Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR

Abstract

Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR,"Abstract. Fuel structural characteristics affect fire behavior including fire intensity, spread rate, flame structure, and duration, therefore, quantifying forest fuel structure has significance in understanding fire behavior as well as providing information for fire management activities (e.g., planned burns, suppression, fuel hazard assessment, and fuel treatment). This paper presents a method of forest fuel strata classification with an integration between terrestrial light detection and ranging (LiDAR) data and geographic information system for automatically assessing forest fuel structural characteristics (e.g., fuel horizontal continuity and vertical arrangement). The accuracy of fuel description derived from terrestrial LiDAR scanning (TLS) data was assessed by field measured surface fuel depth and fuel percentage covers at distinct vertical layers. The comparison of TLS-derived depth and percentage cover at surface fuel layer with the field measurements produced root mean square error values of 1.1 cm and 5.4%, respectively. TLS-derived percentage cover explained 92% of the variation in percentage cover at all fuel layers of the entire dataset. The outcome indicated TLS-derived fuel characteristics are strongly consistent with field measured values. TLS can be used to efficiently and consistently classify forest vertical layers to provide more precise information for forest fuel hazard assessment and surface fuel load estimation in order to assist forest fuels management and fire-related operational activities. It can also be beneficial for mapping forest habitat, wildlife conservation, and ecosystem management.

A Method for Validating the Structural Completeness of Understory Vegetation Models Captured with 3D Remote Sensing

“…Accurate measurement of vegetation at different spatial and temporal scales is critical in informing a range of ecological and natural resource management disciplines [3][4][5]. Estimates of vegetation characteristics such as canopy height, canopy cover, diameter at breast height, and above-ground biomass have been identified as important metrics for carbon accounting, wildlife habitat diversity, precision forestry, fire behaviour modelling and understory forest dynamics for eco-hydrological monitoring [6][7][8][9][10].…”

“…Data capture from above the canopy (satellite, aircraft or drone) is the most common approach to characterising the 3D structure of forests [11][12][13][14]. Known limitations in observing detailed below canopy structure from airborne sensors have led to investigation into terrestrial data capture [7,15,16]. The two methods that have been used previously for capturing 3D information describing forests are laser scanning and image-based point clouds [17].…”

Estimating Fuel Loads and Structural Characteristics of Shrub Communities by Using Terrestrial Laser Scanning

“…In a study in the agro-pastoral ecotone of northern China, Xu et al [84] proposed a model for estimating grass height and canopy cover from TLS data, which explained 70 and 72% of the observed variability respectively. Better results were obtained by Chen et al [85], who, in a study in South-East Australia, established models for estimating the litter depth and percentage cover of the different vertical layers of the understory from TLS metrics, with R 2 values of 0.9 and of 0.8-0.9, respectively. Table 4.…”

A Spatiotemporal Contextual Model for Forest Fire Detection Using Himawari-8 Satellite Data

“…Consequently, there is an urgent need to mitigate the impact caused by this global and recurring phenomenon, and because satellite data has the advantage of wide coverage, timeliness, and low cost, it has been pivotal to meet this requirement for more than four decades [9][10][11]. Using these ideal sensors and platforms, researchers have carried out research on fire risk assessment [12][13][14][15], fuel management [16][17][18], forest fire detection [19][20][21][22][23][24], fire behavior modeling [25][26][27], smoke emissions estimation [28][29][30], and analysis of fire impacts on air quality and FRP (fire radiative power) [31][32][33][34]. In addition, forest fire detection has always been a hot spot of research.…”