A changing climate has brought with it drier, warmer weather, creating conditions for extreme and widespread biomass burning. Wildfires fires are becoming more common, resulting in extensive ecological, economic, and human loss. it is predicted that the area affected by wildfires will double over the next 20-30 years.

Remote sensing technologies provide useful tools for vegetation analysis to inform forest management decisions. In the context of wildfire ecology and prevention, LiDAR is the preferred remote-sensing tool for understanding the forest ecosystem. LiDAR is routinely used to assess forest canopy metrics, providing critical information on forest species, vertical and horizontal vegetation structure, and stand composition to support strategic and tactical wildfire decision-making. Unlike satellite optical imagery, LiDAR can penetrate forest canopy and sense the lower vegetation and ground beneath it. Data derivatives can be linked to environmental drivers related to the spread of fire and burn severity, which are not easily observed using other electro-optical methods. In addition, fuel geometric parameters can be measured using LiDAR, such as canopy height, bulk density, and cover.

VeriDaaS’ next-generation Geiger-mode LiDAR, which was originally designed to provide superior foliage penetration, is the LiDAR technology of choice for forestry applications. Compared with traditional Linear-mode LiDAR, Geiger-mode LiDAR’s high-density and multi-angle looks offer a better chance of seeing through foliage, allowing improved assessment of the forest’s shrub and timber-litter understory. Even with significant occlusion of objects beneath the canopy, detailed extraction of their features is possible with Geiger-mode LiDAR. Geiger-mode LiDAR performs especially well in densely vegetated, complex forests, where conventional sensors can be incapable of penetrating the thick forest canopy.

VeriDaaS’ high-definition Geiger-mode LiDAR can reliably and cost-effectively provide critical forest information across scales—from the individual tree level to the aggregate landscape level—over large areas. This information can be used to inform wildfire behavior and simulation models, forest management, and habitat mapping, as well as to identify priority areas where wildfire prevention efforts should be concentrated.

Wildfire management applications of our data include the following:

  • Analyzing wildfire landscape risk.
  • Derivation of canopy surface models (CSM), canopy height models (CHM) and continuous canopy metrics.
  • Classifying, and estimating biomass and surface fuel volume across forest landscapes.
  • Developing fuel characterization maps for predicting possible wildfire scenarios and prevention requirements, including strategic fuel reduction and tree thinning.
  • Obtaining 3D estimates of canopy bulk density.
  • Predicting crown-fire initiation and fire spread.
  • Identifying areas for prescribed burning to reduce ladder fuels that lead to torching and crown fires.
  • Estimating infrastructure susceptibility to wildfire based on proximal fuel sources.
  • Measuring infrastructure loss.
  • Assessing pre- and post-fire biomass loss, carbon losses, and burn severity.
  • Post-fire succession, ecosystem change and transition.
  • Identifying post-fire structures including fallen and standing wood debris.