Computing fire danger and fire risk on a spatio-temporal scale is of crucial importance in fire management planning, and in the simulation of fire growth and development across a landscape. However, due to the complex nature of forests, fire risk and danger potential maps are considered one of the most difficult thematic layers to build up. Remote sensing and digital terrain data have been introduced for efficient discrete classification of fire risk and fire danger potential. In this study, two time-series data of Landsat imagery were used for determining spatio-temporal change of fire risk and danger potential in Korudag forest planning unit in northwestern Turkey. The method comprised the following two steps: (1) creation of indices of the factors influencing fire risk and danger; (2) evaluation of spatio-temporal changes in fire risk and danger of given areas using remote sensing as a quick and inexpensive means and determining the pace of forest cover change. Fire risk and danger potential indices were based on species composition, stand crown closure, stand development stage, insolation, slope and, proximity of agricultural lands to forest and distance from settlement areas. Using the indices generated, fire risk and danger maps were produced for the years 1987 and 2000. Spatio-temporal analyses were then realized based on the maps produced. Results obtained from the study showed that the use of Landsat imagery provided a valuable characterization and mapping of vegetation structure and type with overall classification accuracy higher than 83%.

Forest fire is one of the high-risk natural disasters in the north-western Anatolia section of Turkey. This paper suggests a new approach based on Geographic Information Systems (GIS), Remote Sensing (RS) and Analytical Hierarchy Process (AHP) for the development of forest fire-risk model. The proposed approach includes human factors as well as environmental factors. In this context, the 12 variables defined under anthropogenic and physical factors in the proposed model are the slope, elevation, aspect, vegetation type, crown closure, Normalized Difference Vegetation Index (NDVI), distance to road, settlement, and agricultural areas, population density, previous fires, and Canadian Forest Fire Weather Index (FWI). For each variable, a layer was created in the GIS database environment. GIS-layers were classified, considering the risk of potentially generating forest-fire of the relevant variables. In addition, to generate risk maps, the weights used in these GIS-layers were obtained by applying the AHP technique. One of the major results of the study shows that the rates of “extreme”, “very high”, “high”, and “moderate” risk areas are 3.87%, 63.46%, 32.13% and 0.53%, respectively. Another important result is that there are not observed the so called “no risk" and "low risk" classes in the region. The results let us to make a conclusion that the natural and human factors having significant contributions the region to be fire-prone. Yet, these results also indicate that rather than emphasizing forest-fire preparedness and mitigation, policy-makers manage forest-fires through reactive, crisis-oriented approaches. In contrast to crisis-based management plans, this study suggests that risk-based preventive plans should be developed and implemented.

Fuels are of great importance in fire behaviour prediction. This paper deals with the prediction of aboveground foliage and branch biomass of calabrian pine (Pinus brutia Ten.) and Anatolian black pine (P. nigra J.F. Arnold subsp. nigra var. caramanica (Loudon) Rehder). The study was based on a total of 418 destructively sampled calabrian and black pine trees and saplings. As a result of the analyses, several regression equations were developed for predicting foliage, fine branch (<0.6 cm), medium branch (0.6–1.0 cm), active fuels (foliage + fine branch), thick branch (1.0–2.5 cm), and total fuel loading. The relationships between fuel biomass and tree properties were determined by multiple linear regressions, considering tree properties as the independent variables, and foliage, branch, active fuel and total biomass as the dependent variables. Tree properties included tree height, crown length, crown width, diameter at breast height and root collar diameter. Results indicated that foliage, branch and total biomass could all be accurately predicted based on the readily measurable and/or predictable tree characteristics. Of the fuel characteristics, crown length, crown width, and height were the three most significant predictors of fuel biomass. The results of this study will not only contribute to the prediction of fire behaviour, but will also be of invaluable use in other forestry disciplines.

This study presents a dynamic model for the prediction of diurnal changes in the moisture content of dead surface fuels in normally stocked Calabrian pine stands under varying weather conditions. The model was developed based on several empirical relationships between moisture contents of dead surface fuels and weather variables, and calibrated using field data collected from three Calabrian stands from three different regions of Turkey (Mugla, southwest; Antalya, south; Trabzon, north-east). The model was tested and validated with independent measurements of fuel moisture from two sets of field observations made during dry and rainy periods. Model predictions showed a mean absolute error (MAE) of 1.19% for litter and 0.90% for duff at Mugla, and 3.62% for litter and 14.38% for duff at Antalya. When two rainy periods were excluded from the analysis at Antalya site, the MAE decreased from 14.38% to 4.29% and R2 increased from 0.25 to 0.83 for duff fuels. Graphical inspection and statistical validation of the model indicated that the diurnal litter and duff moisture dynamics could be predicted reasonably. The model can easily be adapted for other similar fuel types in the Mediterranean region.