Arboriculture & Urban Forestry 48(2): March 2022 Urban Forest Information Management System The urban forest information will be digitized to build the management system. The function of this system includes the introduction of basic data and photos, search functionality, and the addition, modification, and removal of them. Establishing an online street tree man- agement system can effectively grasp the distribution of street trees, inform for pest and disease prevention, and prevent street trees from being encroached or removed. The management agencies can update the database based on the survey data, and the accumu- lated data can be used as the basis for future improve- ment. This system integrates the basic tree tables, street view images, database of roadside trees, information inquiry system, management system, and Web GIS. Urban Forest Action Tutorial System After acquiring the basic survey data, an action infor- mation database will be built. The action tutorial ser- vice can be provided by a mobile application with the functions of GPS and internet access on smartphones. The software will provide pictures, text, or audio based on the features of a road section, allowing for visitors, whether on foot, bike, or vehicle, to gain more knowledge of the urban forest when traveling and improve their overall experience. Based on the 3 main axes described above, we can quickly obtain urban forest tree census data using photovoltaic or drone technologies, digitize urban forest tree cadastral data using geographic informa- tion systems, and establish a management system to assess the urban forest resources under our control. LITERATURE CITED Alonzo M, McFadden JP, Nowak DJ, Roberts DA. 2016. Mapping urban forest structure and function using hyperspectral imagery and lidar data. Urban Forestry & Urban Greening. 17:135-147. https://doi.org/10.1016/j.ufug.2016.04.003 American Public Works Association (APWA). [date unknown]. Urban forestry best management practices for public works managers: Urban forest management plan. Kansas City (MO, USA): APWA Press. https://www2.apwa.net/Documents/ About/CoopAgreements/UrbanForestry/UrbanForestry-4.pdf Bjork A, Erlandsson M, Hakli J, Jaakkola K, Nilsson A, Nummila K, Puntanen V, Sirkka A. 2010. Monitoring environmental performance of the forestry supply chain using RFID. Com- puters in Industry. 62:830-841. https://doi.org/10.1016/j .compind.2011.08.001 Blum J. 2016. Urban forests: Ecosystem services and manage- ment. New York (NY, USA): Apple Academic Press. 316 p. Calders K, Adams J, Armston J, Bartholomeus H, Bauwens S, Bentley LP, Chave J, Danson FM, Demol M, Disney M, 57 Gaulton R, Moorthy SMK, Levick SR, Saarinen N, Schaaf C, Stovall A, Terryn L, Wilkes P, Verbeeck H. 2020. Terrestrial laser scanning in forest ecology: Expanding the horizon. Remote Sensing of Environment. 251:112102. https://doi.org/10.1016/j .rse.2020.112102 Ciesielski M, Sterenczak K. 2019. Accuracy of determining spe- cific parameters of the urban forest using remote sensing. iForest - Biogeosciences and Forestry. 12(6):498-510. https://doi.org/10.3832/ifor3024-012 City of Kirkland. 2013. Urban forestry strategic management plan. Kirkland (WA, USA): City of Kirkland. R-4986. https://www .kirklandwa.gov/files/sharedassets/public/planning-amp -building/urban-forest-management-plan.pdf Climate Action Reserve. 2019. Urban forest management project protocol. Version 1.1. Los Angeles (CA, USA): Climate Action Reserve. https://www.climateactionreserve.org/wp-content/ uploads/2019/04/Urban_Forest_Management_Project _Protocol_V1.1.pdf Coelho-Duarte AP, Daniluk-Mosquera G, Gravina V, Vallejos-Barra O, Ponce-Donoso M. 2021. Tree risk assessment: Component analysis of six visual methods applied in an urban park, Montevideo, Uruguay. Urban Forestry & Urban Greening. 59:1-9. https://doi.org/10.1016/j.ufug.2021.127005 Estornell J, Velázquez-Martí B, Fernández-Sarría A, Martí J. 2018. Lidar methods for measurement of trees in urban forests. Journal of Applied Remote Sensing. 12(4):046009. https:// doi.org/10.1117/1.JRS.12.046009 Farve R. 2014. Using radio frequency identification (RFID) for monitoring trees in the forest: State-of-the-technology inves- tigation. Washington (DC, USA): USDA Forest Service, National Technology & Development Center, Inventory and Monitoring Program. https://www.fs.fed.us/t-d/pubs/pdfpubs/ pdf14191805/pdf14191805dpi100.pdf Goh CL, Rahim RA, Rahiman MHF, Talib MTM, Tee ZC. 2018. Sensing wood decay in standing trees: A review. Sensors and Actuators A: Physical. 269:276-282. https://doi.org/10.1016/j .sna.2017.11.038 Hakli J, Jaakkola K, Pursula P, Huusko M, Nummila K. 2010. UHF RFID based tracking of logs in the forest industry. In: Proceedings of the IEEE International Conference on RFID (IEEE RFID 2010). IEEE RFID 2010; 2010 April 14–16; Orlando, FL, USA. Piscataway (NJ, USA): IEEE Institute of Electrical and Electronics Engineers. p. 245-251. https://doi .org/10.1109/RFID.2010.5467272 Iqbal SPA, Adi W, Eko K, Satria I, Ronni A, Budi PN. 2017. Urban forest topographical mapping using UAV LIDAR. IOP Conference Series: Earth and Environmental Science. 98(1):012034. https://doi.org/10.1088/1755-1315/98/1/012034 Johnstone D, Moore G, Tausz M, Nicolas M. 2010. The measure- ment of wood decay in landscape trees. Arboriculture & Urban Forestry. 36(3):121-127. https://doi.org/10.48044/jauf.2010.016 Johnstone DM, Ades PK, Moore GM, Smith IW. 2007. Predicting wood decay in eucalypts using an expert system and the IML-Resistograph drill. Arboriculture & Urban Forestry. 33(2):76-80. https://doi.org/10.1080/00049158.2018.1500676 Kamaguchi A, Nakao T, Nakai T. 2001. Non-destructive diagno- sis of internal defects of the living trees by the lateral impact vibration method. Tree and Forest Health. 5(2):59-63. https://doi.org/10.18938/treeforesthealth.5.2_59 ©2022 International Society of Arboriculture
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