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

Abstract

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 km 2 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 OPEN ACCESSRemote Sens. 2015, 7 13529 results were observed when assessing the potential for remote predictive mapping by simulating transferability of the model to scenes without training data.

Contributions of Actual and Simulated Satellite SAR Data for Substrate Type Differentiation and Shoreline Mapping in the Canadian Arctic

“…The authors of [16] provide a comprehensive review of the literature related to shoreline mapping using Earth observation data. This section will not be repeated here for brevity.…”

“…The former provides an indication of the purity of nodes a given variable generates, while the latter indicates how accuracy changes when the variable is excluded from model development (by randomly permuting values). Users have the option to remove variables with low importance values, which reduces processing times, and has also been shown to improve model performance [16,27,28]. Per-class probability values, representing the number of trees that voted with the majority divided by the total number of trees, can also be used to provide some indication of the certainty of correct classification [16,26,29].…”

Moving to the RADARSAT Constellation Mission: Comparing Synthesized Compact Polarimetry and Dual Polarimetry Data with Fully Polarimetric RADARSAT-2 Data for Image Classification of Peatlands

“…The RF RADARSAT-2 model began with 50 predictor variables, the simulated RCM compact polarimetry model 32 predictor variables, and the simulated RCM dual polarimetric models had 14 variables. Previous research has shown that model accuracy increases when variables that are ranked to be the least important are removed [49,50]. Thus, we used the Mean Decrease in Accuracy and Gini Index values to manually remove the top five predictor variables with the lowest importance values.…”

“…These measures allow the user to determine which variables have the best predictive power in the final classification. Additionally, variables can be removed from the models that have low importance rankings, which can decrease processing times and improve model accuracy [40,49,50].…”

Mapping the Dabus Wetlands, Ethiopia, Using Random Forest Classification of Landsat, PALSAR and Topographic Data

“…RF was selected for this study because it generally outperforms conventional classifiers such as the Gaussian maximum likelihood classifier [61,62], while performing favorably, or equally well, to other non-parametric approaches; e.g., CART [63,64], Support Vector Machines [32,65,66], Artificial Neural Networks [67], and K-Nearest Neighbor [68]. It is a powerful non-linear and non-parametric classifier that allows for fusion and aggregation of high-dimensional data from various sources (e.g., optical, SAR, and topography [30,69,70]; SAR and topography [21,58,71]; and optical and topography [72][73][74]). RF produces independently constructed classification trees, similar to the Classification and Regression (CART) method, using bootstrapped samples of the original data [75,76].…”