Understanding tree stability through IoT tilt monitoring and terrestrial Photogrammetry

树木是许多人为景观的重要组成部分，提供一系列生态系统服务。然而，尽管有这些优点，树木在风、雪和其他天气事件的影响下折断和倒下时，也会对基础设施和人类生命构成威胁。过去已经开发了几个模型来评估和预测极端天气条件下潜在的树木破坏，但通常它们只能应用于非常特定的环境。物联网传感器网络为监测树枝和树干倾斜提供了低成本的解决方案，可以提高我们对树木稳定性的理解，并通过提供大量数据来改进风险评估，这些数据可以用作预测模型的输入。该项目的物联网将使用三个主要传感组件：用于倾斜测量的分支加速度计和陀螺仪模块，通过地面摄影测量用于树木建筑检索的摄影相机，以及记录风速的风速计。数据全年不间断采集，并通过WiFi模块上传到云端。然后将在数据分析过程中发现的趋势和模式与风强迫模拟的结果进行比较，以测试物理模型与数据驱动方法的有效性。

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.