A Comparison of ALS and Dense Photogrammetric Point Clouds for Individual Tree Detection in Radiata Pine Plantations

Irfan A. Iqbal,Jon Osborn,Christine Stone,Arko Lucieer

doi:10.3390/rs13173536

Abstract

Digital aerial photogrammetry (DAP) has emerged as a potentially cost-effective alternative to airborne laser scanning (ALS) for forest inventory methods that employ point cloud data. Forest inventory derived from DAP using area-based methods has been shown to achieve accuracy similar to that of ALS data. At the tree level, individual tree detection (ITD) algorithms have been developed to detect and/or delineate individual trees either from ALS point cloud data or from ALS- or DAP-based canopy height models. An examination of the application of ITDs to DAP-based point clouds has not yet been reported. In this research, we evaluate the suitability of DAP-based point clouds for individual tree detection in the Pinus radiata plantation. Two ITD algorithms designed to work with point cloud data are applied to dense point clouds generated from small- and medium-format photography and to an ALS point cloud. Performance of the two ITD algorithms, the influence of stand structure on tree detection rates, and the relationship between tree detection rates and canopy structural metrics are investigated. Overall, we show that there is a good agreement between ALS- and DAP-based ITD results (proportion of false negatives for ALS, SFP, and MFP was always lower than 29.6%, 25.3%, and 28.6%, respectively, whereas, the proportion of false positives for ALS, SFP, and MFP was always lower than 39.4%, 30.7%, and 33.7%, respectively). Differences between small- and medium-format DAP results were minor (for SFP and MFP, differences between recall, precision, and F-score were always less than 0.08, 0.03, and 0.05, respectively), suggesting that DAP point cloud data is robust for ITD. Our results show that among all the canopy structural metrics, the number of trees per hectare has the greatest influence on the tree detection rates.

Highlights

Forest inventory has greatly benefitted from the emergence and ongoing development of airborne laser scanning (ALS)
We applied point cloud based individual tree detection (ITD) algorithms to dense point clouds derived from small- and mediumformat digital aerial photogrammetry acquired over a P. radiata plantation and compared our results with those from an ALS point cloud
This suggests that even though the architecture of the ITD algorithms was different, their performance in a homogeneous plantation appears to be comparable for ALS- and Digital aerial photogrammetry (DAP)-based point cloud data

Summary

Introduction

Forest inventory has greatly benefitted from the emergence and ongoing development of airborne laser scanning (ALS). The potential of wide area, contiguous sampling of forest structure using a technology that achieves high point density, high vertical accuracy, and that has high penetrative capacity was recognised early in the commercialisation of ALS [1] and has been the subject of very extensive research and increasingly widespread application [2]. One of two approaches are used to estimate ALS-based forest inventory:. (i) an area-based approach (ABA) (e.g., [9]) or (ii) an individual tree detection (ITD) approach (e.g., [10,11]). In ABA, statistics describing the horizontal and vertical characteristics of canopies are extracted from point cloud data and modelled against spatially coincident ground-based plot data.

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Discussion

Conclusion