Detection of structural tree defects using thermal infrared imaging

Coco Yin Tung Kwok, Man Sing Wong, Hon Li, Karena Ka Wai Hui, Florence Wan Yee Ko, Herman Yiu Kay Shiu, Zihan Kan

Research output: Unpublished conference presentation (presented paper, abstract, poster)Conference presentation (not published in journal/proceeding/book)Academic researchpeer-review

Abstract

Early detection of adverse tree health condition can minimize the risk of unexpected tree failure and increase the plant survival rate by providing timely remedial measures. Conventional arboriculture practice relies on visual inspection to assess the tree health condition and to identify tree defects. However, the hidden defects, e.g. cavity, are not easy to be detected using this method. Some advanced assessments, for example using resistograph and tomography, are usually conducted, but these invasive instruments would cause irreversible damage to the trees. Thermal infrared technology which is a non-invasive method provides an alternative solution to detect abnormal tree condition especially structural defects by comparing the difference in surface temperature between healthy part and unhealthy part of a tree trunk. Although some researchers introduced similar ideas in their studies, most of them interpreted the thermal images of trees with visual interpretation only and thus there is a research gap of how to extract the abnormal tree parts automatically. This paper proposed a methodology by combining k-means clustering and Sobel gradient filter to identify the area of the tree trunk with potential hidden defects. This method first groups the trunk area with similar surface temperature into clusters and then determines the locations with large variations of temperature. By combining these two factors, potential cavities can be identified based on the temperature differences. This method has been exanimated with trees in four species groups, where each group has at least one tree known to have structural defects and one healthy tree. This paper also investigated the variables affecting the ability to detect tree cavities from thermal images, e.g. acquisition time, humidity, temperature, light intensity, weather condition, the distance between camera and tree, and surface roughness of tree bark. The optimized capturing conditions have been determined, and the thermal images captured in these conditions can clearly identify the internal cavities of the tree trunk. The results from this study have been verified by a certified arborist with on-site checking of target trees.

Original languageEnglish
Publication statusPublished - 2020
Event40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019 - Daejeon, Korea, Republic of
Duration: 14 Oct 201918 Oct 2019

Conference

Conference40th Asian Conference on Remote Sensing: Progress of Remote Sensing Technology for Smart Future, ACRS 2019
Country/TerritoryKorea, Republic of
CityDaejeon
Period14/10/1918/10/19

Keywords

  • Structural Tree Defects
  • Thermal Infrared Imaging
  • Tree Cavity

ASJC Scopus subject areas

  • Information Systems

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