Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach

Abdollahi, A; Pradhan, B; Alamri, A

Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach

Abdollahi, A Pradhan, B

Alamri, A

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Publisher:: De Gruyter
Publication Type:: Journal Article
Citation:: International Journal on Smart Sensing and Intelligent Systems, 2023, 16, (1), pp. 20230013
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Abdollahi, A
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Alamri, A
dc.date.accessioned	2024-02-01T00:51:34Z
dc.date.available	2024-02-01T00:51:34Z
dc.date.issued	2023-01-01
dc.identifier.citation	International Journal on Smart Sensing and Intelligent Systems, 2023, 16, (1), pp. 20230013
dc.identifier.issn	1178-5608
dc.identifier.issn	1178-5608
dc.identifier.uri	http://hdl.handle.net/10453/175182
dc.description.abstract	Accurate vegetation analysis is crucial amid accelerating global changes and human activities. Achieving precise characterization with multi-temporal Sentinel-2 data is challenging. In this article, we present a comprehensive analysis of 2021's seasonal vegetation cover in Greater Sydney using Google Earth Engine (GEE) to process Sentinel-2 data. Using the random forest (RF) method, we performed image classification for vegetation patterns. Supplementary factors such as topographic elements, texture information, and vegetation indices enhanced the process and overcome limited input variables. Our model outperformed existing methods, offering superior insights into season-based vegetation dynamics. Multi-temporal Sentinel-2 data, topographic elements, vegetation indices, and textural factors proved to be critical for accurate analysis. Leveraging GEE and rich Sentinel-2 data, our study would benefit decision-makers involved in vegetation monitoring.
dc.language	en
dc.publisher	De Gruyter
dc.relation.ispartof	International Journal on Smart Sensing and Intelligent Systems
dc.relation.isbasedon	10.2478/ijssis-2023-0013
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering, 1007 Nanotechnology
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Regional-Scale Analysis of Vegetation Dynamics Using Satellite Data and Machine Learning Algorithms: A Multi-Factorial Approach
dc.type	Journal Article
utslib.citation.volume	16
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1007 Nanotechnology
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	open_access	*
dc.date.updated	2024-02-01T00:51:33Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	16
utslib.citation.issue	1

Abstract:

Accurate vegetation analysis is crucial amid accelerating global changes and human activities. Achieving precise characterization with multi-temporal Sentinel-2 data is challenging. In this article, we present a comprehensive analysis of 2021's seasonal vegetation cover in Greater Sydney using Google Earth Engine (GEE) to process Sentinel-2 data. Using the random forest (RF) method, we performed image classification for vegetation patterns. Supplementary factors such as topographic elements, texture information, and vegetation indices enhanced the process and overcome limited input variables. Our model outperformed existing methods, offering superior insights into season-based vegetation dynamics. Multi-temporal Sentinel-2 data, topographic elements, vegetation indices, and textural factors proved to be critical for accurate analysis. Leveraging GEE and rich Sentinel-2 data, our study would benefit decision-makers involved in vegetation monitoring.

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http://hdl.handle.net/10453/175182