Identification

Title

Wildfire fuels mapping through artificial intelligence-based methods: A review

Abstract

<span style="-webkit-text-stroke-width:0px;color:rgb(31, 31, 31);display:inline !important;float:none;font-family:ElsevierGulliver, Georgia, "Times New Roman", Times, STIXGeneral, "Cambria Math", "Lucida Sans Unicode", "Microsoft Sans Serif", "Segoe UI Symbol", "Arial Unicode MS", serif, sans-serif;font-size:16px;font-style:normal;font-variant-caps:normal;font-variant-ligatures:normal;font-weight:400;letter-spacing:normal;orphans:2;text-align:start;text-decoration-color:initial;text-decoration-style:initial;text-decoration-thickness:initial;text-indent:0px;text-transform:none;white-space:normal;widows:2;word-spacing:0px;">Understanding fire behavior is a crucial step in wildfire risk assessment and management. Accurate and near real-time knowledge of the spatio-temporal characteristics of fuels is critical for analyzing pre-fire risk mitigation and managing active-fire emergency response. Geospatial modeling and land cover mapping using remote sensing combined with artificial intelligence techniques can provide fuel information at regional scales with high accuracy and resolution, as evidenced by the extensive recent work in the literature that appeared with increasing volume in the open literature. This paper provides a comprehensive survey of the state-of-the-art in wildfire fuel mapping, focusing on the research frontier of fire fuel models, fuel mapping methods, remote sensing data sources, existing datasets/reference maps, and applicable artificial intelligence techniques. The main findings highlight the increasing research on fire fuel mapping worldwide, with a considerable emphasis on multispectral imagery and the Random Forest classifier for its efficacy with limited data. The majority of these studies concentrate on relatively limited geographical scales spanning a small variety of fuel types, thus leaving a gap in regional and national-scale mapping. Further, this review focuses on identifying the major challenges in wildfire fuel mapping and viable solutions as they relate to (i) ground truth data scarcity, (ii) mapping understory vegetation, (iii) temporal latency, and (iv) lack of uncertainty-aware models. Lastly, this paper identifies potential AI-driven solutions that promise a significant leap in fuel mapping and discusses the latest developments and potential future trends in AI-based fuel mapping applications.

Resource type

document

Resource locator

Unique resource identifier

code

https://n2t.net/ark:/85065/d7j107kz

codeSpace

Dataset language

eng

Spatial reference system

code identifying the spatial reference system

Classification of spatial data and services

Topic category

geoscientificInformation

Keywords

Keyword set

keyword value

Text

originating controlled vocabulary

title

Resource Type

reference date

date type

publication

effective date

2016-01-01T00:00:00Z

Geographic location

West bounding longitude

East bounding longitude

North bounding latitude

South bounding latitude

Temporal reference

Temporal extent

Begin position

End position

Dataset reference date

date type

publication

effective date

2025-03-01T00:00:00Z

Frequency of update

Quality and validity

Lineage

Conformity

Data format

name of format

version of format

Constraints related to access and use

Constraint set

Use constraints

Copyright author(s). This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Limitations on public access

None

Responsible organisations

Responsible party

contact position

OpenSky Support

organisation name

UCAR/NCAR - Library

full postal address

PO Box 3000

Boulder

80307-3000

email address

opensky@ucar.edu

web address

http://opensky.ucar.edu/

name: homepage

responsible party role

pointOfContact

Metadata on metadata

Metadata point of contact