@article {bnh-8371, title = {Terrestrial Laser Scanning: an operational tool for fuel hazard mapping?}, journal = {Fire}, volume = {5}, year = {2022}, month = {04/2022}, pages = {85}, abstract = {

Fuel hazard estimates are vital for the prediction of fire behaviour and planning fuel treatment activities. Previous literature has highlighted the potential of Terrestrial Laser Scanning (TLS) to be used to assess fuel properties. However, operational uptake of these systems has been limited due to a lack of a sampling approach that balances efficiency and data efficacy. This study aims to assess whether an operational approach utilising Terrestrial Laser Scanning (TLS) to capture fuel information over an area commensurate with current fuel hazard assessment protocols implemented in South-Eastern Australia is feasible. TLS data were captured over various plots in South-Eastern Australia, utilising both low- and high-cost TLS sensors. Results indicate that both scanners provided similar overall representation of the ground, vertical distribution of vegetation and fuel hazard estimates. The analysis of fuel information contained within individual scans clipped to 4 m showed similar results to that of the fully co-registered plot (cover estimates of near-surface vegetation were within 10\%, elevated vegetation within 15\%, and height estimates of near-surface and elevated strata within 0.05 cm). This study recommends that, to capture a plot in an operational environment (balancing efficiency and data completeness), a sufficient number of non-overlapping individual scans can provide reliable estimates of fuel information at the near-surface and elevated strata, without the need for co-registration in the case study environments. The use of TLS within the rigid structure provided by current fuel observation protocols provides incremental benefit to the measurement of fuel hazard. Future research should leverage the full capability of TLS data and combine it with moisture estimates to gain a full realisation of the fuel hazard.

}, keywords = {fuel hazard, fuel structure, occlusion, remote sensing, Risk assessment, TLS}, doi = {https://doi.org/10.3390/fire5040085}, url = {https://www.mdpi.com/2571-6255/5/4/85}, author = {Luke Wallace and Samuel Hillman and Bryan Hally and Ritu Taneja and Andrew White and James McGlade} } @article {bnh-8129, title = {Kangaroo Island Black Summer fire reconstruction}, number = {685}, year = {2021}, month = {07/2021}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

In the 2019-20 summer, wildfire affected an area of around 200,000 hectares on Kangaroo Island, South Australia, in what has become known as the Black Summer, with significant ongoing social, economic and environmental impacts.

The Advanced Himawari Imager (AHI) onboard the geostationary satellite Himawari-8 provides infrared imagery at 2km spatial resolution at nadir in 10- minute intervals. This allows wildfires to be detected and monitored in quasi-real time using the Biogeographical Region and Individual Geostationary HHMMSS Threshold (BRIGHT) algorithm (Engel, Jones and Reinke, 2020), developed in partnership between the Royal Melbourne Institute of Technology (RMIT) and the Bushfire and Natural Hazard CRC. This report outlines the methods used to verify hotspots detected by the BRIGHT algorithm and reconstruct the Black Summer fires using spatio-temporal clustering.

}, keywords = {black summer, Fire, fire impacts, kangaroo island, reconstructions, remote sensing, Satellite}, issn = {685}, author = {Simon Ramsey and Karin Reinke and Nur Trihantoro and Simon Jones and Chermelle Engel} } @article {bnh-6993, title = {Case study: use of remote sensing data to derive spatial and temporal explicit fuel accumulation curves across defence lands}, number = {579}, year = {2020}, month = {06/2020}, institution = {Bushfire and Natural Hazards CRC}, address = {Melbourne}, abstract = {

Fuel loads are a main driver of fire rate of spread. Therefore, a spatially explicit estimation of fuel loads, coupled with their variation through time may improve wildland fuel management and contribute to the design of more efficient active fire response strategies. However, the high frequency of planned and unplanned fires in large wild areas linked to varying fire severity levels that affect the rate at which fuels re-accumulate, make the continuous monitoring of wildland fuels challenging with field-based survey methods. Here we propose the use of satellite remote sensing to map fuel loads with a revisit time of 16 days. Fuel load maps are produced for five Defence Lands using Landsat and Sentinel-2 optical remote sensing data available at Digital Earth Australia and the National Computation Infrastructure. The fuel load maps are obtained by calculating the time series of the Vegetation Structure Perpendicular Index, an index that measures post-fire disturbance, and fitting these to fuel accumulation curves derived from literature.

}, keywords = {data analysis, defence lands, fuel accumulation, remote sensing}, issn = {579}, author = {Andrea Massetti and Marta Yebra and James Hilton and Christoph R{\"u}diger} } @article {bnh-7380, title = {An early exploration of the use of the Microsoft Azure Kinect for estimation of urban tree Diameter at Breast Height}, journal = {Remote Sensing Letters}, volume = {11}, year = {2020}, month = {09/2020}, pages = {963-972}, abstract = {
Forest and urban tree inventory measurements are increasingly adopting Remote Sensing (RS) techniques due to the accurate and rapid estimates available compared to conventional methods. The focus of this study is to assess the accuracy and potential application of the Microsoft Azure Kinect {\textendash} a lightweight depth sensor {\textendash} for outdoor measurement of tree stem Diameter at Breast Height (DBH). Individual urban trees (n\ =\ 51) were recorded from one viewing angle at a distance of 1 m to 5 m away using the various Field of View (FOV) settings on the depth sensor, from which resultant point clouds provided DBH estimates using a circle-fitting approach. The optimal capture method was observed at a distance of 2 m using the binned Near Field of View (NFOV) setting. Root Mean Square Error (RMSE) of DBH using this method was 8.43 cm; however, after removing trees with irregular or non-circular stems, this improved to 3.53 cm. Variations in ambient light were observed to have little effect on DBH estimates. The results of this study suggest when in an outdoor environment, the Azure Kinect should be used at a distance no greater than 3 m away, using the binned NFOV sensor setting, for DBH estimates.
}, keywords = {DBH, remote sensing, trees}, doi = {https://doi.org/10.1080/2150704X.2020.1802528}, url = {https://www.tandfonline.com/doi/abs/10.1080/2150704X.2020.1802528}, author = {James McGlade and Luke Wallace and Bryan Hally and Andrew White and Karin Reinke and Simon Jones} } @article {bnh-6881, title = {Satellite Remote Sensing Contributions to Wildland Fire Science and Management}, journal = {Current Forestry Reports}, year = {2020}, month = {04/2020}, abstract = {

Purpose

This paper reviews the most recent literature related to the use of remote sensing (RS) data in wildland fire management.

Recent Findings

Studies dealing with pre-fire assessment, active fire detection, and fire effect monitoring are reviewed in this paper. The analysis follows the different fire management categories: fire prevention, detection, and post-fire assessment. Extracting the main trends from each of these temporal sections, recent RS literature shows growing support of the combined use of different sensors, particularly optical and radar data and lidar and optical passive images. Dedicated fire sensors have been developed in the last years, but still, most fire products are derived from sensors that were designed for other purposes. Therefore, the needs of fire managers are not always met, both in terms of spatial and temporal scales, favouring global over local scales because of the spatial resolution of existing sensors. Lidar use on fuel types and post-fire regeneration is more local, and mostly not operational, but future satellite lidar systems may help to obtain operational products. Regional and global scales are also combined in the last years, emphasizing the needs of using upscaling and merging methods to reduce uncertainties of global products. Validation is indicated as a critical phase of any new RS-based product. It should be based on the independent reference information acquired from statistically derived samples.

Summary

The main challenges of using RS for fire management rely on the need to improve the integration of sensors and methods to meet user requirements, uncertainty characterization of products, and greater efforts on statistical validation approaches.

}, keywords = {fire detection, management, remote sensing, Wildfire}, doi = {https://doi.org/10.1007/s40725-020-00116-5}, url = {https://link.springer.com/article/10.1007/s40725-020-00116-5?wt_mc=socialmedia.twitter.1.SEM.ArticleAuthorOnlineFirst}, author = {Emilio Chuvieco and Inmaculada Aguado and Javier Salas and Mariano Garcia and Marta Yebra and Patricia Oliva} } @article {bnh-5688, title = {Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation}, journal = {Remote Sensing}, volume = {11}, year = {2019}, month = {07/2019}, 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 dividing the whole samples 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 samples, with the root mean square error (RMSE) of 19.53\% (Sample 1), 12.64\% (Sample 2) and 15.45\% (Sample 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\% (Sample 1), 26.21\% (Sample 2) and 26.73\% (Sample 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.

}, keywords = {bare soil backscatter linear model, dual polarimetric Sentinel-1A, fuel moisture content, ignition, remote sensing, vegetation backscatter water cloud model}, doi = {https://doi.org/10.3390/rs11131568}, url = {https://www.mdpi.com/2072-4292/11/13/1568}, author = {Long Wang and Xingwen Quan and Binbin He and Marta Yebra and Minfeng Xing and Xiangzhuo Liu} } @mastersthesis {bnh-5778, title = {Remote sensing of tree structure and biomass in north Australia mesic savanna}, volume = {Philosophy}, year = {2019}, month = {05/2019}, school = {Charles Darwin Unviersity}, type = {Doctorate}, address = {Melbourne}, abstract = {

The quantification of Above Ground Biomass (AGB) plays a major role in issues related to greenhouse gas emissions and carbon sequestration, pertaining to global warming and the effects of climate change. In Eucalyptus miniata/tetrodonta dominated open-forest in Australia{\textquoteright}s northern tropical savanna, AGB mapping is challenging, due to the complex structure of the canopy stand, highly dynamic woody cover, vast spatial extent, vulnerability to climatic effects and the impacts of extensive fire.\ 

Remotely sensed data allow for the mapping, quantification and monitoring of AGB at various scales. Quantification of AGB in tropical savanna by common medium and coarse spatial resolution optical sensor data is inappropriate to monitor finer-grained ecological processes responsible for measuring carbon stocks at an individual tree level and are limited in detecting vertical vegetation structure. There is limited research on the utility of airborne LiDAR (Light Detection and Ranging) and alternative high resolution (\< 0.5 m) remote sensing tools for Australian savanna structural assessment. The main goal of this research is to evaluate the efficiency of small footprint airborne LiDAR and determine whether Unmanned Aerial Systems (UAS) and Very High Resolution (VHR) satellite stereo remote sensing data can be used to extract tree biophysical and vertical structural parameters for the purposes of accurately estimating biomass stocks in Australian mesic savannas.\ 

This study utilized a two-phase LiDAR analysis procedure integrating both Individual Tree Detection (ITC) and Area-Based Approaches (ABA) to better understand how the uncertainty of biomass estimation varies with scale. Regression analysis was applied on remote sensing data to develop biomass estimation models based on tree height allometry. This study demonstrated that where field-plot data are spatially limited, it is possible to use a hierarchical integration approach based on AGB uncertainty calculation and calibration to upscale AGB estimates from individual trees to broader landscapes.

}, keywords = {biomass, greenhouse gas, Lidar, northern Australia, remote sensing, tree structure}, url = {https://researchers.cdu.edu.au/en/studentTheses/remote-sensing-of-tree-structure-and-biomass-in-north-australia-m}, author = {Grigorijs Goldbergs} } @mastersthesis {bnh-6627, title = {Measuring fire-induced change in the understorey of an Australian dry sclerophyll forest using remote sensing}, year = {2016}, month = {08/2016}, school = {RMIT University}, address = {Melbourne}, abstract = {

This research investigates the use of remote sensing technologies for measuring and mapping the changes in the forest understorey in response to prescribed burning. Remote sensing has been used extensively to map the burn areas in fire-affected landscapes, but less work has been done focussing on beneath the canopy. Sub-canopy vegetation layers are important for habitat and for understanding the fuel hazards they may pose to the risk of wildfire. Accordingly, instruments and approaches must be able to perform in both pre- and post-burn environments, and be able to provide meaningful measures of change.

Wildfires are increasing in intensity and frequency, and in response prescribed burning is used to mitigate threats posed by them. Quantifying post-fire effects is important for burn severity, ecosystem recovery and post-fire hazard assessments. This information will allow land managers and scientists to understand fires in their environmental, economic and social contexts and help formulate responses and policies accordingly. However, measures of fire effects and fuel hazards which are done via visual assessments are known to be subjective and inconsistent between assessors and over time. What is needed is an improvement in the reporting procedures around quantification of fire effects which are both repeatable and quantifiable.

In this research two remote sensing technologies were used to measure, map and track changes in the understorey of an Australian dry sclerophyll forest. Terrestrial Laser Scanning (TLS) was used to derive vegetation structure variables and HSR (HyperSpectral Radiometry) was used to derive vegetation physiological variables. The study site was located in St. Andrews, Victoria, Australia within which a control plot and three fire treatment plots were set-up and monitored over a two year period, before and after a prescribed fire event conducted in autumn 2012. The datasets collected were used for statistical and spatial analysis of changes in understorey vegetation, and to assess those metrics best suited for describing different vegetation responses to fire effects.

The first part of this research examined the potential of TLS to detect fire-induced change in the forest understorey. From TLS point clouds a total of 18 metrics were extracted which were tested against accuracy and reliability criteria. Three metrics; mean AGHchange (Above Ground Height), median AGHchange and point countchange were shortlisted. To report different post-fire changes in burnt understorey, mean AGHchange metric was used. This metric was able to report fire effects such as total burn area, measures of patchiness, spatial distribution of burnt and unburnt areas, fuel accumulation and prescribed burn efficiency across various temporal scales.

The second part of this research analysed hyperspectral data of the near-surface (grass) and surface fuel layer (litter). Spectral changes in the near-surface fuel layer were observed in Visible (550nm), Near-Infrared (680-750nm) and Middle Infrared (970nm, 1220nm, 1550nm) domains of the electromagnetic spectrum. For the surface fuel layer (litter) changes were observed in the Middle Infrared domain (1140nm, 1225nm and 1700nm). The greatest difference from pre-burn levels for both the fuel layers occurred within the first two weeks post-burn. Spectral indices corresponding to the above determined broad spectral bands were tested to ascertain which were best at characterising burnt from unburnt targets whilst also tracking recovery. Indices such as NDVI, NBR and D720 were found to be the most suitable for near-surface fuel layer whilst D1230 for surface fuel layer.

A preliminary investigation into comparing the change detected by the two remote sensing technologies suggested that physiological change detected by HSR, recorded vegetation recovery as early as six weeks post-burn. Structural change detected by TLS even after two years post-burn was recorded as being close to two weeks post-burn levels. This finding matched well with visual assessments of structural measures (plant cover and height).

The findings of this study suggest that improvements in reporting procedures around quantification of fire effects can be achieved using TLS and HSR technology. TLS-derived structural metric, mean AGHchange can accurately detect quantified measures of fire-induced change in forest understorey that can be validated with field assessments. It can also report post-fire effects at various temporal scales including area burnt, burn patchiness, fuel load accumulation and prescribed burn efficiency. Spectral indices such as NDVI, NBR and D720 were able to accurately detect both vegetation loss and recovery. There is merit in further investigating TLS and HSR in conjunction for quantified and robust reporting of fire effects. The change detected by these technologies can be linked to inform both vegetation recovery and fuel accumulation.

}, keywords = {burn severity, fuel hazard, post-fire effects, prescribed burns, remote sensing}, url = {https://researchbank.rmit.edu.au/view/rmit:161758}, author = {Vaibhav Gupta} }