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1. Orthomosaic images and digital surface models at Drew Point, Beaufort Sea Coast, Alaska, 2018 and 2019

   https://doi.org/10.18739/a2hm52m8p  

   Ward_Jones, Melissa; Jones, Benjamin (January 2025, NSF Arctic Data Center)

   To assess coastal erosion dynamics during the 2018 and 2019 open water seasons at Drew Point, Beaufort Sea Coast, Alaska, we generated orthomosaic images and associated digital surface models from 9 unmanned aerial vehicle (UAV) surveys. UAV surveys were collected on 24 July 2018, 29 July 2018, 3 August 2018, 30 September 2018, 2 August 2019, 6 August 2019, 10 August 2019, 12 August 2019 and 15 August 2019. The digital surface models elevations are at relative sea level (2.2 meters (m) higher than local ellipsoid heights) and have been cleaned up (i.e. noise from waves removed) to only include the coast edge, ~125 m inland from the coast and toppled permafrost blocks in front of the bluff edge. 

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2. Transferability of the Deep Learning Mask R-CNN Model for Automated Mapping of Ice-Wedge Polygons in High-Resolution Satellite and UAV Images

   https://doi.org/10.3390/rs12071085  

   Zhang, Weixing; Liljedahl, Anna K.; Kanevskiy, Mikhail; Epstein, Howard E.; Jones, Benjamin M.; Jorgenson, M. Torre; Kent, Kelcy (April 2020, Remote Sensing)

   State-of-the-art deep learning technology has been successfully applied to relatively small selected areas of very high spatial resolution (0.15 and 0.25 m) optical aerial imagery acquired by a fixed-wing aircraft to automatically characterize ice-wedge polygons (IWPs) in the Arctic tundra. However, any mapping of IWPs at regional to continental scales requires images acquired on different sensor platforms (particularly satellite) and a refined understanding of the performance stability of the method across sensor platforms through reliable evaluation assessments. In this study, we examined the transferability of a deep learning Mask Region-Based Convolutional Neural Network (R-CNN) model for mapping IWPs in satellite remote sensing imagery (~0.5 m) covering 272 km2 and unmanned aerial vehicle (UAV) (0.02 m) imagery covering 0.32 km2. Multi-spectral images were obtained from the WorldView-2 satellite sensor and pan-sharpened to ~0.5 m, and a 20 mp CMOS sensor camera onboard a UAV, respectively. The training dataset included 25,489 and 6022 manually delineated IWPs from satellite and fixed-wing aircraft aerial imagery near the Arctic Coastal Plain, northern Alaska. Quantitative assessments showed that individual IWPs were correctly detected at up to 72% and 70%, and delineated at up to 73% and 68% F1 score accuracy levels for satellite and UAV images, respectively. Expert-based qualitative assessments showed that IWPs were correctly detected at good (40–60%) and excellent (80–100%) accuracy levels for satellite and UAV images, respectively, and delineated at excellent (80–100%) level for both images. We found that (1) regardless of spatial resolution and spectral bands, the deep learning Mask R-CNN model effectively mapped IWPs in both remote sensing satellite and UAV images; (2) the model achieved a better accuracy in detection with finer image resolution, such as UAV imagery, yet a better accuracy in delineation with coarser image resolution, such as satellite imagery; (3) increasing the number of training data with different resolutions between the training and actual application imagery does not necessarily result in better performance of the Mask R-CNN in IWPs mapping; (4) and overall, the model underestimates the total number of IWPs particularly in terms of disjoint/incomplete IWPs. 

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3. Using Object-Oriented Classification for Coastal Management in the East Central Coast of Florida: A Quantitative Comparison between UAV, Satellite, and Aerial Data

   https://doi.org/10.3390/drones3030060  

   Yang, Bo; Hawthorne, Timothy L.; Torres, Hannah; Feinman, Michael (September 2019, Drones)

   High resolution mapping of coastal habitats is invaluable for resource inventory, change detection, and inventory of aquaculture applications. However, coastal areas, especially the interior of mangroves, are often difficult to access. An Unmanned Aerial Vehicle (UAV), equipped with a multispectral sensor, affords an opportunity to improve upon satellite imagery for coastal management because of the very high spatial resolution, multispectral capability, and opportunity to collect real-time observations. Despite the recent and rapid development of UAV mapping applications, few articles have quantitatively compared how much improvement there is of UAV multispectral mapping methods compared to more conventional remote sensing data such as satellite imagery. The objective of this paper is to quantitatively demonstrate the improvements of a multispectral UAV mapping technique for higher resolution images used for advanced mapping and assessing coastal land cover. We performed multispectral UAV mapping fieldwork trials over Indian River Lagoon along the central Atlantic coast of Florida. Ground Control Points (GCPs) were collected to generate a rigorous geo-referenced dataset of UAV imagery and support comparison to geo-referenced satellite and aerial imagery. Multi-spectral satellite imagery (Sentinel-2) was also acquired to map land cover for the same region. NDVI and object-oriented classification methods were used for comparison between UAV and satellite mapping capabilities. Compared with aerial images acquired from Florida Department of Environmental Protection, the UAV multi-spectral mapping method used in this study provided advanced information of the physical conditions of the study area, an improved land feature delineation, and a significantly better mapping product than satellite imagery with coarser resolution. The study demonstrates a replicable UAV multi-spectral mapping method useful for study sites that lack high quality data. 

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4. Mapping tundra ecosystem plant functional type cover, height and aboveground biomass in Alaska and northwest Canada using unmanned aerial vehicles, 2018-2019

   https://doi.org/10.18739/A2R785Q5B  

   Orndahl, Kathleen (January 2022, Arctic Data Center)

   Arctic landscapes are rapidly changing with climate warming. Vegetation communities are restructuring, which in turn impacts wildlife, permafrost, carbon cycling and climate feedbacks. Accurately monitoring vegetation change is thus crucial, but notable mismatches in scale occur between current field and satellite-based monitoring. Remote sensing from unmanned aerial vehicles (UAVs) has emerged as a bridge between field data and satellite imagery mapping. In this work we assess the viability of using high resolution UAV imagery (RGB and multispectral), along with UAV derived Structure from Motion (SfM) to predict cover, height and above-ground biomass of common Arctic plant functional types (PFTs) across a wide range of vegetation community types. We collected field data and UAV imagery from 45 sites across Alaska and northwest Canada. We then classified UAV imagery by PFT, estimated cover and height, and modeled biomass from UAV-derived volume estimates. Here we present datasets summarizing this data. 

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5. Stordalen Mire habitat classifications map (2014)

   https://doi.org/10.5281/zenodo.15042233  

   Palace, Michael; Herrick, Christina (March 2025, Zenodo)

   Stordalen Mire landcover classifications (including 3 permafrost thaw stages: Palsa, Bog, and Fen), based on WorldView-2 satellite imagery acquired on August 8, 2014.  This image is a GeoTIFF with embedded georeferencing information.   FUNDING: National Aeronautics and Space Administration, Interdisciplinary Science program: From Archaea to the Atmosphere (award # NNX17AK10G) National Science Foundation, Biology Integration Institutes Program: EMERGE Biology Integration Institute (award # 2022070) United States Department of Energy Office of Biological and Environmental Research, Genomic Science Program: The IsoGenie Project (grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440) National Science Foundation, MacroSystems program (grant # EF-1241037) 

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