Evaluation and Validation of The MODIS LAI Algorithm with Digital Hemispherical Photography at Bhitar Kanika Mangrove Forest, India

Paramanik, Somnath; Behera, Laxmidhar; Bhattacharya, B. K.; Tripathi, Siddhant

doi:10.1109/igarss.2019.8899056

Cited by 4 publications

(1 citation statement)

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“…The spatial distributions of different species could indicate the effects of tidal inundation and the species-specific salt tolerance capacity. As species-wise ground spectra were unavailable, the spectral signature collected from the image highlighted the differential spectral responses of the three dominant species Heritiera fomes, Excoecaria agallocha, and Avicennia officinalis [66]. The spectral analysis of Avicennia officinalis showed higher reflectance in the NIR region; this may have been caused by the structure of the leaves and the leaf phenology phase.…”

Section: Discussionmentioning

confidence: 99%

Species-Level Classification and Mapping of a Mangrove Forest Using Random Forest—Utilisation of AVIRIS-NG and Sentinel Data

et al. 2021

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Although studies on species-level classification and mapping using multisource data and machine learning approaches are plenty, the use of data with ideal placement of central wavelength and bandwidth at appropriate spatial resolution, for the classification of mangrove species is underreported. The species composition of a mangrove forest has been estimated utilising the red-edge spectral bands and chlorophyll absorption information from AVIRIS-NG and Sentinel-2 data. In this study, three dominant species, Heritiera fomes, Excoecaria agallocha and Avicennia officinalis, have been classified using the random forest (RF) model for a mangrove forest in Bhitarkanika Wildlife Sanctuary, India. Various combinations of reflectance/backscatter bands and vegetation indices derived from Sentinel-2, AVIRIS-NG, and Sentinel-1 were used for species-level discrimination and mapping. The RF model showed maximum accuracy using Sentinel-2, followed by the AVIRIS-NG, in discriminating three dominant species and two mixed compositions. This study indicates the potential of Sentinel-2 data for discriminating various mangrove species owing to the appropriate placement of central wavelength and bandwidth in Sentinel-2 at ≥10 m spatial resolution. The variable importance plots proved that species-level classification could be attempted using red edge and chlorophyll absorption information. This study has wider applicability in other mangrove forests around the world.

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Section: Discussionmentioning

confidence: 99%