Understanding tree stability through IoT tilt monitoring and terrestrial Photogrammetry

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Trees constitute an important part of many anthropogenic landscapes, providing a range of ecosystem services. However, despite their advantages, trees can also pose a risk to infrastructure and human lives, when breaking and falling under the impact of wind, snow and other weather events. Several models have been developed in the past to assess and predict potential tree failure under extreme weather conditions, but usually they can only be applied in a very specific context. The Internet of Things sensor networks provide low-cost solution for the monitoring of branch and trunk tilt which can enhance our understanding of tree stability and improve the risk assessment by supplying high volumes of data, which can be used as an input for predictive models. The IoT in this project will use three main sensing components: on-branch accelerometer and gyroscope modules for tilt measurement, photographic cameras for tree architecture retrieval through terrestrial photogrammetry and anemometer to record the wind speed. The data will be collected continuously throughout the year and uploaded into the cloud via WiFi module. Trends and patterns found in the process of data analysis will then be compared with the results of a wind forcing simulation,to test the effectiveness of physical models against the data-driven approach.