A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing

Yebra, Marta; Quan, Xingwen; Riaño, David; Larraondo, Pablo Rozas; Dijk, Albert I. J. M. van; Cary, Geoffrey J.

doi:10.1016/j.rse.2018.04.053

Cited by 130 publications

(109 citation statements)

References 45 publications

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“…Compared with empirical relationship built upon in-situ measurements, RTMs are physically meaningful and directly address the response in reflectance when vegetation water content changes. A recent study focused on Australia has shown promising outcome of deriving LFM using an inversing technique of RTM [9]. In the future study, we will apply RTMs to achieve a better estimation of LFM.…”

Section: Discussionmentioning

confidence: 95%

Estimation of the Relationship Between Satellite-Derived Vegetation Indices and Live Fuel Moisture Towards Wildfire Risk in Southern California

Whitney

Kim

Jia

et al. 2018

2018 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

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Southern California possesses a Mediterranean climate having semi-arid to arid characteristics and contains shrubland areas at high risk to wildfire. To assess wildfire danger, fire agencies have been monitoring the moisture of vegetation, called live fuel moisture (LFM), using field-based sampling. Unfortunately, spatial and temporal resolution of live fuel moisture data are significantly limited because sampling is labor intensive. Remote sensing satellite data has been used to monitor vegetation moisture content and health of shrublands. Therefore, a potential approach to overcome the limitations of manual measurements of live fuel moisture is to use vegetation indices (VIs) derived from satellite data. The objective of this study is to understand the link between vegetation indices derived from a Moderate Resolution Imaging Spectroradiometer (MODIS) aboard both Terra and Aqua satellites and in-situ live fuel moisture data. In this study, five vegetation indices were calculated using 6 bands of MODIS data within the visible and infrared spectrum collectively with the focus on the three best performing: enhanced vegetation index (EVI), normalized difference water index (NDWI), and visible atmospherically resistant index (VARI). Six sites with multi-year live fuel moisture data collection type were each represented with one pixel of MODIS data with a 500m by 500m spatial resolution covering the time period of February 2000 through December 2017 acquired aboard Terra and June 2002 through December 2017 acquired aboard Aqua. Linear regression was then applied to measure the coefficient of determination (R 2 ) between the vegetation indices and live fuel moisture data. The results show a great variance of R 2 between the sites as well as a variance of best performing VI. The two strongest coefficients of determination, R 2 =0.74 and R 2 =0.72, were calculated at one site for enhanced vegetation index vs. live fuel moisture over a 15-year time period of data collected on Aqua and a 17-year time period of data collected on Terra respectively. The relationship was also affected by annual climate conditions including precipitation. Our results indicate that the satellite data reasonably well-represents the live fuel moisture with higher temporal resolutions over a large area. Utilizing the remote sensing data in wildfire danger assessment will support fire agencies by saving resources for collecting ground data and providing better dataset in both time and space. This will also be beneficial for land management and planning, stakeholders and local governments.

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Section: Discussionmentioning

confidence: 95%

Estimation of the Relationship Between Satellite-Derived Vegetation Indices and Live Fuel Moisture Towards Wildfire Risk in Southern California

Whitney

Kim

Jia

et al. 2018

2018 7th International Conference on Agro-Geoinformatics (Agro-Geoinformatics)

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“…Although global fire models have PFT-specific parametrisations for flammability (Thonicke et al, 2010), such fire-relevant plant characteristics need to be incorporated in DGVMs (Zylstra et al, 2016). Such efforts need to be complemented by calibrating DGVMs against satellite observations that provide relevant information about the spatial distributions of fuel structure (Pettinari and Chuvieco, 2016;Riaño et al, 2002), fuel moisture content (Yebra et al, 2013(Yebra et al, , 2018, fire ignitions and spread (Laurent et al, 2018), fuel consumption (Andela et al, 2016), and fire radiative energy (Kaiser et al, 2012). In summary, besides human-fire interactions, we identified vegetation effects on fire as a main deficiency of fire-enabled dynamic global vegetation models in simulating temporal dynamics of burned area.…”

Section: Improving Vegetation Controls On Fire In Dgvmsmentioning

confidence: 99%

Emergent relationships on burned area in global satellite observations and fire-enabled vegetation models

Forkel

Andela

Harrison

et al. 2018

Preprint

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<p><strong>Abstract.</strong> Recent climate changes increases fire-prone weather conditions and likely affects fire occurrence, which might impact ecosystem functioning, biogeochemical cycles, and society. Prediction of how fire impacts may change in the future is difficult because of the complexity of the controls on fire occurrence and burned area. Here we aim to assess how process-based fire-enabled Dynamic Global Vegetation Models (DGVMs) represent relationships between controlling factors and burned area. We developed a pattern-oriented model evaluation approach using the random forest (RF) algorithm to identify emergent relationships between climate, vegetation, and socioeconomic predictor variables and burned area. We applied this approach to monthly burned area time series for the period 2005&#8211;2011 from satellite observations and from DGVMs from the Fire Model Inter-comparison Project (FireMIP) that were run using a common protocol and forcing datasets. The satellite-derived relationships indicate strong sensitivity to climate variables (e.g. maximum temperature, number of wet days), vegetation properties (e.g. vegetation type, previous-season plant productivity and leaf area, woody litter), and to socioeconomic variables (e.g. human population density). DGVMs broadly reproduce the relationships to climate variables and some models to population density. Interestingly, satellite-derived responses show a strong increase of burned area with previous-season leaf area index and plant productivity in most fire-prone ecosystems which was largely underestimated by most DGVMs. Hence our pattern-oriented model evaluation approach allowed to diagnose that current fire-enabled DGVMs represent some controls on fire to a large extent but processes linking vegetation productivity and fire occurrence need to be improved to accurately simulate the role of fire under global environmental change.</p>

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“…Her notable works include the use of remote-sensing data and geographic information systems to develop tools and framework for wildfire risk assessment [497][498][499]. Her recent works include the development of methods to evaluate fuel moisture and flammability using remote-sensing data [500].…”

Section: Kendra Mclauchlan Is a Professor At Kansas State University mentioning

confidence: 99%

Recognizing Women Leaders in Fire Science: Revisited

Smith

Strand

2018

Fire

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In August, 2018, an editorial in Fire entitled Recognizing Women Leaders in Fire Science was published. This was intended to ignite a conversation into diversity in fire science by highlighting several women leaders in fire research and development. This editorial was released alongside a new Topical Collection in Fire called Diversity Leaders in Fire Science. The response on social media was fantastic, leading to numerous recommendations of women leaders in fire science that had been inadvertently missed in the first editorial. In this editorial, we acknowledge 145 women leaders in fire science to promote diversity across our disciplines. Fire is continually committed to improving diversity and inclusion in all aspects of the journal and welcomes perspectives, viewpoints, and constructive criticisms to help advance that mission.

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A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing

Cited by 130 publications

References 45 publications

Estimation of the Relationship Between Satellite-Derived Vegetation Indices and Live Fuel Moisture Towards Wildfire Risk in Southern California

Estimation of the Relationship Between Satellite-Derived Vegetation Indices and Live Fuel Moisture Towards Wildfire Risk in Southern California

Emergent relationships on burned area in global satellite observations and fire-enabled vegetation models

Recognizing Women Leaders in Fire Science: Revisited

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