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dc.contributorNandy, Subrataen_US
dc.contributorSrinet, Ritikaen_US
dc.contributorViet Luong, Nguyenen_US
dc.contributorGhosh, Surajiten_US
dc.contributorSenthil Kumar, A.en_US
dc.creatorDang, An Thi Ngocen_US
dc.identifier.citationAn Thi Ngoc Dang, Subrata Nandy, Ritika Srinet, Nguyen Viet Luong, Surajit Ghosh, A. Senthil Kumar. (31/12/2018). Forest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnam. Ecological Informatics, 50, pp. 24-32.en_US
dc.description.abstractForest biomass is one of the key measurement for carbon budget accounting, carbon flux monitoring, and climate change studies. Hence, it is essential to develop a credible approach to estimate forest biomass and carbon stocks. Our study applied Sentinel-2 satellite imagery combined with field-measured biomass using Random Forest (RF), a machine learning regression algorithm, to estimate forest aboveground biomass (AGB) in Yok Don National Park, Vietnam. A total of 132 spectral and texture variables were extracted from Sentinel-2 imagery (February 7, 2017) to predict AGB of the National Park using RF algorithm. It was found that a combination of 132 spectral and texture variables could predict AGB with an R2 value of 0.94, RMSE of 34.5 Mgha−1 and % RMSE of 18.3%. RF regression algorithm was further used to reduce the number of variables in such a way that a minimum number of selected variables can be able to estimate AGB at a satisfactory level. A combination of 11 spectral and texture variables was identified based on out-of-bag (OOB) estimation to develop an easy-to-use model for estimating AGB. On validation, the model developed with 11 variables was able to predict AGB with R2=0.81, RMSE=36.67 Mg ha−1 and %RMSE of 19.55%. The results found in the present study demonstrated that Sentinel-2 imagery in conjunction with RF-based regression algorithm has the potential to effectively predict the spatial distribution of forest AGB with adequate accuracy.en_US
dc.publisherElsevier (12 months)en_US
dc.sourceEcological Informatics;50,(2018) Pagination 24-32en_US
dc.subjectspectral variablesen_US
dc.subjectrandom foresten_US
dc.subjectforest biomassen_US
dc.subjecttexture variablesen_US
dc.subjectvariable optimizationen_US
dc.titleForest aboveground biomass estimation using machine learning regression algorithm in Yok Don National Park, Vietnamen_US
dc.typeJournal Articleen_US
cg.creator.idGhosh, Surajit: 0000-0002-3928-2135en_US
cg.subject.agrovocgoal 15 life on landen_US
cg.contributor.centerInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.centerIndian Institute of Remote Sensing - IIRSen_US
cg.contributor.centerCentre for Space Science and Technology Education in Asia and the Pacific - CSSTEAPen_US
cg.contributor.centerVietnam Academy of Science and Technology , Space Technology Institute - VAST - STIen_US
cg.contributor.funderInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.contributor.projectCommunication and Documentation Information Services (CODIS)en_US
cg.contributor.project-lead-instituteInternational Center for Agricultural Research in the Dry Areas - ICARDAen_US
cg.coverage.regionSouthern Asiaen_US
cg.coverage.regionSouth-Eastern Asiaen_US
cg.isijournalISI Journalen_US
dc.identifier.statusTimeless limited accessen_US
cg.subject.sdgSDG 15 - Life on landen_US
cg.journalEcological Informaticsen_US

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