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 |
Lake Ice Concentration, Dynamic Threshold, GOES Imager, Remote Sensing, Shortwave Infrared, Snow Index, Geographical Information System (GIS)
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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
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
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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Download@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}
}
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 SP - 64 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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