BUS606: Operations and Supply Chain Management

Forest Fire Inventory

Forest fire-prone areas were detected by performing a correlation analysis between previous forest fire events and forest fire conditioning factors. The first mandatory stage performed was to prepare a forest fire inventory map for which 177 historical fire locations, as shown in Figure 2, from three years (2013, 2014, and 2015) were gathered. These points were used for the FR technique in performing a correlation between previous forest fire events and their conditioning factors. They were also used for accuracy assessment of fire-prone maps produced by both the techniques for which they were divided into training (n = 124; 70%) and testing (n = 53; 30%) datasets.

Forest Fire Conditioning Factors

A critical influential factor in the quality and precision of the final probability map is the proper selection of forest fire conditioning factors. In this study, seven conditioning factors comprising Land-Use Land Cover (LULC), distance from road, distance from human settlement, elevation, slope, aspect, and distance from the southern international border (with India) were utilized, as shown in Figure 3. The selection of these factors was based on forest fire studies in the region, local knowledge on forest fire, and the availability of spatial layers.

Some spatial layers of these conditioning factors were obtained from offices of the Royal Government of Bhutan: the LULC map of Bhutan 2010 (in vector format) was obtained from the Policy and Planning Division; the road network map of Bhutan 2014 (in vector format) from the Department of Roads; and the human settlement map of Bhutan 2005 (in vector format) from the National Statistical Bureau. Three topographical factors of slope, aspect, and elevation were extracted from a Digital Elevation Model (DEM) for Bhutan created by Jarvis, Reuter. All spatial input layers, including those in vector formats, were converted to a raster format with a standard cell size of 30 m × 30 m following the lowest resolution of the DEM. These input layers were classified prior to the analysis using the popular quantile method. This is because FR and AHP evaluate the impact of each class on the forest fire occurrence separately.

Detailed steps in preparation of the input spatial layers for the AHP and their parameterization are described below. Local expert knowledge from field forest fire managers together with published literature were used to assign fire hazard values for the different classes of the spatial layers.

A LULC – The LULC map contained details of major forest types and other land use types found in Bhutan. As reported by Dorji, people deliberately set fire in chirpine forests for harvesting lemon grass Cymbopogon flexuosus. Since the frequency of forest fire is the highest in chirpine forests, it was assigned the highest fire hazard value, which was then followed by hazard values of blue pine forest and mixed broadleaved-conifer forest. Other land use types were rated as per their contribution to forest fire incidences, based on expert experience and records from past events, as shown in Table 1. Human land use activities could be ignition sources that induce forest fire susceptibility, and hence, agricultural lands were assigned higher hazard values compared to other non-forest land-use types.

Table 1. Land-use and anthropogenic factors that determine an area's proneness to forest fire in Bhutan. Higher values reflect higher proneness. The values are assigned based on empirical field observations and expert judgments.

B Settlement – Based on historical records of forest fires in Bhutan, most fires originated from agricultural lands and areas near human settlements. Thus far, more forest fires in the country have resulted from accidental escape from burning agricultural fields [DoFPS]. Since forested areas located near settlements were highly vulnerable to fire, they were assigned high hazard values than those farther away, similar to the method employed by Opie, March and Sivrikaya, Sağlam, as shown in Table 1.

C Roads – Most roads in Bhutan pass through highly forested areas. Roads influence forest fires during black-topping or surfacing by tar, which is conducted for new road construction and maintenance. Forest fires also occur when road travelers, either on foot or in vehicles, throw igniting substances such as un-extinguished cigarette butts. Therefore, the presence of roads was deemed as increasing an area's vulnerability to forest fire, and thus areas closer to roads were assigned higher hazard values, as shown in Table 1, similar to the approach used by Sowmya and Somashekar.

D International border – Frequent forest fires have been observed in areas near the international border with India, particularly in areas adjoining the Indian states of Assam, Arunachal Pradesh, and West Bengal [DoFPS]. Wildlife managers and poachers deliberately light fires in these states to facilitate the growth of grasses for wild herbivores (Pers. Comm., Sonam Wangdi). Due to contiguous forest cover along many sections of the international border, such fires spread into Bhutan's forests. Therefore, higher values were assigned to areas closer to the southern international border, as shown in Table 1.

E Aspect – Aspect has been widely used for forest fire modeling, because slope direction influences soil moisture and wind speeds that, in turn, affect fire behavior. Moreover, in the northern hemisphere where Bhutan is located, south facing areas receive more sunlight which renders an area dry and prone to forest fire during dry seasons. Hence, the highest hazard value was assigned to areas with a southerly aspect and the lowest to areas with a northerly aspect, as shown in Table 2.

Table 2. Geophysical factors that determine an area's proneness to forest fire in Bhutan. Higher values reflect higher proneness.

F Elevation – Elevation is also considered as a factor influencing forest fire, as it indirectly influences evapotranspiration, temperature, humidity, and precipitation. These conditions, in turn, determine an area's susceptibility to forest fire. In Bhutan, most forest fires are known to occur frequently at lower elevations compared to higher elevations, and hence, higher hazard values were assigned to lower elevated areas, as shown in Table 2.

H Slope – Slope is a well-known contributing factor in forest fire susceptibility mapping. According to, fire travels faster up slope and slowly down slope, meaning the steeper the slopes, the faster the fire travels. Therefore, steeper slopes were accorded higher hazard values, as shown in Table 2, similar to the methods applied by.