Canopy Fuel Load Mapping of Mediterranean Pine Sites Based on Individual Tree-Crown Delineation

Abstract

This study presents an individual tree-crown-based approach for canopy fuel load estimation and mapping in two Mediterranean pine stands. Based on destructive sampling, an allometric equation was developed for the estimation of crown fuel weight considering only pine crown width, a tree characteristic that can be estimated from passive imagery. Two high resolution images were used originally for discriminating Aleppo and Calabrian pines crown regions through a geographic object based image analysis approach. Subsequently, the crown region images were segmented using a watershed segmentation algorithm and crown width was extracted. The overall accuracy of the tree crown isolation expressed through a perfect match between the reference and the delineated crowns was 34.00% for the Kassandra site and 48.11% for the Thessaloniki site, while the coefficient of determination between the ground measured and the satellite extracted crown width was 0.5. Canopy fuel load values estimated in the current study presented mean values from 1.29 ± 0.6 to 1.65 ± 0.7 kg/m 2 similar to other conifers worldwide. Despite the modest accuracies attained in this first study of individual tree crown fuel load mapping, the OPEN ACCESSRemote Sens. 2013, 5 6462 combination of the allometric equations with satellite-based extracted crown width information, can contribute to the spatially explicit mapping of canopy fuel load in Mediterranean areas. These maps can be used among others in fire behavior prediction, in fuel reduction treatments prioritization and during active fire suppression.

Trends in Automatic Individual Tree Crown Detection and Delineation—Evolution of LiDAR Data

“…Ardila et al [16] applied GEOBIA at multiple segmentation scale to locate and delineate tree crowns in urban areas using high resolution imagery and successfully detected 70%-80% of the trees in their study area. Mallinis et al [72] applied GEOBIA multi-resolution segmentation and fuzzy-rule-based classification approach to isolate and delineate tree crown regions from the understory and other land cover classes for canopy fuel load estimation and mapping in heterogeneous pine forests. Chen et al [56] provided a semi-automatic GEOBIA approach within a multi-resolution algorithm to estimate canopy height, above-ground biomass and volume at individual tree crown or small tree cluster level.…”

Predicting crown width and length using nonlinear mixed-effects models: a test of competition measures using Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.)

“…Individual-tree crowns are the basic unit of forest canopy structure (Mallinis et al 2013). Tree crown is an explicit and direct indicator describing tree vigor and health (Zarnoch et al 2004), long-term competitiveness (Biging and Dobbertin 1992), productivity (Stenberg et al 1994), and wood quality (De Kort et al 1991).…”

Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method

“…Crown fuel commonly includes foliage, fine branch wood and arboreal lichens and mosses [10], among which the foliage is the most flammable component [11]. Within this context, the foliage fuel load (FFL) can be regarded as a key fuel variable for crown wildfire danger assessment and typically refers to the dry weight of all tree canopy leaves (both dead and live) per unit area of forest [12,13]. Therefore, accurate estimation and mapping of FFL can assist fire managers in optimizing pre-fire risk warnings and identification of safety zones for firefighters [8,14].…”