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Knowledge and Data-Driven Mapping of Environmental Status Indicators from Remote Sensing and VGI

Confidence Levels, Sensitivity, and the Role of Bathymetry in Coral Reef Remote Sensing

HySpeed Computing, Miami, FL 33243, USA
Center for Spatial Technologies and Remote Sensing, Department of Land Air and Water Resources, University of California, One Shields Avenue, Davis, CA 95616, USA
NSU·Oceanographic Center, Nova Southeastern University, 3301 College Avenue, Fort Lauderdale, FL 33314, USA
Department of Evolutionary Biology and Environmental Studies, University of Zürich, Winterthurerstrasse 190, 8057 Zürich, Switzerland
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(3), 496;
Received: 5 December 2019 / Revised: 28 January 2020 / Accepted: 1 February 2020 / Published: 4 February 2020
Remote sensing is playing an increasingly important role in the monitoring and management of coastal regions, coral reefs, inland lakes, waterways, and other shallow aquatic environments. Ongoing advances in algorithm development, sensor technology, computing capabilities, and data availability are continuing to improve our ability to accurately derive information on water properties, water depth, benthic habitat composition, and ecosystem health. However, given the physical complexity and inherent variability of the aquatic environment, most of the remote sensing models used to address these challenges require localized input parameters to be effective and are thereby limited in geographic scope. Additionally, since the parameters in these models are interconnected, particularly with respect to bathymetry, errors in deriving one parameter can significantly impact the accuracy of other derived parameters and products. This study utilizes hyperspectral data acquired in Hawaii in 2000–2001 and 2017–2018 using NASA’s Classic Airborne Visible/Infrared Imaging Spectrometer to evaluate performance and sensitivity of a well-established semi-analytical inversion model used in the assessment of coral reefs. Analysis is performed at several modeled spatial resolutions to emulate characteristics of different feasible moderate resolution hyperspectral satellites, and data processing is approached with the objective of developing a generalized, scalable, automated workflow. Accuracy of derived water depth is evaluated using bathymetric lidar data, which serves to both validate model performance and underscore the importance of image quality on achieving optimal model output. Data are then used to perform a sensitivity analysis and develop confidence levels for model validity and accuracy. Analysis indicates that derived benthic reflectance is most sensitive to errors in bathymetry at shallower depths, yet remains significant at all depths. The confidence levels provide a first-order method for internal quality assessment to determine the physical extent of where and to what degree model output is considered valid. Consistent results were found across different study sites and different spatial resolutions, confirming the suitability of the model for deriving water depth in complex coral reef environments, and expanding our ability to achieve automated widespread mapping and monitoring of global coral reefs. View Full-Text
Keywords: bathymetry; coral reef; confidence levels; hyperspectral; satellite bathymetry; coral reef; confidence levels; hyperspectral; satellite
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MDPI and ACS Style

Goodman, J.A.; Lay, M.; Ramirez, L.; Ustin, S.L.; Haverkamp, P.J. Confidence Levels, Sensitivity, and the Role of Bathymetry in Coral Reef Remote Sensing. Remote Sens. 2020, 12, 496.

AMA Style

Goodman JA, Lay M, Ramirez L, Ustin SL, Haverkamp PJ. Confidence Levels, Sensitivity, and the Role of Bathymetry in Coral Reef Remote Sensing. Remote Sensing. 2020; 12(3):496.

Chicago/Turabian Style

Goodman, James A., Mui Lay, Luis Ramirez, Susan L. Ustin, and Paul J. Haverkamp 2020. "Confidence Levels, Sensitivity, and the Role of Bathymetry in Coral Reef Remote Sensing" Remote Sensing 12, no. 3: 496.

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