Vol.32, No.04. 2021
Table of Contents
ISSN:1674-9928
CN:31-2050/P
ARTICLE | Oceanography/Sea Ice
Determination of Arctic melt pond fraction and sea ice roughness from Unmanned Aerial Vehicle (UAV) imagery
Correspondence: sujie@ouc.edu.cn
ORCID:
Vol. 29, Issue 3, pp. 181-189 (2018) • DOI
Abstract
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Abstract
Melt ponds on Arctic sea ice are of great significance in the study of the heat balance in the ocean mixed layer, mass and salt balances of Arctic sea ice, and other aspects of the earth-atmosphere system. During the 7th Chinese National Arctic Research Expedition, aerial photographs were taken from an Unmanned Aerial Vehicle over an ice floe in the Canada Basin. Using threshold discrimination and three-dimensional modeling, we estimated a melt pond fraction of 1.63% and a regionally averaged surface roughness of 0.12 for the study area. In view of the particularly foggy environment of the Arctic, aerial images were defogged using an improved dark channel prior based image defog algorithm, especially adapted for the special conditions of sea ice images. An aerial photo mosaic was generated, melt ponds were identified from the mosaic image and melt pond fractions were calculated. Three-dimensional modeling techniques were used to generate a digital elevation model allowing relative elevation and roughness of the sea ice surface to be estimated. Analysis of the relationship between the distributions of melt ponds and sea ice surface roughness shows that melt ponds are smaller on sea ice with higher surface roughness, while broader or deeper melt ponds usually occur in areas where sea ice surface roughness is lower.
Author Address:
1 Physical Oceanography Laboratory/CIMST, Ocean University of China, Qingdao 266100, China;
2 Qingdao National Laboratory for Marine Science and Technology, Qingdao 266100, China;
3 University Corporation for Polar Research, Beijing 100875, China;
4 Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430072, China
2 Qingdao National Laboratory for Marine Science and Technology, Qingdao 266100, China;
3 University Corporation for Polar Research, Beijing 100875, China;
4 Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430072, China
Andreas E L. 1987. A theory for the scalar roughness and the scalar transfer coefficients over snow and sea ice. Bound-Lay Meteorol, 38(1-2): 159-184.
Beckers J F, Renner A H H, Spreen G, et al. 2015. Sea-ice surface roughness estimates from airborne laser scanner and laser altimeter observations in Fram Strait and north of Svalbard. Ann Glaciol, 56(69): 235-244.
Cimoli E, Marcer M, Vandecrux B, et al. 2017. Application of low-cost UASs and digital photogrammetry for high-resolution snow depth mapping in the Arctic. Remote Sensing, 9(11):1144.
Cox G F N, Weeks W F. 1974. Salinity variations in sea ice. J Glaciol, 13(67): 109-120.
Curry J A, Schramm J L, Ebert E E. 1995. Sea ice-albedo climate feedback mechanism. J Climate, 8(2): 240-247.
Dee D P, Uppala S M, Simmons A J, et al. 2011. The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system. Q J Roy Meteor Soc, 137(656): 553-597.
Eicken H, Grenfell T C, Perovich D K, et al. 2004. Hydraulic controls of summer Arctic pack ice albedo. J Geophys Res-Oceans, 109(C8), doi: 10.1029/2003JC001989.
Fetterer F, Untersteiner N. 1998. Observations of melt ponds on Arctic sea ice. J Geophys Res-Oceans, 103(C11): 24821-24835.
He K, Sun J, Tang X. 2011. Single image haze removal using dark channel prior. IEEE T Pattern Anal, 33(12): 2341-2353.
Huang W, Lu P, Lei R, et al. 2016. Melt pond distribution and geometry in high Arctic sea ice derived from aerial investigations. Ann Glaciol, 57(73): 105-118.
Istomina L, Melsheimer C, Huntemann M, et al. 2016. Retrieval of sea ice thickness during melt season from in situ, airborne and satellite imagery//Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 7678-7681.
Li D, Li M. 2014. Research advance and application prospect of unmanned aerial vehicle remote sensing system. Geomatics and Information of Wuhan University, 39(5): 505-513 (in Chinese).
Li X Q, Chen Z, Zhang L T. 2016. 3D model construction of non-metric camera images and its accuracy test. Sci Surv Mapp. 41: 6.
Lu P, Li Z, Lei R, et al. 2011. Aerial observations of melt pond distributions in Arctic summer 2008//International Society of Offshore and Polar Engineers. The Twenty-first International Offshore and Polar Engineering Conference, 948-952.
Nolin A W, Fetterer F M, Scambos T A. 2002. Surface roughness characterizations of sea ice and ice sheets: case studies with MISR data. IEEE T Geosci Remote, 40(7): 1605-1615.
Perovich D K, Grenfell T C, Richter-Menge J A, et al. 2003. Thin and thinner: sea ice mass balance measurements during SHEBA. J Geophys Res-Oceans,108(C3).
Perovich D K, Tucker III W B, Ligett K A. 2002. Aerial observations of the evolution of ice surface conditions during summer. J Geophys Res-Oceans, 107(C10): SHE 24-1-SHE 24-14.
Peterson I K, Prinsenberg S J, Holladay J S. 2008. Observations of sea ice thickness, surface roughness and ice motion in Amundsen Gulf. J Geophys Res-Oceans, 113(C6), doi: 10.1029/2007JC004456.
Rösel A, Kaleschke L, Birnbaum G. 2012. Melt ponds on Arctic sea ice determined from MODIS satellite data using an artificial neural network. The Cryosphere, 6(2): 431-446.
Scharien R K, Yackel J J. 2005. Analysis of surface roughness and morphology of first-year sea ice melt ponds: implications for microwave scattering. IEEE T Geosci Remote, 43(12): 2927-2939.
Tschudi M A, Maslanik J A, Perovich D K. 2008. Derivation of melt pond coverage on Arctic sea ice using modis observations. Remote Sens Environ, 112(5): 2605-2614.
Untersteiner N. 1968. Natural desalination and equilibrium salinity profile of perennial sea ice. J Geophys Res, 73(4): 1251-1257.
Zhang X H, Zhao S L, Chen F T. 2013. The application of Agisoft photoscan in UAV aerial photographic image data processing. Value Engineering, 20: 230-231.
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