Object-based detection of Arctic sea ice and melt ponds using high spatial resolution aerial photographs

Abstract

High resolution aerial photographs used to detect and classify sea ice features can provide accurate physical parameters to refine, validate, and improve climate models. However, manually delineating sea ice and melt ponds is time-consuming and labor-intensive. In this study, an object-based classification algorithm is developed to automatically extract sea ice and melt ponds efficiently from 163 aerial photographs taken during the Chinese National Arctic Research Expedition in summer 2010 (CHINARE 2010) in the Arctic Pacific Sector. The photographs are selected from 599 cloud-free photographs based on their image quality and representativeness in the marginal ice zone (MIZ). The algorithm includes three major steps: (1) the image segmentation groups the neighboring pixels into objects according to the similarity of spectral and textural information; (2) the random forest ensemble classifier distinguishes four general classes: water, general submerged ice (GSI, including melt ponds and submerged ice along ice edges), shadow, and ice/snow; and (3) the polygon neighbor analysis further separates melt ponds and submerged ice from the GSI according to their spatial relationships. The overall classification accuracy for the four general classes is 95.5% based on 178 ground reference objects. Furthermore, the producer's accuracy of 90.8% and user's accuracy of 91.8% are achieved for melt pond detection through 98 independent reference objects. For the 163 photos examined, a total of 19,438 melt ponds larger than 1 m2 are detected, with a pond density of 867.2 km− 2, mean pond size of 32.6 ± 0.03 m2, and mean pond fraction of 0.06 ± 0.006; a total of 42,468 ice floes are detected, with the mean floe size of 173.3 ± 0.1 m2 (majority in 1-30 m2) and mean ice concentration of 46.1 ± 0.5% (ranging from 18.6-98.6%). These results matched well with ship-based visual observations in the MIZ in the same area and time. The method presented in the paper can be applied to data sets of high spatial resolution Arctic sea ice photographs for deriving detailed sea ice concentration, floe size, and melt pond distributions over wider regions, and extracting sea ice physical parameters and their corresponding changes between years.

Department(s)

Geography, Geology, and Planning

Document Type

Article

DOI

https://doi.org/10.1016/j.coldregions.2015.06.014

Keywords

sea ice, melt pond, object-based classification, high spatial resolution imagery

Publication Date

2015

Journal Title

Cold Regions Science and Technology

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