UAV-Based LiDAR Scanning for Individual Tree Detection and Height Measurement in Young Forest Permanent Trials

Francisco Rodríguez-Puerta,Saray Martín-García,Fernando Pérez-Rodríguez,Esteban Gómez-García,Eva Prada

doi:10.3390/rs14010170

Francisco Rodríguez-Puerta, Saray Martín-García + Show 3 more

Open Access

https://doi.org/10.3390/rs14010170

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Abstract

The installation of research or permanent plots is a very common task in growth and forest yield research. At young ages, tree height is the most commonly measured variable, so the location of individuals is necessary when repeated measures are taken and if spatial analysis is required. Identifying the coordinates of individual trees and re-measuring the height of all trees is difficult and particularly costly (in time and money). The data used comes from three Pinus pinaster Ait. and three Pinus radiata D. Don plantations of 0.8 ha, with an age ranging between 2 and 5 years and mean heights between 1 and 5 m. Five individual tree detection (ITD) methods are evaluated, based on the Canopy Height Model (CHM), where the height of each tree is identified, and its crown is segmented. Three CHM resolutions are used for each method. All algorithms used for individual tree detection (ITD) tend to underestimate the number of trees. The best results are obtained with the R package, ForestTools and rLiDAR. The best CHM resolution for identifying trees was always 10 cm. We did not detect any differences in the relative error (RE) between Pinus pinaster and Pinus radiata. We found a pattern in the ITD depending on the height of the trees to be detected: the accuracy is lower when detecting trees less than 1 m high than when detecting larger trees (RE close to 12% versus 1% for taller trees). Regarding the estimation of tree height, we can conclude that the use of the CHM to estimate height tends to underestimate its value, while the use of the point cloud presents practically unbiased results. The stakeout of forestry research plots and the re-measurement of individual tree heights is an operation that can be performed by UAV-based LiDAR scanning sensors. The individual geolocation of each tree and the measurement of heights versus pole and/or hypsometer measurement is highly accurate and cost-effective, especially when tree height reaches 1–1.5 m.

Highlights

In the case of raster-based algorithms that perform with a Canopy Height Model (CHM), we observe that the most accurate resolution for the individual tree detection (ITD) was always the 10 cm resolution of the CHM, regardless of the algorithm used and the two pine species analyzed
The use of unmanned aerial vehicles (UAV) to carry out many forestry measurements is becoming more and more widespread, and it is even common for forest managers to have their own UAV
This paper successfully evaluates three individual tree detection methods based on the canopy height model, and another one based directly on the point cloud

Summary

Introduction

Forest management needs tools to predict the growth of forest stands under different silvicultural systems and climatic scenarios [1]. This information facilitates decision making for both forest owners and managers. Such tools are usually based on growth and yield models, for which it is essential to design research plots that will be successively measured over time (permanent plots). Spatial models include a measure of spatial competition, usually expressed as a function of the distance (or size) between the individual tree

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