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

Abolfazl Abdollahi; Marta Yebra

doi:10.36334/modsim.2023.abdollahi

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The 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.

Original language	English
Title of host publication	Proceedings of the 25th International Congress on Modelling and Simulation
Editors	Vaze, J., Chilcott, C., Hutley, L. and Cuddy, S.M
Place of Publication	Australia
Publisher	Modelling and Simulation Society of Australia and New Zealand Inc.
ISBN (Print)	978-0-9872143-0-0
DOIs	https://doi.org/10.36334/modsim.2023.abdollahi
Publication status	Published - 2023
Event	25th International Congress on Modelling and Simulation, MODSIM 2023 - Darwin, Australia Duration: 9 Jul 2023 → 14 Jul 2023

Conference

Conference	25th International Congress on Modelling and Simulation, MODSIM 2023
Country/Territory	Australia
City	Darwin
Period	9/07/23 → 14/07/23

Access to Document

10.36334/modsim.2023.abdollahi

Cite this

Abdollahi, A., & Yebra, M. (2023). Remote sensing and machine learning techniques for above-ground biomass estimation on a regional scale. In V. J. . C. C. . H. L. A. C. S. M. (Ed.), Proceedings of the 25th International Congress on Modelling and Simulation Modelling and Simulation Society of Australia and New Zealand Inc.. https://doi.org/10.36334/modsim.2023.abdollahi

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

abstract = "The 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.",

author = "Abolfazl Abdollahi and Marta Yebra",

note = "From Stream C2. GIS, remote sensing and data-driven techniques; 25th International Congress on Modelling and Simulation, MODSIM 2023 ; Conference date: 09-07-2023 Through 14-07-2023",

year = "2023",

doi = "10.36334/modsim.2023.abdollahi",

language = "English",

isbn = "978-0-9872143-0-0",

editor = "{Vaze, J., Chilcott, C., Hutley, L. and Cuddy, S.M}",

booktitle = "Proceedings of the 25th International Congress on Modelling and Simulation",

publisher = "Modelling and Simulation Society of Australia and New Zealand Inc.",

}

Abdollahi, A & Yebra, M 2023, Remote sensing and machine learning techniques for above-ground biomass estimation on a regional scale. in VJCCHLACSM (ed.), Proceedings of the 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand Inc., Australia, 25th International Congress on Modelling and Simulation, MODSIM 2023, Darwin, Australia, 9/07/23. https://doi.org/10.36334/modsim.2023.abdollahi

Remote sensing and machine learning techniques for above-ground biomass estimation on a regional scale. / Abdollahi, Abolfazl ; Yebra, Marta.
Proceedings of the 25th International Congress on Modelling and Simulation. ed. / Vaze, J., Chilcott, C., Hutley, L. and Cuddy, S.M. Australia: Modelling and Simulation Society of Australia and New Zealand Inc., 2023.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Remote sensing and machine learning techniques for above-ground biomass estimation on a regional scale

AU - Abdollahi, Abolfazl

AU - Yebra, Marta

N1 - From Stream C2. GIS, remote sensing and data-driven techniques

PY - 2023

Y1 - 2023

N2 - The 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.

AB - The 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.

U2 - 10.36334/modsim.2023.abdollahi

DO - 10.36334/modsim.2023.abdollahi

M3 - Conference contribution

SN - 978-0-9872143-0-0

BT - Proceedings of the 25th International Congress on Modelling and Simulation

A2 - null, Vaze, J., Chilcott, C., Hutley, L. and Cuddy, S.M

PB - Modelling and Simulation Society of Australia and New Zealand Inc.

CY - Australia

T2 - 25th International Congress on Modelling and Simulation, MODSIM 2023

Y2 - 9 July 2023 through 14 July 2023

ER -

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

Abstract

Conference

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