Open access peer-reviewed chapter

Forest Fire Monitoring

Written By

Ahmad AA Alkhatib

Submitted: July 1st, 2017 Reviewed: October 30th, 2017 Published: December 20th, 2017

DOI: 10.5772/intechopen.72059

From the Edited Volume

Forest Fire

Edited by Janusz Szmyt

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Abstract

Thousands of hectares around the globe destroyed by forest fires every year causing tragic loss of houses, properties, lives, fauna and flora. Forest fires are a great menace to ecologically healthy grown forests and protection of the environment. This problem has been the research interest for years, and there are a number of solutions available to resolve this problem. In this chapter, a summary is given for all the technologies that have been used for forest fire detection with explanation of what parameters these systems looking for to understand the fire behaviour.

Keywords

• forest fire detection
• forest fire monitoring
• forest fire system
• forest fire behaviour
• fire early warning systems

1. Introduction

Forests play a vital role in maintaining the Earth’s ecological balance. Unfortunately, the forest fire is usually only observed when it has already spread over a large area, making its control and stoppage arduous and even impossible at times. The result is irreparable damage to the atmosphere and environment, where 30% of CO2 in the atmosphere produced by forest fires [1, 2]. Among other consequences of forest fires are long-term disastrous effects such as impacts on local weather patterns, global warming and extinction of rare species of the flora and fauna.

Forests are vast remote abandoned areas, full of highly combustible material with dry leaves and branches to the Earth surface composites, where these are perfect to act as a fuel source for fire ignition and later fire stages. The fire ignition may be caused through human actions or by natural reasons. The initial stage of ignition is normally referred to as “surface fire” stage. This may then lead to feeding the fire flame, thus becoming “crown fire.” Mostly, at this stage, the fire becomes uncontrollable, and the damage to the landscape may become excessive and could last for a very long time depending on prevailing weather conditions and the terrain.

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2. Problem size

Forest fire is a global environmental problem causing extensive damage every year. According to International Union for Conservation of Nature (IUCN) Report “Global Review of Forest Fire 2000,” wild fire is a natural phenomenon. However, over 90% of all wildland fires are due to human action causing significant forest loss, that is 6–14 million hectares of forest per annum, and 30% of the CO2 in the atmosphere produces from forest fire. This leads to enormous economic losses, damage to environmental, recreational and amenity assets, global warming and loss of life. There is a strong recognition that action is needed to catalyse a strategic international response to forest fires [3].

2.1. In the case of the USA

In the 2003–2004 wildfire sieges, CAL FIRE’s fire suppression costs exceeded $252.3 million; property damage costs exceeded$974 million; 5394 structures were destroyed; and more than 23 people lost their lives as a result of California wildfires. “Increasingly destructive wildfires are ravaging homes and businesses in more than three-fourths of the states. One of the most devastating fires in recent history was the $1 billion Witch Creek Wildfire that decimated vast parts of San Diego County, California, in October 2007. By the time it was fully contained, the fire had burned an estimated 198,000 acres and damaged or destroyed more than 1200 homes and 500 outbuildings.” In terms of scale, the 2007 fire season was second only to last year in acres burned and costs expended. In 2007, there were 27 fires costing over$10 million whose total suppression cost approached $547 million, exclusive of burned area emergency rehabilitation costs. These fires alone accounted for just less than 3 million burned acres. All wildfire acres reported to the National Interagency Coordination Centre in calendar year 2007 totalled 9.32 million acres at a federal cost of approximately$1.8 billion. On 17 June 2002, an estimated $9,403,000 was spent battling 196 wildland fires that scorched over 51,000 acres of land in parts of 11 states in the USA [4]. Hundreds of thousands of acres is burnt within the wildland urban interface (WUI) each year. Each year, over$100 million is spent on suppression efforts and more in the disaster recovery phases of catastrophic, natural and/or human-caused hazards, but the losses continue to mount.

2.2. In the case of Canada

Canada has approximately 10% of the world’s forests. However, about 7400 forest fires have occurred in Canada every year over the last decade, burning an average of 1.9 million hectares of forests. In British Columbia alone in 2006, which was not a peak fire year, there were 2590 forest fires destroying 131,086 hectares and costing $156 million. Fire suppression expenses during the last decade in Canada have exceeded the$500 million and almost hit the $1 billion a year. [Facts about Wildland Fire in Canada, 2012.] On 5 October 2009, for example, a huge fire in the San Bernardino National Forest, CA, burned 3500 acres. More than 500 firefighters found it hard to control due to the strong 72 km/h wind speed [5]. The story continued year after year in Canada (see Table 1). YearNumber of firesNumber of hectares burnedTotal cost (millions) 20062590131,086$156.0
200597634,588$47.2 20042394220,516$164.6
20032473265,050$371.9 200217838539$37.5
200112669677$53.8 2000153917,673$52.7
1999120811,581$21.1 1998266576,574$153.9
199711752960$19.0 1996135820,669$37.1

4.4. Wireless sensor network

The line of sight problem of optical cameras in forests can be solved with the second type of sensors. A new technology called wireless sensor network (WSN) can be deployed in large number of systems, one potential application is forest fire detection. The same printed circuit board integrates the wireless transceiver, sensors and data processing. Sensors are able to influence the physical parameters such as pressure, temperature, gases, radiations, humidity and many other parameters. Sensor network normally deployed in a large-scale random distribution on remote or inaccessible places and under harsh environment for a certain period of lifetime. This technology relied on low data rate and short ranges of communication with multi-hope fashion to reach the sink.

The recent advancement in sensor network technology has made it possible to use this technology in early forest fire detection. A number of studies have considered using WSN in wood fire systems.

Spain used a sensor network and IP cameras to detect the fire ignition by sensors and use the closest camera to provide images for fire. Spain tried four IP cameras where they installed manually in the forest and images are heavy load on such a limited resources network such as sensor network [23].

Forest Fire Surveillance System (FFSS) is a South Korean system. The surveillance is done by sensor network to observe illumination, temperature and humidity. These readings go into a database to make a daily calculation and comparison in order to evaluate the hazards [24].

FireWxNet is a system target to study the forest fire behaviour not detection. The system uses wireless sensor network to provide data for weather status and web cameras to provide images for the fire. The system uses a tiered structure, which starts with directional antennas on the top of mountains and ends with multi-hope sensor network to observe the required environmental parameters. They used web cameras to provide vision data as well, and they equipped the sensors with a small GPS device to provide the location information (see Figure 13) [25].

It is a very smart system proposed in Canada. The system based on fire weather index (FWI) to calculate the probability of fire and the spread speed of the fire. The model provides the fire probability, spread speed, weather observation, moisture content and the fuel codes, which is divided into the following three types to describe the soil content of forest ground [26].

1. Fine Fuel Code (FFMC) represents the litter and fine fuels for 2 cm deep.

2. Duff Moisture Code (DMC) represents moisture content of decomposing organic material for 5–10 cm deep.

3. Drought Code (DC) represents the moisture organic content for 10–20 cm deep (see Figure 14).

It is a Forest Fire Detection project in Pennsylvania [27]. The system uses fire sensors and GPS devices. The project has two aims: (1) rely on the existing technology and (2) replace all the existing fire detection techniques with more efficient ones.

The project plan is to install 12,000 units within 48 months, 4000 devices every 12–15 months. When the sensor detects fire or smoke, a signal is sent through GPS device to Satellite, and then Satellite will forward the signal to monitoring screen and other handheld devices (see Figure 15).

Inner magnolia forest fire research has been done by a system of three parts: (1) monitoring, (2) information management and database system and (3) decision-making system. The system provides a fire simulation from the field images in 3D maps by using Geodatabase and ArcSDE programs for fire simulation [28].

FIRESENSE (Fire Detection and Management through a Multisensor Network for the Protection of Cultural Heritage Areas from the Risk of Fire and Extreme Weather Conditions, FP7-ENV-2009-1-244,088-FIRESENSE) [29] is a Specific Targeted Research Project of the European Union’s 7th Framework Program Environment (including climate change). The FIRESENSE FP7 is a target to monitor remote areas and provide warning system. FIRESENSE is a very advanced system; it relies on IR, optical, temperature sensors, PTZ cameras and weather stations. All these sensors collect and process data to provide a clear understanding for the event to the local authority. The project deployments will be in Turkey, Italy Tunisia and Greece (see Figure 16).

FP7 relies on complicated models, algorithms, concepts and comparisons. They are given as follows:

1. Scene model (Planck’s radiation formula): the heat flux, thermal emitting, smoke, the fire, the reflectance, flickering, absorption emission lines, analysis of the atoms (e.g. potassium) and the molecules (water and carbon dioxide) are characteristics to be investigated.

2. Thermal heat emitted from the background, sunlight reflection, clouds shadow, the buildings and the sky polarisation.

3. Atmosphere gases (N2, O2, CO, CO2, H2O, etc.); each gas behaves and absorbs differently; water vapour concentration; carbon dioxide is more uniformly distributed—its value is larger over industrial cities and vegetation fields than over oceans and deserts. Figure 17 shows the physical aspects related to forest fire detection.

Libelium [30] is a Spanish wireless sensor network company. They named their product Waspmote and proposed it for many WSN applications such as forest fire detection, smart cities, water pollution and many other applications. With regard to forest fire detection, they used Waspmote nodes equipped with GPS device for localisation, and gas boards to measure temperature, carbon dioxide (CO2), carbon monoxide (CO) and humidity for detection. Libelium deployed 90 nodes with solar panels for power scavenging to measure parameters every 5 min (see Figure 18).

Wireless sensor network technology usually deployed in large number that can observe and the surrounding environment, transforming it into electrical signals, to send to the sink in a multi-hop fashion for processing. By this way, there is no need to build towers or set up complicated communication links such as microwave and satellite. WSN works on short communication links fashion and can provide real-time monitoring, where using this technology for forest fire application requires a large number of randomly deployed nodes to provide a reliable network if the key issues were addressed for this network: (1) localisation, (2) coverage, (3) network life span and (4) fire detection method.

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5. Summary of existing techniques

• The first technique is human observation towers, but this technique is inaccurate and inefficient.

• Optical systems were used in many countries, and they also proved inefficiency due to camera manual installation and line of sight and night images problems.

• Satellite scanning is mainly done by two satellites: the Advance Very High Resolution Radiometer (AVHRR), launched in 1998, and the moderate resolution imaging Spectroradiometer (MODIS), launched in 1999. A full scanning for the Earth requires 2 days, which is considered long delay to detect the fire. Satellite images quality is related to weather conditions.

• Finally, WSN started to be considered as a partial solution, where this kind of technology is used together with other technologies such as IP cameras, weather databases and fuel databases.

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Written By

Ahmad AA Alkhatib

Submitted: July 1st, 2017 Reviewed: October 30th, 2017 Published: December 20th, 2017