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Lidar-derived structural-complexity data across four experimental forests.

C Wade Ross1,2, E Louise Loudermilk2, Joseph J O'Brien2

  • 1Tall Timbers, 13093 Henry Beadel Dr, Tallahassee, FL 32312, United States.

Data in Brief
|October 10, 2024
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Summary
This summary is machine-generated.

New lidar data products quantify forest structural complexity, improving wildland fire models like QUIC-Fire. These openly accessible datasets support research in forestry and ecosystem management across multiple experimental forests.

Keywords:
ALSCanopy height modelCrown delineationDigital elevation modelForest structureTree detection

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Area of Science:

  • Ecology and Ecosystem Science
  • Forestry and Wildland Fire Management
  • Geospatial Analysis and Remote Sensing

Background:

  • Ecosystem structural complexity, encompassing biotic and abiotic elements, influences critical functions like light transmittance, habitat provision, and biodiversity.
  • Accurate characterization of forest structure is vital for predicting ecosystem resilience to disturbances such as wildfire and for enhancing wildland fire behavior models.
  • The QUIC-Fire model integrates forest structural complexity metrics for real-time fire spread prediction, but its performance relies on high-quality, localized data inputs.

Purpose of the Study:

  • To develop and provide comprehensive, high-quality data products quantifying forest structural complexity across multiple USDA Forest Service Experimental Forests.
  • To establish a foundation for interdisciplinary research in forestry, wildland fire, hydrology, soil science, and cultural resources.
  • To enhance the usability and adaptability of advanced wildland fire models by providing essential structural data.

Main Methods:

  • Airborne laser scanning (ALS) was employed during the leaf-off season to capture detailed point-cloud data, minimizing foliage interference.
  • Lidar data underwent rigorous processing, including outlier detection, filtering, ground/non-ground classification, and computation of topographic and forest structure metrics.
  • Pixel-level and tree-level metrics were derived, including DEM, slope, aspect, canopy height, foliar height diversity (FHD), and individual tree detection (ITD) metrics.

Main Results:

  • A suite of pixel-level topographic data products (DEM, slope, aspect, TPI, TRI, roughness, flow direction) was generated.
  • Extensive forest structural complexity metrics were computed, including canopy height, FHD, vertical distribution ratio (VDR), canopy rugosity, and canopy cover.
  • Harmonized tree-level data products from individual tree segmentation (ITS) and ITD algorithms were created and made openly accessible.

Conclusions:

  • The developed lidar-derived data products provide crucial, high-resolution information on forest structure and topography.
  • These openly accessible datasets facilitate improved wildland fire modeling and support collaborative research across diverse scientific disciplines.
  • The data products enhance the capacity for effective ecosystem management and resilience assessment within the Southern Research Station focal areas.