Mapping coral reef benthic substrates using hyperspectral space-borne images and spectral libraries

Kutser, Tiit; Miller, Ian; Jupp, David L.B.

doi:10.1016/j.ecss.2006.06.026

Cited by 132 publications

(94 citation statements)

References 29 publications

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“…The Compact Airborne Spectrographic Imager (CASI) is a high spatial resolution hyperspectral airborne sensor, that has been used to map coral reef environments using both unsupervised [8] and supervised [9][10][11] classification approaches. Using a spectral reference library to supervise image classification has been shown to increase classification accuracy in comparison to supervised approaches using image-based statistics [12,13], and has the potential to automate coral reef mapping procedures independent of ancillary field surveys [2,14,15].…”

Section: Introductionmentioning

confidence: 99%

Mapping Coral Reef Benthos, Substrates, and Bathymetry, Using Compact Airborne Spectrographic Imager (CASI) Data

et al. 2014

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This study used a reef-up approach to map coral reef benthos, substrates and bathymetry, with high spatial resolution hyperspectral image data. It investigated a physics-based inversion method for mapping coral reef benthos and substrates using readily available software: Hydrolight and ENVI. Compact Airborne Spectrographic Imager (CASI) data were acquired over Heron Reef in July 2002. The spectral reflectance of coral reef benthos and substrate types were measured in-situ, and using the HydroLight 4.2 radiative transfer model a spectral reflectance library of subsurface reflectance was simulated using water column depths from 0.5-10.0 m at 0.5 m intervals. Using the Spectral Angle Mapper algorithm, sediment, benthic micro-algae, algal turf, crustose coralline algae, macro-algae, and live coral were mapped with an overall accuracy of 65% to a depth of around 8.0 m; in waters deeper than 8.0 m the match between the classified image and field validation data was poor. Qualitative validation of the maps showed accurate mapping of areas dominated by sediment, benthic micro-algae, algal turf, live coral, and macro-algae. A bathymetric map was produced for water column depths 0.5-10.0 m, at 0.5 m intervals, and showed high correspondence with in-situ sonar data (R 2 value of 0.93). OPEN ACCESSRemote Sens. 2014, 6 6424

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Section: Introductionmentioning

confidence: 99%

Mapping Coral Reef Benthos, Substrates, and Bathymetry, Using Compact Airborne Spectrographic Imager (CASI) Data

et al. 2014

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“…In particular, Dekker et al [29] showed that inversion methods applied to airborne hyperspectral data provided moderately accurate retrievals of bathymetry, water column inherent optical properties and benthic reflectance in waters less than 13 m deep with homogeneous to heterogeneous benthic/substrate covers. Phinn et al [32] and reference herein suggested that multi-spectral image bands only provided discrimination between seagrass species and broad cover classes to a depth of 3 m. Kutser et al [33] also noted a limitation mapping coral reef benthos and substrates in water depths greater than 8 m.…”

Section: Image Pre-processing and Classification Strategymentioning

confidence: 99%

Mapping Submerged Habitats and Mangroves of Lampi Island Marine National Park (Myanmar) from in Situ and Satellite Observations

Giardino¹,

Bresciani²,

Fava

et al. 2015

Remote Sensing

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Abstract:In this study we produced the first thematic maps of submerged and coastal habitats of Lampi Island (Myanmar) from in situ and satellite data. To focus on key elements of bio-diversity typically existing in tropical islands the detection of corals, seagrass, and mangrove forests was addressed. Satellite data were acquired from Landsat-8; for the purpose of validation Rapid-Eye data were also used. In situ data supporting image processing were collected in a field campaign performed from 28 February to 4 March 2015 at the time of sensors overpasses. A hybrid approach based on bio-optical modeling and supervised classification techniques was applied to atmospherically-corrected Landsat-8 data. Bottom depth estimations, to be used in the classification process of shallow waters, were in good agreement with depth soundings (R 2 = 0.87). Corals were classified with producer and user accuracies of 58% and 77%, while a lower accuracy (producer and user accuracies of 50%) was found for the seagrass due to the patchy distribution of meadows; accuracies more than 88% were obtained for mangrove forests. The classification indicated the presence of 18 mangroves sites with extension larger than 5 km 2 ; for 15 of those the coexistence of corals and seagrass were also found in the fronting bays, suggesting a significant rate of biodiversity for the study area.

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“…Kutser et al [27] have proved that the bottom type and water depth of the Australian Great Barrier Reef (GBR) can be simultaneously mapped by matching the Hyperion at-sensor radiance data to the simulated at-sensor spectral library. Lafon et al [28] proposed a semi-empirical bathymetric methodology based on the Hydrolight Radiative Transfer code, which was calibrated using in situ bottom reflectance and effective attenuation coefficient measurements and then applied to determine the depth from SPOT images and to develop topographic maps of the tidal inlet of Arcachon.…”

Section: Related Workmentioning

confidence: 99%

Bathymetry of the Coral Reefs of Weizhou Island Based on Multispectral Satellite Images

Huang

Wang

et al. 2017

Remote Sensing

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Shallow water depth measurements using multispectral images are crucial for marine surveying and mapping. At present, relevant studies either depend on the use of other auxiliary data (such as field water depths or water column data) or contain too many unknown variables, thus making these studies suitable only for images that contain enough visible wavebands. To solve this problem, a Quasi-Analytical Algorithm (QAA) approach is proposed in this paper for estimating the water depths around Weizhou Island by developing a QAA to estimate the diffuse attenuation coefficients and simplifying the parameterization of the bathymetric model. The approach contains an initialization sub-approach and a novel global adjustment sub-approach. It is not only independent of other auxiliary data but also greatly reduces the number of unknowns. Experimental results finally demonstrated that the Root Mean Square Errors (RMSEs) were 1.01 m and 0.77 m for the ZY-3 image and the WorldView-3 (WV-3) image, respectively, so the approach is competitive to other QAA bathymetric methods. Besides, the global adjustment sub-approach was also seen to be superior to common smoothing filters: if the Signal-to-Noise Ratio (SNR) is as low as 42, i.e., ZY-3, it can smooth the water depths and improve the accuracies, otherwise can avoid the over-smoothing of water depths.

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Mapping coral reef benthic substrates using hyperspectral space-borne images and spectral libraries

Cited by 132 publications

References 29 publications

Mapping Coral Reef Benthos, Substrates, and Bathymetry, Using Compact Airborne Spectrographic Imager (CASI) Data

Mapping Coral Reef Benthos, Substrates, and Bathymetry, Using Compact Airborne Spectrographic Imager (CASI) Data

Mapping Submerged Habitats and Mangroves of Lampi Island Marine National Park (Myanmar) from in Situ and Satellite Observations

Bathymetry of the Coral Reefs of Weizhou Island Based on Multispectral Satellite Images

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