Regional Level Data Server for Fire Hazard Evaluation and Fuel Treatments Planning

Abstract

Both fire risk assessment and management of wildfire prevention strategies require different sources of data to represent the complex geospatial interaction that exists between environmental variables in the most accurate way possible. In this sense, geospatial analysis tools and remote sensing data offer new opportunities for estimating fire risk and optimizing wildfire prevention planning. Herein, we presented a conceptual design of a server that contained most variables required for predicting fire behavior at a regional level. For that purpose, an innovative and elaborated fuel modelling process and parameterization of all needed environmental and climatic variables were implemented in order to enable to more precisely define fuel characteristics and potential fire behaviors under different meteorological scenarios. The server, open to be used by scientists and technicians, is expected to be the steppingstone for an integrated tool to support decision-making regarding prevention and management of forest fires in Catalonia.

A fire spread simulator to support tactical management decisions for Mediterranean landscapes

“…Combining Cell2Fire to a forest growth and yield simulator able to predict the evolution of fuels across a landscape under different management policies or scenarios, and an optimization module that would explore combinations of management alternatives across time and space, can be the basis of a new decision system oriented to integrate fire into tactical forest planning (Figure 1). While there is a prototype of growth and yield simulator (Gr4Tree, unpublished) based on the same set of models used by González-Olabarria and Pukkala (2011), including new additional models and rules to generate variables required by traditional fire behavior models (Krsnik et al, 2020), these variables are not the currently applied by Cell2Fire. Being the growth and yield simulator based on empirical forest dynamic models, and linking functions, for the region of Catalonia (NE Spain), the adjustment of fuel models depending on forest state and evolution were set match Scott and Burgan (2005).…”

“…To apply this methodology, we selected five fires of medium to large size for which all initial conditions and its evolution were known (ignition allocation and time, fire-weather conditions, final fire perimeter, and stopping time). Another key criterion for the selection was the date of fire occurrence, which had to post-date the landscape data on the arrangement of surface and canopy fuels given in Krsnik et al (2020). The fires were named by combining the municipality and year of occurrence; thus, the five were identified as La Jonquera 2012, Naut Aran 2017, Odena 2015, Valbona 2016, and Vilopriu 2013.…”

“…traditional features based on the size and composition of trees in a stand, Growth4Tree can estimate certain input variables required to run the fire simulator such as bush coverage, canopy characteristics and a standard fuel model. The assignment of the Scott and Burgan fuel models based on forest characteristics follows the rules given in Krsnik et al (2020).…”

“…This is not an easy task, however (He and Mladenoff, 1999), and together with uncertainty regarding future weather and fire initiation conditions has traditionally prevented the use of fire spread simulators in long-term forest planning (Martell, 2001). The availability of new remote sensing tools such as airborne LiDAR has enabled the mapping of fuel over space, but even though there is ongoing progress in this regard (González-Olabarria et al, 2012;Gonzalez-Olabarria et al, 2019), it still has to rely on adjustment by fire experts to represent reality more accurately and avoid the sort of over-simplification produced by statistical models (Krsnik et al, 2020). Therefore, even if current fuel adjustment methods are adequate for tactical planning problems, any future improvements in modeling of fuel accumulation and its relation to forest evolution and management should be incorporated into the forest growth and fire simulation system.…”

Modeling the Susceptibility of Forest Fires Using a Genetic Algorithm: A Case Study in Mountain Areas of Southwestern China

“…e geographical distribution of re susceptibility was an urgent need to implement adequate management actions [3][4][5][6]. It was of importance to perform studies on modeling the susceptibility of forest res [7,8].…”