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

Abstract

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.

Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California

“…The LAI values measured in Elkhorn Slough are high compared to some of the tropical seagrass meadows in Florida and the Bahamas [3,4,33,60], and the beds are considered to be dense seagrass canopies where the underlying sediment is not generally visible. Recent modeling work has shown that the relationship between top of the canopy reflectance of light and LAI decreases exponentially with increasing LAI [4,11]. In other words, the color at the top of the canopy darkens rapidly with initially increases in LAI, but the canopy color "saturates" at some density of LAI ( Figure 4A).…”

“…Chl a = e ln (P/0.06)/0.65 (11) The retrieval ultimately aims to determine benthic reflectance R b . It is important to constrain the range of possible R b spectra to avoid the intrinsic ambiguity between bottom reflectance and water properties following Thompson et al [56].…”

“…Remote sensing techniques are being increasingly employed to monitor the distribution of submerged aquatic vegetation, such as seagrass meadows. These can vary from employing semi-empirical classifications from multichannel images from high-resolution satellites [1][2][3][4][5][6][7][8][9] to semi-analytical algorithms from airborne hyperspectral imagery [4,[10][11][12]. Multichannel broadband sensors like Landsat and Ikonos are quite useful to delineate the presence or absence of benthic vegetation at low cost, but they lack spectral information that is necessary for determining the benthic composition without a priori knowledge or coincident field campaigns [8].…”

“…Radiative transfer models can now incorporate seagrass as a three-dimensional canopy in a moving fluid [34,35]. Sensitivity analyses based on leaf length, position, sediment coverage, and water depth revealed that there are limits to the density of the canopy that can be optically detected, and these limits are dependent on density and current flow that exposes more horizontal leaf area [4,11]. Modeling coupled with image analysis has also revealed that different genera of seagrass, such as the flat-leafed Thalassia testudinum and the cylindrical-leafed Syringodium filiforme, are too spectrally similar to be readily distinguished from hyperspectral remote sensing imagery [11].…”

Seagrass Resource Assessment Using WorldView-2 Imagery in the Redfish Bay, Texas

“…Some investigators used pixel-based classifiers, for examples, the maximum likelihood classifier on the original spectral bands and the spectral indices [14], support vector machines on three sunglint-corrected spectral bands and a depth invariant index [8], classification trees on spectral index after simple atmospheric correction [18]. Some researchers mapped seagrass species by pixels using a physics based model inversion [19], or spectrum matching and look-up tables [10]. Other investigators used the object based image analysis (OBIA) with unsupervised classifier [17] or regression tree classifier [13].…”