Abstract

Evaluating the potential for crown fires remains a pivotal concern in wildfire management because it affects fire behavior, causing them to spread further. In this work, we propose a methodology to assess crown fire potential based on the tree connectivity at crown level from UAS (Unmanned Aircraft Systems)-based Structure-from-Motion Photogrammetry. The approach is usable in a large landscape with the aim of reducing crown fire potential by considering the spatial variability of fuels within a stand. The utilization of UAS for photogrammetry holds immense promise in transforming the approach to assessing and managing forest fires. This cutting-edge technology offers the potential to deliver highly precise and comprehensive data concerning forest structure and connectivity, thereby presenting a groundbreaking opportunity for enhanced forest fire analysis and control.

Details

Title
TREE-LEVEL FUEL CONNECTIVITY TO ASSESS CROWN WILDFIRE POTENTIAL BY UAS-BASED PHOTOGRAMMETRY
Author
Herrero-Huerta, M 1   VIAFID ORCID Logo  ; Sánchez-Jiménez, D 1 ; A del Campo-Sanchez 1 ; Cifuentes-Jimenez, D 1 ; Piedelobo-Martín, L 1 ; P de Andres-Anaya 1 ; Gonzalez-Gonzalez, E 1 ; R Yali Samaniego 1 ; S del Pozo 1 ; Hernandez-Lopez, D 2 ; Lagüela, S 1 ; González-Aguilera, D 1 

1  Department of Cartographic and Land Engineering, Higher Polytechnic School of Avila, Universidad de Salamanca, Hornos Caleros 50, 05003 Ávila, Spain; Department of Cartographic and Land Engineering, Higher Polytechnic School of Avila, Universidad de Salamanca, Hornos Caleros 50, 05003 Ávila, Spain 
2  Institute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, Spain; Institute of Regional Development, University of Castilla-La Mancha, 02071 Albacete, Spain 
Pages
1743-1748
Publication year
2023
Publication date
2023
Publisher
Copernicus GmbH
ISSN
16821750
e-ISSN
21949034
Source type
Conference Paper
Language of publication
English
DOI
https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1743-2023
ProQuest document ID
2901512741
Copyright
© 2023. This work is published under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.