated into the Defense Resiliency Platform (DRP) and formatted for compatibility with Arctic observing networks such as CALM and AON. Beyond scientific monitoring, the project aims to utilize these digital twins for immersive storytelling and digital humanities applications, including VR environments with spatial audio, to document and preserve the memory of these fragile landscapes.</p> <p><strong>Keywords: </strong>Permafrost thaw, Remote Sensing, LiDAR, Birnirk Mounds, Arctic Heritage, GeoAI, Coastal Erosion, Utqiaġvik.</p> <p><strong>Authors</strong></p> <p>Steven Rowell (Project Lead / Geospatial Analysis), Minneapolis College of Art & Design (MCAD) <br>Olaf Kuhlke (Principal Investigator), Minneapolis College of Art & Design (MCAD) <br>Hollie Leggett (Spatial Programmer), Minneapolis College of Art & Design (MCAD) </p> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Cold Regions Research and Engineering Laboratory (ERDC-CRREL) under Contract No. W913E522C0001 and conducted by MCAD as part of the Defense Resiliency Platform (DRP). Partner institutions include UND, University of Minnesota Medical School, Virginia Tech, Stony Brook University, and NCEAS (UC Santa Barbara).</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18790434 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Multi-Modal and Multi-Temporal Remote Sensing for Infrastructure and Heritage Monitoring at the Birnirk Mounds, Utqiaġvik, Alaska (2023–2025) Rowell, Steven Leggett, Hollie Kuhlke, Olaf Permafrost Arctic ecosystem Permafrost ecosystem Alaska Natives/history Remote Sensing Technology Climate Change Climate change impact Digital Twins Photogrammetry Unmanned Aerial Devices geospatial Spatial Analysis Archaeology <p><strong>Abstract</strong></p> <p>This presentation details a multi-year (2023-2025) monitoring project at the Birnirk National Historic Landmark in Utqiaġvik, Alaska, an area characterized by ice-rich coastal bluffs and significant cultural heritage features currently at risk from rapid permafrost thaw and coastal erosion. Utilizing a multi-modal remote sensing approach, the study integrates aerial LiDAR (RESEPI XT-32M2X), terrestrial LiDAR (Matterport Pro3), aerial photogrammetry (Structure from Motion), and long-wave infrared (LWIR) thermal imaging.</p> <p>The methodology focuses on creating high-resolution 3D datasets to quantify vertical change and shoreline retreat through Difference of DEMs (DoD) and M3C2 point-cloud distance analysis. A key finding highlights the utility of LWIR as a qualitative cueing layer to identify subsurface drainage and ice-wedge collapse pathways before physical failure is visible. Furthermore, the project explores the use of "GeoAI" (ML-assisted geospatial analysis) for early-warning risk targeting of heritage sites and community infrastructure. Data products, including tiled point clouds and time-series rasters, are being integrated into the Defense Resiliency Platform (DRP) and formatted for compatibility with Arctic observing networks such as CALM and AON. Beyond scientific monitoring, the project aims to utilize these digital twins for immersive storytelling and digital humanities applications, including VR environments with spatial audio, to document and preserve the memory of these fragile landscapes.</p> <p><strong>Keywords: </strong>Permafrost thaw, Remote Sensing, LiDAR, Birnirk Mounds, Arctic Heritage, GeoAI, Coastal Erosion, Utqiaġvik.</p> <p><strong>Authors</strong></p> <p>Steven Rowell (Project Lead / Geospatial Analysis), Minneapolis College of Art & Design (MCAD) <br>Olaf Kuhlke (Principal Investigator), Minneapolis College of Art & Design (MCAD) <br>Hollie Leggett (Spatial Programmer), Minneapolis College of Art & Design (MCAD) </p> <p><strong>Acknowledgments</strong></p> <p>This work was supported by the Cold Regions Research and Engineering Laboratory (ERDC-CRREL) under Contract No. W913E522C0001 and conducted by MCAD as part of the Defense Resiliency Platform (DRP). Partner institutions include UND, University of Minnesota Medical School, Virginia Tech, Stony Brook University, and NCEAS (UC Santa Barbara).</p> |
| title | Multi-Modal and Multi-Temporal Remote Sensing for Infrastructure and Heritage Monitoring at the Birnirk Mounds, Utqiaġvik, Alaska (2023–2025) |
| topic | Permafrost Arctic ecosystem Permafrost ecosystem Alaska Natives/history Remote Sensing Technology Climate Change Climate change impact Digital Twins Photogrammetry Unmanned Aerial Devices geospatial Spatial Analysis Archaeology |
| url | https://doi.org/10.5281/zenodo.18790434 |