ALLena: Thaw depth measurements of the active layer in the Lena River Delta region from 1998 to 2022, Northeastern Siberia
Active layer thickness (ALT) is one of currently two Essential Climate Variables related directly to permafrost regions as defined by the Global Climate Observing System (GCOS) at the World Meteorological Organization (WMO). ALT is determined as the maximum thaw depth at the end of the thawing season and is being monitored across the Arctic to better understand climate change impacts on landscape, permafrost, hydrology, vegetation, and soil biogeochemistry. In addition, spatially and/or temporally distributed measurements of thaw depth (TD) in the active layer during the thawing period are often recorded during field work in permafrost regions for various research purposes and process understanding. The ALLena dataset presents a compilation of thaw depth (TD) measurements of the active layer in the Lena River Delta region based on published and unpublished field data collected during Russian and joint German-Russian expeditions from 1998 to 2022. The dataset contains data from different areas of the Lena Delta region with clustering in research-intensive areas, e.g. in the vicinity of Research Station Samoylov Island, but also includes sites adjacent to the delta such as Muostakh Island, Bykovsky Peninsula and Tiksi settlement surroundings. The compiled data is distributed across various ecosystems and geomorphological units underlain by different types of deposits and characterized by various relief positions, microtopography, soil and vegetation cover (e.g. active floodplains, polygonal tundra on Holocene Lena Delta terraces, remnants of Yedoma Ice Complex, etc.). TD measurements were conducted as opportunistic single as well as targeted TD transects or grid measurements. For some sites measurements of TD were repeated at the same locations in different years. TD was measured one time per site or several times per site within a few centimeters to several meters to capture small-scale heterogeneity depending on e.g. microcrotopography or vegetation type. This is the main datatable which contains discrete TD values if they were measured one to three times per site. In case of more than three TD values per measurement site, the datatable provides the minimum, maximum, and mean TD as well as the number of measurements that were averaged. For sites with more than three TD measurements per site, separate detailed tables are provided. The dataset presents a highly valuable compilation of TD data over a uniquely large Arctic region and long time span. It might complement the Circumpolar Active Layer Monitoring (CALM) program and other published ALT and TD datasets for other permafrost regions and may be used for e.g. parametrization or validation of permafrost modelling and remote sensing studies.
ALLena: Thaw depth measurements of the active layer in the Lena River Delta region from 1998 to 2022, Northeastern Siberia, detailed table 'Detailed_data_TD_Veremeeva'
Active layer thickness (ALT) is one of currently two Essential Climate Variables related directly to permafrost regions as defined by the Global Climate Observing System (GCOS) at the World Meteorological Organization (WMO). ALT is determined as the maximum thaw depth at the end of the thawing season and is being monitored across the Arctic to better understand climate change impacts on landscape, permafrost, hydrology, vegetation, and soil biogeochemistry. In addition, spatially and/or temporally distributed measurements of thaw depth (TD) in the active layer during the thawing period are often recorded during field work in permafrost regions for various research purposes and process understanding. The ALLena dataset presents a compilation of thaw depth (TD) measurements of the active layer in the Lena River Delta region based on published and unpublished field data collected during Russian and joint German-Russian expeditions from 1998 to 2022. The dataset contains data from different areas of the Lena Delta region with clustering in research-intensive areas, e.g. in the vicinity of Research Station Samoylov Island, but also includes sites adjacent to the delta such as Muostakh Island, Bykovsky Peninsula and Tiksi settlement surroundings. The compiled data is distributed across various ecosystems and geomorphological units underlain by different types of deposits and characterized by various relief positions, microtopography, soil and vegetation cover (e.g. active floodplains, polygonal tundra on Holocene Lena Delta terraces, remnants of Yedoma Ice Complex, etc.). TD measurements were conducted as opportunistic single as well as targeted TD transects or grid measurements. For some sites measurements of TD were repeated at the same locations in different years. TD was measured one time per site or several times per site within a few centimeters to several meters to capture small-scale heterogeneity depending on e.g. microcrotopography or vegetation type. The main datatable (doi:10.1594/PANGAEA.973813) contains discrete TD values if they were measured one to three times per site. In case of more than three TD values per measurement site, the main datatable provides the minimum, maximum, and mean TD as well as the number of measurements that were averaged. For sites with more than three TD measurements per site, separate detailed tables like this one are provided. The dataset presents a highly valuable compilation of TD data over a uniquely large Arctic region and long time span. It might complement the Circumpolar Active Layer Monitoring (CALM) program and other published ALT and TD datasets for other permafrost regions and may be used for e.g. parametrization or validation of permafrost modelling and remote sensing studies.
ALLena: Thaw depth measurements of the active layer in the Lena River Delta region from 1998 to 2022, Northeastern Siberia, detailed table 'Detailed_data_TD_Ulrich'
ALLena: Thaw depth measurements of the active layer in the Lena River Delta region from 1998 to 2022, Northeastern Siberia, detailed table 'Detailed_data_TD_Schwamborn'
ALLena: Thaw depth measurements of the active layer in the Lena River Delta region from 1998 to 2022, Northeastern Siberia, detailed table 'Detailed_data_TD_Nitzbon'
ALLena: Thaw depth measurements of the active layer in the Lena River Delta region from 1998 to 2022, Northeastern Siberia, detailed table 'Detailed_data_TD_Fedorov-Davydov'
ALLena: Thaw depth measurements of the active layer in the Lena River Delta region from 1998 to 2022, Northeastern Siberia, detailed table 'Detailed_data_TD_Evgrafova'
ALLena: Thaw depth measurements of the active layer in the Lena River Delta region from 1998 to 2022, Northeastern Siberia, detailed table 'Detailed_data_TD_Boike'
ALLena: Thaw depth measurements of the active layer in the Lena River Delta region from 1998 to 2022, Northeastern Siberia, detailed table 'Detailed_data_TD_Minke'
ALLena: Thaw depth measurements of the active layer in the Lena River Delta region from 1998 to 2022, Northeastern Siberia, detailed table 'Detailed_data_TD_Grosse'
ALLena: Thaw depth measurements of the active layer in the Lena River Delta region from 1998 to 2022, Northeastern Siberia, detailed table 'Detailed_data_TD_Lashchinskiy'
Database of Ice-Rich Yedoma Permafrost (IRYP)
We synthesized data on the circum-Arctic and sub-Arctic distribution and thickness of Yedoma for compiling a preliminary circum-polar Yedoma map. For compiling this map, we used maps of the previous Yedoma coverage estimates, included the digitized areas from Grosse et al. (2013) as well as extracted areas of potential Yedoma distribution from additional surface geological and Quaternary geological maps: 1.: 1:500,000: Q-51-V,G; P-51-A,B; P-52-A,B; Q-52-V,G; P-52-V,G; Q-51-A,B; R-51-V,G; R-52-V,G; R-52-A,B 2.: 1:1,000,000: P-50-51; P-52-53; P-58-59; Q-42-43; Q-44-45; Q-50-51; Q-52-53; Q-54-55; Q-56-57; Q-58-59; Q-60-1; R-(40)-42; R-43-(45); R-(45)-47; R-48-(50); R-51; R-53-(55); R-(55)-57; R-58-(60); S-44-46; S-47-49; S-50-52; S-53-55 3.: 1:2,500,000: Quaternary map of the territory of Russian Federation 4.: Alaska Permafrost Map The digitalization was done using GIS techniques (ArcGIS) and vectorization of raster Images (Adobe Photoshop and Illustrator). Data on Yedoma thickness are obtained from boreholes and exposures reported in the scientific literature. The map and database are still preliminary and will have to undergo a technical and scientific vetting and review process. In their current form, we included a range of attributes for Yedoma area polygons based on lithological and stratigraphical information from the original source maps as well as a confidence level for our classification of an area as Yedoma (3 stages: confirmed, likely, or uncertain). In its current version, our database includes more than 365 boreholes and exposures and more than 2000 digitized Yedoma areas. We expect that the database will continue to grow. In this preliminary stage, we estimate the Northern Hemisphere Yedoma deposit area to cover approximately 625,000 km². We estimate that 53% of the total Yedoma area today is located in the tundra zone, 47% in the taiga zone. Separated from west to east, 29% of the Yedoma area is found in North America and 71 % in North Asia. The latter include 9% in West Siberia, 11% in Central Siberia, 44% in East Siberia and 7% in Far East Russia. Adding the recent maximum Yedoma region (including all Yedoma uplands, thermokarst lakes and basins, and river valleys) of 1.4 million km² (Strauss et al., 2013, doi:10.1002/2013GL058088) and postulating that Yedoma occupied up to 80% of the adjacent formerly exposed and now flooded Beringia shelves (1.9 million km², down to 125 m below modern sea level, between 105°E - 128°W and >68°N), we assume that the Last Glacial Maximum Yedoma region likely covered more than 3 million km² of Beringia.
Database of Ice-Rich Yedoma Permafrost Version 2 (IRYP v2)
Ice-rich permafrost in the circum-Arctic and sub-Arctic, such as late Pleistocene Yedoma, are especially prone to degradation due to climate change or human activity. When Yedoma deposits thaw, large amounts of frozen organic matter and biogeochemically relevant elements return into current biogeochemical cycles. Building on previous mapping efforts, the objective of this paper is to compile the first digital pan-Arctic Yedoma map and spatial database of Yedoma coverage. Therefore, we 1) synthesized, analyzed, and digitized geological and stratigraphical maps allowing identification of Yedoma occurrence at all available scales, and 2) compiled field data and expert knowledge for creating Yedoma map confidence classes. We used GIS-techniques to vectorize maps and harmonize site information based on expert knowledge. Hence, here we synthesize data on the circum-Arctic and sub-Arctic distribution and thickness of Yedoma for compiling a preliminary circum-polar Yedoma map. To harmonize the different datasets and to avoid merging artifacts, we applied map edge cleaning while merging data from different database layers. For the digitalization and spatial integration, we used Adobe Photoshop CS6 (Version: 13.0 x64), Adobe Illustrator CS6 (Version 16.0.3 x64), Avenza MAPublisher 9.5.4 (Illustrator Plug-In) and ESRI ArcGIS 10.6.1 for Desktop (Advanced License). Generally, we followed workflow of figure 2 of the related publication (IRYP Version 2, Strauss et al 2021, https://doi.org/10.3389/feart.2021.758360). We included a range of attributes for Yedoma areas based on lithological and stratigraphic information from the source maps and assigned three different confidence levels of the presence of Yedoma (confirmed, likely, or uncertain). Using a spatial buffer of 20 km around mapped Yedoma occurrences, we derived an extent of the Yedoma domain. Our result is a vector-based map of the current pan-Arctic Yedoma domain that covers approximately 2,587,000 km², whereas Yedoma deposits are found within 480,000 km² of this region. We estimate that 35% of the total Yedoma area today is located in the tundra zone, and 65% in the taiga zone. With this Yedoma mapping, we outlined the substantial spatial extent of late Pleistocene Yedoma deposits and created a unique pan-Arctic dataset including confidence estimates.
DARTS: Multi-year database of AI detected retrogressive thaw slumps (RTS) in hotspots of the circum-arctic permafrost region - v1.2
This dataset, called DARTS, comprises footprints of active parts of retrogressive thaw slump (RTS) identified and quantified using an automated deep learning approach in RTS hotspots across the Arctic and Subarctic permafrost regions. We utilized multispectral PlanetScope imagery with a spatial resolution of ~3 meters (m), complemented by ArcticDEM (Digital Elevation Model) and derived datasets, including slope, relative elevation, and Landsat-derived change trends. The dataset covers an area of 1.6 million square-kilometers (km²), with at least one coverage between 2021 and 2023, and provides annual coverage for approximately 900,000 km². In several highly active key sites, such as Banks Island, Peel Plateau, and Novaya Zemlya, we extended the data frequency and temporal coverage to 2018-2023. We mapped a total of more than 43,000 individual RTS and ALD, many of them multiple times. We offer two levels of datasets; Level 1: RTS footprints per image with timestamps; and Level 2: annually aggregated RTS footprints. Essential metadata includes image footprints, dataset coverage, timestamps, and model-specific information. To enhance reproducibility and further use, the training labels, processing code, and model checkpoints are publicly available. This version, v1.2, is the second revision of the first openly accessible release. The dataset will be maintained and continuously updated in both spatial and temporal extent. It can be used for mapping and quantifying RTS, analyzing spatio-temporal patterns of RTS dynamics, or serving as input for landscape dynamics models. ### Citation Ingmar Nitze, Konrad Heidler, Nina Nesterova, Jonas Küpper, Emma Schütt, Tobias Hölzer, Sophia Barth, Mark J. Lara, Anna Liljedahl, & Guido Grosse. (2025). DARTS: Multi-year database of AI detected retrogressive thaw slumps (RTS) in hotspots of the circum-arctic permafrost region - v1.2. Arctic Data Center. doi:10.18739/A22B8VD7C. ### Methods __Step 1: Ethical Research Practices__ All data were collected using remote sensing data (satellite imagery), and no local community members were involved at any point in the research process. __Step 2: Data acquisition__ We used PlanetScope multi-spectral satellite imagery as our main data source for extracting RTS footprints, supplemented by relative elevation and slope data from ArcticDEM and Landsat trends from 2000 to 2019 to analyze temporal changes. PlanetScope imagery includes four spectral bands with a ground sampling distance of 3.7 to 4.1 meters and a high revisit frequency of less than one day. We utilized both PlanetScope Scenes (PSScene) and PlanetScope OrthoTiles (PSOrthoTile). Our dataset covers approximately 1.64 million km², focusing on RTS hotspots across the Arctic from 2021 to 2023, with core regions (900,000 km²) receiving annual coverage from 2021 to 2023. For all Planet Scenes we calculated Normalized Difference Vegetation Index (NDVI) for enhanced vegetation information. The final input dataset consists of ten layers, totaling 17,169 images covering about 8.35 million km² of imagery, with the majority from our key period of 2021 to 2023. __Step 3:__ Methods for our deep learning dataset processing are divided into training and inference workflows, both sharing common preprocessing steps. We utilized PyTorch and the Segmentation Models for PyTorch library to train and predict retrogressive thaw slumps. The DARTS dataset was created using our custom thaw-slump-segmentation package, which is available on github (https://github.com/initze/thaw-slump-segmentation). __Step 4: DL (deep learning) Model Setup__ The model training process involved several iterative steps, including label creation, data preprocessing, training with internal validation, hyperparameter tuning, and model ensembling. Labeling followed procedures from Nitze et al. (2021), primarily using PlanetScope imagery and auxiliary datasets to distinguish RTS from stable ground. We performed six iterations of labeling, focusing on areas with active RTS and gradually incorporating more negative samples to reduce false positives. Nitze, I., Heidler, K., Barth, S., &amp; Grosse, G. (2021). Developing and Testing a Deep Learning Approach for Mapping Retrogressive Thaw Slumps. Remote Sensing, 13(21). https://doi.org/10.3390/rs13214294 __Step 5: Model Training__ Training was based on the Unet++ architecture with a resnet34 backbone, refined through six cycles. We optimized parameters such as learning rates and tile sizes, finding a tile size of 1024 pixels most effective. Extensive data augmentation techniques were applied using the albumentations library. The final training dataset included 3749 features from 198 image scenes. __Step 6: Model Data Ensembling__ To address false positives, we created an ensemble model by averaging output probabilities and binarizing them at a threshold of 0.5. Post-processing involved removing detected RTS objects near image borders and those smaller than 32 pixels, as well as filtering polygons overlapping significantly with water bodies using ESRI land cover data. __Step 7: Validation__ Validation of our dataset involved multiple strategies at different stages. Initially, we conducted internal model performance validation, focusing on pixel-level accuracy to assess the overlap of feature polygons during deep learning model training against a hold-out validation subset. This provided estimates of the model's performance before ensembling and cleaning. The dataset validation involved (1) the visual confirmation of detected features in Northwest (NW) Siberia, (2) the comparison of the output data against an independent internal RTS polygon dataset, and the comparison with comparing them to manually digitized features and the RTS location dataset by (3) Lewkowicz et al. (2024) and (4) van der Sluijs &amp; Kokelj (2023). This validation was performed at the object level, where we checked intersections between our dataset and reference datasets due to a lack of comprehensive ground truth data. AI Model Performance Validation: During model training, we evaluated pixel-wise accuracy using standard metrics such as precision, recall, F1 score, and Intersection over Union (IoU) against a hold-out validation subset. This validation was specific to the initial deep learning models. Dataset Validation: For manual confirmation in NW Siberia, we assessed 3,668 features over an area of approximately 105,000 km² using various satellite basemaps. Each feature was classified as true positive, false positive, or uncertain. In Central and Eastern Siberia, we analyzed model performance across eight regions by creating reference datasets for RTS polygons to estimate precision, recall, and F1 scores for the years 2021 to 2023. We also compared DARTS with published RTS point locations in five regions of NW Canada (Lewkowicz, 2024). Furthermore, we compared DARTS with published RTS polygon footprints in two regions of NW Canada (van der Sluijs and Kokelj, 2023).
40-year monthly mean AVHRR GAC Land Surface Temperature data for the Pan-Arctic region (Pan-Arctic AVHRR LST)
This data collection contains 40 years of monthly mean daytime AVHRR Global Area Coverage (GAC) land surface temperature (LST) data. This dataset covers the 1981-2020 perdiod and covers the whole globe above 50° latitude. The spatial extent of the dataset is the following : (-180°, 50°N) ; (180°, 90°N) The LST monthly mean composites are computed from daily daytime LST files, that were generated from the EUMETSAT AVHRR PyGAC FDR (https://navigator.eumetsat.int/product/EO:EUM:DAT:0862) as described in Dupuis et al. (2024). These daily LST files contain only cloud-free pixels and pixels with sufficient quality regarding satellite zenith angle and error margin from the radiative transfer modelling. The probabilistic cloud mask from the CLARA-A3 (https://navigator.eumetsat.int/product/EO:EUM:DAT:0874) dataset has been used. The LST monthly means do not contain any water masks, as potential users might have different requirements regarding water masks. The dataset has been validated against in situ data from the SURFRAD (https://gml.noaa.gov/grad/surfrad/overview.html), ARM (https://arm.gov/capabilities/observatories/nsa) and KIT (https://www.imk-asf.kit.edu/english/skl_stations.php) networks. AVHRR GAC LST daytime monthly mean composites: daily land surface temperature data are averaged to monthly composites for every afternoon and mid-day satellite (10 different satellites). Satellite identifiers: AVN07 : NOAA 7 AVN09 : NOAA 9 AVN11 : NOAA 11 AVN14 : NOAA 14 AVN16 : NOAA 16 AVN18 : NOAA 18 AVN19 : NOAA 19 AVMEA : MetOp-A AVMEB : MetOp-B AVMEC : MetOp-C The LST files are available as a gridded product in the WGS84 coordinate reference system and are distributed as NetCDF files. The dataset covers the pan-Arctic region (-180°, 90°, 180°, 50°) at a spatial resolution of 0.05°x0.05° pixel size. Each *.nc file contains one variable (LST) with three dimensions (time, lat, lon) and five coordinates (time, lat, lon, band and spatial_ref). ###Citation Dupuis, S., Wunderle, S., & Göttsche, F. (2024). 40-year monthly mean AVHRR GAC Land Surface Temperature data for the Pan-Arctic region (Pan-Arctic AVHRR LST) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.13361744 Dupuis, S., Göttsche, F.-M., & Wunderle, S. (2024). Temporal stability of a new 40-year daily AVHRR land surface temperature dataset for the pan-Arctic region. The Cryosphere, 18(12), 6027-6059. https://doi.org/10.5194/tc-18-6027-2024 ###Information about funding sources that supported the collection of the data: Dr. Alfred Bretscher Fund (University of Bern)
DARTS: Multi-year database of AI detected retrogressive thaw slumps (RTS) and active layer detachment slides (ALD) in hotspots of the circum-arctic permafrost region - v1
This dataset, called DARTS, comprises footprints of retrogressive thaw slump (RTS) and active layer detachments slides (ALD) identified and quantified using an automated deep learning approach in RTS hotspots across the Arctic and Subarctic permafrost regions. We utilized multispectral PlanetScope imagery with a spatial resolution of 3 meters (m), complemented by ArcticDEM (Digital Elevation Models) and derived datasets, including slope, relative elevation, and Landsat-derived change trends. The dataset covers an area of 1.6 million square-kilometers (km²), with at least one coverage between 2021 and 2023, and provides annual coverage for approximately 900,000 km². In several highly active key sites, such as Banks Island, Peel Plateau, and Novaya Zemlya, we extended the data frequency and temporal coverage to 2018-2023. We mapped a total of more than 43,000 individual RTS and ALD, many of them multiple times. We offer two levels of datasets; Level 1: RTS footprints per image with timestamps; and Level 2: annually aggregated RTS footprints. Essential metadata includes image footprints, dataset coverage, timestamps, and model-specific information. To enhance reproducibility and further use, the training labels, processing code, and model checkpoints are publicly available. This version, v1, is the first openly accessible release. The dataset will be maintained and continuously updated in both spatial and temporal extent. It can be used for mapping and quantifying RTS, analyzing spatio-temporal patterns of RTS dynamics, or serving as input for landscape dynamics models. ### __Citation__ __In order to use these data, you must cite this data set with the following citation:__ Ingmar Nitze, Konrad Heidler, Nina Nesterova, Jonas Küpper, Emma Schütt, Tobias Hölzer, Sophia Barth, Mark J Lara, Anna Liljedahl, & Guido Grosse. (2024). DARTS: Multi-year database of AI detected retrogressive thaw slumps (RTS) and active layer detachment slides (ALD) in hotspots of the circum-arctic permafrost region - v1. Arctic Data Center. doi:10.18739/A2RR1PP44.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Cape Blossom, Alaska (7 cm)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 29.12 pts/m² and 10.33 pts/m², respectively. This dataset covers approximately 9.62 km² of Cape Blossom in Alaska, with all images collected on 25 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Cape Blossom, Alaska (10 cm)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 7.55 pts/m² and 2.86 pts/m², respectively. This dataset covers approximately 23.28 km² of Cape Blossom in Alaska, with all images collected on 27 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Cape Blossom, Alaska (20 cm)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 3.66 pts/m² and 1.34 pts/m², respectively. This dataset covers approximately 25.22 km² of Cape Blossom in Alaska, with all images collected on 25 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Trail Valley Creek, Canada (subset 08)
As part of the MOSES airborne campaign led by the Alfred Wegener Institute in 2018, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 11.92 px/m³ and 3.96 px/m³ respectively. This dataset covers approximately 15.46 km² of Trail Valley Creek, Canada, with all images collected on 22 August 2018. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Trail Valley Creek, Canada (subset 07)
As part of the MOSES airborne campaign led by the Alfred Wegener Institute in 2018, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 14.12 px/m³ and 5.05 px/m³ respectively. This dataset covers approximately 21.41 km² of Trail Valley Creek, Canada, with all images collected on 22 August 2018. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Trail Valley Creek, Canada (subset 06)
As part of the MOSES airborne campaign led by the Alfred Wegener Institute in 2018, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 13.96 px/m³ and 4.74 px/m³ respectively. This dataset covers approximately 21.40 km² of Trail Valley Creek, Canada, with all images collected on 22 August 2018. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Trail Valley Creek, Canada (subset 05)
As part of the MOSES airborne campaign led by the Alfred Wegener Institute in 2018, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 14.48 px/m³ and 4.74 px/m³ respectively. This dataset covers approximately 21.39 km² of Trail Valley Creek, Canada, with all images collected on 22 August 2018. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Trail Valley Creek, Canada (subset 04)
As part of the MOSES airborne campaign led by the Alfred Wegener Institute in 2018, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 15.63 px/m³ and 4.98 px/m³ respectively. This dataset covers approximately 21.38 km² of Trail Valley Creek, Canada, with all images collected on 22 August 2018. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Trail Valley Creek, Canada (subset 03)
As part of the MOSES airborne campaign led by the Alfred Wegener Institute in 2018, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 15.68 px/m³ and 4.35 px/m³ respectively. This dataset covers approximately 21.37 km² of Trail Valley Creek, Canada, with all images collected on 22 August 2018. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Trail Valley Creek, Canada (subset 02)
As part of the MOSES airborne campaign led by the Alfred Wegener Institute in 2018, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 16.19 px/m³ and 5.15 px/m³ respectively. This dataset covers approximately 21.36 km² of Trail Valley Creek, Canada, with all images collected on 22 August 2018. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Trail Valley Creek, Canada (subset 01)
As part of the MOSES airborne campaign led by the Alfred Wegener Institute in 2018, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 12.36 pts/m³ and 4.28 pts/m³ respectively. This dataset covers approximately 17.35 km² of Trail Valley Creek, Canada, with all images collected on 22 August 2018. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Southern Noatak Valley, Alaska (subset 04)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 2.79 pts/m² and 1.03 pts/m², respectively. This dataset covers approximately 31.07 km² of the southern Noatak Valley in Alaska, with all images collected on 2 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Southern Noatak Valley, Alaska (subset 03)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 3.22 pts/m² and 1.15 pts/m², respectively. This dataset covers approximately 31.21 km² of the southern Noatak Valley in Alaska, with all images collected on 2 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Southern Noatak Valley, Alaska (subset 02)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 3.48 pts/m² and 1.26 pts/m², respectively. This dataset covers approximately 31.00 km² of the southern Noatak Valley in Alaska, with all images collected on 2 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Southern Noatak Valley, Alaska (subset 01)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 3.56 pts/m² and 1.27 pts/m², respectively. This dataset covers approximately 27.43 km² of the southern Noatak Valley in Alaska, with all images collected on 2 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Southern Baldwin Peninsula, Alaska (subset 03)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 3.88 pts/m² and 1.39 pts/m², respectively. This dataset covers approximately 25.53 km² of the southern Baldwin Peninsula in Alaska, with all images collected on 28 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Southern Baldwin Peninsula, Alaska (subset 02)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 3.96 pts/m² and 1.45 pts/m², respectively. This dataset covers approximately 33.81 km² of the southern Baldwin Peninsula in Alaska, with all images collected on 28 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Southern Baldwin Peninsula, Alaska (subset 01)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 4.74 pts/m² and 1.70 pts/m², respectively. This dataset covers approximately 26.33 km² of the southern Baldwin Peninsula in Alaska, with all images collected on 28 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Teshekpuk Lake, Alaska (subset 11)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 13.20 pts/m² and 5.39 pts/m², respectively. This dataset covers approximately 10.19 km² north of Teshekpuk Lake in Alaska, with all images collected on 23 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Teshekpuk Lake, Alaska (subset 10)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 17.29 pts/m² and 6.46 pts/m², respectively. This dataset covers approximately 9.74 km² north of Teshekpuk Lake in Alaska, with all images collected on 23 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Teshekpuk Lake, Alaska (subset 09)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 16.79 pts/m² and 6.45 pts/m², respectively. This dataset covers approximately 10.73 km² north of Teshekpuk Lake in Alaska, with all images collected on 23 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Teshekpuk Lake, Alaska (subset 08)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 19.40 pts/m² and 7.25 pts/m², respectively. This dataset covers approximately 10.06 km² north of Teshekpuk Lake in Alaska, with all images collected on 23 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Teshekpuk Lake, Alaska (subset 07)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 19.66 pts/m² and 7.26 pts/m², respectively. This dataset covers approximately 9.94 km² north of Teshekpuk Lake in Alaska, with all images collected on 23 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Teshekpuk Lake, Alaska (subset 06)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 19.09 pts/m² and 6.52 pts/m², respectively. This dataset covers approximately 8.91 km² north of Teshekpuk Lake in Alaska, with all images collected on 23 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Teshekpuk Lake, Alaska (subset 05)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 20.17 pts/m² and 7.11 pts/m², respectively. This dataset covers approximately 8.63 km² north of Teshekpuk Lake in Alaska, with all images collected on 23 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Teshekpuk Lake, Alaska (subset 04)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 17.09 pts/m² and 6.12 pts/m², respectively. This dataset covers approximately 9.29 km² north of Teshekpuk Lake in Alaska, with all images collected on 23 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Teshekpuk Lake, Alaska (subset 03)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 15.65 pts/m² and 5.96 pts/m², respectively. This dataset covers approximately 12.64 km² north of Teshekpuk Lake in Alaska, with all images collected on 23 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Teshekpuk Lake, Alaska (subset 02)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 18.55 pts/m² and 6.54 pts/m², respectively. This dataset covers approximately 11.20 km² north of Teshekpuk Lake in Alaska, with all images collected on 23 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Teshekpuk Lake, Alaska (subset 01)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 15.83 pts/m² and 5.73 pts/m², respectively. This dataset covers approximately 6.35 km² north of Teshekpuk Lake in Alaska, with all images collected on 23 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Noatak Valley, Alaska (subset 04)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 23.83 pts/m² and 8.42 pts/m², respectively. This dataset covers approximately 13.36 km² of the northern Noatak Valley in Alaska, with all images collected on 2 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Noatak Valley, Alaska (subset 03)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 27.18 pts/m² and 9.22 pts/m², respectively. This dataset covers approximately 12.96 km² of the northern Noatak Valley in Alaska, with all images collected on 2 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Noatak Valley, Alaska (subset 02)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 27.10 pts/m² and 9.15 pts/m², respectively. This dataset covers approximately 12.56 km² of the northern Noatak Valley in Alaska, with all images collected on 2 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Noatak Valley, Alaska (subset 01)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 28.77 pts/m² and 9.89 pts/m², respectively. This dataset covers approximately 12.16 km² of the northern Noatak Valley in Alaska, with all images collected on 2 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Kitluk Coast, Alaska (subset 04)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 9.45 pts/m² and 3.14 pts/m², respectively. This dataset covers approximately 25.29 km² of the Kitluk Coast the northern Seward Peninsula in Alaska, with all images collected on 9 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Kitluk Coast, Alaska (subset 03)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 9.80 pts/m² and 3.26 pts/m², respectively. This dataset covers approximately 25.83 km² of the Kitluk Coast the northern Seward Peninsula in Alaska, with all images collected on 9 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Kitluk Coast, Alaska (subset 02)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 9.70 pts/m² and 3.22 pts/m², respectively. This dataset covers approximately 24.60 km² of the Kitluk Coast the northern Seward Peninsula in Alaska, with all images collected on 9 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Northern Kitluk Coast, Alaska (subset 01)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 8.58 pts/m² and 3.06 pts/m², respectively. This dataset covers approximately 21.95 km² of the Kitluk Coast the northern Seward Peninsula in Alaska, with all images collected on 9 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Kougarok River, Alaska (subset 05)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 6.56 pts/m² and 2.51 pts/m², respectively. This dataset covers approximately 21.89 km² around the Kougarok River in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Kougarok River, Alaska (subset 04)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 8.11 pts/m² and 2.92 pts/m², respectively. This dataset covers approximately 21.89 km² around the Kougarok River in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Kougarok River, Alaska (subset 03)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 7.85 pts/m² and 2.66 pts/m², respectively. This dataset covers approximately 21.89 km² around the Kougarok River in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Kougarok River, Alaska (subset 02)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 9.21 pts/m² and 3.17 pts/m², respectively. This dataset covers approximately 21.89 km² around the Kougarok River in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Kougarok River, Alaska (subset 01)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 9.16 pts/m² and 3.14 pts/m², respectively. This dataset covers approximately 21.89 km² around the Kougarok River in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Kobuk Delta, Alaska (subset 02)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 2.28 pts/m² and 1.28 pts/m², respectively. This dataset covers approximately 43.74 km² of the Kobuk Delta in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Kobuk Delta, Alaska (subset 01)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 20 cm, as well as photogrammetric point clouds in RGB and NIR at 2.86 pts/m² and 1.10 pts/m², respectively. This dataset covers approximately 40.40 km² of the Kobuk Delta in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Ketik Fire Scar, Alaska (subset 09)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 36.17 pts/m² and 12.62 pts/m², respectively. This dataset covers approximately 6.66 km² of the Ketik fire scar in Alaska, with all images collected on 27 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Ketik Fire Scar, Alaska (subset 08)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 36.11 pts/m² and 12.70 pts/m², respectively. This dataset covers approximately 9.49 km² of the Ketik fire scar in Alaska, with all images collected on 27 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Ketik Fire Scar, Alaska (subset 07)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 37.42 pts/m² and 12.65 pts/m², respectively. This dataset covers approximately 9.39 km² of the Ketik fire scar in Alaska, with all images collected on 27 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Ketik Fire Scar, Alaska (subset 06)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 37.36 pts/m² and 12.48 pts/m², respectively. This dataset covers approximately 9.46 km² of the Ketik fire scar in Alaska, with all images collected on 27 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Ketik Fire Scar, Alaska (subset 05)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 37.02 pts/m² and 11.77 pts/m², respectively. This dataset covers approximately 9.49 km² of the Ketik fire scar in Alaska, with all images collected on 27 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Ketik Fire Scar, Alaska (subset 04)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 35.13 pts/m² and 11.51 pts/m², respectively. This dataset covers approximately 9.60 km² of the Ketik fire scar in Alaska, with all images collected on 27 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Ketik Fire Scar, Alaska (subset 03)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 35.84 pts/m² and 12.29 pts/m², respectively. This dataset covers approximately 9.60 km² of the Ketik fire scar in Alaska, with all images collected on 27 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Ketik Fire Scar, Alaska (subset 02)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 36.63 pts/m² and 12.29 pts/m², respectively. This dataset covers approximately 9.49 km² of the Ketik fire scar in Alaska, with all images collected on 27 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Ketik Fire Scar, Alaska (subset 01)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 34.79 pts/m² and 11.80 pts/m², respectively. This dataset covers approximately 8.17 km² of the Ketik fire scar in Alaska, with all images collected on 27 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Imuruk Volcanic Field, Alaska (subset 05)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 8.33 pts/m² and 3.01 pts/m², respectively. This dataset covers approximately 16.71 km² of the Imuruk volcanic field in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Imuruk Volcanic Field, Alaska (subset 04)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 13.04 pts/m² and 4.48 pts/m², respectively. This dataset covers approximately 16.83 km² of the Imuruk volcanic field in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Imuruk Volcanic Field, Alaska (subset 03)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 12.85 pts/m² and 4.68 pts/m², respectively. This dataset covers approximately 16.94 km² of the Imuruk volcanic field in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Imuruk Volcanic Field, Alaska (subset 02)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 12.08 pts/m² and 4.26 pts/m², respectively. This dataset covers approximately 17.06 km² of the Imuruk volcanic field in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Imuruk Volcanic Field, Alaska (subset 01)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 9.77 pts/m² and 3.56 pts/m², respectively. This dataset covers approximately 17.18 km² of the Imuruk volcanic field in Alaska, with all images collected on 10 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Central North Slope, Alaska (subset 04)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 8.92 pts/m² and 3.11 pts/m², respectively. This dataset covers approximately 14.54 km² of the central North Slope in Alaska, with all images collected on 29 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Central North Slope, Alaska (subset 03)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 8.57 pts/m² and 3.00 pts/m², respectively. This dataset covers approximately 15.08 km² of the central North Slope in Alaska, with all images collected on 29 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Central North Slope, Alaska (subset 02)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 8.78 pts/m² and 3.07 pts/m², respectively. This dataset covers approximately 15.15 km² of the central North Slope in Alaska, with all images collected on 29 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of the Central North Slope, Alaska (subset 01)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 9.00 pts/m² and 3.15 pts/m², respectively. This dataset covers approximately 13.79 km² of the central North Slope in Alaska, with all images collected on 29 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Buckland Fire Scar, Alaska (subset 06)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 39.37 pts/m² and 13.29 pts/m², respectively. This dataset covers approximately 8.53 km² of the Buckland Fire scar in Alaska, with all images collected on 27 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Buckland Fire Scar, Alaska (subset 05)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 38.39 pts/m² and 12.95 pts/m², respectively. This dataset covers approximately 7.77 km² of the Buckland Fire scar in Alaska, with all images collected on 27 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Buckland Fire Scar, Alaska (subset 04)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 39.26 pts/m² and 13.01 pts/m², respectively. This dataset covers approximately 7.00 km² of the Buckland Fire scar in Alaska, with all images collected on 27 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Buckland Fire Scar, Alaska (subset 03)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 36.23 pts/m² and 12.77 pts/m², respectively. This dataset covers approximately 7.69 km² of the Buckland Fire scar in Alaska, with all images collected on 27 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Buckland Fire Scar, Alaska (subset 02)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 35.36 pts/m² and 13.04 pts/m², respectively. This dataset covers approximately 8.25 km² of the Buckland Fire scar in Alaska, with all images collected on 27 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Buckland Fire Scar, Alaska (subset 01)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 32.61 pts/m² and 11.30 pts/m², respectively. This dataset covers approximately 11.58 km² of the Buckland Fire scar in Alaska, with all images collected on 27 June 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Anaktuvuk River Fire Scar (subset 05)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 23.22 pts/m² and 8.44 pts/m², respectively. This dataset covers approximately 6.21 km² of the Anaktuvuk River fire scar in Alaska, with all images collected on 22 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Anaktuvuk River Fire Scar (subset 04)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 24.33 pts/m² and 8.47 pts/m², respectively. This dataset covers approximately 7.76 km² of the Anaktuvuk River fire scar in Alaska, with all images collected on 22 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Anaktuvuk River Fire Scar (subset 03)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 24.91 pts/m² and 8.58 pts/m², respectively. This dataset covers approximately 7.64 km² of the Anaktuvuk River fire scar in Alaska, with all images collected on 22 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Anaktuvuk River Fire Scar (subset 02)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 22.36 pts/m² and 7.52 pts/m², respectively. This dataset covers approximately 7.52 km² of the Anaktuvuk River fire scar in Alaska, with all images collected on 22 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Anaktuvuk River Fire Scar (subset 01)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 24.86 pts/m² and 8.60 pts/m², respectively. This dataset covers approximately 5.81 km² of the Anaktuvuk River fire scar in Alaska, with all images collected on 22 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Meade Fire Scar, Alaska (subset 02)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 9.11 pts/m² and 3.21 pts/m², respectively. This dataset covers approximately 21.10 km² of the Meade fire scar in Alaska, with all images collected on 29 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Meade Fire Scar, Alaska (subset 01)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 9.82 pts/m² and 3.44 pts/m², respectively. This dataset covers approximately 31.63 km² of the Meade fire scar in Alaska, with all images collected on 29 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Lake Basins on the Northern Seward Peninsula, Alaska (West, subset 02)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 8.82 pts/m² and 3.04 pts/m², respectively. This dataset covers approximately 15.38 km² of drained thermokarst lake basins on the northern Seward Peninsula in Alaska, with all images collected on 9 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Lake Basins on the Northern Seward Peninsula, Alaska (West, subset 01)
As part of the Perma-X airborne campaign led by the Alfred Wegener Institute in 2021, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 8.93 pts/m² and 3.18 pts/m², respectively. This dataset covers approximately 18.30 km² of drained thermokarst lake basins on the northern Seward Peninsula in Alaska, with all images collected on 9 July 2021. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Drew Point, Alaska (subset 05)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 8.11 pts/m² and 3.02 pts/m², respectively. This dataset covers approximately 20.51 km² of Drew Point in Alaska, with all images collected on 30 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Drew Point, Alaska (subset 04)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 7.76 pts/m² and 2.95 pts/m², respectively. This dataset covers approximately 22.14 km² of Drew Point in Alaska, with all images collected on 30 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Drew Point, Alaska (subset 03)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 7.85 pts/m² and 2.98 pts/m², respectively. This dataset covers approximately 22.21 km² of Drew Point in Alaska, with all images collected on 30 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Drew Point, Alaska (subset 02)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 8.15 pts/m² and 3.05 pts/m², respectively. This dataset covers approximately 22.27 km² of Drew Point in Alaska, with all images collected on 30 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Drew Point, Alaska (subset 01)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 10 cm, as well as photogrammetric point clouds in RGB and NIR at 8.89 pts/m² and 3.27 pts/m², respectively. This dataset covers approximately 17.38 km² of Drew Point in Alaska, with all images collected on 30 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Cape Simpson, Alaska (subset 05)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 24.98 pts/m² and 9.42 pts/m², respectively. This dataset covers approximately 4,83 km² of Cape Simpson in Alaska, with all images collected on 19 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
Super-high-resolution aerial imagery, digital surface model and 3D point cloud of Cape Simpson, Alaska (subset 04)
As part of the ThawTrend-Air airborne campaign led by the Alfred Wegener Institute in 2019, we collected super-high-resolution multispectral imagery of permafrost landscapes with the Modular Aerial Camera System (MACS), developed by the German Aerospace Center. From these images, we photogrammetrically processed four-band orthophotos (blue, green, red, near-infrared) and digital surface models at a spatial resolution of 7 cm, as well as photogrammetric point clouds in RGB and NIR at 27.34 pts/m² and 9.89 pts/m², respectively. This dataset covers approximately 4.38 km² of Cape Simpson in Alaska, with all images collected on 19 July 2019. This super-high-resolution dataset provides opportunities for generating detailed training datasets of permafrost landform inventories, a baseline for change detection for thermokarst and thermo-erosion processes, and upscaling of field measurements to lower-resolution satellite observations.
GeoTiffs all tiles and virtual mosaic DSM
GeoTiffs all tiles and virtual mosaic Ortho
Maximum coverage of the dataset with at least one coverage between 2018-2023
Footprints of RTS and ALD features, maximum extent aggregated per calendar year
Coverage of individual image scenes, used for the processing of DARTS Level 1 datasets
FEATURES: DARTS_NitzeEtAl_v1-2_features_2018-2023_level1.gpkg
STYLE: DARTS_NitzeEtAl_v1-2_features_2018-2023_layerstyle.sld
COVERAGE: DARTS_NitzeEtAl_v1-2_coverage_2018-2023_level1.parquet
Annual coverage of input images for 2018-2023
Metadata: DARTS_Multi_year_database_of_AI_detected_retrogre.xml
FEATURES: DARTS_NitzeEtAl_v1-2_features_2018-2023_level2.parquet
Maximum coverage of the dataset with at least annual coverage between 2021-2023
FEATURES: DARTS_NitzeEtAl_v1-2_features_2018-2023_level1.parquet
FEATURES: Metadata: DARTS_NitzeEtAl_v1-2_features_2018-2023_level1_description.xlsx
COVERAGE: DARTS_NitzeEtAl_v1-2_coverage_2018-2023_level1.gpkg
FEATURES: DARTS_NitzeEtAl_v1-2_features_2018-2023_level2.gpkg
FEATURES: Metadata: DARTS_NitzeEtAl_v1-2_features_2018-2023_level2_description.xlsx
COVERAGE: DARTS_NitzeEtAl_v1-2_coverage_2018-2023_union.parquet
FEATURES: Metadata: DARTS_Multi_year_database_of_AI_detected.xml
COVERAGE: DARTS_NitzeEtAl_v1-2_coverage_2018-2023_level2.parquet
COVERAGE: DARTS_NitzeEtAl_v1-2_coverage_2018-2023_level2.gpkg
*ZIP containing Shapefiles from doi.pangaea.de/10.1594/PANGAEA.861733:* iryp_data_frames.shp iryp_field_photos.shp iryp_field_sites.shp QG1000k_quaternary_deposits_thickness.shp QG1000k_yedoma_russia.shp QG2500k_yedoma_russia.shp SG500k_yedoma_russia.shp SG7200k_yedoma_alaska.shp western_yukon_loess.shp
Footprints of RTS and ALD features, processed on individual image scenes
photographs of Ice-Rich Yedoma permafrost sites
COVERAGE: DARTS_NitzeEtAl_v1-2_coverage_2021-2023_intersect.gpkg
*ZIP containing feature-layer from doi.pangaea.de/10.1594/PANGAEA.861733:* iryp_data_frames iryp_field_photos iryp_field_sites QG1000k_quaternary_deposits_thickness QG1000k_yedoma_russia QG2500k_yedoma_russia SG500k_yedoma_russia SG7200k_yedoma_alaska western_yukon_loess *Description: * IRYP_yedoma_digitalization.doc *Tables:* QG1000k_genetic_description iryp_field_sites_detail
STYLE: DARTS_NitzeEtAl_v1-2_features_2018-2023_layerstyle.qml
FEATURES: Metadata: DARTS_NitzeEtAl_v1-2_features_2018-2023_level1_description.csv
COVERAGE: DARTS_NitzeEtAl_v1-2_coverage_2021-2023_intersect.parquet
COVERAGE: DARTS_NitzeEtAl_v1-2_coverage_2018-2023_union.gpkg
FEATURES: Metadata: DARTS_NitzeEtAl_v1-2_features_2018-2023_level2_description.csv
Publication this dataset is a supplement to
Strauss J, Laboor S, Schirrmeister L, Fedorov AN, Fortier D, Froese D, Fuchs M, Günther F, Grigoriev M, Harden J, Hugelius G, Jongejans LL, Kanevskiy M, Kholodov A, Kunitsky V, Kraev G, Lozhkin A, Rivkina E, Shur Y, Siegert C, Spektor V, Streletskaya I, Ulrich M, Vartanyan S, Veremeeva A, Anthony KW, Wetterich S, Zimov N and Grosse G (2021) Circum-Arctic Map of the Yedoma Permafrost Domain. Front. Earth Sci. 9:758360. doi: 10.3389/feart.2021.758360