Journal
METHODSX
Volume 8, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.mex.2021.101484
Keywords
Terrestrial LiDAR; Wildland fuels; Forestry metrics; Monitoring protocols
Categories
Funding
- US Fish and Wildlife Service
- USDA Forest Service Southern Research Station
- Department of Defense Strategic Environmental Research and Development Program [RC-2641]
- USDA Forest Service Northern Research Station
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Integrating Terrestrial Laser Scanning (TLS) methods into forest monitoring strategies can standardize data collection, improve efficiency, and easily adapt to new environmental measurements. This new method simplifies extraction of forestry, fuels, and ecological vegetation variables from a single TLS point cloud, streamlines data collection process, and produces data suitable for models and decision support frameworks.
Traditional forestry, ecology, and fuels monitoring methods can be costly and error-prone, and are often used beyond their original assumptions due to difficulty or unavailability of more appropriate methods. These traditional methods tend to be rigid and may not be useful for detecting new ecological changes or required data at modern levels of precision [1] . The integration of Terrestrial Laser Scanning (TLS) methods into forest monitoring strategies can cost effectively standardize data collection, improve efficiency, and reduce error, with datasets that can easily be analyzed to better inform management decisions. Affordable (sub-$20K) off-the-shelf TLS units-such as the Leica BLK360- have been used commercially in the built environment but have untapped potential in the natural world for monitoring. Here, we provide a methodology that successfully integrates LiDAR scanning with existing monitoring methods. This new method: Allows for simplified and quick extraction of forestry, fuels and ecological vegetation variables from a single TLS point cloud and quick transect sampling. Streamlines the data collection process, removes sampling bias, and produces data that can be easily processed to provide inputs for models and decision support frameworks. Is adaptable to integrate additional or new environmental measurements. (C) 2021 The Authors. Published by Elsevier B.V.
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