Classification of Coral Reefs in the South China Sea by Combining Airborne LiDAR Bathymetry Bottom Waveforms and Bathymetric Features
- 14 August 2018
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Geoscience and Remote Sensing
- Vol. 57 (2), 815-828
- https://doi.org/10.1109/tgrs.2018.2860931
Abstract
Geographic information describing coral reefs plays an important role in constructing electronic chart systems and protecting the ecological environment of the ocean. To derive geographic information of coral reefs more effectively, this paper proposes a methodology to detect coral reefs by combining airborne LiDAR bathymetry (ALB) bottom waveform and bathymetric feature data. A feature vector was established by deriving bottom waveform variables (the peak amplitude, pulsewidth, area, skewness, kurtosis, and backscatter cross section) and bathymetric variables (the depth standard deviation, slope, bathymetric position index, Gaussian curvature, mean curvature, and roughness). Using a support vector machine classifier, coral reefs were detected by distinguishing two classes (coral reefs and others) on the seafloor. To evaluate the classification performance of coral reefs, the developed method was applied to Yuanzhi Island, South China Sea surveys, and verified by field data (aerial digital camera images and underwater video images). The results showed that the classification overall accuracy of coral reefs can be greatly improved from 80.59%/90.31% when ALB bottom waveform or bathymetric variables features were used separately to 93.57% when using a combination of ALB bottom waveform and bathymetric features. In addition, the kappa coefficient can also be greatly improved from approximately 0.61/0.80 to 0.87. And the new proposed method performs better compared to the current classification method using ALB data to detect coral reefs with an overall accuracy of 90.92% and Kappa of 0.81. This highlights the potential of ALB data, combining waveform data and bathymetric data, for precisely detecting coral reefs in shallow water areas.Keywords
Funding Information
- National Key R&D Program of China (2018YFF0212203, 2017YFC1405006, 2016YFB0501705)
- National Natural Science Foundation of China (41506111, 41506210)
- National Science and Technology Major Project on High-resolution Earth Observation (11-Y20A12-9001-17/18, 42-Y20A11-9001-17/18)
- shandong provincial key research and development program (2018GHY115002)
This publication has 60 references indexed in Scilit:
- Human deforestation outweighs future climate change impacts of sedimentation on coral reefsNature Communications, 2013
- Enhancing Coral Health Detection Using Spectral Diversity Indices from WorldView-2 Imagery and Machine LearnersRemote Sensing, 2012
- Capability of the Sentinel 2 mission for tropical coral reef mapping and coral bleaching detectionRemote Sensing of Environment, 2012
- A prior feature SVM-MRF based method for mouse brain segmentationNeuroImage, 2012
- Carotenoid Distribution in Living Cells of Haematococcus pluvialis (Chlorophyceae)PLOS ONE, 2011
- Predicting Species Diversity of Benthic Communities within Turbid Nearshore Using Full-Waveform Bathymetric LiDAR and Machine LearnersPLOS ONE, 2011
- Mapping coral reef benthic substrates using hyperspectral space-borne images and spectral librariesEstuarine, Coastal and Shelf Science, 2006
- Fundamental quantitative methods of land surface analysisGeoderma, 2002
- Change detection in shallow coral reef environments using Landsat 7 ETM+ dataRemote Sensing of Environment, 2001
- A Coefficient of Agreement for Nominal ScalesEducational and Psychological Measurement, 1960