Next Article in Journal
Learning-Based Algal Bloom Event Recognition for Oceanographic Decision Support System Using Remote Sensing Data
Previous Article in Journal
Improving the Accuracy of the Water Surface Cover Type in the 30 m FROM-GLC Product
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2015, 7(10), 13528-13563; doi:10.3390/rs71013528

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

1
Environment Canada, National Wildlife Research Centre, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada
2
Department of Geography and Environmental Studies, Carleton University, 1125 Colonel By Drive Ottawa, ON K1S 5B6, Canada
*
Author to whom correspondence should be addressed.
Academic Editors: Magaly Koch and Prasad S. Thenkabail
Received: 10 September 2015 / Revised: 30 September 2015 / Accepted: 10 October 2015 / Published: 19 October 2015
View Full-Text   |   Download PDF [1283 KB, uploaded 19 October 2015]   |  

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. View Full-Text
Keywords: RADARSAT-2; Landsat 5; classification; Random Forest; Arctic; shorelines RADARSAT-2; Landsat 5; classification; Random Forest; Arctic; shorelines
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Banks, S.; Millard, K.; Pasher, J.; Richardson, M.; Wang, H.; Duffe, J. Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model. Remote Sens. 2015, 7, 13528-13563.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top