LiDAR data acquisition and processing for ecology applications

• URL: http://arxiv.org/abs/2401.05891v1
• Date: Thu, 11 Jan 2024 13:03:27 GMT
• Title: LiDAR data acquisition and processing for ecology applications
• Authors: Ion Ciobotari and Adriana Pr\'incipe and Maria Alexandra Oliveira and Jo\~ao Nuno Silva
• Abstract summary: Terrestrial laser scanners (TLS) have been used in ecology to reconstruct the 3D structure of vegetation. The orientation of LiDAR was modified to make observations in the vertical plane and a motor was integrated for its rotation. From the data generated, histograms of point density variation along the vegetation height were created.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The collection of ecological data in the field is essential to diagnose, monitor and manage ecosystems in a sustainable way. Since acquisition of this information through traditional methods are generally time-consuming, due to the capability of recording large volumes of data in short time periods, automation of data acquisition sees a growing trend. Terrestrial laser scanners (TLS), particularly LiDAR sensors, have been used in ecology, allowing to reconstruct the 3D structure of vegetation, and thus, infer ecosystem characteristics based on the spatial variation of the density of points. However, the low amount of information obtained per beam, lack of data analysis tools and the high cost of the equipment limit their use. This way, a low-cost TLS (<10k$) was developed along with data acquisition and processing mechanisms applicable in two case studies: an urban garden and a target area for ecological restoration. The orientation of LiDAR was modified to make observations in the vertical plane and a motor was integrated for its rotation, enabling the acquisition of 360 degree data with high resolution. Motion and location sensors were also integrated for automatic error correction and georeferencing. From the data generated, histograms of point density variation along the vegetation height were created, where shrub stratum was easily distinguishable from tree stratum, and maximum tree height and shrub cover were calculated. These results agreed with the field data, whereby the developed TLS has proved to be effective in calculating metrics of structural complexity of vegetation.

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