Mapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engine

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cg.contributor.affiliationInternational Water Management Instituteen
cg.contributor.donorIndian Space Research Organisationen
cg.coverage.countryIndia
cg.coverage.iso3166-alpha2IN
cg.coverage.regionSouthern Asia
cg.coverage.subregionHimalayan Foothill
cg.creator.identifierSurajit Ghosh: 0000-0002-3928-2135en
cg.identifier.doihttps://doi.org/10.1080/01431161.2020.1766147en
cg.identifier.iwmilibraryH050791en
cg.isijournalISI Journalen
cg.issn0143-1161en
cg.issue18en
cg.journalInternational Journal of Remote Sensingen
cg.reviewStatusPeer Reviewen
cg.volume41en
dc.contributor.authorSrinet, R.en
dc.contributor.authorNandy, S.en
dc.contributor.authorPadalia, H.en
dc.contributor.authorGhosh, Surajiten
dc.contributor.authorWatham, T.en
dc.contributor.authorPatel, N. R.en
dc.contributor.authorChauhan, P.en
dc.date.accessioned2021-11-20T09:53:36Zen
dc.date.available2021-11-20T09:53:36Zen
dc.identifier.urihttps://hdl.handle.net/10568/116172
dc.titleMapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engineen
dcterms.abstractPlant functional types (PFTs) have been widely used to represent the vegetation characteristics and their interlinkage with the surrounding environment in various earth system models. The present study aims to generate a PFT map for the Northwest Himalayan (NWH) foothills of India using seasonality parameters, topographic conditions, and climatic information from various satellite data and products using Random Forest (RF) algorithm in Google Earth Engine (GEE) platform. The seasonality information was extracted by carrying out a harmonic analysis of Normalized Difference Vegetation Index (NDVI) time-series (2008 to 2018) from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance 8 day 500 m data (MOD09A1). For topographic information, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) derived aspect and Multi-Scale Topographic Position Index (MTPI) were used, whereas, for climatic variables, WorldClim V2 Bioclimatic (Bioclim) variables were used. RF, a machine learning classifier, was used to generate a PFT map using these datasets. The overall accuracy of the resulting PFT map was found to be 83.33% with a Kappa coefficient of 0.71. The present study provides an effective approach for PFT classification using different well-established, freely available satellite data and products in the GEE platform. This approach can also be implemented in different ecological settings by using various meaningful variables at varying resolutions.en
dcterms.accessRightsLimited Access
dcterms.available2020-06-30en
dcterms.bibliographicCitationSrinet, R.; Nandy, S.; Padalia, H.; Ghosh, Surajit; Watham, T.; Patel, N. R.; Chauhan, P. 2020. Mapping plant functional types in Northwest Himalayan foothills of India using random forest algorithm in Google Earth Engine. International Journal of Remote Sensing, 41(18):7296-7309. [doi: https://doi.org/10.1080/01431161.2020.1766147]en
dcterms.extent7296-7309en
dcterms.issued2020-09-16en
dcterms.languageen
dcterms.licenseCopyrighted; all rights reserved
dcterms.publisherInforma UK Limiteden
dcterms.subjectforestsen
dcterms.subjecthighlandsen
dcterms.subjectnormalized difference vegetation indexen
dcterms.subjectecosystemsen
dcterms.subjecttime series analysisen
dcterms.subjectmoderate resolution imaging spectroradiometeren
dcterms.subjectdigital elevation modelsen
dcterms.subjectclimatic factorsen
dcterms.subjectmappingen
dcterms.typeJournal Article

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