Tropical Mangrove Species Classification Using Random Forest Algorithm and Very High-Resolution Satellite Imagery
Abstract
The spectral mixing was a challenge that principally found in the pixel-based classification method, particularly in the species level. The objective of this study was to evaluate the effectiveness of the random forest (RF) algorithm in order to improve the accuracy of the tropical mangrove species classification in Pak Phanang mangrove conservation, Nakhon Si Thammarat Province. The study utilized the very high-resolution, the Quickbird image, which was pre-calibrated using radiometric and geometric correction to incorporate with the field observation data. The process divided the input data into the training and the validation sets. The training process adjusted the input parameters for instances, the tree depth, the number of sample node, and the number of trees to acquire the best RF classification model. The validation compared the classified result with the conventional pixel-based maximum likelihood classification (MLC). The overall accuracy (OA), the kappa statistic, and the Z-statistic were indications of the RF classification evaluation. The result revealed that the RF algorithm achieved higher efficiency with the overall accuracy of 78.00% and 0.72 for the kappa statistic. Meanwhile, for MLC, the OA and the kappa statistic presented 56.00% and 0.44, respectively. The Z statistic (Z = 3.68) result also significantly confirmed the difference between RF and MLC at the 95% confidence level.
Keywords : random forest, very high resolution satellite imagery, classification, tropical mangrove species
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