Accuracy Assessment of Hyperspectral Classification of Coral Reef Features

Holden, H.; LeDrew, E.

doi:10.1080/10106049908542147

Cited by 14 publications

(6 citation statements)

References 18 publications

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“…Numerous studies have successfully integrated fieldwork and various remote sensing techniques that have identified coral morphologies, habitat maps, live coral indices, coral reef resilience indicators, and other general reef categories [8,15,16,21,23,33,34]. Likewise, the ability to detect coral bleaching [35][36][37][38] and diseased states in corals [39], continues to improve with recent advances in remotes sensing. Even so, coral reef managers are yet calling for an improved understanding of biodiversity (species) estimates derived through remote sensing [1,2,31].…”

Section: Introductionmentioning

confidence: 99%

Linking Coral Reef Remote Sensing and Field Ecology: It’s a Matter of Scale

Lucas

Goodman

2014

JMSE

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Remote sensing shows potential for assessing biodiversity of coral reefs. Important steps in achieving this objective are better understanding the spectral variability of various reef components and correlating these spectral characteristics with field-based ecological assessments. Here we analyze >9400 coral reef field spectra from southwestern Puerto Rico to evaluate how spectral variability and, more specifically, spectral similarity between species influences estimates of biodiversity. Traditional field methods for estimating reef biodiversity using photoquadrats are also included to add ecological context to the spectral analysis. Results show that while many species can be distinguished using in situ field spectra, the addition of the overlying water column significantly reduces the ability to differentiate species, and even groups of species. This indicates that the ability to evaluate biodiversity with remote sensing decreases with increasing water depth. Due to the inherent spectral similarity amongst many species, including taxonomically dissimilar species, remote sensing underestimates biodiversity and represents the lower limit of actual species diversity.

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Section: Introductionmentioning

confidence: 99%

Linking Coral Reef Remote Sensing and Field Ecology: It’s a Matter of Scale

Lucas

Goodman

2014

JMSE

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“…Several models exist (e.g., Hydrolight), or are under development to perform such corrections, however they are generally designed for analysis and modeling of a single spectrum and not for full‐image, per‐pixel correction of hyperspectral data. Limited examples are available of the application of water column corrections to field or modeled spectra [ Young et al , 1995; Clark et al , 2000; Holden and LeDrew , 2000, 2001, 2002; Kutser et al , 2003; Mobley et al , 2003] or directly to hyperspectral data [ Gao et al , 2000; Goodman et al , 2003].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the detection and classification of coral reef and associated benthic habitats: A hyperspectral remote sensing approach

Mishra¹,

Narumalani²,

Rundquist³

et al. 2007

J. Geophys. Res.

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[1] Coral reefs and associated benthic habitats are heterogeneous in nature. A remote sensor designed to discriminate these environments requires a high number of narrow, properly placed bands which are not currently available in existing satellite sensors. Optical hyperspectral sensors mounted on aerial platforms seem to be appropriate for overcoming the lack of both high spectral and spatial resolution of satellite sensors. This research presents results of an innovative coral reef application by such a sensor. Using hyperspectral Airborne Imaging Spectroradiometer for Applications (AISA) Eagle data, the approach presented solves the confounding influence of water column attenuation on substrate reflectance on a per-pixel basis. The hyperspectral imagery was used in band ratio algorithms to derive water depth and water column optical properties (e.g., absorption and backscattering coefficients). The water column correction technique produced a bottom albedo image which revealed that the dark regions comprised of sea grasses and benthic algae had albedo values %15%, whereas sand-and coral-dominated areas had albedos >30% and %15-35%, respectively. The retrieved bottom albedo image was then used to classify the benthos, generating a detailed map of benthic habitats, followed by accuracy assessment.

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“…This information can be collected using imaging spectrometers (e.g., hyperspectral scanners) or with individual point based spectroscopy [14]. Spectroscopy has been used to distinguish between live coral and other coral reef benthos in the past, but these studies have largely been limited to in situ underwater or closerange measurements [15][16][17][18][19][20]. Capturing data in that way is time intensive and provides limited coverage.…”

Section: Introductionmentioning

confidence: 99%

“…In using drones, a major consideration is the influence of the water column and the nature of its influence under different light and environmental conditions, such as waves. With varying depths and water quality, there is likely increased confusion between more spectrally similar classes such as algae and coral due to uneven attenuation throughout spectral signatures [18]. For example, it has been found that with higher chlorophyll or sediment content in the water, more algae will likely be classified as coral [22].…”

Section: Introductionmentioning

confidence: 99%

Assessing the Potential of Remotely-Sensed Drone Spectroscopy to Determine Live Coral Cover on Heron Reef

Cornet

Joyce

2021

Drones

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Coral reefs, as biologically diverse ecosystems, hold significant ecological and economic value. With increased threats imposed on them, it is increasingly important to monitor reef health by developing accessible methods to quantify coral cover. Discriminating between substrate types has previously been achieved with in situ spectroscopy but has not been tested using drones. In this study, we test the ability of using point-based drone spectroscopy to determine substrate cover through spectral unmixing on a portion of Heron Reef, Australia. A spectral mixture analysis was conducted to separate the components contributing to spectral signatures obtained across the reef. The pure spectra used to unmix measured data include live coral, algae, sand, and rock, obtained from a public spectral library. These were able to account for over 82% of the spectral mixing captured in each spectroscopy measurement, highlighting the benefits of using a public database. The unmixing results were then compared to a categorical classification on an overlapping mosaicked drone image but yielded inconclusive results due to challenges in co-registration. This study uniquely showcases the potential of using commercial-grade drones and point spectroscopy in mapping complex environments. This can pave the way for future research, by increasing access to repeatable, effective, and affordable technology.

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Accuracy Assessment of Hyperspectral Classification of Coral Reef Features

Cited by 14 publications

References 18 publications

Linking Coral Reef Remote Sensing and Field Ecology: It’s a Matter of Scale

Linking Coral Reef Remote Sensing and Field Ecology: It’s a Matter of Scale

Enhancing the detection and classification of coral reef and associated benthic habitats: A hyperspectral remote sensing approach

Assessing the Potential of Remotely-Sensed Drone Spectroscopy to Determine Live Coral Cover on Heron Reef

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