Abstract
The Random Forest algorithm was used to classify 86 Wide Fine Quadrature Polarized RADARSAT-2 scenes, five Landsat 5 scenes, and a Digital Elevation Model covering an area approximately 81,000 km2 in size, and representing the entirety of Dease Strait, Coronation Gulf and Bathurst Inlet, Nunavut. The focus of this research was to assess the potential to operationalize shoreline sensitivity mapping to inform oil spill response and contingency planning. The impact of varying the training sample size and reducing model data load were evaluated. Results showed that acceptable accuracies could be achieved with relatively few training samples, but that higher accuracies and greater probabilities of correct class assignment were observed with larger sample sizes. Additionally, the number of inputs to the model could be greatly reduced without impacting overall performance. Optimized models reached independent accuracies of 91% for seven land cover types, and classification probabilities between 0.77 and 0.98 (values for latter represent per-class averages generated from independent validation sites). Mixed results were observed when assessing the potential for remote predictive mapping by simulating transferability of the model to scenes without training data.
Highlights
Arctic marine shorelines are sensitive environments that can experience both immediate and long-term perturbations from oil spills, which may occur more frequently as a result of increased energy resource development and transportation in the Canadian Arctic [1,2,3,4,5,6]
Three model iterations were deemed sufficient to represent the variability of outputs since models generated with the same training sample size tended to predict the same classes at the same locations, and tended to achieve similar accuracies, Kappa statistic values, and probabilities
This research has demonstrated the potential to classify shore and near-shore land cover types to acceptable levels (e.g., >~80%) using relatively few training samples (i.e., 25 points per-class). This result is relevant for mapping remote, Arctic shorelines since these areas are often difficult and expensive to access, and tend to make up only a fraction of the total image, which can make it harder to collect a large quantity of ground data
Summary
Arctic marine shorelines are sensitive environments that can experience both immediate and long-term perturbations from oil spills, which may occur more frequently as a result of increased energy resource development and transportation in the Canadian Arctic [1,2,3,4,5,6]. Shallow incidence angle images provided the best overall class separability, and when the three intensity channels (HH, HV and VV in dB) were combined with SPOT-4 imagery as inputs to the Maximum Likelihood classifier, the authors achieved overall accuracies of 76% and 86% for the Richards Island and Tuktoyaktuk Harbour sites, respectively. While it was not known what the weather conditions were immediately prior to each acquisition, potential for classifier transferability was demonstrated as the authors showed that values for many classes were consistent between the two study areas when compared at like incidence angles
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