Automatic Tree Crown Extraction from UAS Multispectral Imagery for the Detection of Bark Beetle Disturbance in Mixed Forests

Robert Minařík,Jakub Langhammer,Theodora Lendzioch

doi:10.3390/rs12244081

Abstract

Multispectral imaging using unmanned aerial systems (UAS) enables rapid and accurate detection of pest insect infestations, which are an increasing threat to midlatitude natural forests. Pest detection at the level of an individual tree is of particular importance in mixed forests, where it enables a sensible forest management approach. In this study, we propose a method for individual tree crown delineation (ITCD) followed by feature extraction to detect a bark beetle disturbance in a mixed urban forest using a photogrammetric point cloud (PPC) and a multispectral orthomosaic. An excess green index (ExG) threshold mask was applied before the ITCD to separate targeted coniferous trees from deciduous trees and backgrounds. The individual crowns of conifer trees were automatically delineated as (i) a full tree crown using marker-controlled watershed segmentation (MCWS), Dalponte2016 (DAL), and Li 2012 (LI) region growing algorithms or (ii) a buffer (BUFFER) around a treetop from the masked PPC. We statistically compared selected spectral and elevation features extracted from automatically delineated crowns (ADCs) of each method to reference tree crowns (RTC) to distinguish between the forest disturbance classes and two tree species. Moreover, the effect of PPC density on the ITCD accuracy and feature extraction was investigated. The ExG threshold mask application resulted in the excellent separability of targeted conifer trees and the increasing shape similarity of ADCs compared to RTC. The results revealed a strong effect of PPC density on treetop detection and ITCD. If the PPC density is sufficient (>10 points/m2), the ADCs produced by DAL, MCWS, and LI methods are comparable, and the extracted feature statistics of ADCs insignificantly differ from RTC. The BUFFER method is less suitable for detecting a bark beetle disturbance in the mixed forest because of the simplicity of crown delineation. It caused significant differences in extracted feature statistics compared to RTC. Therefore, the point density was found to be more significant than the algorithm used. We conclude that automatic ITCD methods may constitute a substitute for the time-consuming manual tree crown delineation in tree-based bark beetle disturbance detection and sanitation of individual infested trees using the suggested methodology and high-density (>20 points/m2, 10 points/m2 minimum) PPC.

Highlights

Insect disturbances are natural processes in forest ecosystems to maintain healthy and heterogeneous forests [1]
This study aimed to compare current individual tree crown delineation (ITCD) methods for individual tree crown feature extractions from unmanned aircraft systems (UAS) multispectral data to distinguish between the forest disturbance classes in a mixed urban forest
The BUFFER method is less suitable for detecting a bark beetle disturbance in a mixed forest using the individual tree-based approach because of the simplicity of crown delineation around a treetop

Summary

Introduction

Insect disturbances are natural processes in forest ecosystems to maintain healthy and heterogeneous forests [1]. Forest ecosystems can experience increased magnitude and frequency insect disturbances due to interaction from increasing temperatures and drought [2,3]. One of the most significant threats to the temperate forests of Central Europe are climate-change driven bark beetle Ips typographus L. Longer vegetation seasons, and drought combined with an increased frequency of windstorms allow for the establishment of additional generations of the Ips typographus and a population overgrowth [4,5]. The stressors include changes in forest soil properties, tree species diversity, higher temperatures, and carbon dioxide content [7,8]. Pest insect infestation is a serious threat to urban forests that can affect forest health and economic losses [9]. Urban forests are under active forest management, which allows rapid individual tree sanitation. It is necessary to use an appropriate method for the detection of individual infested trees

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