Anders Siggins

A simple technique for co-registration of terrestrial LiDAR observations for forestry applications

Light detection and ranging (LiDAR) from terrestrial platforms provides unprecedented detail about the three-dimensional structure of forest canopies. Although airborne laser scanning is designed to yield a relatively homogeneous distribution of returns, the radial perspective of terrestrial laser scanning (TLS) results in a rapid decrease of number of returns with increasing distance from the instrument. Additionally, when used in forested environments, significant parts of the area under investigation may be obscured by tree trunks and understorey. A possible approach to mitigate this effect is to combine TLS observations acquired at different locations to obtain multiple perspectives of an area under investigation. The denser and more evenly distributed observations then allow a spatially explicit and more comprehensive study of forest characteristics. This study demonstrates a simple approach to combine TLS observations made at multiple locations using bright reference targets as tie-points. Results show this technique was able to accurately combine the different TLS data sets (root mean square error (RMSE): 0.04–0.7 m, coefficient of determination (R 2): 0.70–0.99). Terrain elevations from TLS system were highly correlated with field-measured terrain heights (R 2: 0.70–0.98).

Incorporating vegetation attenuation in radiant heat flux modelling

Models of radiant heat flux (RHF) are critical for understanding wildfire behaviour and the effect a fire may have on homes and people. Various models have been presented in the literature for wildfire RHF, many being based on the Stephan–Boltzmann equation for radiative heat transfer. Most models simplify the fire and receiver interaction by considering a single fuel type at a given separation distance from a receiving point (e.g. on a building requiring protection). However, wildfire is an inherently spatial phenomenon, in that a fire may progress across the landscape towards a building across complex terrain and through spatially varying fuel types. This spatial variation influences the fire behaviour as well as the level of RHF incident on the building. In this study, we present methods for incorporating spatially varying topography and fuels into existing RHF modelling equations. In this way, we achieve a time-dependent profile of the RHF incident on homes, while accounting for attenuation due to fuels and topography that lie between the building and the fire front. The model is applied to the prediction of damage in a fire that occurred in South Australia in 2005. Although only coarse spatial information was available for determining the spatial distribution of fuels, modelled RHF was a significant indicator of house damage. Attenuation due to vegetation between homes and the fire was shown to reduce the modelled RHF exposure of homes. However, this was not shown to increase the significance of predicted house damage in the case of this fire event.

Predicting Growth, Carbon Sequestration and Salinity Impacts of Forestry Plantations

Farm forestry is an increasingly important form of diversifying farm income and helping to deal with environmental issues including dryland salinity, global warming and climate variability. Here we briefly describe the development, use and spatial application of improved versions of the plantation growth model, 3-PG, to provide estimates of productivity and carbon sequestration as well as salinity impacts. Several forestry scenarios using eucalypt species and Pinus radiata were tested with application to the Corangamite Catchment in south western Victoria, Australia.

Mobile Field Data Collection for Post Bushfire Analysis and African Farmers

In recent years CSIRO has been trialling field data collection using mobile devices such as phones and tablets. Two recent tools that have been developed by CSIRO are the CSIRO Surveyor (Post Bushfire House Surveyor) and DroidFarmer. Challenges tackled include mapping field documents to mobile data through QR (Quick Response) codes, rapid input of survey data, accurate capture of GPS locations and offline operation. Throughout this paper we detail the design choices made for these systems. We give details of how well field data collection was performed and discuss our planned future developments in this space.

A 3 dimensional ray tracing approach to modelling bushfire radiant heat flux for houses using LiDar derived vegetation voxel data and quadratic polygonal fire fronts

This paper describes the development of vulnerability assessment methods using a new approach to estimate radiant heat flux (RHF) exposure and consequent house response at a landscape scale. The model uses a three dimensional representation of the landscape, house location, vegetation structure based on LiDar data and the landscape scale gridded fire arrival conditions as inputs. The report presents the exploratory implementation of the approach on the Pine Ridge Road region affected by the 7th of February 2009 bushfire in Victoria. The 3D RHF ray tracing model provides a more robust prediction of house loss through bushfire, provided the approach direction can be determined.