What is LiDAR and how is it used in forest fire management?

Light Detection and Ranging, or LiDAR, uses laser light to generate detailed three-dimensional maps of the Earth's surface. In the context of forest fire management, bi-temporal LiDAR data, collected at two different times, allows for the analysis of forest structures at the individual tree level. By comparing pre- and post-fire data, the impacts of the fire can be assessed with greater accuracy, informing better forest management and fire prevention strategies. This contrasts with traditional methods that often lack the necessary detail for targeted interventions.

What is the cPAC method and how does it utilize LiDAR data to assess fire severity?

Tree crown Profile Area Change, or cPAC, is a method that uses LiDAR data to assess fire severity at the individual tree level. By comparing pre- and post-fire LiDAR data, cPAC quantifies changes in the profile area of tree crowns. This involves segmenting individual tree crowns from pre-fire LiDAR-derived canopy height models (CHMs) and then analyzing the change in profile area between pre- and post-fire height percentile curves to determine fire-induced damage.

What are the key advantages of using the cPAC method for assessing forest fire severity?

The cPAC method offers enhanced accuracy in assessing fire severity compared to traditional methods and even simpler LiDAR metrics. It provides insights into the variable impacts of fire across a landscape by focusing on individual trees. Furthermore, the use of bi-temporal data allows for a direct assessment of changes in forest structure caused by fire. In the American Fire study, cPAC outperformed other LiDAR metrics and Landsat imagery, demonstrating its precision in illustrating tree biomass loss.

What are the limitations to consider when using cPAC for forest fire assessment and management?

While cPAC represents a significant advancement, its widespread adoption faces limitations. The cost of LiDAR data acquisition can be substantial, and specialized expertise is needed to process and interpret the data effectively. Overcoming these obstacles is crucial for the broader implementation of cPAC and similar LiDAR-based methods in forest fire management, which can lead to more targeted and effective interventions.

What are the broader implications of using bi-temporal LiDAR data and methods like cPAC for forest management and wildfire prevention?

Bi-temporal LiDAR data and methods like cPAC have significant implications for forest management and wildfire prevention. The enhanced accuracy and detailed insights offered by these technologies enable more informed decisions about resource allocation, targeted interventions, and long-term forest management strategies. Accurately measuring fire severity on individual trees helps to focus efforts on areas that need the most attention after a fire. This contributes to more effective ecosystem restoration and resilience in the face of increasing fire threats.

LiDAR scanning a forest, showing fire damage.

Can LiDAR Technology Help Us Predict and Prevent Forest Fires?

Lena Kashyap in Climate & Environment August 2025 • 4 min read.

"A deeper look into how individual tree assessments are changing forest management and wildfire prevention using bi-temporal LiDAR data for more precise fire severity mapping"

Forest fires are a natural part of many ecosystems, but their increasing frequency and intensity pose significant risks to both the environment and human communities. Understanding and accurately assessing the severity of these fires is crucial for effective forest management and restoration efforts. Traditional methods, such as on-the-ground surveys and satellite imagery analysis, have limitations in providing the detailed information needed for targeted interventions.

Enter Light Detection and Ranging (LiDAR) technology, a remote sensing method that uses laser light to create highly detailed three-dimensional maps of the Earth's surface. LiDAR offers a unique opportunity to analyze forest structures at the individual tree level, providing a more precise understanding of fire impacts. By comparing pre- and post-fire LiDAR data, we can now assess fire severity with unprecedented accuracy, leading to better-informed decisions about forest management and fire prevention strategies.

This article explores how bi-temporal LiDAR data—that is, LiDAR data collected at two different points in time—is transforming forest fire assessment. We'll delve into a new method called tree crown Profile Area Change (cPAC), which quantifies fire severity for individual trees, offering a more nuanced and effective approach to managing our forests in the face of increasing fire threats.

Understanding Tree Crown Profile Area Change (cPAC) and LiDAR

LiDAR scanning a forest, showing fire damage.

The cPAC method leverages the detailed structural information captured by LiDAR to assess fire severity at the level of individual trees. This involves comparing pre- and post-fire LiDAR data to identify changes in the profile area of tree crowns. The process begins with segmenting individual tree crowns from pre-fire LiDAR-derived canopy height models (CHMs).

Once the tree crowns are identified, fire severity is calculated by analyzing the change in the profile area between pre- and post-fire height percentile curves. These curves are generated from LiDAR point clouds within each tree segment, providing a detailed representation of the tree's vertical structure. By comparing the area under these curves before and after a fire, the cPAC method quantifies the extent of fire-induced damage to individual trees.

Enhanced Accuracy: cPAC provides a more accurate assessment of fire severity compared to traditional methods and simple LiDAR metrics.
Individual Tree Analysis: By focusing on individual trees, cPAC offers insights into the variable impacts of fire across a landscape.
Bi-temporal Data: Comparing pre- and post-fire data allows for a direct assessment of changes in forest structure due to fire.

In a study assessing the American Fire in Sierra Nevada, California, the cPAC method demonstrated its effectiveness by showing a strong correlation with in-situ basal area changes (R²=0.75). This outperformed other LiDAR-metrics like canopy cover change (R2=0.36) and tree height change (R2=0.37), as well as Landsat imagery-derived Normalized Burn Ratio differences (R2=0.20). The results indicate that cPAC is a superior method for illustrating tree biomass loss with higher precision.

Looking Ahead: The Future of LiDAR in Forest Fire Management

The cPAC method represents a significant step forward in our ability to assess and manage forest fires. By providing detailed, tree-level insights into fire severity, this approach enables more targeted and effective interventions. However, the widespread adoption of cPAC and similar LiDAR-based methods depends on addressing existing limitations, such as the cost of data acquisition and the need for specialized expertise.