Dr Yang Chen’s study used Light Detection and Ranging (LiDAR) to measure landscape-scale forest fuels to generate a time effective, feasible and objective method for forest fuel hazard assessment.
Currently, firefighters and land managers still rely on empirical knowledge to visually assess forest fuel characteristics of distinct fuel layers. The visual assessment method provides a subjective description of fuel properties that can lead to unreliable fire behaviour prediction and hazard estimation.
Yang’s research investigated the application of the LiDAR technique in quantifying forest fuel properties, including fuel structural characteristics and litter-bed fuel load at a landscape scale. Her findings indicate that LiDAR allows a more efficient and accurate description of fuel structural characteristics and estimation of litter-bed fuel load. The results from her study can assist fire hazard assessment, fuel reduction treatment and fire behaviour prediction.
Yang is currently working at CSIRO as a research scientist socialising in deep learning and earth observation.
Her thesis is available here.
Student project
Resources credited
| Type | Released |
Title |
Download | Key Topics |
|---|---|---|---|---|
| Presentation-Slideshow | 24 Oct 2016 | Forest fuel structural measurement and fuel load estimation using LiDAR data |
 Save (660.29 KB) |
fire, fuel reduction, modelling |
| 07 Jul 2015 | Yang Chen PhD Progress Report 2015 |
Save (65.17 KB) |
