Tracking Tree Health in Urban Spaces with Raspberry Pi @Raspberry_Pi #PiDay #RaspberryPi

A research team at the University of British Columbia from Electrical and Computer Engineering and Forestry is using ground sensors, drones, mobile data, and AI to understand tree health and socio-ecological parameters in urban green spaces. Here’s more from Brian Chami and the research team at UBC:

Climate change is expected to worsen the frequency and intensity of extreme weather events, including severe storms, cyclones, and thunderstorms, causing substantial damage to urban natural assets and forests  [1]. IoT systems have allowed for such low-cost, low power networks of sensors to optimize the efficiency of city operations and services, including urban forest management [2]. In addition, natural disaster management tools have emerged to provide proactive measures for damage mitigation and ensure effective response and rapid recovery post disasters, such as fallen trees onto buildings, roads, and infrastructure [3, 4]. In particular, Micro-Electro-Mechanical Systems (MEMS) sensors, e.g. inertial sensors, have been adopted for various applications including tree-dynamics monitoring, the natural ways in which trees sway in response to environmental factors, such as wind, rain, temperature, and humidity levels [5,6] , and associated preliminary tree mortality assessment [4].

Inertial measurement unit (IMU) sensors have also been used in tree fall detection for post-natural disasters assessment systems (e.g. severe storms) [3]. In our approach, we use a 6DoF – IMU (3-axis accelerometer/gyroscope, MPU6050, InvenSense Inc.) to monitor variations in tree dynamics due to wind/drought stress. The sensors are attached to trunks and branches of two Northern red oak trees (Quercus rubra), one mature and another relatively younger, pinoak, and western Cedar.

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