Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric and LiDAR Point Clouds in an Urban Park

Ebadat Ghanbari Parmehr,Marco Amati

doi:10.3390/rs13112062

Abstract

Estimation of urban tree canopy parameters plays a crucial role in urban forest management. Unmanned aerial vehicles (UAV) have been widely used for many applications particularly forestry mapping. UAV-derived images, captured by an onboard camera, provide a means to produce 3D point clouds using photogrammetric mapping. Similarly, small UAV mounted light detection and ranging (LiDAR) sensors can also provide very dense 3D point clouds. While point clouds derived from both photogrammetric and LiDAR sensors can allow the accurate estimation of critical tree canopy parameters, so far a comparison of both techniques is missing. Point clouds derived from these sources vary according to differences in data collection and processing, a detailed comparison of point clouds in terms of accuracy and completeness, in relation to tree canopy parameters using point clouds is necessary. In this research, point clouds produced by UAV-photogrammetry and -LiDAR over an urban park along with the estimated tree canopy parameters are compared, and results are presented. The results show that UAV-photogrammetry and -LiDAR point clouds are highly correlated with R2 of 99.54% and the estimated tree canopy parameters are correlated with R2 of higher than 95%.

Highlights

Urban trees play a critical role in greening and sustainably managing cities [1,2,3,4]
Unmanned Aerial Vehicles (UAV) offer an advantage for highly detailed canopy mapping over a small area allowing access to tree parameters on the public and private estate, without the need for physical on the ground access that would be required with terrestrial scanning
The accuracy of the geometry of the points decreases towards the edge of the scan because photogrammetry depends on the field of view and Light Detection And Ranging (LiDAR) depends on the off-nadir scan angle

Summary

Introduction

Urban trees play a critical role in greening and sustainably managing cities [1,2,3,4]. The traditional measurement methods for tree canopy parameters such as height, diameter, area, and volume of canopies are expensive due to time and labour especially when high accuracy is required over the area of a local government. 3D mapping methods rely on either Light Detection And Ranging (LiDAR) or Photogrammetric 3D measurements. Remotely sensed 3D mapping tree canopy methods can estimate tree canopy parameters accurately over a large area [5,6,7,8]. Both LiDAR and photogrammetric methods generate 3D point clouds which can be used to estimate required parameters through further point cloud processing.

Results

Discussion

Conclusion