Forest fuel type classification: Review of remote sensing techniques, constraints and future trends.

Abdollahi, A; Yebra, M

Forest fuel type classification: Review of remote sensing techniques, constraints and future trends.

Abdollahi, A

Yebra, M

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Publisher:: ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: J Environ Manage, 2023, 342, pp. 118315
Issue Date:: 2023-09-15

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Field	Value	Language
dc.contributor.author	Abdollahi, A https://orcid.org/0000-0002-1704-4670
dc.contributor.author	Yebra, M
dc.date.accessioned	2024-01-10T06:06:03Z
dc.date.available	2023-05-31
dc.date.available	2024-01-10T06:06:03Z
dc.date.issued	2023-09-15
dc.identifier.citation	J Environ Manage, 2023, 342, pp. 118315
dc.identifier.issn	0301-4797
dc.identifier.issn	1095-8630
dc.identifier.uri	http://hdl.handle.net/10453/174220
dc.description.abstract	Improved forest management plans require a better understanding of wildfire risk and behavior to enhance the conservation of biodiversity and plan effective risk mitigation activities across the landscape. More particularly, for spatial fire hazard and risk assessing as well as fire intensity and growth modeling across a landscape, an adequate knowledge of the spatial distribution of key forest fuels attributes is required. Mapping fuel attributes is a challenging and complicated procedure because fuels are highly variable and complex. To simplify, classification schemes are used to summarize the large number of fuel attributes (e.g., height, density, continuity, arrangement, size, form, etc.) into fuel types which groups vegetation classes with a similar predicted fire behavior. Remote sensing is a cost-effective and objective technology that have been used to regularly map fuel types and have demonstrated greater success compared to traditional field surveys, especially with recent advancements in remote sensing data acquisition and fusion techniques. Thus, the main goal of this manuscript is to provide a comprehensive review of the recent remote sensing approaches used for fuel type classification. We build on findings from previous review manuscripts and focus on identifying the key challenges of different mapping approaches and the research gaps that still need to be filled in. To improve classification outcomes, more research into developing state-of-the-art deep learning algorithms with integrated remote sensing data sources is encouraged for future research. This review can be used as a guideline for practitioners, researchers, and decision-makers in the domain of fire management service.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
dc.relation.ispartof	J Environ Manage
dc.relation.isbasedon	10.1016/j.jenvman.2023.118315
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Environmental Sciences
dc.subject.mesh	Biodiversity
dc.subject.mesh	Fires
dc.subject.mesh	Forests
dc.subject.mesh	Remote Sensing Technology
dc.subject.mesh	Wildfires
dc.subject.mesh	Fires
dc.subject.mesh	Biodiversity
dc.subject.mesh	Remote Sensing Technology
dc.subject.mesh	Forests
dc.subject.mesh	Wildfires
dc.subject.mesh	Remote Sensing Technology
dc.subject.mesh	Forests
dc.subject.mesh	Biodiversity
dc.subject.mesh	Fires
dc.subject.mesh	Wildfires
dc.title	Forest fuel type classification: Review of remote sensing techniques, constraints and future trends.
dc.type	Journal Article
utslib.citation.volume	342
utslib.location.activity	England
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 Information, Systems and Modelling
utslib.copyright.status	open_access	*
dc.date.updated	2024-01-10T06:06:01Z
pubs.publication-status	Published
pubs.volume	342

Abstract:

Improved forest management plans require a better understanding of wildfire risk and behavior to enhance the conservation of biodiversity and plan effective risk mitigation activities across the landscape. More particularly, for spatial fire hazard and risk assessing as well as fire intensity and growth modeling across a landscape, an adequate knowledge of the spatial distribution of key forest fuels attributes is required. Mapping fuel attributes is a challenging and complicated procedure because fuels are highly variable and complex. To simplify, classification schemes are used to summarize the large number of fuel attributes (e.g., height, density, continuity, arrangement, size, form, etc.) into fuel types which groups vegetation classes with a similar predicted fire behavior. Remote sensing is a cost-effective and objective technology that have been used to regularly map fuel types and have demonstrated greater success compared to traditional field surveys, especially with recent advancements in remote sensing data acquisition and fusion techniques. Thus, the main goal of this manuscript is to provide a comprehensive review of the recent remote sensing approaches used for fuel type classification. We build on findings from previous review manuscripts and focus on identifying the key challenges of different mapping approaches and the research gaps that still need to be filled in. To improve classification outcomes, more research into developing state-of-the-art deep learning algorithms with integrated remote sensing data sources is encouraged for future research. This review can be used as a guideline for practitioners, researchers, and decision-makers in the domain of fire management service.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/174220