A physics-based method for the remote sensing of seagrasses

Hedley, John D.; Russell, Brandon; Randolph, Kaylan; Dierssen, Heidi M.

doi:10.1016/j.rse.2015.12.001

Cited by 47 publications

(56 citation statements)

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“…The steps in the development of the physics-based model are similar to those described in Hedley and Enríquez (2010), Hedley et al (2015), so here the description is briefer and focuses on the key differences in the current work. The two species considered are T. testudinium and S. filiforme, for readability these are henceforth referred to simply as Thalassia and Syringodium.…”

Section: Overviewmentioning

confidence: 99%

“…In addition, the underlying model can be used for sensitivity analysis before image processing. While sensitivity analyses and uncertainty propagation are key tools for predicting capability and informing on sensor design (Lubin et al, 2001;Hochberg and Atkinson, 2003;Hedley et al, 2012bHedley et al, , 2015Botha et al, 2013) their results are not often directly compared to practical image analyses, to determine if the predictions of the sensitivity analysis are borne out in practice.…”

Section: Introductionmentioning

confidence: 99%

“…Physics-based inversion methods have been applied in seagrass environments (Dekker et al, 2011;Hedley et al, 2015) and are in theory more transferable, since they can be parameterized generically and are not linked to any specific site or imagery. Being based on a physical model rather than statistical inference, these methods also facilitate greater understanding of the fundamental limitations and uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…As an advance to previous work (Hedley et al, 2015) the new model incorporates two species, Thalassia testudinum and Syringodium filiforme, and incorporates uncertainty in the leaf reflectance of both species, in addition to variation in canopy structure, water optical properties and depth. Here we describe the application of the model in a sensitivity analysis and to hyperspectral imagery of Florida Bay.…”

Section: Introductionmentioning

confidence: 99%

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Remote Sensing of Seagrass Leaf Area Index and Species: The Capability of a Model Inversion Method Assessed by Sensitivity Analysis and Hyperspectral Data of Florida Bay

Hedley¹,

Russell

Randolph

et al. 2017

Front. Mar. Sci.

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The capability for mapping two species of seagrass, Thalassia testudinium and Syringodium filiforme, by remote sensing using a physics based model inversion method was investigated. The model was based on a three-dimensional canopy model combined with a model for the overlying water column. The model included uncertainty propagation based on variation in leaf reflectances, canopy structure, water column properties, and the air-water interface. The uncertainty propagation enabled both a-priori predictive sensitivity analysis of potential capability and the generation of per-pixel error bars when applied to imagery. A primary aim of the work was to compare the sensitivity analysis to results achieved in a practical application using airborne hyperspectral data, to gain insight on the validity of sensitivity analyses in general. Results showed that while the sensitivity analysis predicted a weak but positive discrimination capability for species, in a practical application the relevant spectral differences were extremely small compared to discrepancies in the radiometric alignment of the model with the imagery-even though this alignment was very good. Complex interactions between spectral matching and uncertainty propagation also introduced biases. Ability to discriminate LAI was good, and comparable to previously published methods using different approaches. The main limitation in this respect was spatial alignment with the imagery with in situ data, which was heterogeneous on scales of a few meters. The results provide insight on the limitations of physics based inversion methods and seagrass mapping in general. Complex models can degrade unpredictably when radiometric alignment of the model and imagery is not perfect and incorporating uncertainties can have non-intuitive impacts on method performance. Sensitivity analyses are upper bounds to practical Hedley et al.Remote Sensing of Seagrasses capability, incorporating a term for potential systematic errors in radiometric alignment may be advisable. While T. testudinium and S. filiforme were too spectrally similar to be discriminated purely on spectral grounds, mapping of these, and other species may be achievable by exploiting co-incident factors based on ecological zonation.

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Section: Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Remote Sensing of Seagrass Leaf Area Index and Species: The Capability of a Model Inversion Method Assessed by Sensitivity Analysis and Hyperspectral Data of Florida Bay

Hedley¹,

Russell

Randolph

et al. 2017

Front. Mar. Sci.

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“…Knudby et al [11] further applied this ALUT method to Landsat 8 data for producing the bathymetric maps for shallow waters in Canada. Recently, Hedley et al [33] further presented a method for mapping the water depths and the leaf area index (LAI) of seagrasses according to the idea that variations in reflectance is caused by leaf length, leaf position, sediment coverage on leaves, water depth, and solar zenith angle, etc. Sylvain et al [34] also presented a novel statistical semi-analytic method for mapping water depth and water quality using hyperspectral remote-sensing data, which is based on local maximum likelihood (ML) estimation performed on subimages.…”

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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Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

Muller‐Karger

Hestir

Ade

et al. 2018

Ecological Applications

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119

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The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

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A physics-based method for the remote sensing of seagrasses

Cited by 47 publications

References 48 publications

Remote Sensing of Seagrass Leaf Area Index and Species: The Capability of a Model Inversion Method Assessed by Sensitivity Analysis and Hyperspectral Data of Florida Bay

Remote Sensing of Seagrass Leaf Area Index and Species: The Capability of a Model Inversion Method Assessed by Sensitivity Analysis and Hyperspectral Data of Florida Bay

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

Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems

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