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 Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

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 [...]

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 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.

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