Using LiDAR UAV to understand the characteristics of forest fires
Forest fires are becoming more common due to climate change and bring considerable ecological and economic loss. Understanding the characteristics of a fire – its’ severity and distribution within a forest – will quantify its’ impact. The data will indicate the likely level of post-fire regeneration and ecosystem recovery. This in turn will help develop post-fire forestry management plans. The information can also be used to better estimate and model fires in the future to aid prevention and improve fire containment.
Wildfires don’t burn evenly through forests. What remains of trees and vegetation after a fire is a good indication of its’ severity. In a forested landscape some areas may be barely scorched. However, in other parts the trees will be severely damaged with branches and leaves fully burnt.
In this study the research team used both high-resolution LiDAR (Light Detection and Ranging), captured from a drone and satellite optical imagery. They assessed the site of the Yeste fire in the province of Albacete, SE Spain. The team wanted to compare the data acquired using the two different technologies to appraise the potential of this approach for the future.
LiDAR UAV mapping overcomes the limitations of satellite imagery
Satellite optical imagery is being increasingly used to examine the extent of forest fires. However, these sensors cannot penetrate through the canopy down to the low level vegetation and the ground below. LiDAR overcomes this constraint. A major benefit of drone mounted LiDAR is the ability to fly over the study area at low altitude. This allows the collation of high resolution data through the many vegetation layers. The lasers can penetrate through the gaps in the foliage. The resulting 3 dimensional point cloud displays the canopy, the lower vegetation and the ground beneath. Individual trees can be detected and the vertical profile of the canopy and vegetation can be viewed.
The study sites
The Yeste fire occurred in July 2017 and in total 3,217 hectares of forest were burnt. Six months later the LiDAR drone mapping survey was undertaken using the Routescene UAV LiDAR system. This was supplied to the team at Toledo University by Grafinta, Routescene’s distributor in Spain. Three distinct areas were surveyed in detail. Each of 5-6 hectares in size and with a different level of fire severity (high, moderate and low). Along with a nearby control area that was not burnt.
Location in SE Spain of UAV LiDAR mapping flights using the Routescene UAV LidarPod
The different fire severity areas surveyed
Detail of the LiDAR UAV mapping flights
The flights were made at an altitude of 40m above the ground (footprint on the ground was 33 × 204m). Flown in parallel lines 40m apart with an overlap of 50%. The flight speed was between 5 and 7m/s. At this flight height, the spatial distance (spacing) among points was 0.05m. The mean density was 300 points per m². The horizontal accuracy was <2cm and the vertical accuracy <10cm.
Watch the video of the team collecting data using the Routescene UAV LiDAR system
The Routescene Lidar system mounted underneath the drone
Analysis of the UAV LiDAR data
Complex analysis of the LiDAR UAV data was undertaken including establishing ground from non-ground points. The team used Routescene’s LidarViewer Pro software to create Digital Terrain Models (DTMs) and Canopy Height Models.
Point cloud of one of the study areas using Routescene LidarViewer Pro LiDAR data processing software
Raw point cloud (above) and non-ground points removed to display the Ground Points (below)
Further analysis enabled the team to classify the data into three clusters. It was found that crown leaf area index (LAI), crown leaf area density (LAD), crown volume, tree height and tree height skewness, among others, were the most significant tree structure variables to distinguish the 3 cluster groups.
Point clouds of the areas surveyed with UAV LiDAR mapping system
Then the information was crossed referenced with fire severity levels derived from satellite images. These were acquired both a few months before and after the fire.
LiDAR UAV mapping enables a fine-grain assessment of fire severity
Relatively few studies have used LiDAR drone data to characterize crown damage after fires at the individual tree level. With these LiDAR UAV metrics, the team distinguished crown fire from surface fire through changes in the understory Leaf Area Index and understory and midstory vegetation. They found that unburned and low-severity burned areas were more diverse in tree structures than moderate and high severity burned ones. This data was previously not attainable using satellite imagery.
The project demonstrated the potential to distinguish post-fire plant structures in detail using UAV LiDAR data. When crossed with satellite-based fire severity metrics, the high resolution results will allow researchers to estimate the impact of fire on single trees not just whole forested areas.
Point clouds of individual trees categorized by fire severity level: UB – Unburned LS – Low severity MS – Moderate severity HS – High severity
LiDAR UAV – Powerful tool to assess forests and refine fire management
Olga Viedma from the Department of Environmental Sciences, University of Castilla-La Mancha, Spain, commented, “UAV LiDAR, with its level of detail, was the obvious next choice of technology and natural evolution to further examine the levels of damage to individual trees. I am delighted to say the Routescene UAV LiDAR system worked well and proved to be very useful. We have since carried out further studies on forest fires using the system in central Spain and the Canary Islands.”
In conclusion, the high resolution drone LiDAR data was extremely valuable. It allowed the assessment of forest structure and patterns using metrics which are ecologically meaningful down to an individual tree level. This data can be used to predict fire risk and develop more precise and site-specific fire impact studies. It can also be used to create post-fire management plans.
Read the more detailed scientific published paper. Postfire Tree Structure from High-Resolution LiDAR and RBR Sentinel 2A Fire Severity Metrics in a Pinus halepensis-Dominated Burned Stand
See other uses of drone LiDAR for forestry management.