Terrestrial Mobile LiDAR for High Resolution Forest Inventory

Imagine using a handheld/backpack system or your smartphone to scan a forest plot and derive a complete inventory of every individual tree by species, volume, grade, and economic value. As digital technologies are advancing quickly allowing autonomous cars and trucks to move on our highways, the dream to use these digital technologies for automated forest inventory is very close to becoming a reality.

Both hardware (e.g., LiDAR and digital cameras) and software (e.g., AI) are advancing quickly and becoming economically affordable. This can allow advanced computing (e.g., AI) and multi-dimensional data from various sources to be used in forest systems to improve information gathering and decision-making.

Improvements in digital technology have helped lay the foundation for their potential application in forest productivity and management. However, the integration of these digital technologies into forest inventory is still lacking, hindering inventory automation and data-driven traits selection for tree improvement and precision management.

HabibBackpackinMartell.jpg

ABOVE: Proposed data acquisition modality: Backpack system deployed in Martell Forest in Febrary 2021.

Principal Investigators

  • Ayman Habib, Professor, Civil Engineering, Purdue University
  • Songlin Fei, Professor, Forestory & Natural Resources, Purdue University

Collaborators

  • Guofan Shao, Professor, Forestry & Natural Resources, Purdue University
  • Joey Gallion, Forestry Inventory Program Manager, Indiana Department of Natural Resources

Objectives

The objective of the research is to develop a terrestrial system/platform and biometrics extraction algorithms for fine-detail, automated measurements and trait characterization for forest plantations. The system is based on an integrated RGB camera, LiDAR, and Global Positioning System/Inertial Navigation Systems (GPS/INS) units. The integrated sensors can be mounted onboard a backpack or an all-terrain vehicle.

  1. Optimize system integration, data logging, and deployment.
  2. Develop data processing and biometrics extraction algorithms.
  3. Share tools and methods with HTIRC researchers and stakeholders in trainings and workshops.
BELOW: Examples of a profile along LiDAR point clouds captured by a UAV (green) and the Backpack system (blue) over a plantation in Martell Forest on September 25, 2020 and February 27, 2021, respectively (note the high level of detail furnished by the Backpack system).
AKAM_Habib_Fei_HTIRC_Backpack_UAS.jpg
AKAM_Habib_Fei_HTIRC_Backpack.jpg
A more detailed point cloud from the February 27, 2021 mission in Martell Forest is available at https://youtu.be/x8aE1vjM4_A

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