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 Sensing

Sarah Banks³  Koreen Millard¹  Jon Pasher³  Murray Richardson¹  Huili Wang³  Jason Duffe³  Magaly Koch² 

[1] Department of Geography and Environmental Studies, Carleton University, 1125 Colonel By Drive Ottawa, ON K1S 5B6, Canada; E-Mails:Environment Canada, National Wildlife Research Centre, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada;;Environment Canada, National Wildlife Research Centre, 1125 Colonel By Drive, Ottawa, ON K1S 5B6, Canada; E-Mails:

关键词: RADARSAT-2;    Landsat 5;    classification;    Random Forest;    Arctic;    shorelines;   

DOI  :  10.3390/rs71013528

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.