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Species-level classification and mapping of a mangrove forest using random forest—utilisation of AVIRIS-NG and sentinel data

Species-level classification and mapping of a mangrove forest using random forest—utilisation of AVIRIS-NG and sentinel data
Species-level classification and mapping of a mangrove forest using random forest—utilisation of AVIRIS-NG and sentinel data
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.
2072-4292
Behera, Mukunda Dev
c518f934-4dea-40bd-a947-a561686ee674
Barnwal, Surbhi
844ef9c2-06c5-40d0-be7b-6cc9103e0c44
Paramanik, Somnath
8fb0a9ec-ddf2-4ceb-a749-131a401c3753
Das, Pulakesh
bf9629da-26f6-42b2-bc9e-c0673ecc2880
Bhattyacharya, Bimal Kumar
c003c502-8b42-4e22-b051-b33e8c0284bd
Jagadish, Buddolla
b305f398-f49d-441b-8a53-167060bc4bce
Roy, Parth S.
6f6214ce-214e-4a6c-accc-e9ca1c012007
Ghosh, Sujit Madhab
a43fa7bd-10af-4508-b8b1-3ff85dc317ae
Behera, Soumit Kumar
66900de5-89c5-47e0-97eb-18c6fa530a94
Behera, Mukunda Dev
c518f934-4dea-40bd-a947-a561686ee674
Barnwal, Surbhi
844ef9c2-06c5-40d0-be7b-6cc9103e0c44
Paramanik, Somnath
8fb0a9ec-ddf2-4ceb-a749-131a401c3753
Das, Pulakesh
bf9629da-26f6-42b2-bc9e-c0673ecc2880
Bhattyacharya, Bimal Kumar
c003c502-8b42-4e22-b051-b33e8c0284bd
Jagadish, Buddolla
b305f398-f49d-441b-8a53-167060bc4bce
Roy, Parth S.
6f6214ce-214e-4a6c-accc-e9ca1c012007
Ghosh, Sujit Madhab
a43fa7bd-10af-4508-b8b1-3ff85dc317ae
Behera, Soumit Kumar
66900de5-89c5-47e0-97eb-18c6fa530a94

Behera, Mukunda Dev, Barnwal, Surbhi, Paramanik, Somnath, Das, Pulakesh, Bhattyacharya, Bimal Kumar, Jagadish, Buddolla, Roy, Parth S., Ghosh, Sujit Madhab and Behera, Soumit Kumar (2021) Species-level classification and mapping of a mangrove forest using random forest—utilisation of AVIRIS-NG and sentinel data. Remote Sensing, 13 (11). (doi:10.3390/rs13112027).

Record type: Article

Abstract

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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Accepted/In Press date: 13 May 2021
Published date: 21 May 2021

Identifiers

Local EPrints ID: 484780
URI: http://eprints.soton.ac.uk/id/eprint/484780
ISSN: 2072-4292
PURE UUID: a55afe44-e6cd-4691-af67-900e5977deea
ORCID for Somnath Paramanik: ORCID iD orcid.org/0000-0002-4509-8801

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Date deposited: 21 Nov 2023 17:46
Last modified: 12 Jun 2024 02:10

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Contributors

Author: Mukunda Dev Behera
Author: Surbhi Barnwal
Author: Somnath Paramanik ORCID iD
Author: Pulakesh Das
Author: Bimal Kumar Bhattyacharya
Author: Buddolla Jagadish
Author: Parth S. Roy
Author: Sujit Madhab Ghosh
Author: Soumit Kumar Behera

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