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AI's Role in Burn Analysis

Gazelle Ecosolutions

3 min read

What is Burn Analysis?

We mentioned before in another blog article the importance of canopy cover in forestry management. But how does that proxy for tree health relate to burn analysis? 

In short, we can summarize that burn analysis is the assessment and monitoring of areas affected by wildfires after the fact. It helps in understanding the extent of damage, identifying areas needing rehabilitation, possibly scoping how an area has improved over time after a fire, and planning for future fire prevention strategies.

Role of Burn Analysis

Burn analysis plays a significant role in identifying areas where burns have occurred and monitoring land use and land cover (LULC) changes. It provides insights into the severity of wildfires, helping to estimate economic losses, especially in terms of timber value.

As climate change has worsened, new research from The Australian National University (ANU), the University of Sheffield, and the University of Cambridge shows that severe wildfires are putting global timber production at risk. The study found that from 2001 to 2021, wildfires destroyed 18.5 to 24.7 million hectares of timber-producing forest, resulting in a loss of approximately 393 to 667 million cubic meters of industrial timber worth $45 to 77 billion. 

This alarming trend highlights the urgent need for accurate burn analysis data. It is essential for stakeholders such as forestry managers, environmental agencies, and landowners to have this data for recovery efforts, resource allocation, and developing strategies to mitigate future risks.

How It’s Being Analyzed Now

Normalized Burn Ratio analysis being done on a plot

Advanced tools like ArcGIS facilitate burn analysis by enabling the classification of LULC. These tools are often used by remote sensing analysts who have spent years in training at colleges or workplaces that can afford these expensive software packages. Supervised or unsupervised classification methods help in mapping and monitoring changes in burned areas over time.

One of the key metrics in burn analysis is the Normalized Burn Ratio (NBR), which helps in assessing fire severity by comparing pre- and post-fire imagery. Cloud computing tools–like Google Earth Engine–are also used fairly extensively for large-scale analysis and integration of various data sources (Landsat, Sentinel, etc.)

If you take a look at imagery intelligence companies like Overwatch Imaging, they use advanced technologies to provide real-time wildfire intelligence. Their systems utilize high-resolution aerial imagery and powerful automated software to identify and monitor wildfire activity. Often, they’ll include multiple spectral bands, including visible (RGB), near-infrared (NIR), shortwave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR). This multi-spectral approach–combined with their real-time processing capabilities–supports wildfire management efforts, assisting first responders and keeping people safe.

How It Can Be Improved

But while current methods are effective, there is potential for improvement through the integration of artificial intelligence (AI) and remote sensing technologies. AI can automate the burn analysis process, making it more efficient and tailored to specific needs. For example, AI can provide customized algorithms that consider factors like burn severity, vegetation type, and recovery potential. This customization can enhance accuracy and relevance, particularly for report generation and comparative analyses over time.

Additionally, smaller wildfire teams and landowners who cannot afford expensive solutions like Overwatch Imaging can benefit from AI and cloud-based tools. These technologies can offer cost-effective alternatives for accurate and timely burn analysis.

Introducing Gazelle

Gazelle is an AI remote sensing tool designed to handle all aspects of burn analysis once reserved for large corporations with the manpower and talent to do so. By leveraging AI, Gazelle can streamline the process of analyzing burn areas, integrating Google Earth Engine queries and other remote sensing data sources. This AI-driven approach offers precise, real-time insights into wildfire impacts and land recovery, aiding in better decision-making for forestry management and conservation efforts.

Gazelle's capabilities include:

  • Automating burn assessments with high accuracy.

  • Customizing LULC algorithms to meet specific user requirements.

  • Generating detailed reports and comparisons of burn areas over time.

By working with Gazelle’s AI system, forestry managers, environmental scientists, and general analysts can enhance their monitoring and management practices. This ensures sustainable practices and amplifies them with quick and easy remote sensing technology.

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