Forest fuel moisture content estimation from MODIS data

2005 ◽  
Author(s):  
Mei Zhou ◽  
Guangmeng Guo ◽  
Gary Z. Wang ◽  
Junhui Zhao
Keyword(s):  
Moisture Content ◽  
Fuel Moisture ◽  
Modis Data ◽  
Forest Fuel ◽  
Fuel Moisture Content

scholarly journals Evaluating the Effects of Projected Climate Change on Forest Fuel Moisture Content

2014 ◽  
Vol 37 ◽  
pp. 65-69
Author(s):  
Kellen Nelson ◽  
Daniel Tinker
Keyword(s):  
Climate Change ◽  
Moisture Content ◽  
Stand Structure ◽  
Canopy Cover ◽  
Fire Season ◽  
Climate Change Scenarios ◽  
Fuel Moisture ◽  
Mature Forest ◽  
Forest Fuel ◽  
Fuel Moisture Content

Understanding how live and dead forest fuel moisture content (FMC) varies with seasonal weather and stand structure will improve researchers’ and forest managers’ ability to predict the cumulative effects of weather on fuel drying during the fire season and help identify acute conditions that foster wildfire ignition and high rates of fire spread. No studies have investigated the efficacy of predicting FMC using mechanistic water budget models at daily time scales through the fire season nor have they investigated how FMC may vary across space. This study addresses these gaps by (1) validating a novel mechanistic live FMC model and (2) applying this model with an existing dead FMC model at three forest sites using five climate change scenarios to characterize how FMC changes through time and across space. Sites include post-fire 24-year old forest, mature forest with high canopy cover, and mature forest affected by the mountain pine beetle with moderate canopy cover. Climate scenarios include central tendency, warm/dry, warm/wet, hot/dry, and hot/wet.

scholarly journals Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568

2020 ◽  
Vol 12 (2) ◽  
pp. 206
Author(s):  
Long Wang ◽  
Xingwen Quan ◽  
Binbin He ◽  
Marta Yebra ◽  
Minfeng Xing ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Moisture Content ◽  
Fuel Moisture ◽  
Forest Fuel ◽  
Fuel Moisture Content

The authors wish to make the following corrections to this paper [1]: 1 [...]

scholarly journals Corrigendum to: Estimation of surface dead fine fuel moisture using automated fuel moisture sticks across a range of forests worldwide

2020 ◽  
Vol 29 (6) ◽  
pp. 560
Author(s):  
Jane G. Cawson ◽  
Petter Nyman ◽  
Christian Schunk ◽  
Gary J. Sheridan ◽  
Thomas J. Duff ◽  
...  
Keyword(s):  
Moisture Content ◽  
Measurement Techniques ◽  
Field Measurements ◽  
Future Research ◽  
Fuel Moisture ◽  
Potential Solution ◽  
Forest Types ◽  
Wildfire Management ◽  
Forest Fuel ◽  
Fuel Moisture Content

Field measurements of surface dead fine fuel moisture content (FFMC) are integral to wildfire management, but conventional measurement techniques are limited. Automated fuel sticks offer a potential solution, providing a standardised, continuous and real-time measure of fuel moisture. As such, they are used as an analogue for surface dead fine fuel but their performance in this context has not been widely evaluated. We assessed the ability of automated fuel sticks to predict surface dead FFMC across a range of forest types. We combined concurrent moisture measurements of the fuel stick and surface dead fine fuel from 27 sites (570 samples), representing nine broad forest fuel categories. We found a moderate linear relationship between surface dead FFMC and fuel stick moisture for all data combined (R2=0.54), with fuel stick moisture averaging 3-fold lower than surface dead FFMC. Relationships were typically stronger for individual forest fuel categories (median R2=0.70; range=0.55–0.87), suggesting the sticks require fuel-specific calibration for use as an analogue of surface dead fine fuel. Future research could identify fuel properties that will enable more generalised calibration functions.

scholarly journals Estimation of surface dead fine fuel moisture using automated fuel moisture sticks across a range of forests worldwide

2020 ◽  
Vol 29 (6) ◽  
pp. 548
Author(s):  
Jane G. Cawson ◽  
Petter Nyman ◽  
Christian Schunk ◽  
Gary J. Sheridan ◽  
Thomas J. Duff ◽  
...  
Keyword(s):  
Moisture Content ◽  
Measurement Techniques ◽  
Field Measurements ◽  
Future Research ◽  
Fuel Moisture ◽  
Potential Solution ◽  
Forest Types ◽  
Wildfire Management ◽  
Forest Fuel ◽  
Fuel Moisture Content

Field measurements of surface dead fine fuel moisture content (FFMC) are integral to wildfire management, but conventional measurement techniques are limited. Automated fuel sticks offer a potential solution, providing a standardised, continuous and real-time measure of fuel moisture. As such, they are used as an analogue for surface dead fine fuel but their performance in this context has not been widely evaluated. We assessed the ability of automated fuel sticks to predict surface dead FFMC across a range of forest types. We combined concurrent moisture measurements of the fuel stick and surface dead fine fuel from 27 sites (570 samples), representing nine broad forest fuel categories. We found a moderate linear relationship between surface dead FFMC and fuel stick moisture for all data combined (R2=0.54), with fuel stick moisture averaging 3-fold lower than surface dead FFMC. Relationships were typically stronger for individual forest fuel categories (median R2=0.70; range=0.55–0.87), suggesting the sticks require fuel-specific calibration for use as an analogue of surface dead fine fuel. Future research could identify fuel properties that will enable more generalised calibration functions.

Retrieval of forest fuel moisture content using a coupled radiative transfer model

2017 ◽  
Vol 95 ◽  
pp. 290-302 ◽  
Author(s):  
Xingwen Quan ◽  
Binbin He ◽  
Marta Yebra ◽  
Changming Yin ◽  
Zhanmang Liao ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Moisture Content ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Fuel Moisture ◽  
Forest Fuel ◽  
Fuel Moisture Content

scholarly journals Estimation of Herbaceous Fuel Moisture Content Using Vegetation Indices and Land Surface Temperature from MODIS Data

2013 ◽  
Vol 5 (6) ◽  
pp. 2617-2638 ◽  
Author(s):  
Momadou Sow ◽  
Cheikh Mbow ◽  
Christelle Hély ◽  
Rasmus Fensholt ◽  
Bienvenu Sambou
Keyword(s):  
Moisture Content ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Indices ◽  
Fuel Moisture ◽  
Modis Data ◽  
Fuel Moisture Content

Predicting 1-H Dead Fuel Moisture Content at Regional Scales Using Machine Learning from Himawari-8 Data

2021 ◽  
Author(s):  
Chunquan Fan ◽  
Binbin He ◽  
Peng Kong ◽  
Hao Xu ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Moisture Content ◽  
Fuel Moisture ◽  
Fuel Moisture Content

Effects of distribution of bulk density and moisture content on shrub fires

2013 ◽  
Vol 22 (5) ◽  
pp. 625 ◽  
Author(s):  
Ambarish Dahale ◽  
Selina Ferguson ◽  
Babak Shotorban ◽  
Shankar Mahalingam
Keyword(s):  
Moisture Content ◽  
Spatial Variation ◽  
Bulk Density ◽  
Solid Fuel ◽  
Numerical Solutions ◽  
Propagation Speed ◽  
Fire Spread ◽  
Fuel Moisture ◽  
Combustion Heat ◽  
Fuel Moisture Content

Formulation of a physics-based model, capable of predicting fire spread through a single elevated crown-like shrub, is described in detail. Predictions from the model, obtained by numerical solutions to governing equations of fluid dynamics, combustion, heat transfer and thermal degradation of solid fuel, are found to be in fairly good agreement with experimental results. In this study we utilise the physics-based model to explore the importance of two parameters – the spatial variation of solid fuel bulk density and the solid fuel moisture content – on the burning of an isolated shrub in quiescent atmosphere. The results suggest that vertical fire spread rate within an isolated shrub and the time to initiate ignition within the crown are two global parameters significantly affected when the spatial variation of the bulk density or the variation of fuel moisture content is taken into account. The amount of fuel burnt is another parameter affected by varying fuel moisture content, especially in the cases of fire propagating through solid fuel with moisture content exceeding 40%. The specific mechanisms responsible for the reduction in propagation speed in the presence of higher bulk densities and moisture content are identified.

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