Coral Reef Mapping of UAV: A Comparison of Sun Glint Correction Methods

Although methods were proposed for eliminating sun glint effects from airborne and satellite images over coral reef environments, a method was not proposed previously for unmanned aerial vehicle (UAV) image data. De-glinting in UAV image analysis may improve coral distribution mapping accuracy result compared with an uncorrected image classification technique. The objective of this research was to determine accuracy of coral reef habitat classification maps based on glint correction methods proposed by Lyzenga et al., Joyce, Hedley et al., and Goodman et al.. The UAV imagery collected from the coral-dominated Pulau Bidong (Peninsular Malaysia) on 20 April 2016 was analyzed in this study. Images were pre-processed with the following two strategies: Strategy-1 was the glint removal technique applied to the whole image, while Strategy-2 used only the regions impacted by glint instead of the whole image. Accuracy measures for the glint corrected images showed that the method proposed by Lyzenga et al. following Strategy-2 could eliminate glints over the branching coral—Acropora (BC), tabulate coral—Acropora + Montipora (TC), patch coral (PC), coral rubble (R), and sand (S) with greater accuracy than the other four methods using Strategy-1. Tested in two different coral environments (Site-1: Pantai Pasir Cina and Site-2: Pantai Vietnam), the glint-removed UAV imagery produced reliable maps of coral habitat distribution with finer details. The proposed strategies can potentially be used to remove glint from UAV imagery and may improve usability of glint-affected imagery, for analyzing spatiotemporal changes of coral habitats from multi-temporal UAV imagery.

[1]  Muhammad I. Nadzri,et al.  Can ensemble techniques improve coral reef habitat classification accuracy using multispectral data? , 2020, Geocarto International.

[2]  Heidi M. Dierssen,et al.  Water Column Optical Properties of Pacific Coral Reefs Across Geomorphic Zones and in Comparison to Offshore Waters , 2019, Remote. Sens..

[3]  Mazlan Hashim,et al.  Potential of Earth Observation (EO) technologies for seagrass ecosystem service assessments , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[4]  J. Bujang,et al.  Assessment of the impact of coastal reclamation activities on seagrass meadows in Sungai Pulai estuary, Malaysia, using Landsat data (1994–2017) , 2018, International Journal of Remote Sensing.

[5]  James L. Hench,et al.  Very high resolution mapping of coral reef state using airborne bathymetric LiDAR surface-intensity and drone imagery , 2018, Fine Resolution Remote Sensing of Species in Terrestrial and Coastal Ecosystems.

[6]  Mark Parsons,et al.  UAVs, Hyperspectral Remote Sensing, and Machine Learning Revolutionizing Reef Monitoring , 2018, Sensors.

[7]  Takashi Nakamura,et al.  Assessment of Machine Learning Algorithms for Automatic Benthic Cover Monitoring and Mapping Using Towed Underwater Video Camera and High-Resolution Satellite Images , 2018, Remote. Sens..

[8]  David Hernández-López,et al.  Automatic Hotspot and Sun Glint Detection in UAV Multispectral Images , 2017, Sensors.

[9]  Blake M. Allan,et al.  Applications of unmanned aerial vehicles in intertidal reef monitoring , 2017, Scientific Reports.

[10]  Chuiqing Zeng,et al.  The impacts of environmental variables on water reflectance measured using a lightweight unmanned aerial vehicle (UAV)-based spectrometer system , 2017 .

[11]  Yue Zhang,et al.  Versatile time-dependent spatial distribution model of sun glint for satellite-based ocean imaging , 2017 .

[12]  Carl J. Legleiter,et al.  Removing sun glint from optical remote sensing images of shallow rivers , 2017 .

[13]  Diego González-Aguilera,et al.  Drones—An Open Access Journal , 2017 .

[14]  Mazlan Hashim,et al.  Marine and human habitat mapping for the Coral Triangle Initiative region of Sabah using Landsat and Google Earth imagery , 2016 .

[15]  Scott F. Heron,et al.  Remote Sensing of Coral Reefs for Monitoring and Management: A Review , 2016, Remote. Sens..

[16]  Javier Marcello,et al.  Automatic Sun Glint Removal of Multispectral High-Resolution Worldview-2 Imagery for Retrieving Coastal Shallow Water Parameters , 2016, Remote. Sens..

[17]  J. Bujang,et al.  Application of Landsat images to seagrass areal cover change analysis for Lawas, Terengganu and Kelantan of Malaysia , 2015 .

[18]  Gonzalo Pajares,et al.  Overview and Current Status of Remote Sensing Applications Based on Unmanned Aerial Vehicles (UAVs) , 2015 .

[19]  J. Bujang,et al.  The application of remote sensing to seagrass ecosystems: an overview and future research prospects , 2015 .

[20]  P. Shanmugam,et al.  A robust method for removal of glint effects from satellite ocean colour imagery , 2014 .

[21]  Morton J. Canty,et al.  Image Analysis, Classification and Change Detection in Remote Sensing: With Algorithms for ENVI/IDL and Python, Third Edition , 2014 .

[22]  I. Colomina,et al.  Unmanned aerial systems for photogrammetry and remote sensing: A review , 2014 .

[23]  Dongzhi Zhao,et al.  Review of coral reef ecosystem remote sensing , 2014 .

[24]  A. Hodgson,et al.  Unmanned Aerial Vehicles (UAVs) for Surveying Marine Fauna: A Dugong Case Study , 2013, PloS one.

[25]  K. S. Lee,et al.  Simulation Studies on the Electrical Power Potential Harnessed by Tidal Current Turbines , 2013 .

[26]  Oliver Zielinski,et al.  Sunglint Detection for Unmanned and Automated Platforms , 2012, Sensors.

[27]  Menghua Wang,et al.  Evaluation of sun glint models using MODIS measurements , 2010 .

[28]  Samantha J. Lavender,et al.  Sun Glint Correction of High and Low Spatial Resolution Images of Aquatic Scenes: a Review of Methods for Visible and Near-Infrared Wavelengths , 2009, Remote. Sens..

[29]  Jaan Praks,et al.  A sun glint correction method for hyperspectral imagery containing areas with non-negligible water leaving NIR signal , 2009 .

[30]  Dar A. Roberts,et al.  A forward image model for passive optical remote sensing of river bathymetry , 2009 .

[31]  S. Ustin,et al.  Influence of atmospheric and sea-surface corrections on retrieval of bottom depth and reflectance using a semi-analytical model: a case study in Kaneohe Bay, Hawaii. , 2008, Applied optics.

[32]  Wei Li,et al.  Improving the description of sunglint for accurate prediction of remotely sensed radiances , 2008 .

[33]  R. Steneck,et al.  Coral Reefs Under Rapid Climate Change and Ocean Acidification , 2007, Science.

[34]  D. Jupp,et al.  Mapping coral reef benthic substrates using hyperspectral space-borne images and spectral libraries , 2006 .

[35]  M. Canty Image Analysis, Classification, and Change Detection in Remote Sensing , 2006 .

[36]  Fred J. Tanis,et al.  Multispectral bathymetry using a simple physically based algorithm , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[37]  Georg Martin,et al.  Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters—a Baltic Sea case study , 2006 .

[38]  Re Mount,et al.  Acquisition of Through-water Aerial Survey Images: Surface Effects and the Prediction of Sun Glitter and Subsurface Illumination , 2005 .

[39]  John D. Hedley,et al.  Technical note: Simple and robust removal of sun glint for mapping shallow‐water benthos , 2005 .

[40]  D. Bellwood,et al.  Confronting the coral reef crisis , 2004, Nature.

[41]  Serge Andréfouët,et al.  Spectral reflectance of coral , 2004, Coral Reefs.

[42]  E. LeDrew,et al.  Remote sensing of coral reefs and their physical environment. , 2004, Marine pollution bulletin.

[43]  Serge Andréfouët,et al.  Sea surface correction of high spatial resolution Ikonos images to improve bottom mapping in near-shore environments , 2003, IEEE Trans. Geosci. Remote. Sens..

[44]  Tiit Kutser,et al.  Modeling spectral discrimination of Great Barrier Reef benthic communities by remote sensing instruments , 2003 .

[45]  S. Bailey,et al.  Correction of Sun glint Contamination on the SeaWiFS Ocean and Atmosphere Products. , 2001, Applied optics.

[46]  S. Sathiya Keerthi,et al.  Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.

[47]  Chris D. Clark,et al.  Coral reef habitat mapping: how much detail can remote sensing provide? , 1997 .

[48]  S. K. McFeeters The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[49]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[50]  Russell G. Congalton,et al.  A review of assessing the accuracy of classifications of remotely sensed data , 1991 .

[51]  D. Lyzenga Passive remote sensing techniques for mapping water depth and bottom features. , 1978, Applied optics.

[52]  Jacob Cohen A Coefficient of Agreement for Nominal Scales , 1960 .

[53]  T. Harmel,et al.  Sunglint correction of the Multi-Spectral Instrument (MSI)-SENTINEL-2 imagery over inland and sea waters from SWIR bands , 2018 .

[54]  H. Dierssen,et al.  The Influence of a Sandy Substrate, Seagrass, or Highly Turbid Water on Albedo and Surface Heat Flux , 2018 .

[55]  K. Moffett,et al.  Remote Sens , 2015 .

[56]  Chris Roelfsema,et al.  Integrating field data with high spatial resolution multispectral satellite imagery for calibration and validation of coral reef benthic community maps , 2010 .

[57]  Karen E. Joyce,et al.  A method for mapping live coral cover using remote sensing , 2005 .

[58]  D. Lyzenga Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data , 1981 .