An evaluation of approaches used to determine the distribution and biomass of emergent and submerged aquatic macrophytes over large spatial scales

Vis, Chantal; Hudon, Christiane; Carignan, Richard

doi:10.1016/s0304-3770(03)00105-0

Cited by 120 publications

(82 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Several authors, in fact, reported data that highlighted how July-August is the period of maximum rhizophyte biomass in temperate regions (Vis et al 2003;Bolpagni et al 2007) where usually plant senescence only starts at the end of September (Bolpagni et al 2007;Pierobon et al 2010).…”

Section: Image Processingmentioning

confidence: 99%

“…Conversely, aquatic plants and their properties are not as easily detectable as terrestrial vegetation. Vis et al (2003), however, demonstrated that remote sensing techniques are useful for investigating the shifts in the assemblages of primary producers by monitoring the distribution of aquatic macrophytes and changes in the layout of aquatic plant communities. As pointed out by a review of extant literature, major technical difficulties are met when vegetation is submerged or flooded; in fact the presence of water can alter the spectral characteristics of the images (an exhaustive discussion is presented in Silva et al 2008).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Retrospective assessment of macrophytic communities in southern Lake Garda (Italy) from in situ and MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) data

et al. 2012

View full text Add to dashboard Cite

show abstract

Section: Image Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Retrospective assessment of macrophytic communities in southern Lake Garda (Italy) from in situ and MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) data

et al. 2012

View full text Add to dashboard Cite

show abstract

“…Conventional field surveys of macrophyte communities are difficult to implement, requiring considerable time and expense to carry out over large spatial scales [4]. Even worse, traditional field-based surveys are commonly hindered by limited accessibility [5]. The technology of remote sensing has the advantage of a broad coverage and frequent data acquisitions, which makes it a valuable tool for the assessment of macrophyte distribution, vegetation status and related biophysical parameters.…”

Section: Introductionmentioning

confidence: 99%

Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis

Zhou

Sathyendranath

et al. 2018

Remote Sensing

View full text Add to dashboard Cite

Optical remote sensing of aquatic vegetation in shallow water is an essential aid to ecosystem protection, but it is difficult because the spectral characteristics of the vegetation are sensitive to external features such as water background effects, atmospheric effects, and the structural properties of the canopy. A global sensitivity analysis of an aquatic vegetation radiative transfer model provides invaluable background for algorithm development for use in optical remote sensing. Here, we use the extended Fourier Amplitude Sensitivity Test (EFAST) method for the modelling. Four different cases were identified by subdividing the ranges of water depth and leaf area index (LAI) involved. The results indicate that the reflectance of emergent vegetation is affected mainly by the concentrations of chlorophyll a + b in leaves (Cab), leaf inclination distribution function parameter (LIDFa) and LAI. The parameter LAI is influential in sparse vegetation cases whereas Cab and LIDFa are influential in dense vegetation cases. Canopy reflectance for submerged vegetation is dominated by water parameters. Relatively, LAI and Cab are highly sensitive vegetation parameters. The analysis is extended to vegetation index as well, which takes the Sentinel-2A as the reference sensor. It shows that NDAVI (Normalized Difference Aquatic Vegetation Index) is suitable for retrieving LAI in all cases except deep-sparse for emergent vegetation, whereas NDVI (Normalized Difference Vegetation Index) would be better in the deep-sparse case. NDVI, NDAVI and WAVI (Water Adjusted Vegetation Index), respectively, are suitable for retrieving Cab, Car and LIDFa in dense cases. For submerged vegetation, the sensitivity of LAI to NDAVI is relatively high only in the shallow-sparse case. The adjustment factor L in SAVI and WAVI fails to suppress the sensitivity to water constituent parameters. The sensitivity of LAI and Cab to NDVI in deep cases is relatively higher than that to the other indices, which may provide clues for the construction of inversion algorithms in macrophyte remote sensing in the aquatic environment using spectral signatures in the visible and near infrared regions.

show abstract

“…It is known that different types of aquatic vegetation have subtly different spectral reflectance signatures, which differ greatly from open water and non-vegetated areas (Marshall and Lee 1994, Ozesmi and Bauer 2002, Penuelas et al 1993. However, in the case of mixed beds, the varying contribution of each emergent macrophytes species to the total coverage remains difficult (Underwood et al 2006, Vis et al 2003.…”

Section: Remote Sensing and Gis For Aquatic Plant Studiesmentioning

confidence: 99%

Integration of multispectral satellite and hyperspectral field data for aquatic macrophyte studies

John

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Aquatic macrophytes (AM) can serve as useful indicators of water pollution along the littoral zones. The spectral signatures of various AM were investigated to determine whether species could be discriminated by remote sensing. In this study the spectral readings of different AM communities identified were done using the ASD Fieldspec® Hand Held spectro-radiometer in the wavelength range of 325 -1075nm. The collected specific reflectance spectra were applied to space borne multi-spectral remote sensing data from Worldview-2, acquired on 26 th March 2011. The dimensionality reduction of the spectro-radiometric data was done using the technique principal components analysis (PCA). Out of the different PCA axes generated, 93.472 % variance of the spectra was explained by the first axis. The spectral derivative analysis was done to identify the wavelength where the greatest difference in reflectance is shown. The identified wavelengths are 510, 690, 720, 756, 806, 885, 907 and 923 nm. The output of PCA and derivative analysis were applied to Worldview-2 satellite data for spectral subsetting. The unsupervised classification was used to effectively classify the AM species using the different spectral subsets. The accuracy assessment of the results of the unsupervised classification and their comparison were done. The overall accuracy of the result of unsupervised classification using the band combinations Red-Edge, Green, Coastal blue & Red-edge, Yellow, Blue is 100%. The band combinations NIR-1, Green, Coastal blue & NIR-1, Yellow, Blue yielded an accuracy of 82.35%. The existing vegetation indices and new hyper-spectral indices for the different type of AM communities were computed. Overall, results of this study suggest that high spectral and spatial resolution images provide useful information for natural resource managers especially with regard to the location identification and distribution mapping of macrophyte species and their communities.

show abstract

An evaluation of approaches used to determine the distribution and biomass of emergent and submerged aquatic macrophytes over large spatial scales

Cited by 120 publications

References 22 publications

Retrospective assessment of macrophytic communities in southern Lake Garda (Italy) from in situ and MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) data

Retrospective assessment of macrophytic communities in southern Lake Garda (Italy) from in situ and MIVIS (Multispectral Infrared and Visible Imaging Spectrometer) data

Canopy Reflectance Modeling of Aquatic Vegetation for Algorithm Development: Global Sensitivity Analysis

Integration of multispectral satellite and hyperspectral field data for aquatic macrophyte studies

Contact Info

Product

Resources

About