Hyperspectral remote sensing of coral reefs by semi-analytical model inversion – Comparison of different inversion setups

Petit, Tristan; Bajjouk, Touria; Mouquet, Pascal; Rochette, Sébastien; Vozel, Benôıt; Delacourt, Christophe

doi:10.1016/j.rse.2017.01.004

Cited by 53 publications

(38 citation statements)

References 45 publications

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“…Common methods applied to satellite imagery include the inversion of physically based models [28] based on former studies by Lee et al (1999Lee et al ( , 2001 [29,30]. Several authors extended the method [31] to a wide range of applications, including SAV mapping from Sentinel-2 [32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics of Submerged Aquatic Vegetation in a Deep Lake from Sentinel-2 Data

Ghirardi

Bolpagni

Bresciani

et al. 2019

Water

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We mapped the extent of submerged aquatic vegetation (SAV) of Lake Iseo (Northern Italy, over the 2015–2017 period based on satellite data (Sentinel 2 A-B) and in-situ measurements; the objective was to investigate its spatiotemporal variability. We focused on the southern sector of the lake, the location of the shallowest littorals and the most developed macrophyte communities, mainly dominated by Vallisneria spiralis and Najas marina. The method made use of both in-situ measurements and satellite data (22 Sentinel 2 A-B images) that were atmospherically corrected with 6SV code and processed with the BOMBER (Bio-Optical Model-Based tool for Estimating water quality and bottom properties from Remote sensing images). This modeling system was used to estimate the different substrate coverage (bare sediment, dense stands of macrophytes with high albedo, and sparse stand of macrophytes with low albedo). The presented results substantiate the existence of striking inter- and intra-annual variations in the spatial-cover patterns of SAV. Intense uprooting phenomena were also detected, mainly affecting V. spiralis, a species generally considered a highly plastic pioneer taxon. In this context, remote sensing emerges as a very reliable tool for mapping SAV with satisfactory accuracy by offering new perspectives for expanding our comprehension of lacustrine macrophyte dynamics and overcoming some limitations associated with traditional field surveys.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal Dynamics of Submerged Aquatic Vegetation in a Deep Lake from Sentinel-2 Data

Ghirardi

Bolpagni

Bresciani

et al. 2019

Water

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“…Spectral optimization is then applied to determine the values of the scalar variables whose modeled R rs (R M rs ) best matches the sensor-derived R rs (R sens rs ). SA models based on Lee et al [15] include BRUCE [16], SAMBUCA [17] and BOMBER [18] and those described in Jay et al [19] and Petit et al [20]. However, use of a single endmember to model bottom reflectance (as in HOPE) confounds benthic retrievals for heterogeneous pixels or other pixels not represented by the single endmember.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Shallow-Water Remote Sensing with an Enhanced Benthic Classifier

2018

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Hyperspectral remote sensing inversion models utilize spectral information over optically shallow waters to retrieve optical properties of the water column, bottom depth and reflectance, with the latter used in benthic classification. Accuracy of these retrievals is dependent on the spectral endmember(s) used to model the bottom reflectance during the inversion. Without prior knowledge of these endmember(s) current approaches must iterate through a list of endmember-a computationally demanding task. To address this, a novel lookup table classification approach termed HOPE-LUT was developed for selecting the likely benthic endmembers of any hyperspectral image pixel. HOPE-LUT classifies a pixel as sand, mixture or non-sand, then the latter two are resolved into the three most likely classes. Optimization subsequently selects the class (out of the three) that generated the best fit to the remote sensing reflectance. For a coral reef case, modeling results indicate very high benthic classification accuracy (>90%) for depths less than 4 m of common coral reef benthos. These accuracies decrease substantially with increasing depth due to the loss of bottom information, especially the spectral signatures. We applied this technique to hyperspectral airborne imagery of Heron Reef, Great Barrier Reef and generated benthic habitat maps with higher classification accuracy compared to standard inversion models.

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“…high spectral resolution and broad spectral coverage to enable addressing a large variety of topics and questions, such as deriving water depth, water properties and benthic habitat by optimization [9,10,20], coral reef monitoring [20], or biofilm characterization [8,21]; -high spatial resolution (<10 cm) to study structures with complex geometry, such as coral reefs [20], or small features, such as the holes of mangrove crabs [22].…”

Section: Introductionmentioning

confidence: 99%

Direct Georeferencing of a Pushbroom, Lightweight Hyperspectral System for Mini-UAV Applications

et al. 2018

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Abstract:Hyperspectral imagery has proven its potential in many research applications, especially in the field of environmental sciences. Currently, hyperspectral imaging is generally performed by satellite or aircraft platforms, but mini-UAV (Unmanned Aerial Vehicle) platforms (<20 kg) are now emerging. On such platforms, payload restrictions are critical, so sensors must be selected according to stringent specifications. This article presents the integration of a light pushbroom hyperspectral sensor onboard a multirotor UAV, which we have called Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations). This article depicts the system design: the UAV platform, the imaging module, the navigation module, and the interfacing between the different elements. Pushbroom sensors offer a better combination of spatial and spectral resolution than full-frame cameras. Nevertheless, data georectification has to be performed line by line, the quality of direct georeferencing being limited by mechanical stability, good timing accuracy, and the resolution and accuracy of the proprioceptive sensors. A georegistration procedure is proposed for geometrical pre-processing of hyperspectral data. The specifications of Hyper-DRELIO surveys are described through two examples of surveys above coastal or inland waters, with different flight altitudes. This system can collect hyperspectral data in VNIR (Visible and Near InfraRed) domain above small study sites (up to about 4 ha) with both high spatial resolution (<10 cm) and high spectral resolution (1.85 nm) and with georectification accuracy on the order of 1 to 2 m.

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Hyperspectral remote sensing of coral reefs by semi-analytical model inversion – Comparison of different inversion setups

Cited by 53 publications

References 45 publications

Spatiotemporal Dynamics of Submerged Aquatic Vegetation in a Deep Lake from Sentinel-2 Data

Spatiotemporal Dynamics of Submerged Aquatic Vegetation in a Deep Lake from Sentinel-2 Data

Hyperspectral Shallow-Water Remote Sensing with an Enhanced Benthic Classifier

Direct Georeferencing of a Pushbroom, Lightweight Hyperspectral System for Mini-UAV Applications

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