LiDAR-Based Wildfire Prevention in WUI: The Automatic Detection, Measurement and Evaluation of Forest Fuels

Abstract

This paper describes a methodology using LiDAR point clouds with an ultra-high resolution in the characterization of forest fuels for further wildfire prevention and management. Biomass management strips were defined in three case studies using a particular Spanish framework. The data were acquired through a UAV platform. The proposed methodology allows for the detection, measurement and characterization of individual trees, as well as the analysis of shrubs. The individual tree segmentation process employed a canopy height model, and shrub cover LiDAR-derived models were used to characterize the vegetation in the strips. This way, the verification of the geometric legal restrictions was performed automatically and objectively using decision trees and GIS tools. As a result, priority areas, where wildfire prevention efforts should be concentrated in order to control wildfires, can be identified.

Forestry Remote Sensing from Unmanned Aerial Vehicles: A Review Focusing on the Data, Processing and Potentialities

“…Usually, CHMs or DSMs, point clouds and spectral information are used in tree segmentation processes [49]. Depending on the type of data, there are several algorithms for tree detection, being the detection rate dependent on the forest type-usually higher in coniferous stands than broadleaves [50][51][52], tree density and clustering [52][53][54] and not in the algorithm used [53].…”

“…Figure 2 presents an example of such approach in an area mainly composed by maritime pine trees. Regarding the methods for tree detection using point clouds, there are two methods widely used: point cloud segmentation and the layer stacking [49]. The point cloud segmentation consists into first locating the local maxima points and consequently these points are iteratively assigned to trees based on a distance threshold of similar characteristics into homogeneous regions [58].…”

“…In this scope, multispectral data acquired from UAVs has been used in tree species classification, but, in some cases, the data has been limited to RGB imagery and/or to modified RGB cameras to acquire near-infrared (NIR) or Red Edge channels using [65][66][67], also known as colour infrared (CIR) imagery. However, recently the development of small hyperspectral Regarding the methods for tree detection using point clouds, there are two methods widely used: point cloud segmentation and the layer stacking [49]. The point cloud segmentation consists into first locating the local maxima points and consequently these points are iteratively assigned to trees based on a distance threshold of similar characteristics into homogeneous regions [58].…”

“…Regarding forest fire prevention, some countries adopted legislation towards vegetation management to increase the safety of populations in wildland-urban interfaces. This way, Fernández-Álvarez et al [49] proposed a methodology for monitoring the compliance of identified vegetation with the fire prevention legislation, using UAV LiDAR-driven models in wildland-urban interfaces. The data processing workflow consisted of filtering the LiDAR point cloud to obtain DTM, DSM, and consequently CHM and shrub cover.…”

Effectiveness of Sentinel-2 in Multi-Temporal Post-Fire Monitoring When Compared with UAV Imagery

“…In this context, remote sensing platforms are being used as a capable tool for mapping burned areas, evaluating the characteristics of active fires and characterizing post-fire ecological effects and regeneration [7]. In the past decade, the use of unmanned aerial vehicles (UAVs) has increased for agroforestry applications [8] and are now being used for forest fire prevention [9], canopy fuel estimation [10], fire monitoring [11,12] and to support firefighting operations [13]. Likewise, studies using UAV-based imagery in post-fire monitoring have been concerned with surveying [14], calibrating satellite-based burn severity indices [15], assessing post-fire vegetation recovery [16], mapping fire severity [17,18], studying forest recovery dynamics [19] and sapling identification [20].…”