Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection

Jonathan Dash,Thomas Paul,Michael Watt,Grant Pearse

doi:10.3390/rs9020156

Abstract

The spread of exotic conifers from commercial plantation forests has significant economic and ecological implications. Accurate methods for invasive conifer detection are required to enable monitoring and guide control. In this research, we combined spectral information from aerial imagery with data from airborne laser scanning (ALS) to develop methods to identify invasive conifers using remotely-sensed data. We examined the effect of ALS pulse density and the height threshold of the training dataset on classification accuracy. The results showed that adding spectral values to the ALS metrics/variables in the training dataset led to significant increases in classification accuracy. The most accurate models (kappa range of 0.773–0.837) had either four or five explanatory variables, including ALS elevation, the near-infrared band and different combinations of ALS intensity and red and green bands. The best models were found to be relatively invariant to changes in pulse density (1–21 pls/m2) or the height threshold (0–2 m) used for the inclusion of data in the training dataset. This research has extended and improved the methods for scattered single tree detection and offered valuable insight into campaign settings for the monitoring of invasive conifers (tree weeds) using remote sensing approaches.

Highlights

Exotic conifers are the foundation of the plantation forest industry in many Southern Hemisphere countries, providing significant economic and social benefits
The objectives of this research were to (i) compare the accuracy of detection models developed using various combinations of airborne laser scanning (ALS) data and aerially-acquired spectral data and (ii) determine the sensitivity of classification accuracy in these models to the height threshold used for inclusion and the density of the ALS data
This suggests that the uncalibrated intensity values used in this study would likely have little value in classification models for invasive conifers as they would overlap the values from all other vegetation types

Summary

Introduction

Exotic conifers are the foundation of the plantation forest industry in many Southern Hemisphere countries, providing significant economic and social benefits. A number of exotic conifer species have become invasive and, under certain environmental settings, propagate beyond the plantation boundary and are serious tree weeds [1,2,3]. In New Zealand, invasive conifers, often referred to as ‘wilding conifers’, are dominantly invading indigenous and semi-native grass and shrublands across large areas of the South and North Island [3,4]. Invasive conifers are estimated to occupy, with highly variable densities (from less than one tree per hectare to full cover), an area equivalent to the national plantation forest estate [5]. A range of chemical and physical control methods can be deployed to control invasive conifers [7], but these depend on successful detection of individuals and characterisation of the infestation level to be deployed cost effectively

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Methods

Results

Discussion

Conclusion