Forest mapping by geoinformatics for landscape fire behaviour modelling in coastal forests, Greece
Journal
International Journal of Remote Sensing
Date Issued
March 25, 2013
DOI
10.1080/01431161.2013.779399
Abstract
This study aims at quantifying and mapping fire-related characteristics of forest structure
through field inventories, statistics, remote sensing, and geographical information
systems in the island of Lesvos, northeast Aegean Sea, Greece. Simulation of fire
behaviour requires forest biomass inputs that describe surface fuel types/models along
with canopy fuel properties, such as canopy cover, stand height, crown base height, and
crown bulk density, to accurately predict surface and crown fire spread and spotting
potential. Forest canopy characteristics and other vegetation attributes were sampled
and derived in over 100 field plots, the majority of which were located in coastal pine
forest stands. Regression models involving four dependent forest stand variables (stand
height, canopy cover, crown base height, and crown bulk density) were developed using
generalized additive models. The values of adjusted R2 were 0.72 for stand height,
0.68 for canopy cover, 0.51 for crown base height, and 0.33 for crown bulk density.
These regression models were used to create forest fuel characteristics layers, which can
be used as inputs to fire management applications and state-of-the-art landscape-scale
fire behaviour model
through field inventories, statistics, remote sensing, and geographical information
systems in the island of Lesvos, northeast Aegean Sea, Greece. Simulation of fire
behaviour requires forest biomass inputs that describe surface fuel types/models along
with canopy fuel properties, such as canopy cover, stand height, crown base height, and
crown bulk density, to accurately predict surface and crown fire spread and spotting
potential. Forest canopy characteristics and other vegetation attributes were sampled
and derived in over 100 field plots, the majority of which were located in coastal pine
forest stands. Regression models involving four dependent forest stand variables (stand
height, canopy cover, crown base height, and crown bulk density) were developed using
generalized additive models. The values of adjusted R2 were 0.72 for stand height,
0.68 for canopy cover, 0.51 for crown base height, and 0.33 for crown bulk density.
These regression models were used to create forest fuel characteristics layers, which can
be used as inputs to fire management applications and state-of-the-art landscape-scale
fire behaviour model