ORCID Profile
0000-0002-4049-9315
Current Organisations
Bushfire & Natural Hazards Cooperative Research Centre
,
Australian National University
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Research Topics
In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Top 5 Research Topics
ANZSRC Field of Research (FoR)
Ecological Applications | Photogrammetry and Remote Sensing | Surfacewater Hydrology | Evolutionary Impacts of Climate Change | Ecological Impacts of Climate Change | Ecosystem Function | Ecological Physiology |
ANZSRC Socio-Economic Objective (SEO)
Effects of Climate Change and Variability on Australia (excl. Social Impacts) | Ecosystem Assessment and Management of Forest and Woodlands Environments | Mountain and High Country Flora, Fauna and Biodiversity | Natural Hazards in Forest and Woodlands Environments | Mountain and High Country Land and Water Management | Ecosystem Assessment and Management of Mountain and High Country Environments
Related Links
Publications
Radio observations of supernova remnant G1.9+0.3
Publisher: Oxford University Press (OUP)
Date: 09-12-2019
Abstract: We present 1–10 GHz radio continuum flux density, spectral index, polarization, and rotation measure (RM) images of the youngest known Galactic supernova remnant (SNR) G1.9+0.3, using observations from the Australia Telescope Compact Array. We have conducted an expansion study spanning eight epochs between 1984 and 2017, yielding results consistent with previous expansion studies of G1.9+0.3. We find a mean radio continuum expansion rate of (0.78 ± 0.09) per cent yr−1 (or ∼8900 km s−1 at an assumed distance of 8.5 kpc), although the expansion rate varies across the SNR perimetre. In the case of the most recent epoch between 2016 and 2017, we observe faster-than-expected expansion of the northern region. We find a global spectral index for G1.9+0.3 of −0.81 ± 0.02 (76 MHz–10 GHz). Towards the northern region, however, the radio spectrum is observed to steepen significantly (∼−1). Towards the two so-called (east and west) ‘ears’ of G1.9+0.3, we find very different RM values of 400–600 and 100–200 rad m2, respectively. The fractional polarization of the radio continuum emission reaches (19 ± 2) per cent, consistent with other, slightly older, SNRs such as Cas A.
Estimation of live fuel moisture content from MODIS images for fire risk assessment
Publisher: Elsevier BV
Date: 04-2008
Using alternative soil moisture estimates in the McArthur Forest Fire Danger Index
Publisher: CSIRO Publishing
Date: 2017
DOI: 10.1071/WF16217
Abstract: McArthur’s Forest Fire Danger Index (FFDI) incorporates the Keetch–Byram Drought Index (KBDI) estimate of soil dryness. Improved approaches for estimating soil moisture now exist, with potential for informing the calculation of FFDI. We evaluated the effect, compared with KBDI, of two alternative methods of estimating soil moisture: the rainfall-based Antecedent Precipitation Index and soil moisture from the Soil Moisture Ocean Salinity satellite mission. These methods were used to calculate FFDI over a s le period of 5years (2010–14) at seven locations around Australia. The effect of substituting the alternatives for KBDI, and of entirely replacing the Drought Factor (DF) (a measure of fuel availability in FFDI) with the alternatives was explored by studying the effect on magnitude, distribution and timing of FFDI and associated Fire Danger Rating (FDR). Both approaches predicted drier soil conditions than KBDI, resulting in fewer Low–Moderate FDR days and more days of High FDR and above. The alternative methods replacing KBDI had little effect on seasonal patterns of FDR. Of all approaches, replacing DF entirely with the soil moisture alternatives most closely mimicked McArthur’s FFDI. Overall, if alternative measures of soil moisture are adopted for FFDI, the entire replacement of the DF term should be considered.
Forest fuel type classification: Review of remote sensing techniques, constraints and future trends
Publisher: Elsevier BV
Date: 09-2023
The vegetation structure perpendicular index for wildfire severity and forest recovery monitoring
Publisher: IEEE
Date: 07-2018
Supplementary material to Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties
Publisher: Copernicus GmbH
Date: 13-04-2022
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
Publisher: MDPI AG
Date: 07-01-2020
DOI: 10.3390/RS12020206
Abstract: The authors wish to make the following corrections to this paper [1]: 1 [...]
Historical background and current developments for mapping burned area from satellite Earth observation
Publisher: Elsevier BV
Date: 05-2019
Primary and secondary effects of climate variability on net ecosystem carbon exchange in an evergreen Eucalyptus forest
Publisher: Elsevier BV
Date: 12-2013
Projecting live fuel moisture content via deep learning
Publisher: CSIRO Publishing
Date: 09-03-2023
DOI: 10.1071/WF22188
Abstract: Background Live fuel moisture content (LFMC) is a key environmental indicator used to monitor for high wildfire risk conditions. Many statistical models have been proposed to predict LFMC from remotely sensed data however, almost all these estimate current LFMC (nowcasting models). Accurate modelling of LFMC in advance (projection models) would provide wildfire managers with more timely information for assessing and preparing for wildfire risk. Aims The aim of this study was to investigate the potential for deep learning models to predict LFMC across the continental United States 3 months in advance. Method Temporal convolutional networks were trained and evaluated using a large database of field measured s les, as well as year-long time series of MODerate resolution Imaging Spectroradiometer (MODIS) reflectance data and Parameter-elevation Relationships on Independent Slopes Model (PRISM) meteorological data. Key results The proposed 3-month projection model achieved an accuracy (root mean squared error (RMSE) 27.52% R2 0.47) close to that of the nowcasting model (RMSE 26.52% R2 0.51). Conclusions The study is the first to predict LFMC with a 3-month lead-time, demonstrating the potential for deep learning models to make reliable LFMC projections. Implications These findings are beneficial for wildfire management and risk assessment, showing proof-of-concept for providing advance information useful to help mitigate the effect of catastrophic wildfires.
Integrating plant physiology into simulation of fire behavior and effects
Publisher: Wiley
Date: 14-02-2023
DOI: 10.1111/NPH.18770
Abstract: 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 vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine‐scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine‐scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process‐based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.
The polar night shift: seasonal dynamics and drivers of Arctic Ocean microbiomes revealed by autonomous sampling
Publisher: Springer Science and Business Media LLC
Date: 11-12-2021
DOI: 10.1038/S43705-021-00074-4
Abstract: The Arctic Ocean features extreme seasonal differences in daylight, temperature, ice cover, and mixed layer depth. However, the ersity and ecology of microbes across these contrasting environmental conditions remain enigmatic. Here, using autonomous s lers and sensors deployed at two mooring sites, we portray an annual cycle of microbial ersity, nutrient concentrations and physical oceanography in the major hydrographic regimes of the Fram Strait. The ice-free West Spitsbergen Current displayed a marked separation into a productive summer (dominated by diatoms and carbohydrate-degrading bacteria) and regenerative winter state (dominated by heterotrophic Syndiniales, radiolarians, chemoautotrophic bacteria, and archaea). The autumn post-bloom with maximal nutrient depletion featured Coscinodiscophyceae , Rhodobacteraceae (e.g. Amylibacter ) and the SAR116 clade. Winter replenishment of nitrate, silicate and phosphate, linked to vertical mixing and a unique microbiome that included Magnetospiraceae and Dadabacteriales, fueled the following phytoplankton bloom. The spring-summer succession of Phaeocystis , Grammonema and Thalassiosira coincided with ephemeral peaks of Aurantivirga , Formosa, Polaribacter and NS lineages, indicating metabolic relationships. In the East Greenland Current, deeper s ling depth, ice cover and polar water masses concurred with weaker seasonality and a stronger heterotrophic signature. The ice-related winter microbiome comprised Bacillaria , Naviculales , Polarella , Chrysophyceae and Flavobacterium ASVs. Low ice cover and advection of Atlantic Water coincided with diminished abundances of chemoautotrophic bacteria while others such as Phaeocystis increased, suggesting that Atlantification alters microbiome structure and eventually the biological carbon pump. These insights promote the understanding of microbial seasonality and polar night ecology in the Arctic Ocean, a region severely affected by climate change.
The Role of Climate in Ignition Frequency
Publisher: MDPI AG
Date: 10-05-2023
DOI: 10.3390/FIRE6050195
Abstract: Many fire management agencies aim to detect and suppress all ignitions within their jurisdiction and may benefit from understanding the causes of year-to-year variation. Ignition variation is likely to be associated with climatically driven changes in fuel quantity or moisture in landscapes where ignition sources vary little over similar temporal scales. We assessed how monthly ignitions varied in south-eastern Australia in response to climate anomalies over approximately nine years. Once accounting for seasonal effects, ignitions increased with temperature and vapour pressure deficit anomalies and decreased with precipitation anomalies. These findings indicate that climatic conditions play a role in limiting ignition frequency via effects on fuel moisture. Our models predicted that warmer and drier conditions during our study caused considerable increases in ignition frequency compared to the long-term average reference period (1961–1990). Like many fire-prone landscapes around the world, our study area is projected to become hotter and drier because of climate change. Fire management agencies will need to improve fire detection and suppression capabilities under a future climate.
Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires
Publisher: MDPI AG
Date: 02-11-2020
DOI: 10.3390/RS12213590
Abstract: Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R2 = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management.
Assessing the sensitivity of multi-frequency vegetation optical depth to biomass and canopy moisture content: towards an ecological-oriented evaluation
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-10931
Abstract: & & Multi-decadal records of global Vegetation Optical Depth (VOD) derived from spaceborne microwave sensors provide novel opportunities to observe and analyze both the current state as well as the temporal changes of vegetation. Theoretically, VOD is sensitive to the biomass and moisture content of vegetation. Past studies found relations between VOD and leaf area index (LAI), productivity, biomass, and vegetation water status. In addition, VOD has been used to investigate or estimate changes in biomass, vegetation isohydricity, and tree mortality. However, VOD is not directly measured with ground observations and therefore difficult to evaluate. Several VOD products exist that have been retrieved using different satellite sensors, microwave frequencies (e.g. Ku, X, C, and L-bands) and retrieval algorithms. These products show differences in both their temporal (e.g. short-term variability) as well as spatial dynamics (e.g. their relation with LAI). Hence from a user point-of-view, it is difficult to assess which VOD products might be the most suitable for a certain ecological application.& & & & Here we aim to develop and present initial results of an ecological-oriented assessment of several VOD products. Based on the theoretical assumption that VOD is sensitive to vegetation biomass and moisture content, we assess the co-varying sensitivities of high- (Ku, X, C-bands) and low-frequency (L-band) VOD products to biomass and moisture content within a consistent evaluation framework. High-frequency VOD was taken from the recent developed VODCA products and low-frequency VOD from SMAP and SMOS retrievals. Biomass was derived from global above-ground biomass maps and MODIS LAI. Canopy moisture content was estimated from MODIS retrievals.& & & & The first results confirm previous findings that VOD is both sensitive to biomass and moisture content. High-frequency VOD products are mainly sensitive to short-term changes in canopy biomass and moisture content and low frequency VOD to woody biomass. However, we also found that high-frequency VOD shows high sensitivity to aboveground biomass in Savannahs and boreal forests. Also low-frequency VOD includes a clear signal of vegetation moisture that cannot be explained by biomass changes. This suggests that multi-frequency VOD products and estimates of vegetation biomass and moisture content should be integrated and jointly analyzed to provide a consistent picture of ecosystem dynamics.& &
A global canopy water content product from AVHRR/Metop
Publisher: Elsevier BV
Date: 04-2020
Towards an Integrated Approach to Wildfire Risk Assessment: When, Where, What and How May the Landscapes Burn
Publisher: MDPI AG
Date: 22-05-2023
DOI: 10.3390/FIRE6050215
Abstract: This paper presents a review of concepts related to wildfire risk assessment, including the determination of fire ignition and propagation (fire danger), the extent to which fire may spatially overlap with valued assets (exposure), and the potential losses and resilience to those losses (vulnerability). This is followed by a brief discussion of how these concepts can be integrated and connected to mitigation and adaptation efforts. We then review operational fire risk systems in place in various parts of the world. Finally, we propose an integrated fire risk system being developed under the FirEUrisk European project, as an ex le of how the different risk components (including danger, exposure and vulnerability) can be generated and combined into synthetic risk indices to provide a more comprehensive wildfire risk assessment, but also to consider where and on what variables reduction efforts should be stressed and to envisage policies to be better adapted to future fire regimes. Climate and socio-economic changes entail that wildfires are becoming even more a critical environmental hazard extreme fires are observed in many areas of the world that regularly experience fire, yet fire activity is also increasing in areas where wildfires were previously rare. To mitigate the negative impacts of fire, those responsible for managing risk must leverage the information available through the risk assessment process, along with an improved understanding on how the various components of risk can be targeted to improve and optimize the many strategies for mitigation and adaptation to an increasing fire risk.
Comparison of contrasting optical and LiDAR fire severity remote sensing methods in a heterogeneous forested landscape in south-eastern Australia
Publisher: Informa UK Limited
Date: 03-04-2022
Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China
Publisher: MDPI AG
Date: 08-10-2019
DOI: 10.3390/F10100887
Abstract: 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 still remains unexplored over the southwest China ecosystem, an area historically vulnerable to wildfires. To this end, we took 10-years of LFMC dynamics retrieved from Moderate Resolution Imaging Spectrometer (MODIS) reflectance product using the physical Radiative Transfer Model (RTM) and the wildfire events extracted from the MODIS Burned Area (BA) product to explore the relations between LFMC and forest/grassland fire occurrence across the subtropical highland zone (Cwa) and humid subtropical zone (Cwb) over southwest China. The statistical results of pre-fire LFMC and cumulative burned area show that distinct pre-fire LFMC critical thresholds were identified for Cwa (151.3%, 123.1%, and 51.4% for forest, and 138.1%, 72.8%, and 13.1% for grassland) and Cwb (115.0% and 54.4% for forest, and 137.5%, 69.0%, and 10.6% for grassland) zones. Below these thresholds, the fire occurrence and the burned area increased significantly. Additionally, a significant decreasing trend on LFMC dynamics was found during the days prior to two large fire events, Qiubei forest fire and Lantern Mountain grassland fire that broke during the 2009/2010 and 2015/2016 fire seasons, respectively. The minimum LFMC values reached prior to the fires (49.8% and 17.3%) were close to the lowest critical LFMC thresholds we reported for forest (51.4%) and grassland (13.1%). Further LFMC trend analysis revealed that the regional median LFMC dynamics for the 2009/2010 and 2015/2016 fire seasons were also significantly lower than the 10-year LFMC of the region. Hence, this study demonstrated that the LFMC dynamics explained wildfire occurrence in these fire-prone regions over southwest China, allowing the possibility to develop a new operational wildfire danger forecasting model over this area by considering the satellite-derived LFMC product.
A hybrid method for segmenting individual trees from airborne lidar data
Publisher: Elsevier BV
Date: 08-2019
Understanding Unplanned Fire Ignition Patterns to Improve Early Fire Detection and Resource Deployment
Publisher: MDPI
Date: 09-08-2022
The Vegetation Structure Perpendicular Index (VSPI): A forest condition index for wildfire predictions
Publisher: Elsevier BV
Date: 04-2019
Continental-scale prediction of live fuel moisture content using soil moisture information
Publisher: Elsevier BV
Date: 09-2021
Improving burn severity retrieval by integrating tree canopy cover into radiative transfer model simulation
Publisher: Elsevier BV
Date: 2020
RADAR-Vegetation Structural Perpendicular Index (R-VSPI) for the Quantification of Wildfire Impact and Post-Fire Vegetation Recovery
Publisher: MDPI AG
Date: 29-06-2022
DOI: 10.3390/RS14133132
Abstract: The precise information on fuel characteristics is essential for wildfire modelling and management. Satellite remote sensing can provide accurate and timely measurements of fuel characteristics. However, current estimates of fuel load changes from optical remote sensing are obstructed by seasonal cloud cover that limits their continuous assessments. This study utilises remotely sensed Synthetic-Aperture Radar (SAR) (Sentinel-1 backscatter) data as an alternative to optical-based imaging (Sentinel-2 scaled surface reflectance). SAR can penetrate clouds and offers high-spatial and medium-temporal resolution datasets and can hence complement the optical dataset. Inspired by the optical-based Vegetation Structural Perpendicular Index (VSPI), an SAR-based index termed RADAR-VSPI (R-VSPI) is introduced in this study. R-VSPI characterises the spatio-temporal changes in fuel load due to wildfire and the subsequent vegetation recovery thereof. The R-VSPI utilises SAR backscatter (σ°) from the co-polarized (VV) and cross-polarized (VH) channels at a centre frequency of 5.4 GHz. The newly developed index is applied over major wildfire events that occurred during the “Black Summer” wildfire season (2019–2020) in southern Australia. The condition of the fuel load was mapped every 5 (any orbit) to 12 (same orbit) days at an aggregated spatial resolution of 110 m. The results show that R-VSPI was able to quantify fuel depletion by wildfire (relative to healthy vegetation) and monitor its subsequent post-fire recovery. The information on fuel condition and heterogeneity improved at high-resolution by adapting the VSPI on a dual-polarization SAR dataset (R-VSPI) compared to the historic forest fuel characterisation methods (that used visible and infrared bands only for fuel estimations). The R-VSPI thus provides a complementary source of information on fuel load changes in a forest landscape compared to the optical-based VSPI, in particular when optical observations are not available due to cloud cover.
Burn Severity Estimation in Northern Australia Tropical Savannas Using Radiative Transfer Model and Sentinel-2 Data
Publisher: IEEE
Date: 07-2019
Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth
Publisher: Copernicus GmbH
Date: 02-01-2023
Abstract: Abstract. The moisture content of vegetation canopies controls various ecosystem processes such as plant productivity, transpiration, mortality, and flammability. Leaf moisture content (here defined as the ratio of leaf water mass to leaf dry biomass, or live-fuel moisture content, LFMC) is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread, and is widely measured at field sites around the globe. LFMC can be retrieved from satellite observations in the visible and infrared domain of the electromagnetic spectrum, which is however h ered by frequent cloud cover or low sun elevation angles. As an alternative, vegetation water content can be estimated from satellite observations in the microwave domain. For ex le, studies at local and regional scales have demonstrated the link between LFMC and vegetation optical depth (VOD) from passive microwave satellite observations. VOD describes the attenuation of microwaves in the vegetation layer. However, neither were the relations between VOD and LFMC investigated at large or global scales nor has VOD been used to estimate LFMC. Here we aim to estimate LFMC from VOD at large scales, i.e. at coarse spatial resolution, globally, and at daily time steps over past decadal timescales. Therefore, our objectives are: (1) to investigate the relation between VOD from different frequencies and LFMC derived from optical sensors and a global database of LFMC site measurements (2) to test different model structures to estimate LFMC from VOD and (3) to apply the best-performing model to estimate LFMC at global scales. Our results show that VOD is medium to highly correlated with LFMC in areas with medium to high coverage of short vegetation (grasslands, croplands, shrublands). Forested areas show on average weak correlations, but the variability in correlations is high. A logistic regression model that uses VOD and additionally leaf area index as predictor to account for canopy biomass reaches the highest performance in estimating LFMC. Applying this model to global VOD and LAI observations allows estimating LFMC globally over decadal time series at daily temporal s ling. The derived estimates of LFMC can be used to assess large-scale patterns and temporal changes in vegetation water status, drought conditions, and fire dynamics.
Assessment of generalized allometric models for aboveground biomass estimation: A case study in Australia
Publisher: Elsevier BV
Date: 08-2020
Investigating Live Fuel Moisture Content Estimation in Fire-Prone Shrubland from Remote Sensing Using Empirical Modelling and RTM Simulations
Publisher: MDPI AG
Date: 14-07-2020
DOI: 10.3390/RS12142251
Abstract: Previous research has demonstrated that remote sensing can provide spectral information related to vegetation moisture variations essential for estimating live fuel moisture content (LFMC), but accuracy and timeliness still present challenges to using this information operationally. Consequently, many regional administrations are investing important resources in field c aigns for LFMC monitoring, often focusing on indicator species to reduce s ling time and costs. This paper compares different remote sensing approaches to provide LFMC prediction of Cistus ladanifer, a fire-prone shrub species commonly found in Mediterranean areas and used by fire management services as an indicator species for wildfire risk assessment. Spectral indices (SI) were derived from satellite imagery of different spectral, spatial, and temporal resolution, including Sentinel-2 and two different reflectance products of the Moderate Resolution Imaging Spectrometer (MODIS) MCD43A4 and MOD09GA. The SI were used to calibrate empirical models for LFMC estimation using on ground field LFMC measurements from a monospecific shrubland area located in Madrid (Spain). The empirical models were fitted with different statistical methods: simple (LR) and multiple linear regression (MLR), non-linear regression (NLR), and general additive models with splines (GAMs). MCD43A4 images were also used to estimate LFMC from the inversion of radiative transfer models (RTM). Empirical model predictions and RTM simulations of LFMC were validated and compared using an independent s le of LFMC values observed in the field. Empirical models derived from MODIS products and Sentinel-2 data showed R2 between estimated and observed LFMC from 0.72 to 0.75 and mean absolute errors ranging from 11% to 13%. GAMs outperformed regression methods in model calibration, but NLR had better results in model validation. LFMC derived from RTM simulations had a weaker correlation with field data (R2 = 0.49) than the best empirical model fitted with MCD43A4 images (R2 = 0.75). R2 between observations and LFMC derived from RTM ranged from 0.56 to 0.85 when the validation was performed for each year independently. However, these values were still lower than the equivalent statistics using the empirical models (R2 from 0.65 to 0.94) and the mean absolute errors per year for RTM were still high (ranging from 25% to 38%) compared to the empirical model (ranging 7% to 15%). Our results showed that spectral information derived from Sentinel-2 and different MODIS products provide valuable information for LFMC estimation in C. ladanifer shrubland. However, both empirical and RTM approaches tended to overestimate the lowest LFMC values, and therefore further work is needed to improve predictions, especially below the critical LFMC threshold used by fire management services to indicate higher flammability ( %). Although lower extreme LFMC values are still difficult to estimate, the proposed empirical models may be useful to identify when the critical threshold for high fire risk has been reached with reasonable accuracy. This study demonstrates that remote sensing data is a promising source of information to derive reliable and cost-effective LFMC estimation models that can be used in operational wildfire risk systems.
Development of a framework for fire risk assessment using remote sensing and geographic information system technologies
Publisher: Elsevier BV
Date: 2010
The contribution of soil, topographic, and vegetation traits to plant-water sensitivity
Publisher: Copernicus GmbH
Date: 28-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-10934
Abstract: & & Spatio-temporal patterns of plant water uptake, loss, and storage are a first-order control on photosynthesis, evapotranspiration dynamics, and thus, land-atmosphere interactions. These patterns depend on temporally variable hydrometeorological conditions but also on geographically varying characteristics. These include, but are not limited to, topographic and soil properties that influence rainfall infiltration and water distribution in the unsaturated zone and vegetation properties, such as rooting depth, stomatal and xylem properties, leaf area, and more. Understanding how these different factors interact to control the overall dynamics of plant water uptake is fundamental to understanding the response of vegetation to hydrologic variations, but has traditionally been hindered by data limitations. In situ measurements are too sparse to sufficiently span the range of possible variations across different geographic factors. Remote sensing estimates of plant water uptake either are not available or (in the case of ET estimates) are sufficiently indirect that they are unlikely to correctly account for all of the factors above. Here, we study the effects of different geographic factors on plant-water interactions by analyzing the dynamics of live fuel moisture content (LFMC, defined as the vegetation water content ided by dry biomass) determined from Sentinel-1 synthetic aperture radar and Landsat multispectral observations. LFMC directly reflects vegetation water content and therefore patterns of plant water uptake and evapotranspiration. We quantify the & quot lant-water sensitivity& quot by using an auto-regressive model comparing LFMC to climate and analyze the spatial patterns of plant water sensitivity at 4 km resolution across the Western United States. No in idual factor explains a majority of the spatial patterns in plant-water sensitivity. With the exception of the maximum soil conductance, no soil, topographic, or vegetation traits exerts a dominant control on plant water sensitivity. However, when aggregated, soil characteristics explain about twice as much variability in plant water sensitivity as topographic or plant characteristics do, despite little previous recognition of the influence of soil hydraulic properties on plant-water interactions.& & &
Discovery of a pulsar-powered bow shock nebula in the Small Magellanic Cloud supernova remnant DEM S5
Publisher: Oxford University Press (OUP)
Date: 08-04-2019
DOI: 10.1093/MNRAS/STZ971
Excess low-frequency noise in YBCO rf SQUIDs in weak magnetic fields
Publisher: Elsevier BV
Date: 10-1997
Satellite Remote Sensing Contributions to Wildland Fire Science and Management
Publisher: Springer Science and Business Media LLC
Date: 30-04-2020
Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties
Publisher: Copernicus GmbH
Date: 16-03-2023
Abstract: Abstract. Vegetation attenuates the microwave emission from the land surface. The strength of this attenuation is quantified in models in terms of the parameter vegetation optical depth (VOD) and is influenced by the vegetation mass, structure, water content, and observation wavelength. Earth observation satellite sensors operating in the microwave frequencies are used for global VOD retrievals, enabling the monitoring of vegetation at large scales. VOD has been used to determine above-ground biomass, monitor phenology, or estimate vegetation water status. VOD can be also used for constraining land surface models or modelling wildfires at large scales. Several VOD products exist, differing by frequency/wavelength, sensor, and retrieval algorithm. Numerous studies present correlations or empirical functions between different VOD datasets and vegetation variables such as the normalized difference vegetation index, leaf area index, gross primary production, biomass, vegetation height, or vegetation water content. However, an assessment of the joint impact of land cover, vegetation biomass, leaf area, and moisture status on the VOD signal is challenging and has not yet been done. This study aims to interpret the VOD signal as a multi-variate function of several descriptive vegetation variables. The results will help to select VOD at the most suitable wavelength for specific applications and can guide the development of appropriate observation operators to integrate VOD with large-scale land surface models. Here we use VOD from the Land Parameter Retrieval Model (LPRM) in the Ku, X, and C bands from the harmonized Vegetation Optical Depth Climate Archive (VODCA) dataset and L-band VOD derived from Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) sensors. The leaf area index, live-fuel moisture content, above-ground biomass, and land cover are able to explain up to 93 % and 95 % of the variance (Nash–Sutcliffe model efficiency coefficient) in 8-daily and monthly VOD within a multi-variable random forest regression. Thereby, the regression reproduces spatial patterns of L-band VOD and spatial and temporal patterns of Ku-, X-, and C-band VOD. Analyses of accumulated local effects demonstrate that Ku-, X-, and C-band VOD are mostly sensitive to the leaf area index, and L-band VOD is most sensitive to above-ground biomass. However, for all VODs the global relationships with vegetation properties are non-monotonic and complex and differ with land cover type. This indicates that the use of simple global regressions to estimate single vegetation properties (e.g. above-ground biomass) from VOD is over-simplistic.
Global 5-km resolution estimates of secondary evaporation including irrigation through satellite data assimilation
Publisher: Copernicus GmbH
Date: 24-01-2018
Abstract: Abstract. A portion of globally generated surface and groundwater resources evaporates from wetlands, water bodies and irrigated areas. This secondary evaporation of blue water directly affects the remaining water resources available for ecosystems and human use. At the global scale, a lack of detailed water balance studies and direct observations limits our understanding of the magnitude and spatial and temporal distribution of secondary evaporation. Here, we propose a methodology to assimilate satellite-derived information into the landscape hydrological model W3 at an unprecedented 0.05° or c. 5 km resolution globally. The assimilated data are all derived from MODIS observations, including surface water extent, surface albedo, vegetation cover, leaf area index, canopy conductance, and land surface temperature (LST). The information from these products is imparted on the model in a simple but efficient manner, through a combination of direct insertion of surface water extent, evaporation flux adjustment based on LST, and parameter nudging for the other observations. The resulting water balance estimates were evaluated against river basin discharge records and the water balance of closed basins and demonstrably improved water balance estimates compared to ignoring secondary evaporation (e.g., bias improved from +38 mm/d to +2 mm/d). The evaporation estimates derived from assimilation were combined with global mapping of irrigation crops to derive a minimum estimate of irrigation water requirements (I0), representative of optimal irrigation efficiency. Our I0 estimates were lower than published country-level estimates of irrigation water use produced by alternative estimation methods, for reasons that are discussed. We estimate that 16 % of globally generated water resources evaporate before reaching the oceans, enhancing total terrestrial evaporation by 6.1 • 1012 m3 y−1 or 8.8 %. Of this volume, 5 % is evaporated from irrigation areas, 58% from terrestrial water bodies and 37 % from other surfaces. Model-data assimilation at even higher spatial resolutions can achieve a further reduction in uncertainty but will require more accurate and detailed mapping of surface water dynamics and areas equipped for irrigation.
Estimation of Forest Structure with the Vegetation Structure Perpendicular Index (VSPI) for Dynamic Fire Spread Simulations
Publisher: IEEE
Date: 07-2019
Retrieval of forest fuel moisture content using a coupled radiative transfer model
Publisher: Elsevier BV
Date: 09-2017
Live fuel moisture content estimation from MODIS: A deep learning approach
Publisher: Elsevier BV
Date: 09-2021
Mapping Live Fuel Moisture Content and Flammability for Continental Australia Using Optical Remote Sensing
Publisher: IEEE
Date: 07-2018
Innovative strategies and corporate profitability: the positive resources dependence from political network
Publisher: Elsevier BV
Date: 04-2020
Estimation of dry matter content in leaves using normalized indexes and prospect model inversion
Publisher: Informa UK Limited
Date: 28-10-2012
Evaluating the Sentinel-2a Satellite Data for Fuel Moisture Content Retrieval
Publisher: IEEE
Date: 07-2019
Convergence in critical fuel moisture and fire weather thresholds associated with fire activity in the pyroregions of Mediterranean Europe
Publisher: Elsevier BV
Date: 02-2022
DOI: 10.1016/J.SCITOTENV.2021.151462
Abstract: Wildfires are becoming an increasing threat to many communities worldwide. There has been substantial progress towards understanding the proximal causes of increased fire activity in recent years at regional and national scales. However, subcontinental scale examinations of the commonalities and differences in the drivers of fire activity across different regions are rare in the Mediterranean zone of the European Union (EUMed). Here, we first develop a new classification of EUMed pyroregions, based on grouping different ecoregions with similar seasonal patterns of burned area. We then examine the thresholds associated with fire activity in response to different drivers related to fuel moisture, surface meteorology and atmospheric stability. We document an overarching role for variation in dead fuel moisture content (FM
Estimación de variables esenciales de la vegetación en un ecosistema de dehesa utilizando factores de reflectividad simulados estacionalmente
Publisher: Universitat Politecnica de Valencia
Date: 23-06-2020
Abstract: Mixed vegetation systems such as wood pastures and shrubby pastures are vital for extensive and sustainable livestock production as well as for the conservation of bio ersity and provision of ecosystem services, and are mostly located in areas that are expected to be more strongly affected by climate change. However, the structural characteristics, phenology, and the optical properties of the vegetation in these mixed -ecosystems such as savanna-like ecosystems in the Iberian Peninsula which combines herbaceous and/or shrubby understory with a low density tree cover, constitute a serious challenge for the remote sensing studies. This work combines physical and empirical methods to improve the estimation of essential vegetation variables: leaf area index ( em LAI /em , m sup /sup / m sup /sup ), leaf (C sub ab,leaf /sub , μg / cm sup /sup ) and canopy(C sub ab,canopy /sub , g / m sup /sup ) chlorophyll content, and leaf (C sub m, leaf /sub , g / cm sup /sup ) and canopy (C sub m,canopy /sub , g / m sup /sup ) dry matter content in a dehesa ecosystem. For this purpose, a spectral simulated database for the four main phenological stages of the highly dynamic herbaceous layer (summer senescence, autumn regrowth, greenness peak and beginning of senescence), was built by coupling PROSAIL and FLIGHT radiative transfer models. This database was used to calibrate different predictive models based on vegetation indices (VI) proposed in the literature which combine different spectral bands as well as Partial Least Squares Regression (PLSR) using all bands in the simulated spectral range (400-2500 nm). PLSR models offered greater predictive power ( em R sup /sup /em ≥ 0.93, em RRMSE /em ≤ 10.77 %) both for the leaf and canopy- level variables. The results suggest that directional and geometric effects control the relationships between simulated reflectance factors and the foliar parameters. High seasonal variability is observed in the relationship between biophysical variables and IVs, especially for em LAI /em and em C sub ab /sub /em , which is confirmed in the PLSR analysis. The models developed need to be validated with spectral data obtained either with proximal or remote sensors.
Actual evapotranspiration estimation by ground and remote sensing methods: the Australian experience
Publisher: Wiley
Date: 16-12-2011
DOI: 10.1002/HYP.8391
Specific leaf area and vapour pressure deficit control live fuel moisture content
Publisher: Wiley
Date: 06-02-2023
Abstract: The live fuel moisture content (LFMC) is an important precondition for wildfire activity, yet it remains challenging to predict LFMC due to the dynamic interplay between atmospheric and hydrological conditions that determine the plant's access to, and loss of water. We monitored LFMC and a range of plant water‐use traits (predawn and midday leaf water potentials [Ψ leaf ]), leaf traits (specific leaf area [SLA]), hydrological status (soil water content [SWC] in the shallow layer and full profile) and atmospheric variables (air temperature, vapour pressure deficit [VPD], CO 2 concentrations) in a mature eucalypt woodland at the Eucalyptus Free‐Air CO 2 Enrichment (EucFACE) facility during a drought. We combined plant traits, hydrological status and atmospheric variables into a biophysical model to predict LFMC dynamics, and compared these with predictions of LFMC based on a satellite model and established relationships between Ψ leaf and LFMC from pressure–volume curves. Predawn Ψ leaf could be well predicted from changes in SWC, but variation in midday Ψ leaf and LFMC were more responsive to atmospheric than hydrological variables. The biophysical model explained up to 89% of variability in LFMC and outperformed established approaches to predict LFMC. SLA was the single most important variable to predict LFMC, followed by VPD, which explained 33% of the remaining variability in LFMC. Our study demonstrates that the co‐variation of plant traits and atmospheric and hydrological conditions affect LFMC during drought, suggesting a new way forward for predicting LFMC by combining biophysical and satellite‐based models of LFMC with seasonal forecasts of meteorological and hydrological variables. Read the free Plain Language Summary for this article on the Journal blog.
Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties
Publisher: Copernicus GmbH
Date: 13-04-2022
DOI: 10.5194/BG-2022-85
Abstract: Abstract. Vegetation attenuates the microwave emission from the land surface. The strength of this attenuation is quantified in models in terms of the parameter Vegetation Optical Depth (VOD), and is influenced by the vegetation mass, structure, water content, and observation wavelength. Earth observation satellites operating in the microwave frequencies are used for global VOD retrievals, enabling the monitoring of vegetation status at large scales. VOD has been used to determine above-ground biomass, monitor phenology or estimate vegetation water status. VOD can be also used for constraining land surface models or modelling wildfires at large scale. Several VOD products exist differing by frequency/wavelength, sensor, and retrieval algorithm. Numerous studies present correlations or empirical functions between different VOD datasets and vegetation variables such as normalised difference vegetation index, leaf area index, gross primary production, biomass, vegetation height or vegetation water content. However, an assessment of the joint impact of land cover, vegetation biomass, leaf area, and moisture status on the VOD signal is challenging and has not yet been done. This study aims to interpret the VOD signal as a multi-variate function of several descriptive vegetation variables. The results will help to select certain VOD wavelengths for specific applications and can guide the development of appropriate observation operators to integrate VOD with large-scale land surface models. Here we use VOD from the Land Parameter Retrieval Model (LPRM) of Ku-, X- and C-bands of the harmonised VODCA dataset and level 3 L-band derived from SMOS and SMAP sensors. Within a multivariable regression random forest model for simulating these VOD signals, leaf area index, live-fuel moisture content, above-ground biomass, and land cover are able to explain up to 0.95 of the variance (coefficient of determination). Thereby, the variance in L-band VOD is reproduced spatially and for Ku-, X- and C-band VOD spatially as well as temporally. Analyses of accumulated local effects demonstrate that Ku-, X- and C-band VOD is mostly sensitive to leaf area index and L-band VOD to above-ground biomass. However, for all VODs the global relationships with vegetation properties are non-monotonic and complex and differ with land cover type. This indicates that the use of simple global regressions to estimate single vegetation properties (e.g. above-ground biomass) from VOD is over-simplistic.
Relationships between Burn Severity and Environmental Drivers in the Temperate Coniferous Forest of Northern China
Publisher: MDPI AG
Date: 17-12-2021
DOI: 10.3390/RS13245127
Abstract: Burn severity is a key component of fire regimes and is critical for quantifying fires’ impacts on key ecological processes. The spatial and temporal distribution characteristics of forest burn severity are closely related to its environmental drivers prior to the fire occurrence. The temperate coniferous forest of northern China is an important part of China’s forest resources and has suffered frequent forest fires in recent years. However, the understanding of environmental drivers controlling burn severity in this fire-prone region is still limited. To fill the gap, spatial pattern metrics including pre-fire fuel variables (tree canopy cover (TCC), normalized difference vegetation index (NDVI), and live fuel moisture content (LFMC)), topographic variables (elevation, slope, and topographic radiation aspect index (TRASP)), and weather variables (relative humidity, maximum air temperature, cumulative precipitation, and maximum wind speed) were correlated with a remote sensing-derived burn severity index, the composite burn index (CBI). A random forest (RF) machine learning algorithm was applied to reveal the relative importance of the environmental drivers mentioned above to burn severity for a fire. The model achieved CBI prediction accuracy with a correlation coefficient (R) equal to 0.76, root mean square error (RMSE) equal to 0.16, and fitting line slope equal to 0.64. The results showed that burn severity was mostly influenced by flammable live fuels and LFMC. The elevation was the most important topographic driver, and meteorological variables had no obvious effect on burn severity. Our findings suggest that in addition to conducting strategic fuel reduction management activities, planning the landscapes with fire-resistant plants with higher LFMC when possible (e.g., “Green firebreaks”) is also indispensable for lowering the burn severity caused by wildfires in the temperate coniferous forests of northern China.
Integrating geospatial information into fire risk assessment
Publisher: CSIRO Publishing
Date: 2014
DOI: 10.1071/WF12052
Abstract: Fire risk assessment should take into account the most relevant components associated to fire occurrence. To estimate when and where the fire will produce undesired effects, we need to model both (a) fire ignition and propagation potential and (b) fire vulnerability. Following these ideas, a comprehensive fire risk assessment system is proposed in this paper, which makes extensive use of geographic information technologies to offer a spatially explicit evaluation of fire risk conditions. The paper first describes the conceptual model, then the methods to generate the different input variables, the approaches to merge those variables into synthetic risk indices and finally the validation of the outputs. The model has been applied at a national level for the whole Spanish Iberian territory at 1-km2 spatial resolution. Fire danger included human factors, lightning probability, fuel moisture content of both dead and live fuels and propagation potential. Fire vulnerability was assessed by analysing values-at-risk and landscape resilience. Each input variable included a particular accuracy assessment, whereas the synthetic indices were validated using the most recent fire statistics available. Significant relations (P 0.001) with fire occurrence were found for the main synthetic danger indices, particularly for those associated to fuel moisture content conditions.
A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products
Publisher: Elsevier BV
Date: 09-2013
Characterising vegetation fuel moisture conditions from microwave satellite observations for fire danger prediction at continental to global scales
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-10860
Abstract: & & Spatial patterns and temporal changes in live fuel moisture content (LFMC) have been intensively estimated from satellite observations in the optical domain of the electromagnetic spectrum. Such estimates are valuable to predict regional to local variations in fire danger (Yebra et al., 2018). However, optical satellite measurements saturate fast in dense canopies and are generally h ered during cloud cover. Microwave satellite observations can penetrate clouds and the canopy (dependent on the wavelength) and hence have been intensively used to derive surface soil moisture (SSM) or vegetation optical depth (VOD), which is a proxy for vegetation water content (Moesinger et al., 2019). However, the relationship of microwave VOD to LFMC and the predictive capabilities of VOD for fire dynamics have not yet been investigated at large scales. Here we aim to assess how VOD reflects changes in LFMC and the sensitivity of VOD to different properties of fire dynamics such as fire occurrence, size, burned area, and fire radiative power.& & & & We compared VOD in different microwave bands (Ku-, X-, and C-band) from the VODCA dataset (Moesinger et al., 2019) with LFMC from MODIS retrievals (Yebra et al., 2018). Our results demonstrate that VOD and LFMC are moderately to highly correlated but the strength and shape of the relationship depends on land cover type. In a preliminary analysis, we then predicted the probability of fire occurrence (Andela et al., 2019) and fire radiative power (Kaiser et al., 2012) from VOD, SSM, and climate data using the random forest machine learning approach. The initial results show that VOD is a skilful predictor for continental-scale fire dynamics. Furthermore, our results suggest that the combination of LFMC from optical satellites with microwave SSM and VOD might allow to comprehensively estimate ecosystem fuel moisture conditions. Hence microwave satellite observations will be valuable for the development of integrated fire danger prediction systems.& & & & & & & & & & strong& References& /strong& & & & & Andela, N., Morton, D.C., Giglio, L., Paugam, R., Chen, Y., Hantson, S., Werf, G.R. van der, Randerson, J.T., 2019. The Global Fire Atlas of in idual fire size, duration, speed and direction. Earth Syst. Sci. Data 11, 529& #8211 . 0.5194/essd-11-529-2019& & & & Kaiser, J.W., Heil, A., Andreae, M.O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M.G., Suttie, M., van der Werf, G.R., 2012. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9, 527& #8211 . 0.5194/bg-9-527-2012& & & & Moesinger, L., Dorigo, W., Jeu, R. de, Schalie, R. van der, Scanlon, T., Teubner, I., Forkel, M., 2019. The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA. Earth Syst. Sci. Data Discuss. 1& #8211 . 0.5194/essd-2019-42& & & & Yebra, M., Quan, X., Ria& #241 o, D., Rozas Larraondo, P., van Dijk, A.I.J.M., Cary, G.J., 2018. A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing. Remote Sens. Environ. 212, 260& #8211 . 0.1016/j.rse.2018.04.053& &
Drought-related leaf functional traits control spatial and temporal dynamics of live fuel moisture content
Publisher: Elsevier BV
Date: 05-2022
Controlled field experiment clarifies the influence of soil moisture on litter moisture content
Publisher: Elsevier BV
Date: 03-2022
Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation
Publisher: MDPI AG
Date: 02-07-2019
DOI: 10.3390/RS11131568
Abstract: Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage of remote sensing data from multiple sensors to derive FMC however, little attention has been devoted to the usage of the C-band Sentinel-1A data. In this study, we aimed to test the performance of C-band Sentinel-1A data for multi-temporal retrieval of forest FMC by coupling the bare soil backscatter linear model with the vegetation backscatter water cloud model (WCM). This coupled model that linked the observed backscatter directly to FMC, was firstly calibrated using field FMC measurements and corresponding synthetic aperture radar (SAR) backscatters (VV and VH), and then a look-up table (LUT) comprising of the modelled VH backscatter and FMC was built by running the calibrated model forwardly. The absolute difference (MAEr) of modelled and observed VH backscatters was selected as the cost function to search the optimal FMC from the LUT. The performance of the presented methodology was verified using the three-fold cross-validation method by iding the whole s les into equal three parts. Two parts were used for the model calibration and the other one for the validation, and this was repeated three times. The results showed that the estimated and measured forest FMC were consistent across the three validation s les, with the root mean square error (RMSE) of 19.53% (S le 1), 12.64% (S le 2) and 15.45% (S le 3). To further test the performance of the C-band Sentinel-1A data for forest FMC estimation, our results were compared to those obtained using the optical Landsat 8 Operational Land Imager (OLI) data and the empirical partial least squares regression (PLSR) method. The latter resulted in higher RMSE between estimated and measured forest FMC with 20.11% (S le 1), 26.21% (S le 2) and 26.73% (S le 3) than the presented Sentinel-1A data-based method. Hence, this study demonstrated that the good capability of C-band Sentinel-1A data for forest FMC retrieval, opening the possibility of developing a new operational SAR data-based methodology for forest FMC estimation.
Global vegetation gross primary production estimation using satellite-derived light-use efficiency and canopy conductance
Publisher: Elsevier BV
Date: 06-2015
Habitat highs and lows: Using terrestrial and UAV LiDAR for modelling avian species richness and abundance in a restored woodland
Publisher: Elsevier BV
Date: 02-2023
Regional estimation of woodland moisture content by inverting Radiative Transfer Models
Publisher: Elsevier BV
Date: 05-2013
Dry Live Fuels Increase the Likelihood of Lightning‐Caused Fires
Publisher: American Geophysical Union (AGU)
Date: 02-08-2023
DOI: 10.1029/2022GL100975
Abstract: Live fuel moisture content (LFMC) is a key determinant of landscape ignition potential, but quantitative estimates of its effects on wildfire are lacking. We present a causal inference framework to isolate the effect of LFMC from other drivers like fuel type, fuel amount, and meteorology. We show that in California when LFMC is below a critical flammability threshold, the likelihood of fires is 1.8 times as high statewide (2.25% vs. 1.27%) and 2.5 times as high in shrubs, compared to when LFMC is greater than the threshold. This risk ratio is times when LFMC is 10% less than the threshold. Between 2016 and 2021, the risk ratio was highest in 2020 (2.3 times), potentially contributing to the record‐breaking wildfire activity in 2020. Our estimates can inform several wildfire prediction and management applications, including wildfire suppression, prescribed burn planning, and public safety power shutoff implementation.
Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
Publisher: MDPI AG
Date: 17-10-2018
DOI: 10.3390/RS10101654
Abstract: Fire Spread Rate (FSR) can indicate how fast a fire is spreading, which is especially helpful for wildfire rescue and management. Historically, images obtained from sun-orbiting satellites such as Moderate Resolution Imaging Spectroradiometer (MODIS) were used to detect active fire and burned area at the large spatial scale. However, the daily revisit cycles make them inherently unable to extract FSR in near real-time (hourly or less). We argue that the Himawari-8, a next generation geostationary satellite with a 10-min temporal resolution and 0.5–2 km spatial resolution, may have the potential for near real-time FSR extraction. To that end, we propose a novel method (named H8-FSR) for near real-time FSR extraction based on the Himawari-8 data. The method first defines the centroid of the burned area as the fire center and then the near real-time FSR is extracted by timely computing the movement rate of the fire center. As a case study, the method was applied to the Esperance bushfire that broke out on 17 November, 2015, in Western Australia. Compared with the estimated FSR using the Commonwealth Scientific and Industrial Research Organization (CSIRO) Grassland Fire Spread (GFS) model, H8-FSR achieved favorable performance with a coefficient of determination (R2) of 0.54, mean bias error of –0.75 m/s, mean absolute percent error of 33.20% and root mean square error of 1.17 m/s, respectively. These results demonstrated that the Himawari-8 data are valuable for near real-time FSR extraction, and also suggested that the proposed method could be potentially applicable to other next generation geostationary satellite data.
Better managing fire in flammable tree plantations
Publisher: Elsevier BV
Date: 2023
Modelling wildfire occurrence at regional scale from land use/cover and climate change scenarios
Publisher: Elsevier BV
Date: 11-2021
Retrieval of Fuel Moisture Content from Himawari-8 Product: Towards Real-Time Wildfire Risk Assessment
Publisher: IEEE
Date: 07-2018
Generation of a Species-Specific Look-Up Table for Fuel Moisture Content Assessment
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2009
Multi-modal temporal CNNs for live fuel moisture content estimation
Publisher: Elsevier BV
Date: 10-2022
Deriving comprehensive forest structure information from mobile laser scanning observations using automated point cloud classification
Publisher: Elsevier BV
Date: 08-2016
A fuel moisture content and flammability monitoring methodology for continental Australia based on optical remote sensing
Publisher: Elsevier BV
Date: 06-2018
Representing vapour and capillary rise from the soil improves a leaf litter moisture model
Publisher: Elsevier BV
Date: 09-2022
Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: Solving the ill-posed inverse problem
Publisher: Elsevier BV
Date: 16-11-2009
Sea-ice melt determines seasonal phytoplankton dynamics and delimits the habitat of temperate Atlantic taxa as the Arctic Ocean atlantifies
Publisher: Cold Spring Harbor Laboratory
Date: 05-05-2023
DOI: 10.1101/2023.05.04.539293
Abstract: The Arctic Ocean is one of the regions where anthropogenic environmental change is progressing most rapidly and drastically. The impact of rising temperatures and decreasing sea ice on Arctic marine microbial communities is yet not well understood. Microbes form the basis of food webs in the Arctic Ocean, providing energy for larger organisms. Previous studies have shown that Atlantic taxa associated with low light are robust to more polar conditions. In this study, we compared to which extent sea ice melt influences light-associated phytoplankton dynamics and bio ersity over two years at two mooring locations in the Fram Strait. One mooring is deployed in pure Atlantic water, and the second in the intermittently ice-covered Marginal Ice Zone. Time-series analysis of licon sequence variants abundance over a two-year period, allowed us to identify communities of co-occurring taxa that exhibit similar patterns throughout the annual cycle. We then examined how alterations in environmental conditions affect the prevalence of species. During high abundance periods of diatoms, polar phytoplankton populations dominated, while temperate taxa were weakly represented. Generally, polar pelagic and ice-associated taxa (such as Fragilariopsis cylindrus or Melosira arctica ) were more prevalent in Atlantic conditions whereas temperate taxa (such as Odontella aurita or Proboscia alata ) have limited potential to persist in colder ice-impacted waters. In contrast to previous assumptions, we think that sea-ice melt acts as a barrier to the horizontal extent of temperate diatoms by preventing their succession at places strongly influenced by polar conditions such as the melting sea ice.
Laboratory Measurements of Plant Drying
Publisher: American Society for Photogrammetry and Remote Sensing
Date: 05-2014
Evaluation of optical remote sensing to estimate actual evapotranspiration and canopy conductance
Publisher: Elsevier BV
Date: 02-2013
Estimation of forest surface fuel load using airborne lidar data
Publisher: SPIE
Date: 18-10-2016
DOI: 10.1117/12.2239715
Model-driven estimation of closed and open shrublands live fuel moisture content
Publisher: Informa UK Limited
Date: 04-11-2022
Adaptive wildfire mitigation approaches
Publisher: American Association for the Advancement of Science (AAAS)
Date: 09-09-2022
Unveiling the Factors Responsible for Australia’s Black Summer Fires of 2019/2020
Publisher: MDPI AG
Date: 04-09-2021
DOI: 10.3390/FIRE4030058
Abstract: The summer season of 2019–2020 has been named Australia’s Black Summer because of the large forest fires that burnt for months in southeast Australia, affecting millions of Australia’s citizens and hundreds of millions of animals and capturing global media attention. This extensive fire season has been attributed to the global climate crisis, a long drought season and extreme fire weather conditions. Our aim in this study was to examine the factors that have led some of the wildfires to burn over larger areas for a longer duration and to cause more damage to vegetation. To this end, we studied all large forest and non-forest fires ( km2) that burnt in Australia between September 2019 and mid-February 2020 (Australia’s Black Summer fires), focusing on the forest fires in southeast Australia. We used a segmentation algorithm to define in idual polygons of large fires based on the burn date from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) active fires product and the Moderate Resolution Imaging Spectroradiometer (MODIS) burnt area product (MCD64A1). For each of the wildfires, we calculated the following 10 response variables, which served as proxies for the fires’ extent in space and time, spread and intensity: fire area, fire duration (days), the average spread of fire (area/days), fire radiative power (FRP as detected by NASA’s MODIS Collection 6 active fires product (MCD14ML)), two burn severity products, and changes in vegetation as a result of the fire (as calculated using the vegetation health index (VHI) derived from AVHRR and VIIRS as well as live fuel moisture content (LFMC), photosynthetic vegetation (PV) and combined photosynthetic and non-photosynthetic vegetation (PV+NPV) derived from MODIS). We also computed more than 30 climatic, vegetation and anthropogenic variables based on remotely sensed derived variables, climatic time series and land cover datasets, which served as the explanatory variables. Altogether, 391 large fires were identified for Australia’s Black Summer. These included 205 forest fires with an average area of 584 km2 and 186 non-forest fires with an average area of 445 km2 63 of the forest fires took place in southeast (SE) Australia (the area between Fraser Island, Queensland, and Kangaroo Island, South Australia), with an average area of 1097 km2. Australia’s Black Summer forest fires burnt for more days compared with non-forest fires. Overall, the stepwise regression models were most successful at explaining the response variables for the forest fires in SE Australia (n = 63 median-adjusted R2 of 64.3%), followed by all forest fires (n = 205 median-adjusted R2 of 55.8%) and all non-forest fires (n = 186 median-adjusted R2 of 48.2%). The two response variables that were best explained by the explanatory variables used as proxies for fires’ extent, spread and intensity across all models for the Black Summer forest and non-forest fires were the change in PV due to fire (median-adjusted R2 of 69.1%) and the change in VHI due to fire (median-adjusted R2 of 66.3%). Amongst the variables we examined, vegetation and fuel-related variables (such as previous frequency of fires and the conditions of the vegetation before the fire) were found to be more prevalent in the multivariate models for explaining the response variables in comparison with climatic and anthropogenic variables. This result suggests that better management of wildland–urban interfaces and natural vegetation using cultural and prescribed burning as well as planning landscapes with less flammable and more fire-tolerant ground cover plants may reduce fire risk to communities living near forests, but this is challenging given the sheer size and ersity of ecosystems in Australia.
Author Correction: Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications
Publisher: Springer Science and Business Media LLC
Date: 29-11-2019
DOI: 10.1038/S41597-019-0315-Z
Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.
The influence of soil moisture on surface and sub-surface litter fuel moisture simulation at five Australian sites
Publisher: Elsevier BV
Date: 03-2021
Global trends in vegetation seasonality in the GIMMS NDVI3g and their robustness
Publisher: Elsevier BV
Date: 02-2021
A New Fire Danger Index Developed by Random Forest Analysis of Remote Sensing Derived Fire Sizes
Publisher: MDPI AG
Date: 29-09-2022
DOI: 10.3390/FIRE5050152
Abstract: Studies using remote sensing data for fire danger prediction have primarily relied on fire ignitions data to develop fire danger indices (FDIs). However, these data may only represent conditions suitable for ignition but may not represent fire danger conditions causing escalating fire size. The fire-related response variable’s scalability is a key factor that forms a basis for an FDI to include a broader range of fire danger conditions. Remote sensing derived fire size is a scalable fire characteristic encapsulating all possible fire sizes that previously occurred in the landscape, including extreme fire events. Consequently, we propose a new FDI that uses remote sensing derived fire size as a response variable. We computed fire sizes from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument burned area. We applied random forest (RF) and logistic regression (LR) to develop the FDI for Australia. RF models performed better than LR, and the higher predicted probabilities demonstrated higher chances for ignited fires to be escalated to larger fire sizes at a regional scale across Australia. However, the predicted probabilities cannot be related to the specific range of fire sizes due to data limitations. Further research with higher temporal and spatial resolution data of both the response and predictor variables can help establish a better relationship between a specific range of fire sizes and the predicted probabilities.
Global Canopy Conductance (Gc)
Publisher: No publisher found
Date: 2001
Global fuel moisture content mapping from MODIS
Publisher: Elsevier BV
Date: 09-2021
Wildfires: Australia needs national monitoring agency
Publisher: Springer Science and Business Media LLC
Date: 12-08-2020
Sea-ice derived meltwater stratification slows the biological carbon pump: results from continuous observations
Publisher: Springer Science and Business Media LLC
Date: 12-2021
DOI: 10.1038/S41467-021-26943-Z
Abstract: The ocean moderates the world’s climate through absorption of heat and carbon, but how much carbon the ocean will continue to absorb remains unknown. The North Atlantic Ocean west (Baffin Bay/Labrador Sea) and east (Fram Strait/Greenland Sea) of Greenland features the most intense absorption of anthropogenic carbon globally the biological carbon pump (BCP) contributes substantially. As Arctic sea-ice melts, the BCP changes, impacting global climate and other critical ocean attributes (e.g. bio ersity). Full understanding requires year-round observations across a range of ice conditions. Here we present such observations: autonomously collected Eulerian continuous 24-month time-series in Fram Strait. We show that, compared to ice-unaffected conditions, sea-ice derived meltwater stratification slows the BCP by 4 months, a shift from an export to a retention system, with measurable impacts on benthic communities. This has implications for ecosystem dynamics in the future warmer Arctic where the seasonal ice zone is expected to expand.
Global 5 km resolution estimates of secondary evaporation including irrigation through satellite data assimilation
Publisher: Copernicus GmbH
Date: 27-09-2018
DOI: 10.5194/HESS-22-4959-2018
Abstract: Abstract. A portion of globally generated surface and groundwater resources evaporates from wetlands, waterbodies and irrigated areas. This secondary evaporation of “blue” water directly affects the remaining water resources available for ecosystems and human use. At the global scale, a lack of detailed water balance studies and direct observations limits our understanding of the magnitude and spatial and temporal distribution of secondary evaporation. Here, we propose a methodology to assimilate satellite-derived information into the landscape hydrological model W3 at an unprecedented 0.05∘, or ca. 5 km resolution globally. The assimilated data are all derived from MODIS observations, including surface water extent, surface albedo, vegetation cover, leaf area index, canopy conductance and land surface temperature (LST). The information from these products is imparted on the model in a simple but efficient manner, through a combination of direct insertion of the surface water extent, an evaporation flux adjustment based on LST and parameter nudging for the other observations. The resulting water balance estimates were evaluated against river basin discharge records and the water balance of closed basins and demonstrably improved water balance estimates compared to ignoring secondary evaporation (e.g., bias improved from +38 to +2 mm yr−1). The evaporation estimates derived from assimilation were combined with global mapping of irrigation crops to derive a minimum estimate of irrigation water requirements (I0), representative of optimal irrigation efficiency. Our I0 estimates were lower than published country-level estimates of irrigation water use produced by alternative estimation methods, for reasons that are discussed. We estimate that 16 % of globally generated water resources evaporate before reaching the oceans, enhancing total terrestrial evaporation by 6.1×1012 m3 yr−1 or 8.8 %. Of this volume, 5 % is evaporated from irrigation areas, 58 % from terrestrial waterbodies and 37 % from other surfaces. Model-data assimilation at even higher spatial resolutions can achieve a further reduction in uncertainty but will require more accurate and detailed mapping of surface water dynamics and areas equipped for irrigation.
A Live Fuel Moisture Content Product from Landsat TM Satellite Time Series for Implementation in Fire Behavior Models
Publisher: MDPI AG
Date: 27-05-2020
DOI: 10.3390/RS12111714
Abstract: Live Fuel Moisture Content (LFMC) contributes to fire danger and behavior, as it affects fire ignition and propagation. This paper presents a two layered Landsat LFMC product based on topographically corrected relative Spectral Indices (SI) over a 2000–2011 time series, which can be integrated into fire behavior simulation models. Nine chaparral s ling sites across three Landsat-5 Thematic Mapper (TM) scenes were used to validate the product over the Western USA. The relations between field-measured LFMC and Landsat-derived SIs were strong for each in idual site but worsened when pooled together. The Enhanced Vegetation Index (EVI) presented the strongest correlations (r) and the least Root Mean Square Error (RMSE), followed by the Normalized Difference Infrared Index (NDII), Normalized Difference Vegetation Index (NDVI) and Visible Atmospherically Resistant Index (VARI). The relations between LFMC and the SIs for all sites improved after using their relative values and relative LFMC, increasing r from 0.44 up to 0.69 for relative EVI (relEVI), the best predictive variable. This relEVI served to estimate the herbaceous and woody LFMC based on minimum and maximum seasonal LFMC values. The understory herbaceous LFMC on the woody pixels was extrapolated from the surrounding pixels where the herbaceous vegetation is the top layer. Running simulations on the Wildfire Analyst (WFA) fire behavior model demonstrated that this LFMC product alone impacts significantly the fire spatial distribution in terms of burned probability, with average burned area differences over 21% after 8 h burning since ignition, compared to commonly carried out simulations based on constant values for each fuel model. The method could be applied to Landsat-7 and -8 and Sentinel-2A and -2B after proper sensor inter-calibration and topographic correction.
Estimating leaf moisture content at global scale from passive microwave satellite observations of vegetation optical depth
Publisher: Copernicus GmbH
Date: 05-04-2022
Abstract: Abstract. The moisture content of vegetation canopies controls various ecosystem processes such as plant productivity, transpiration, mortality and flammability. Leaf moisture content (here defined as the ratio of leaf water mass to leaf dry biomass, or live-fuel moisture content, LFMC) is a vegetation property that is frequently used to estimate flammability and the danger of fire occurrence and spread and is widely measured at field sites around the globe. LFMC can be retrieved from satellite observations in the visible and infrared domain of the electromagnetic spectrum, which is however h ered by frequent cloud cover or low sun elevation angles. As an alternative, vegetation water content can be estimated from satellite observations in the microwave domain. For ex le, studies at local and regional scales have demonstrated the link between LFMC and vegetation optical depth (VOD) from passive microwave satellite observations. VOD describes the attenuation of microwaves in the vegetation layer. However, neither were the relations between VOD and LFMC investigated at large or global scales nor has VOD been used to estimate LFMC. Here we aim to estimate LFMC from VOD at large scales, i.e. at coarse spatial resolution, globally, and at daily time steps over decadal time series. Therefore, our objectives are 1) to investigate the relation between VOD from different frequencies and LFMC derived from optical sensors and a global database of LFMC site measurements 2) to test different model structures to estimate LFMC from VOD and 3) to apply the best-performing model to estimate LFMC at global scales. Our results show that VOD is medium to highly correlated with LFMC in areas with medium to high coverage of short vegetation (grasslands, croplands, shrublands). Forested areas show on average weak correlations but the variability in correlations is high. A logistic regression model that uses VOD and additionally leaf area index as predictor to account for canopy biomass reaches the highest performance. Applying this model to global VOD and LAI observations allows estimating LFMC globally over decadal time series at daily temporal s ling. The derived estimates of LFMC can be used to assess large-scale patterns and temporal changes in vegetation water status and fire dynamics.
A multiscale morphological algorithm for improvements to canopy height models
Publisher: Elsevier BV
Date: 09-2019
Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR
Publisher: SPIE-Intl Soc Optical Eng
Date: 07-12-2016
A radiative transfer model-based method for the estimation of grassland aboveground biomass
Publisher: Elsevier BV
Date: 02-2017
The extraordinary 2019/20 Australian bushfire season: contributing processes and environmental impacts
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-3804
Abstract: & & The recent Australian summer witnessed bushfire at a scale that is without historical precedent. We analysed the scale and severity of the fires, the main processes contributing to their scale, and environmental consequences that have already become apparent.& We did this by combining satellite-derived information of vegetation cover, biomass and history, of soil and vegetation moisture content, and of fire extent and severity. More than 80,000 km2 was burnt, much of it native forest. Fire severity varied, but was overall greater than in preceding years. A critical factor contributing to fire conditions was a multi-year drought in Eastern Australia, which culminated in 2019 with the hottest and driest year in more than a century. During the fire season, fire danger conditions were further exacerbated by oceanic modes in the Indian and Southern Oceans, which limited circulation and caused excessive heating of the Australian land mass. Fuel availability in forests was unusually high. Reasons for this were several, including afforestation and regrowth as well as effective fire suppression in preceding years, while a contributing role for CO& sub& & /sub& fertilisation is also plausible. Combined with the drought and associated vegetation mortality, this created a high and flammable fuel load. The fires strongly affected Australia& #8217 s total living carbon pool, which was already depleted by several years of below-average rainfall. Greenhouse gas releases associated with drought and bushfires are not considered in official emission accounts, but are of comparable magnitude. The smoke emissions also caused direct health impacts, affecting cities like Sydney, Melbourne and Canberra for prolonged periods. Most of the burnt forests are resilient to fire and will regenerate, assuming rainfall conditions improve. The severity, scale and connectedness of some of the fire complexes suggest ecological recolonization may be very slow, while a number of threatened species may not recover. Perhaps most concerning, some of the forests affected had burnt only years before, whereas other areas contained vegetation communities not experiencing fire for centuries, raising questions about their ability to regenerate and possibly permanent ecological regime shifts.& &
The FrogPhone: A novel device for real-time frog call monitoring
Publisher: Wiley
Date: 04-12-2019
Climate change induced declines in fuel moisture may turn currently fire-free Pyrenean mountain forests into fire-prone ecosystems
Publisher: Elsevier BV
Date: 11-2021
Plant-water sensitivity regulates wildfire vulnerability
Publisher: Springer Science and Business Media LLC
Date: 07-02-2022
DOI: 10.1038/S41559-021-01654-2
Abstract: Extreme wildfires extensively impact human health and the environment. Increasing vapour pressure deficit (VPD) has led to a chronic increase in wildfire area in the western United States, yet some regions have been more affected than others. Here we show that for the same increase in VPD, burned area increases more in regions where vegetation moisture shows greater sensitivity to water limitation (plant-water sensitivity R 2 = 0.71). This has led to rapid increases in human exposure to wildfire risk, both because the population living in areas with high plant-water sensitivity grew 50% faster during 1990–2010 than in other wildland–urban interfaces and because VPD has risen most rapidly in these vulnerable areas. As plant-water sensitivity is strongly linked to wildfire vulnerability, accounting for ecophysiological controls should improve wildfire forecasts. If recent trends in VPD and demographic shifts continue, human wildfire risk will probably continue to increase.
Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications
Publisher: Springer Science and Business Media LLC
Date: 21-08-2019
DOI: 10.1038/S41597-019-0164-9
Abstract: Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 s ling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 in idual records based on in situ destructive s les used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.
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 behaviour
Publisher: Elsevier BV
Date: 03-2021
Estimation of Fuel Conditions for Fire Danger Assessment
Publisher: Springer Berlin Heidelberg
Date: 2009
Beyond the pond: Terrestrial habitat use by frogs in a changing climate
Publisher: Elsevier BV
Date: 09-2020
Estimating fire severity and carbon emissions over Australian tropical savannahs based on passive microwave satellite observations
Publisher: Informa UK Limited
Date: 17-04-2018
Development of a predictive model for estimating forest surface fuel load in Australian eucalypt forests with LiDAR data
Publisher: Elsevier BV
Date: 11-2017
Related Organisations
Bushfire & Natural Hazards Cooperative Research Centre
Location: Australia
Related Funding Activities
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE190100039
Start Date: 07-2019
End Date: 07-2024
Amount: $1,120,000.00
Funder: Australian Research Council
View Funded ActivityLinkage Projects - Grant ID: LP190100436
Start Date: 10-2020
End Date: 10-2024
Amount: $524,027.00
Funder: Australian Research Council
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