Volume 6, Issue 2, December 2018, Page: 64-73
Application of Dynamic Threshold in a Lake Ice Detection Algorithm
Peter Dorofy, Department of Civil and Environmental Engineering, Rowan University, Glassboro, USA
Rouzbeh Nazari, Department of Civil and Environmental Engineering, Rowan University, Glassboro, USA
Peter Romanov, NOAA Cooperative Remote Sensing Science and Technology Center (CREST), City University of New York, New York, USA
Received: Jul. 23, 2018;       Accepted: Aug. 2, 2018;       Published: Aug. 29, 2018
DOI: 10.11648/j.ajrs.20180602.12      View  282      Downloads  21
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.
Lake Ice Concentration, Dynamic Threshold, GOES Imager, Remote Sensing, Shortwave Infrared, Snow Index, Geographical Information System (GIS)
To cite this article
Peter Dorofy, Rouzbeh Nazari, Peter Romanov, Application of Dynamic Threshold in a Lake Ice Detection Algorithm, American Journal of Remote Sensing. Vol. 6, No. 2, 2018, pp. 64-73. doi: 10.11648/j.ajrs.20180602.12
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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