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

Autor:	Partha Sarathi Roy, Somnath Paramanik, Buddolla Jagadish, Bimal Kumar Bhattyacharya, Sujit Madhab Ghosh, Soumit K. Behera, Surbhi Barnwal, Pulakesh Das, Mukunda Dev Behera
Rok vydání:	2021
Předmět:	010504 meteorology & atmospheric sciences Science 0211 other engineering and technologies Red edge 02 engineering and technology Excoecaria agallocha AVIRIS-NG 01 natural sciences red edge Bhitarkanika Wildlife Sanctuary random forest species-level classification Avicennia officinalis 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing biology Spectral bands Vegetation biology.organism_classification Random forest General Earth and Planetary Sciences Environmental science Heritiera fomes Mangrove
Zdroj:	Remote Sensing, Vol 13, Iss 2027, p 2027 (2021) Remote Sensing; Volume 13; Issue 11; Pages: 2027
ISSN:	2072-4292
DOI:	10.3390/rs13112027
Popis:	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.
Databáze:	OpenAIRE
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