the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Super-high-resolution aerial imagery datasets of permafrost landscapes in Alaska and northwestern Canada
Ingmar Nitze
Inge Grünberg
Jennika Hammar
Simon Schäffler
Daniel Hein
Matthias Gessner
Tilman Bucher
Jörg Brauchle
Jörg Hartmann
Torsten Sachs
Julia Boike
Abstract. Permafrost landscapes across the Arctic are very susceptible to a warming climate and are currently experiencing rapid change. High-resolution remote sensing datasets present a valuable source of information to better analyze and quantify current permafrost landscape characteristics and impacts of climate change on the environment. In particular, aerial datasets can provide further understanding of permafrost landscapes in transition due to local and widespread thaw. We here present a new dataset of super-high-resolution digital orthophotos, photogrammetric point clouds, and digital surface models that we acquired over permafrost landscapes in northwestern Canada, northern, and western Alaska. The imagery was collected with the Modular Aerial Camera System (MACS) during aerial campaigns conducted by the Alfred Wegener Institute in the summers of 2018, 2019, and 2021. The MACS was specifically developed by the German Aerospace Center (DLR) for operation under challenging light conditions in polar environments. It features cameras in the optical and the near-infrared wavelengths with up to 16 megapixels. We processed the images to four-band (blue – green – red – near-infrared) orthomosaics, digital surface models with spatial resolutions of 7 to 20 cm, and 3D point clouds with point densities up to 44 pts/m3. 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. All three regional dataset collections, along with supporting data, are available via PANGAEA; the DOIs are listed in the Code and Data Availability Section.
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Tabea Rettelbach et al.
Status: open (until 20 Dec 2023)
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RC1: 'Comment on essd-2023-193', Anonymous Referee #1, 30 Sep 2023
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The article is well-written, I have no comments except for one on Figure 3, which is missing the "ng" in the word "processi". Other than that, the authors might consider citing four articles that are valuable from the point of view of thermokarst lakes:
Chen, X., Mu, C., Jia, L., Li, Z., Fan, C., Mu, M., Peng, X., & Wu, X. (2021). High-resolution dataset of thermokarst lakes on the Qinghai-Tibetan Plateau. Earth System Science Data Discussions, 1–23.
Hughes-Allen, L., Bouchard, F., Laurion, I., Séjourné, A., Marlin, C., Hatté, C., Costard, F., Fedorov, A., & Desyatkin, A. (2021). Seasonal patterns in greenhouse gas emissions from thermokarst lakes in Central Yakutia (Eastern Siberia). Limnology and Oceanography, 66(S1), S98–116. https://doi.org/10.1002/lno.11665.
Janiec, P., Nowosad, J., & Zwoliński, Zb. (2023). A machine learning method for Arctic lakes detection in the permafrost areas of Siberia, European Journal of Remote Sensing, 56:1, 2163923, DOI: 10.1080/22797254.2022.2163923.
Wu, Y., Duguay, C. R., & Xu, L. (2021). Assessment of machine learning classifiers for global lake ice cover map ping from MODIS TOA reflectance data. Remote Sensing of Environment, 253, 112206. https://doi.org/10.1016/j.rse.2020.112206.Citation: https://doi.org/10.5194/essd-2023-193-RC1 -
RC2: 'Comment on essd-2023-193', Anonymous Referee #2, 16 Nov 2023
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General comments:
This paper describes super-high-resolution aerial imagery datasets of permafrost landscapes in Alaska and northwestern Canada. To the best of our knowledge, acquiring aerial remote sensing imagery involves a substantial investment of human and financial resources. Consequently, the diverse datasets provided by this study offer robust support for a multitude of research endeavors. The authors have comprehensively expounded on various aspects, including flight design, data preprocessing, product generation, and product release, effectively showcasing intricate procedural details to the readers. The paper exhibits a well-structured format, clear logic, and authentic English expression, rendering it a high-quality scientific contribution. Nonetheless, a few queries and suggestions persist, and I would greatly appreciate it if the authors could address them.
Specific comments:
In the Abstract, the authors describe parameters such as spatial resolution and point cloud density of the generated datasets. However, there is no mention of an overview of the dataset size and specific product accuracy. It is recommended that the authors include a brief description of the product quantity (e.g., the total number of orthophotos and the number of point cloud datasets) as well as the product quality (e.g., geometric errors, visual quality of the images, etc.) to provide readers with a more intuitive presentation.
Page 4, Figure 1. The black lines in the graph appear to be somewhat irregular and contain breakpoints. Could the authors explain the significance of designing flight paths in this manner? Additionally, what are the factors that lead to interruptions in the flight route?
Page 6, lines 146-148. The authors mention that rainfall may affect the state of water bodies and the local hydrological conditions. Did the authors take into consideration the characteristics of rainfall when designing the flight paths?
Page 6, line 152. The authors mention that the MACS sensor is specifically designed for the tough environment of the Arctic region. What distinguishes this device from typical equipment? While the author has provided references, it is recommended to briefly describe in the main text the reasons for the suitability of this device for the Arctic region.
Page 8, lines 182-184. The authors have only provided grid-stitched data and have not presented strip-stitched data. Based on my experience, stitching strip data from UAV or manned-aircraft flights can be more challenging than grid data, and it often results in significant missing when using automated stitching software like Pix4D. Did the authors encounter this issue during data processing? If so, have you undertaken any specific measures to address it?
Page 9, line 200. What specific aspects are included in the “cleaning operations”? Were these operations carried out manually or automatically using software or programs?
Page 9, line 206. In the flight experiment, RGB and NIR band data were collected. Are the DSNU parameters used consistent for different bands? What determines the choice of these parameters?
Page 9, line 209. I would like to express my significant concern: The authors have decomposed the original RGB images into three bands. Can each of these bands quantitatively reflect the radiometric information of the Earth’s surface, or are these band values relative? If it is the latter case, the application scenarios for the “multispectral” data obtained by the authors will be greatly limited, perhaps only supporting qualitative research rather than quantitative research. In my experience, obtaining accurate surface reflectance information requires the use of ground-based calibration panels, which seems to be lacking in this study. Additionally, if possible, please provide the central wavelengths and full-width half-maximum (FWHM) information for the R/G/B/NIR bands.
Page 10, Figure 3. In the image fusion process, what method was used for blending overlapping areas of images? (e.g., “blending”, “averaging”, etc.)
Page 12, line 239. The authors mention creating multiple subprojects, but was color correction and geometric correction applied to the orthophotos generated from these subprojects to facilitate their subsequent applications by users? In other words, are the images ready for use without any additional processing, or do they require special treatment?
Page 15, line 334. When the author standardized the spatial resolution of the images, which upscaling algorithm was used for the data with higher spatial resolution? Different upscaling algorithms may be suitable for different image data types.
Page 18, Figure 7. There appear to be horizontal stripes in the stitched image. What is the reason behind these stripes? The spacing between these stripes seems regular and not consistent with the explanation given in section 5.2, “Changing illumination”. Is there a method to remove these stripes?
Page 24, line 419. The statement may not be accurate, as there could be inherent errors associated with onboard GPS positioning itself.
Technical corrections:
Page 2, lines 29-32. The sentence “In addition, …, in the permafrost region.” appears somewhat lengthy. It is recommended to split it into two sentences to clarify the cause-and-effect relationship.
Page 5, line 98. “The mean annual air temperatures 1990-2020 were …” should be “The mean annual air temperatures for 1990-2020 were …”.
Page 6, line 132. In the sentence “50 to 90% permafrost coverage”: The expression “50” is not properly formatted and should be written as “50%” to avoid potential ambiguity. “50” and “50%” represent two different numerical values.
Page 7, Figure 2. In the title: “… the two right sensors the RGB …” should be “… the two right sensors are the RGB …”.
Page 12, line 261. Where is Sec. A? Appendix?
Page 13, line 286. The order of letters within the parentheses is incorrect. It should be (B-G-R-NIR) instead of the current sequence.
Page 19. The page number obstructs the main text.
Page 23, Figure 11. In the title: The numbering of subfigures is incorrect. It should be (a) and (b), (c) and (d)...
Page 24, lines 410-411. “where” should be “were”.
Page 35. The page number obstructs the main text.
Citation: https://doi.org/10.5194/essd-2023-193-RC2
Tabea Rettelbach et al.
Data sets
Aerial imagery datasets of permafrost landscapes in Alaska and northwestern Canada acquired by the Modular Aerial Camera System Tabea Rettelbach, Ingmar Nitze, Inge Grünberg, Jennika Hammar, Simon Schäffler, Daniel Hein, Matthias Gessner, Tilman Bucher, Jörg Brauchle, Jörg Hartmann, Torsten Sachs, Julia Boike, and Guido Grosse https://doi.pangaea.de/10.1594/PANGAEA.961577
Model code and software
MACS Processing Ingmar Nitze and Tabea Rettelbach https://github.com/awi-response/macs_processing
Tabea Rettelbach et al.
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