Abstract
Wetlands play a key role in regional and global environments, and are critically linked to major issues such as climate change, wildlife habitat, biodiversity, water quality protection, and global carbon and methane cycles. Remotely-sensed imagery provides a means to detect and monitor wetlands on large scales and with regular frequency. In this project, methodologies were developed to classify wetlands (Open Bog, Treed Bog, Open Fen, Treed Fen, and Swamps) from multi-source remotely sensed data using advanced classification algorithms. The data utilized included multispectral optical and thermal data (Landsat-5) and Radar imagery from RADARSAT-2 and Sentinel-1. The goals were to determine the best way to combine the aforementioned imagery to classify wetlands, and determine the most significant image features. Classification algorithms investigated in this study were Naive Bayes, K-Nearest Neighbor (K-NN), Support Vector Machine (SVM), and Random Forest (RF). Based on the test results in the study area in Northern Ontario, Canada (49°31′.34N, 80°43′37.04W), a RF based classification methodology produced the most accurate classification result (87.51%). SVM, in some cases, produced results of comparable or better accuracy than RF. Our work also showed that the use of surface temperature (an untraditional feature choice) could aid in the classification process if the image is from an abnormally warm spring. This study found that wetlands were best classified using the NDVI (Normalized Difference Vegetative Index) calculated from optical imagery obtained in the spring months, radar backscatter coefficients, surface temperature, and ancillary data such as surface slope, computed through either an RF or SVM classifier. It was also found that preselection of features using Log-normal or RF variable importance analysis was an effective way of identifying low quality features and to a lesser extent features which were of higher quality.
Highlights
Wetlands play key roles in regional and global environments and are critically linked to major issues such as climate change, wildlife habitat health, and biodiversity
Other studies have approached this problem by incorporating Radio Detection and Ranging (RADAR) or Light Detection and Ranging (LiDAR) based measurements with Landsat Thermatic Mapper (TM) imagery to aid in their classification methodologies
It was found that analysis of features using gross statistical analysis in the form of the Log-normal distance and an iterative regression approach in the form of the Random Forest (RF) predictor importance value were an effective means of identifying which features were of high quality and should be used in classification and which features were of low quality and should be either ignored or removed from classification
Summary
Wetlands play key roles in regional and global environments and are critically linked to major issues such as climate change, wildlife habitat health, and biodiversity. It is found to have good class separation when one class dominated the classification area (>30 m2), but not when mixtures of wetlands types were of the same order as the sensor resolution [13]. For these Landsat derived maps, accuracy levels varied between 30 and 82%, depending on the techniques used [13,14,15,16,17,18,19,20,21]. Resulting classification accuracies range from ~63% to 92%, again, depending on the methodologies and class definitions used [15,15,21,27,28,29,30,31]
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