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Remote sensing and machine learning techniques for above-ground biomass estimation on a regional scale

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dc.contributor.authorAbdollahi, Abolfazl
dc.contributor.authorYebra, Marta
dc.contributor.editorVaze, J.
dc.contributor.editorChilcott, C.
dc.contributor.editorHutley, L.
dc.contributor.editorCuddy, S.M
dc.coverage.spatialDarwin, NT, Australia
dc.date.accessioned2024-06-11T05:21:10Z
dc.date.available2024-06-11T05:21:10Z
dc.date.created9-14 July 2023
dc.date.issued2023
dc.date.updated2024-05-12T08:16:13Z
dc.description.abstractThe present research centers around the assessment of biomass at a regional scale in the Greater Sydney region of Australia. This is achieved through the integration of data from the Sentinel-2 satellite, spaceborne Light Detection and Ranging (LiDAR) observations from the Global Ecosystem Dynamics Investigation (GEDI), and the utilization of machine learning algorithms.
dc.format.mimetypeapplication/pdfen_AU
dc.identifier.isbn978-0-9872143-0-0
dc.identifier.urihttps://hdl.handle.net/1885/733713135
dc.language.isoen_AUen_AU
dc.provenanceCreative Commons Attribution 4.0 International CC BY License (http://creativecommons.org/licenses/by/4.0)
dc.publisherModelling and Simulation Society of Australia and New Zealand Inc.
dc.relation.ispartofseries25th International Congress on Modelling and Simulation (MODSIM2023)
dc.rights© 2023
dc.rights.licenseCreative Commons Attribution 4.0 International CC BY License
dc.rights.urihttp://creativecommons.org/licenses/by/4.0
dc.sourceProceedings of the 25th International Congress on Modelling and Simulation
dc.source.urihttps://mssanz.org.au/modsim2023/
dc.titleRemote sensing and machine learning techniques for above-ground biomass estimation on a regional scale
dc.typeConference paper
dcterms.accessRightsOpen Access
local.contributor.affiliationAbdollahi, Abolfazl, College of Science, ANU
local.contributor.affiliationYebra, Marta, College of Science, ANU
local.contributor.authoruidAbdollahi, Abolfazl, u1122682
local.contributor.authoruidYebra, Marta, u5620051
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor410205 - Fire ecology
local.identifier.absfor410402 - Environmental assessment and monitoring
local.identifier.absfor401304 - Photogrammetry and remote sensing
local.identifier.ariespublicationu1122682xPUB8
local.identifier.doi10.36334/modsim.2023.abdollahi
local.publisher.urlhttps://mssanz.org.au/modsim2023/
local.type.statusPublished Version

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