Abstract

Spatial information on the distribution of biomass is an important issue for monitoring and managing the environment. It is a prerequisite for successful forest fire management and for predicting fire intensity and fire behaviour, but estimates of biomass are time consuming and expensive and need to be done depending on size classes. We propose a method that takes into account the contemporary use of an allometric approach and remote sensed data from Very High Resolution (VHR) satellite images  to obtain distribution maps of biomass subdivided into different components while keeping plant-destructive collection of data to a minimum. To test the feasibility of distributing biomass into classes, we subdivided biomass into two size classes according to the size of leaves (thickness) and branches (diameter). This is an approach that can be adapted to both fuel classes or to estimation of the ligneaous component. We haphazardly selected eight areas, within the Site of Community Importance “Monteferrato e Monte Iavello” (Italy), where easy-to-measure characteristics (height, diameter, cover) of vascular plants were collected. Regression equations between easy-to-measure vegetation characteristics and biomass values were derived to estimate the biomass of each area in the two size classes. Then, we evaluated the relationships between the normalized difference vegetation index (NDVI) and the estimated biomass values for each area using regression equations and size class. The equations that resulted from the regression analysis were used to create maps of biomass using NDVI map. Such a procedure allows the identification of features otherwise lost when the vegetation is represented only by vegetation class labels. This includes the orientation of vegetation lines which may favor the spread of fire in a given direction; information that may be useful for hazard management and prevention.

Key words: Allometric equation, Pinus pinaster, Erica scoparia, biomass size class, Very High Resolution (VHR) satellite imagery.