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Arboriculture & Urban Forestry Online
Volume 48, Issue 2 — March 2022
https://www.isa-arbor.com/Publications/Arboriculture-Urban-Forestry

Intelligent Survey Technologies and Applications for Urban Forests in Taiwan

Jan-Chang Chen, Chun-Hung Wei, Yi-Ta Hsieh, Shang-Chuan Huang, and Ping-Hsun Peng

Abstract: Background: Roadside trees play an important role in urban landscaping. They are not only related to urban scenes, traffic safety, quality of life, and health, but also closely related to ecology and cultural development. Thus, effective, intelligent management of an area of urban roadside trees will become an important topic. Methods: This paper evaluates survey technologies and management techniques utilized in many cities of Taiwan, including surveys of roadside trees, risk assessment, and precious protected trees. A roadside tree management database was built using a geographic information system (GIS). Results: The number of urban forest trees exceeded 100,000 in our surveys, and many types of intelligent survey instruments were used to survey the trees, including real-time kinematic (RTK) and non-destructive detection instruments, radio frequency identification (RFID), in-vehicle light detection and ranging (LiDAR), and panoramic streetscape systems. A tree management system can be constructed by introducing the digitized information, which is based on a basic survey of trees. The survey stage primarily relies on manual surveys, in-vehicle LiDAR, and RFID, and then a visualized database retrieval system will be proposed using GIS. This system can be utilized for the health and foundation management of trees and the whole spatial planning of urban forests, among others. Conclusion: This research attempts to summarize the trends in intelligent management of urban forests using our practical experiences with the goal that it will be a reference for the future intelligent construction of urban forests.

Keywords: Geographic Information System (GIS); In-Vehicle Light Detection and Ranging (LiDAR); Non-Destructive Detection Instruments; Urban Forest; Urban Scenes.

https://doi.org/10.48044/jauf.2022.005


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