Forest fire fuel through the lens of remote sensing: Review of approaches, challenges and future directions in the remote sensing of biotic determinants of fire …

MG Gale, GJ Cary, AIJM Van Dijk, M Yebra - Remote Sensing of …, 2021 - Elsevier
Forested environments are subject to large and high intensity unplanned fire events, owing
to, among other factors, the high quantity and complex structure of fuel in these …

Satellite remote sensing contributions to wildland fire science and management

E Chuvieco, I Aguado, J Salas, M García… - Current Forestry …, 2020 - Springer
Purpose This paper reviews the most recent literature related to the use of remote sensing
(RS) data in wildland fire management. Recent Findings Studies dealing with pre-fire …

Integrating plant physiology into simulation of fire behavior and effects

LT Dickman, AK Jonko, RR Linn, I Altintas… - New …, 2023 - Wiley Online Library
Wildfires are a global crisis, but current fire models fail to capture vegetation response to
changing climate. With drought and elevated temperature increasing the importance of …

[HTML][HTML] Global fuel moisture content mapping from MODIS

X Quan, M Yebra, D Riaño, B He, G Lai, X Liu - International Journal of …, 2021 - Elsevier
Fuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver.
This study presents the first daily FMC product at a global scale and 500 m pixel resolution …

Machine learning for predicting forest fire occurrence in Changsha: An innovative investigation into the introduction of a forest fuel factor

X Wu, G Zhang, Z Yang, S Tan, Y Yang, Z Pang - Remote Sensing, 2023 - mdpi.com
Affected by global warming and increased extreme weather, Hunan Province saw a phased
and concentrated outbreak of forest fires in 2022, causing significant damage and impact …

Live fuel moisture content estimation from MODIS: A deep learning approach

L Zhu, GI Webb, M Yebra, G Scortechini, L Miller… - ISPRS Journal of …, 2021 - Elsevier
Live fuel moisture content (LFMC) is an essential variable to model fire danger and
behaviour. This paper presents the first application of deep learning to LFMC estimation …

Soil moisture retrieval using SAR backscattering ratio method during the crop growing season

M Xing, L Chen, J Wang, J Shang, X Huang - Remote Sensing, 2022 - mdpi.com
Soil moisture content (SMC) is an indispensable basic element for crop growth and
development in agricultural production. Obtaining accurate information on SMC in real time …

[HTML][HTML] SAR-enhanced mapping of live fuel moisture content

K Rao, AP Williams, JF Flefil, AG Konings - Remote Sensing of …, 2020 - Elsevier
Assessing wildfire risk presents several challenges due to uncertainty in fuel flammability
and ignition potential. Live fuel moisture content (LFMC)-the mass of water per unit dry …

Effects of live fuel moisture content on wildfire occurrence in fire-prone regions over southwest China

K Luo, X Quan, B He, M Yebra - Forests, 2019 - mdpi.com
Previous studies have shown that Live Fuel Moisture Content (LFMC) is a crucial driver
affecting wildfire occurrence worldwide, but the effect of LFMC in driving wildfire occurrence …

Live fuel moisture content mapping in the Mediterranean Basin using random forests and combining MODIS spectral and thermal data

A Cunill Camprubi, P González-Moreno… - Remote Sensing, 2022 - mdpi.com
Remotely sensed vegetation indices have been widely used to estimate live fuel moisture
content (LFMC). However, marked differences in vegetation structure affect the relationship …