Coral Reef Change Detection in Remote Pacific Islands Using Support Vector Machine Classifiers

Abstract

Despite the abundance of research on coral reef change detection, few studies have been conducted to assess the spatial generalization principles of a live coral cover classifier trained using remote sensing data from multiple locations. The aim of this study is to develop a machine learning classifier for coral dominated benthic cover-type class (CDBCTC) based on ground truth observations and Landsat images, evaluate the performance of this classifier when tested against new data, then deploy the classifier to perform CDBCTC change analysis of multiple locations. The proposed framework includes image calibration, support vector machine (SVM) training and tuning, statistical assessment of model accuracy, and temporal pixel-based image differencing. Validation of the methodology was performed by cross-validation and train/test split using ground truth observations of benthic cover from four different reefs. These four locations (Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island) as well as two additional locations (Kiritimati Island and Tabuaeran Island) were then evaluated for CDBCTC change detection. The in-situ training accuracy against ground truth observations for Palmyra Atoll, Kingman Reef, Baker Island Atoll, and Howland Island were 87.9%, 85.7%, 69.2%, and 82.1% respectively. The classifier attained generalized accuracy scores of 78.8%, 81.0%, 65.4%, and 67.9% for the respective locations when trained using ground truth observations from neighboring reefs and tested against the local ground truth observations of each reef. The classifier was trained using the consolidated ground truth data of all four sites and attained a cross-validated accuracy of 75.3%. The CDBCTC change detection analysis showed a decrease in CDBCTC of 32% at Palmyra Atoll, 25% at Kingman Reef, 40% at Baker Island Atoll, 25% at Howland Island, 35% at Tabuaeran Island, and 43% at Kiritimati Island. This research establishes a methodology for developing a robust classifier and the associated Controlled Parameter Cross-Validation (CPCV) process for evaluating how well the model will generalize to new data. It is an important step for improving the scientific understanding of temporal change within coral reefs around the globe.

Detection of Coral Reef Bleaching Based on Sentinel-2 Multi-Temporal Imagery: Simulation and Case Study

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“…Brightness in a NIR band was utilized to deglint the visible wavelength bands based on the linear relationships between NIR and visible bands (Hedley et al, 2005). To facilitate further analysis, pixels containing boats, whitecaps (sea foam), clouds and their shadows, and land were masked (Gapper et al, 2019).…”

Seagrass Depth Distribution Mirrors Coastal Development in the Mexican Caribbean – An Automated Analysis of 800 Satellite Images

“…The remote sensing analysis was implemented as an "objective" processing chain that could be consistently and repeatedly applied free from potential bias due to data or operator choices. This excluded commonly used habitat classification or machine learning approaches for seagrass mapping (Knudby and Nordlund, 2011;Lyons et al, 2013;Roelfsema et al, 2014;Gapper et al, 2019) since the location and time points of training data could not be excluded as spatial or temporal bias factors (and in any case training data appropriate for the scale of the analysis was lacking). Instead, we used a radiative transfer model inversion method to simultaneously estimate LAI and depth (Hedley et al, 2016(Hedley et al, , 2017.…”

Aircraft Target Classification for Conventional Narrow-Band Radar with Multi-Wave Gates Sparse Echo Data

“…According to the difference of micro-Doppler effects [45] of rotating parts due to the difference in structure and rotating speed, features of the amplitude deviation coefficient, time domain waveform entropy and frequency domain waveform entropy are extracted to classify targets. Finally, features extracted from multi-wave gates sparse echo data are weighted and fused to train and test the support vector machine (SVM) [46][47][48] model for classification. Experimental results show that the proposed algorithm can improve the classification probability, and four wave gates echo data in weighted features fusion used to extract features is the optimal wave gate number for target classification.…”