Estimating Biophysical Parameters of Individual Trees in an Urban Environment Using Small Footprint Discrete-Return Imaging Lidar

Rupesh Shrestha,Randolph H Wynne

doi:10.3390/rs4020484

Rupesh Shrestha, Randolph H Wynne

Open Access

https://doi.org/10.3390/rs4020484

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Abstract

Quantification of biophysical parameters of urban trees is important for urban planning, and for assessing carbon sequestration and ecosystem services. Airborne lidar has been used extensively in recent years to estimate biophysical parameters of trees in forested ecosystems. However, similar studies are largely lacking for individual trees in urban landscapes. Prediction models to estimate biophysical parameters such as height, crown area, diameter at breast height, and biomass for over two thousand individual trees were developed using best subsets multiple linear regression for a study area in central Oklahoma, USA using point cloud distributional metrics from an Optech ALTM 2050 lidar system. A high level of accuracy was attained for estimating individual tree height (R2 = 0.89), dbh (R2 = 0.82), crown diameter (R2 = 0.90), and biomass (R2 = 0.67) using lidar-based metrics for pooled data of all tree species. More variance was explained in species-specific estimates of biomass (R2 = 0.68 for Juniperus virginiana to 0.84 for Ulmus parviflora) than in estimates from broadleaf deciduous (R2 = 0.63) and coniferous (R2 = 0.45) taxonomic groups—or the data set analysed as a whole (R2 = 0.67). The metric crown area performed particularly well for most of the species-specific biomass equations, which suggests that tree crowns should be delineated accurately, whether manually or using automatic individual tree detection algorithms, to obtain a good estimation of biomass using lidar-based metrics.

Highlights

Urban trees perform ecosystem functions such as sequestering carbon, improving air quality and providing general amenities [1,2]
This study is one of the early attempts at estimating biophysical parameters for individual trees in an urban area based on point-based lidar distributional metrics
When estimating the biomass of individual trees during an urban forest inventory, a priori stratification of trees by species affords the use of lidar-based biomass equations developed for the particular species rather than those developed for broader classes

Summary

Introduction

Urban trees perform ecosystem functions such as sequestering carbon, improving air quality and providing general amenities [1,2]. Urban trees in the USA store 700 million tonnes of carbon (tC) with a gross carbon sequestration rate of 22.8 million tC/yr [3]. Uncertainties exist in such quantification due to a lack of direct measurements of urban tree allometry and biomass [5]. 10% of the trees in urban areas in the USA are publicly-owned; the remaining 90% are on private property [6]. The management of urban trees varies immensely. Urban trees are subjected to different stresses—they often grow on compacted soil, are subjected to intense pruning, have very little space in which to grow, are improperly staked, etc. Unlike traditional forests, where trees experience a change in growth and allocation after certain events like thinning, low density of trees in urban environments reduces potential competition for light and other resources [8]

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