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LiDAR data acquisition and processing for ecology applications

Ion Ciobotari, Adriana Príncipe, Maria Alexandra Oliveira, João Nuno Silva

TL;DR

The paper addresses the need for scalable ecological data collection by developing a low-cost terrestrial LiDAR system (LCLS) that rotates a vertically oriented LiDAR to achieve 360° coverage with enhanced vertical resolution. It details an end-to-end architecture, hardware rotation, tilt estimation, and data processing that yields georeferenced point clouds suitable for vegetation-structure analysis. Through controlled and field evaluations, the study demonstrates accurate distance measurements, viable tilt corrections, and vegetation metrics such as height histograms and Shannon diversity indices, while comparing performance against a mobile MLS (VUX). The findings show that LCLS can capture detailed vegetation structure in small plots at a lower cost than commercial TLS, with potential extensions to mobile configurations and RGB integration for improved classification and broader ecological applications.

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.

LiDAR data acquisition and processing for ecology applications

TL;DR

The paper addresses the need for scalable ecological data collection by developing a low-cost terrestrial LiDAR system (LCLS) that rotates a vertically oriented LiDAR to achieve 360° coverage with enhanced vertical resolution. It details an end-to-end architecture, hardware rotation, tilt estimation, and data processing that yields georeferenced point clouds suitable for vegetation-structure analysis. Through controlled and field evaluations, the study demonstrates accurate distance measurements, viable tilt corrections, and vegetation metrics such as height histograms and Shannon diversity indices, while comparing performance against a mobile MLS (VUX). The findings show that LCLS can capture detailed vegetation structure in small plots at a lower cost than commercial TLS, with potential extensions to mobile configurations and RGB integration for improved classification and broader ecological applications.

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.
Paper Structure (29 sections, 6 equations, 17 figures, 9 tables)

This paper contains 29 sections, 6 equations, 17 figures, 9 tables.

Table of Contents

  1. Introduction
  2. Low cost LiDAR system (LCLS) arquitecture
  3. Implementation
  4. LiDAR sensor
  5. Hardware implementation for sensor rotation
  6. LiDAR rotation control
  7. LiDAR tilt
  8. Data acquisition and processing
  9. LCLS evaluation
  10. Cost
  11. Technical performance
  12. Configuration and calibration
  13. Distance assessment
  14. Position sensors assessment
  15. Tilt measurements
  16. ...and 14 more sections

Figures (17)

  • Figure 1: Block diagram of the system with explicit connections between its various components.
  • Figure 2: (a) Standard orientation of the LiDAR sensor; (b) Proposed orientation of the LiDAR sensor
  • Figure 3: Low Cost stationary LiDAR system. This system is composed by the LiDAR sensor (A), a turntable (B) and a step motor (C).
  • Figure 4: Electric connection between sensors and motor controller.
  • Figure 5: Graphical representation of an object 6 degrees of freedom (x, y and z axis and pitch, roll and yaw angles).
  • ...and 12 more figures