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Application of Dynamic Threshold in a Lake Ice Detection Algorithm

Received: 23 July 2018    Accepted: 2 August 2018    Published: 29 August 2018
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Abstract

The traditional method involved in the classification of surface types such as water, ice, and snow rely on thresholds values of spectral properties that are fixed. However, the use of daily fixed thresholds leaves a substantial number of either unclassified and/or misclassified ice and water pixels. In this study, a new dynamic threshold technique is proposed to identify and map lake ice cover in the imagery of GOES-I to P series satellites. In addition, dynamic threshold can be used as an alternative solution to Bidirectional Reflectance Distribution Function (BRDF) models. The technique has been applied using GOES-13 imager data over Lake Michigan, one of five of the Great Lakes. Nine scenes based on an hourly acquisition of a single day are used to visually sample water and ice pixels. A threshold for the visible (0.62 µm) and the reflective component of the mid-infrared (3.9 µm) is determined for each scene by the intersection of the probability distributions of the water and ice samples. The thresholds are used as decision thresholds in a binary test algorithm applied on a per-pixel basis. Both fixed threshold (single scene) and dynamic thresholds (multiple scenes) have been compared. Dynamic threshold shows a significant gain in classified pixels over fixed threshold. A preliminary quantitative assessment is introduced to evaluate the algorithm’s performance using sensitivity and specificity testing. The classification results show a sensitivity of 98% when delineating thick ice and water and 87% when delineating thick/thin ice and water. Implementing a dynamic threshold, can be used in constructing ice maps in applications that benefit from high temporal resolution imagery.

Published in American Journal of Remote Sensing (Volume 6, Issue 2)
DOI 10.11648/j.ajrs.20180602.12
Page(s) 64-73
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

Lake Ice Concentration, Dynamic Threshold, GOES Imager, Remote Sensing, Shortwave Infrared, Snow Index, Geographical Information System (GIS)

References
[1] Nazari, R., Khanbilvardi, R., and Cryosphere, N. C. 2011. “Fractional Ice mapping and monitoring for the future GOES-R Advanced Baseline Imager (ABI).” AGU Fall Meeting Abstracts. doi: 10.2011/AGUFM. C31A0595N.
[2] Nazari, R., and Khanbilvardi, R. 2011. “Application of dynamic threshold in sea and lake ice mapping and monitoring.” International Journal of Hydrology Science and Technology. 1 (1/2): 37-46. doi: 10.1504/IJHST.2011.040739.
[3] Romanov, P., Gutman, G., and Csiszar, I. 2000. Automated monitoring of snow cover over North America with multispectral satellite data. J. Appl. Meteorol. 39 (11): 1866–1880. doi: 10.1175/1520-0450 (2000) 039<1866:AMOSCO>2.0. CO; 2.
[4] Dorofy, P., Nazari, R., Romanov, P., and Key, J. 2016 “Development of a Mid-Infrared Sea and Lake Ice Index (MISI) Using the GOES Imager.” Remote Sens. 8 (12): 1015. doi: 10.3390/rs8121015.
[5] Liu, Y., Key, J., and Mahoney, R. 2016. “Sea and Freshwater ice concentration from VIIRS on Suomi NPP and the future JPSS satellites.” Remote Sens. 8 (6): 523. doi: 10.3390/rs8060523.
[6] Singh, P. Snow and glacier hydrology (37). Springer Science & Business Media. 2001.
[7] “Retrieval of surface albedo from space.” www2.hawaii.edu, 2017. Accessed 1 Feb 2017. http://www2.hawaii.edu/~jmaurer/albedo/.
[8] Sandmeier, S. R., and Strahler, A. H. 2000. “BRDF laboratory measurements.” Remote Sensing Reviews. 18 (2-4): 481-502. doi: 10.1080/02757250009532398.
[9] Dumont, M., Brissaud, O., Picard, G., Schmitt, B., Gallet, J. C., and Arnaud, Y. 2010. “High-accuracy measurements of snow Bidirectional Reflectance Distribution Function at visible and NIR wavelengths–comparison with modelling results.” Atmospheric Chemistry and Physics. 10 (5): 2507-2520. doi: 10.5194/acp-10-2507-2010.
[10] Gatebe, C. K., and King, M. D. 2016. “Airborne spectral BRDF of various surface types (ocean, vegetation, snow, desert, wetlands, cloud decks, smoke layers) for remote sensing applications.” Remote Sensing of Environment. 179 (15): 131-148. doi: 10.1016/j.rse.2016.03.029.
[11] Noguchi, K., Richter, A., Rozanov, V., Rozanov, A., Burrows, J. P., Irie, H. and Kita, K. 2014. “Effect of surface BRDF of various land cover types on geostationary observations of tropospheric NO2.” Atmospheric Measurement Techniques. 7 (10): 3497-3508. doi: 10.5194/amt-7-3497-2014.
[12] Daxiang, X., Debao, T., Xiongfei, W., and Qiao, W. 2015. “A Dynamic Threshold Cloud Detecting Approach based on the Brightness Temperature from FY-2 VISSR Data.” The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences. 40 (7): 617. doi: 10.5194/isprsarchives-XL-7-W3-617-2015.
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  • APA Style

    Peter Dorofy, Rouzbeh Nazari, Peter Romanov. (2018). Application of Dynamic Threshold in a Lake Ice Detection Algorithm. American Journal of Remote Sensing, 6(2), 64-73. https://doi.org/10.11648/j.ajrs.20180602.12

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    ACS Style

    Peter Dorofy; Rouzbeh Nazari; Peter Romanov. Application of Dynamic Threshold in a Lake Ice Detection Algorithm. Am. J. Remote Sens. 2018, 6(2), 64-73. doi: 10.11648/j.ajrs.20180602.12

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    AMA Style

    Peter Dorofy, Rouzbeh Nazari, Peter Romanov. Application of Dynamic Threshold in a Lake Ice Detection Algorithm. Am J Remote Sens. 2018;6(2):64-73. doi: 10.11648/j.ajrs.20180602.12

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  • @article{10.11648/j.ajrs.20180602.12,
      author = {Peter Dorofy and Rouzbeh Nazari and Peter Romanov},
      title = {Application of Dynamic Threshold in a Lake Ice Detection Algorithm},
      journal = {American Journal of Remote Sensing},
      volume = {6},
      number = {2},
      pages = {64-73},
      doi = {10.11648/j.ajrs.20180602.12},
      url = {https://doi.org/10.11648/j.ajrs.20180602.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajrs.20180602.12},
      abstract = {The traditional method involved in the classification of surface types such as water, ice, and snow rely on thresholds values of spectral properties that are fixed. However, the use of daily fixed thresholds leaves a substantial number of either unclassified and/or misclassified ice and water pixels. In this study, a new dynamic threshold technique is proposed to identify and map lake ice cover in the imagery of GOES-I to P series satellites. In addition, dynamic threshold can be used as an alternative solution to Bidirectional Reflectance Distribution Function (BRDF) models. The technique has been applied using GOES-13 imager data over Lake Michigan, one of five of the Great Lakes. Nine scenes based on an hourly acquisition of a single day are used to visually sample water and ice pixels. A threshold for the visible (0.62 µm) and the reflective component of the mid-infrared (3.9 µm) is determined for each scene by the intersection of the probability distributions of the water and ice samples. The thresholds are used as decision thresholds in a binary test algorithm applied on a per-pixel basis. Both fixed threshold (single scene) and dynamic thresholds (multiple scenes) have been compared. Dynamic threshold shows a significant gain in classified pixels over fixed threshold. A preliminary quantitative assessment is introduced to evaluate the algorithm’s performance using sensitivity and specificity testing. The classification results show a sensitivity of 98% when delineating thick ice and water and 87% when delineating thick/thin ice and water. Implementing a dynamic threshold, can be used in constructing ice maps in applications that benefit from high temporal resolution imagery.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Application of Dynamic Threshold in a Lake Ice Detection Algorithm
    AU  - Peter Dorofy
    AU  - Rouzbeh Nazari
    AU  - Peter Romanov
    Y1  - 2018/08/29
    PY  - 2018
    N1  - https://doi.org/10.11648/j.ajrs.20180602.12
    DO  - 10.11648/j.ajrs.20180602.12
    T2  - American Journal of Remote Sensing
    JF  - American Journal of Remote Sensing
    JO  - American Journal of Remote Sensing
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    EP  - 73
    PB  - Science Publishing Group
    SN  - 2328-580X
    UR  - https://doi.org/10.11648/j.ajrs.20180602.12
    AB  - The traditional method involved in the classification of surface types such as water, ice, and snow rely on thresholds values of spectral properties that are fixed. However, the use of daily fixed thresholds leaves a substantial number of either unclassified and/or misclassified ice and water pixels. In this study, a new dynamic threshold technique is proposed to identify and map lake ice cover in the imagery of GOES-I to P series satellites. In addition, dynamic threshold can be used as an alternative solution to Bidirectional Reflectance Distribution Function (BRDF) models. The technique has been applied using GOES-13 imager data over Lake Michigan, one of five of the Great Lakes. Nine scenes based on an hourly acquisition of a single day are used to visually sample water and ice pixels. A threshold for the visible (0.62 µm) and the reflective component of the mid-infrared (3.9 µm) is determined for each scene by the intersection of the probability distributions of the water and ice samples. The thresholds are used as decision thresholds in a binary test algorithm applied on a per-pixel basis. Both fixed threshold (single scene) and dynamic thresholds (multiple scenes) have been compared. Dynamic threshold shows a significant gain in classified pixels over fixed threshold. A preliminary quantitative assessment is introduced to evaluate the algorithm’s performance using sensitivity and specificity testing. The classification results show a sensitivity of 98% when delineating thick ice and water and 87% when delineating thick/thin ice and water. Implementing a dynamic threshold, can be used in constructing ice maps in applications that benefit from high temporal resolution imagery.
    VL  - 6
    IS  - 2
    ER  - 

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Author Information
  • Department of Civil and Environmental Engineering, Rowan University, Glassboro, USA

  • Department of Civil and Environmental Engineering, Rowan University, Glassboro, USA

  • NOAA Cooperative Remote Sensing Science and Technology Center (CREST), City University of New York, New York, USA

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