Wildfires on the Rise: Identifying High-risk Areas with AI

Indra den Bakker
May 7, 2019 · 6 min read

2018 has become the fourth warmest year on record triggering severe natural hazards that affected over 62 million people globally. The World Meteorological Organization (WMO) estimates most of the damage was due to the extreme weather events caused by the accelerating global climate change impacts. According to the WMO report, over 1,600 deaths were linked to heat waves and wildfires in Europe, Japan, and the U.S. over the year.

In Sweden, fires destroyed unprecedented 25,000 hectares of forests, and abnormal wildfires have ravaged across the United Kingdom, Ireland, Germany, Latvia, Norway, and even the Arctic Circle.

Last July, Greece has suffered the worst loss of life from wildfires in the last decade in the world with at least 99 deaths reported. In November, the persistent severe winds and dry conditions resulted in a fire disaster across California killing 85 people, destroying the town of Paradise and over 18 000 houses in the area. This was the biggest wildfire toll in the United States over the last century. The total losses in 2018 across the United States from wildfires reached $24 billion, the biggest financial toll ever.

In Canada, over 1.35 million hectares burnt in total in British Columbia and extensive heavy smoke had engulfed cities on the West coast including Vancouver and Seattle.

Indonesia has experienced strong droughts for half a year, battering Java from July to October. Afghanistan lost a substantial part of crops due to droughts and long-term drought has persisted in Pakistan, where 2018 became the fourth-driest year since 1961. Extreme drought conditions were also reported in Iran, Uruguay, Brazil, and most of all Argentina, where the agricultural losses were estimated at $5.9 billion.


The destructive power and frequency of the fires are accelerating around the world due to a combination of risk factors: increased temperatures, decreased soil moisture, changes in vegetation mix, plant density, and moisture content. Speed of the wind, relative humidity, rainfall dynamics, and climate variability are also significant factors of risk.

Source: National Geographic

Climate change accelerates the frequency of wildfires extending the hot periods further each year inducing stronger droughts conducive to forest fires. Unusually dry weather converts green vegetation into dry, easily flammable fuel, warm temperatures enhance combustion, and strong winds spread the fire faster and further.

The Intergovernmental Panel on Climate Change projects further increases in mean temperature in most land and ocean regions, more hot extremes, droughts, and precipitation deficits posing major risks for forest ecosystems, business, and local communities.

This interactive map illustrates the projected temperature increases around the US considering numerous future climate forecasts as generated by 27 different climate models using 12 different measures to describe the climate including minimum and maximum temperature and total precipitation for each season.


The UNEP’s 2019 Global Environment Outlook concludes that coping with the damages resulting from climate change, deforestation, and biodiversity loss requires smart governance and sustainable management of natural resources, where forests play a crucial role. Innovative tools for data analysis like Artificial Intelligence (AI) and Machine Learning allow obtaining advanced diversified data about ecosystems from both Earth and space.

The traditional manual surveys and ground measurements are costly and time-consuming, but they can now be replaced with alternative methods. Novel tools like Deep Learning enable better and faster support for decision-making in emergency management expanding our understanding of the patterns for predictive modelling of wildfires.

Artificial Intelligence, Big Data, Machine Learning, and Remote Sensing methods can analyze enormous amounts of data in order to predict, prevent, assess, and monitor wildfires effectively. Utilizing information on historical fire events the system identifies the key factors, predicts risks, and impacts generating explicit spatial distribution models and maps of the wildfire probability.

A special tool Firemap has been developed for conducting data-driven predictive modelling and analyzing fires with a high risk of rapid spread that allows exploring various potential scenarios, as well as real-time fire forecasting.

Firemap facilitates quick access to data regarding weather conditions and fires in the past, reports the current situation, and offers weather forecasts using satellite detections, information on landscapes and vegetation for planning effective fire response strategies.

Decision support systems using AI, Big Data, and Machine Learning can improve emergency management for environmental security, facilitate developing tactical firefighting strategies and ecosystem remediation. Microsoft has created AI for Earth to promote sustainable technological solutions to global challenges like preserving biodiversity, sustainable natural resource management, and climate change mitigation.

Source: Microsoft AI

Another brilliant example is the Web-GIS wildfire prevention and management system (AEGIS) aimed at reducing the potential human, environmental, and property losses using an integrated user-friendly decision support tool for managing wildland fire hazards in Greece.

The AEGIS platform enables online access to spatial and field inventory data, land cover maps, high-resolution multispectral satellite images (RapidEye), and wildfire simulation tools for early fire warning, fire planning, control, and coordination.

AEGIS applies artificial neural networks (ANNs) for comprehensive wildfire ignition risk assessment integrating essential parameters, training methods, activation functions, pre-processing methods, and network structures to produce fire hazard prediction maps. The system also incorporates weather information obtained from remote automatic weather stations and weather forecast maps.


Forests serve as a natural carbon sink that absorbs the CO2 emissions from the atmosphere, playing a crucial role in climate change mitigation. Our team applies AI and advanced information systems to enable the government authorities, fire protection agencies, and the forest industry players to beware of the high-risk factors and areas in the forests.

We assist our clients in planning adequate preventive and control measures for pro-active wildfire risk assessment, fire behaviour prediction, effective emergency management, and sustainable forest governance to maximize the carbon sequestration.

Source: 20tree.ai

At 20tree.ai we utilize advanced technological innovations to create insights into the key risk factors associated with the climate variability, forest composition, vegetation density, and behavioural patterns of local communities.

For climate analysis, we use EUMETSAT satellite data with live updates about ground surface temperature and humidity uploaded every 15 minutes. For vegetation analysis, we use 0.3 m to 10 m high-resolution satellite data creating insights into forest quantity and quality, tree species, biomass, and health of trees. For community analysis, we use night-time satellite imagery to look at populated areas and expansion of urban areas.

We help the governments, companies, and communities to understand the environmental risks, predict their impacts, and prepare to face them with minimum losses. We consider forest fire hazards from a data perspective analyzing key risk factors like drought levels, total biomass, people mobility and activities, air, and ground temperatures, and wind conditions.

Our Forest Intelligence Platform provides instant access to the forest fire risk maps designed to aid in developing sustainable forest management strategies, forest carbon stock conservation, wildfire prevention, climate mitigation, and adaptation actions.

Our innovative approach minimizes the risks and vulnerability, strengthens the resilience and adaptive capacity of the fragile forest ecosystems, forest-dependent communities, and business via information access and analysis for improved forest governance.

We uncover unique opportunities for decision-makers, business owners, and other stakeholders to obtain critical information, predict further dynamics, and take adequate actions for achieving the social, economic, and environmental goals stated in the Paris Agreement and the UN 2030 Agenda for Sustainable Development.

If you’re interested to learn more about how AI can detect wildfires at an early stage and identify high-risk areas you can request a demo here.


Exploring forests from the sky.

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