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

Abstract

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.

Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine

“…In previous studies, the dynamics of water column attenuation have posed a challenge in bathymetry estimation. Thus, previous bathymetric algorithms have relied on field calibration or complex physical modeling to simulate a broad range of water conditions [19,51,60]. However, these methods are not sufficient to overcome the dynamics of water column attenuation at a global scale.…”

The Future Is Big—and Small: Remote Sensing Enables Cross-Scale Comparisons of Microbiome Dynamics and Ecological Consequences

“…(B) In seagrass meadows, the infectious pathogen Labyrinthula zosterae causes seagrass wasting disease by invading plant tissue and attacking chloroplasts, with severe outbreaks causing shoot mortality and meadow decline ( 58 ); aerial images can detect changes in meadow extent ( 17 ) and may detect damage to seagrass tissue ( 18 ). (C) Intertidal and subtidal coral reefs are another system where remote sensing of microbial dynamics underpinning ecosystem disturbance (e.g., coral bleaching) may be possible as algorithms advance and hyperspectral sensors become more affordable ( 18 , 21 , 32 ). Figure created by Lillian R. Aoki, with feedback and contributions from all authors.…”

“…Seagrasses create habitat that supports biodiversity but are declining globally due to multiple stressors, including disease ( 29 , 30 ). Meadows grow in shallow coastal waters; intertidal seagrass can be mapped at low tide by UAVs ( 17 , 31 ), and water correction algorithms allow mapping of subtidal meadows ( 32 , 33 ). We can derive ecosystem-level characteristics such as plant biomass and above-ground carbon stocks from UAVs and satellite measurements in response to disease outbreaks ( 34 , 35 ).…”

Underwater Topography Inversion in Liaodong Shoal Based on GRU Deep Learning Model

“…Kerr et al [14] develop an approach for predicting water depth in tropical carbonate landscapes from a multispectral satellite image without the need for ground-truth data. Goodman et al [15] utilized hyperspectral data to evaluate the performance and sensitivity of a representative semi-analytical inversion model for deriving water depth and benthic surface reflectance. With the development of airborne LiDAR, a series of bathymetry research with higher accuracy has been carried out [16][17][18].…”