Estimating Changes in Leaf Area, Leaf Area Density, and Vertical Leaf Area Profile for Mango, Avocado, and Macadamia Tree Crowns Using Terrestrial Laser Scanning

Dan Wu,Kasper Johansen,Andrew Robson,Stuart Phinn,Christopher Searle,Jasmine Muir

doi:10.3390/rs10111750

Abstract

Vegetation metrics, such as leaf area (LA), leaf area density (LAD), and vertical leaf area profile, are essential measures of tree-scale biophysical processes associated with photosynthetic capacity, and canopy geometry. However, there are limited published investigations of their use for horticultural tree crops. This study evaluated the ability of light detection and ranging (LiDAR) for measuring LA, LAD, and vertical leaf area profile across two mango, macadamia and avocado trees using discrete return data from a RIEGL VZ-400 Terrestrial Laser Scanning (TLS) system. These data were collected multiple times for individual trees to align with key growth stages, essential management practices, and following a severe storm. The first return of each laser pulse was extracted for each individual tree and classified as foliage or wood based on TLS point cloud geometry. LAD at a side length of 25 cm voxels, LA at the canopy level and vertical leaf area profile were calculated to analyse tree crown changes. These changes included: (1) pre-pruning vs. post-pruning for mango trees; (2) pre-pruning vs. post-pruning for macadamia trees; (3) pre-storm vs. post-storm for macadamia trees; and (4) tree leaf growth over a year for two young avocado trees. Decreases of 34.13 m2 and 8.34 m2 in LA of mango tree crowns occurred due to pruning. Pruning for the high vigour mango tree was mostly identified between 1.25 m and 3 m. Decreases of 38.03 m2 and 16.91 m2 in LA of a healthy and unhealthy macadamia tree occurred due to pruning. After flowering and spring flush of the same macadamia trees, storm effects caused a 9.65 m2 decrease in LA for the unhealthy tree, while an increase of 34.19 m2 occurred for the healthy tree. The tree height increased from 11.13 m to 11.66 m, and leaf loss was mainly observed between 1.5 m and 4.5 m for the unhealthy macadamia tree. Annual increases in LA of 82.59 m2 and 59.97 m2 were observed for two three-year-old avocado trees. Our results show that TLS is a useful tool to quantify changes in the LA, LAD, and vertical leaf area profiles of horticultural trees over time, which can be used as a general indicator of tree health, as well as assist growers with improved pruning, irrigation, and fertilisation application decisions.

Highlights

Light detection and ranging (LiDAR) is an active remote sensing technology
Calders et al [10] later proved that correcting topography was essential for leaf area index (LAI) and plant area volume density (PAVD) calculations from terrestrial laser scanning (TLS), whilst Kaasalainen et al [11] and Calders et al [12] combined TLS measurements and three-dimensional (3D) quantitative structure modelling [13] to produce robust and highly accurate aboveground biomass calculations
This study assessed the ability of TLS to calculate leaf area (LA) and leaf area density (LAD) of mango, avocado, and macadamia tree crops

Summary

Introduction

Light detection and ranging (LiDAR) is an active remote sensing technology. A laser scanner emits laser pulses to precisely and accurately measure distances between the scanner and each survey point based on the speed of light and the time each laser pulse travels. To assist in the development of improved management practices, such as targeted limb removal and the variable rate application of foliar sprays, it is essential that high spatial resolution TLS be evaluated for the accurate measure of LA and LAD within horticultural tree crops [29]. RemoteOSuenrs.s2t0u1d8,y10a,ixmFeOdR tPoEEcRalRcEuVlaIEtWe tree level LA, voxel (25 cm in side length) LAD, and vertica3l olef a18f area profiles using TLS for mango, avocado, and macadamia tree crops. This information provides the tfhouenfoduantidonattiondetovedleovpeolorcphoardchmaradnamgaenmaegnetmpelannt sptlainlosrteadilotoreidndtoivindduiavlitdreueasl,tsruecehs,assupchreacsispiornecsipsiroany sdporsaaygedsoasangdespraunndinpgrubnaisnegdboansetdheoLnAthaetLaApatrtaicpualratrichueliagrhhtefiogrhetafcohr etraeceh.

Data Registration

Data Classification and Extraction

Findings

Conclusions