BUS606: Operations and Supply Chain Management

Forest fire is an environmental catastrophe that threatens the safety of humans, infrastructure, and biodiversity. Global climate change has led to the considerable decrease in precipitation and increase in the temperature, further influencing the occurrence of forest fires. There are other factors influencing forest fire increase, such as a longer arid season and contributing anthropogenic activities. For example, the evidence for declining forest resilience to wildfires under climate change has been documented by Stevens-Rumann, Kemp, Allen, Macalady, and Rother and Veblen. Abatzoglou and Williams stated the increased fire activity in the western US and in the US Northern Rockies has been driven by both rising temperatures and widespread drought, particularly since 2000. These factors have altered the trend and frequency of forest fires at an alarming rate in many regions of the world. It is, therefore, vital to have a reliable and precise approach to predict areas susceptible to forest fire. An accurate forest fire-prone map can assist forest management and planning authorities in allocating relevant resources, emergency responses, and early warning systems. Several methods have been proposed and tested to map forest fire-prone regions. These approaches can be categorized into three major groups of physics-based techniques, statistical techniques, and machine learning techniques .

Many physics-based techniques existed, such as EMBYR, FARSITE – Fire Area Simulator, FIRETEC, FDS, and LANDIS-II. Combinations of equations on fluid mechanics, combustion of canopy biomass, and heat transfer mechanisms are required for physics-based methods to recognize fire-prone areas including predicted forest fire periods. The main weak point of these approaches is the difficulty in measuring the amount of inherent errors. Another disadvantage of the physics-based method is the requirement of having detailed data. For instance, data on locations and sizes of trees, fuel mass, soil moisture etc., have to be collected over large areas, making this a difficult task. Therefore, these techniques may not be applicable in data-poor regions. Machine learning methods, such as the Artificial Neural Network, Support Vector Machine, and Decision Tree, are considerably time consuming and software dependent with a high computer capacity. As such, these techniques also may not be practicable for regions with limited resources but requiring urgent actions.

However, statistical methods are more appropriate for forest fire susceptibility modeling in the case of large study areas, particularly in combination with Geographic Information System (GIS) technology. GIS and Remote Sensing (RS) techniques make it considerably easy to collect and assess spatial data on large regions with different scales and resolutions. Statistical methods are easily comprehensible and implementable, because they do not require specific tailor-made software. Statistical methods, such as Frequency Ratio (FR), quantitatively evaluate the correlation between conditioning factors and forest fire occurrence without involving any expert opinion in the analysis. Conversely, qualitative methods like the Analytical Hierarchical Process (AHP) are based on knowledge of fire experts, but are also easily implementable if there is adequate knowledge of previous forest fire occurrences.

In this study, FR and AHP techniques were utilized for the spatial prediction of forest fire-prone areas based on a case study in Bhutan, which represents a data-poor country. Bhutan has more than 72% forest cover, which is the highest in the world in terms of proportion of land covered with forest. Every year, several forest fires are reported in various parts of the country. For instance, between 1993 and 2005, a staggering 868 forest fires were reported with 128,368 hectares of forested area reported to be burnt. The country's highly rugged terrain, which is compounded by a large accumulation of fuel load, poses significant challenges and risks in preventing and containing wildfires. Almost 30% of the country is covered with coniferous forest, which is considered as the most flammable forest type because of an accumulation of resin and dried needles. Usually, the dry undercover of pine forests during sustained dry period that lasts for more than six months makes this forest type prone to wildfires. Repeated forest fires can potentially jeopardize sustainable management of Bhutan's forests. Currently, there are no maps of forest fire-prone areas in Bhutan. This has greatly hindered the government, particularly the Department of Forests and Park Services, in developing an effective forest fire management strategy.

The main objective of this study is to generate forest fire-prone area maps and compare the efficiency of statistical-based and knowledge-based techniques to predict fire-prone areas, using the case of forest fire incidences in Bhutan. In addition, we test if Moderate Resolution Imaging Spectroradiometer (MODIS) fire points were reliable for predicting forest fires in the case of Bhutan. Using the outcome of this study, we intend to assist the Bhutan Government in forest fire prevention and management.