Sensor Networks and Smart Green Cities: Mapping Urban Tree Phenology

传感器网络和智能绿色城市：绘制城市树木物候学

• 批准号: 2299573
• 金额: --
• 依托单位: University of Nottingham
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2299573
• 关键词: Sensor; Networks; Smart; Green; Cities

This PhD project aims to provide automatic insights into urban trees from ground level, high spatial and high temporal datasets. The project output will be designed to support the decision making, planning, and management of green infrastructure for sustainable smart green cities. Data from novel Sensor Networks and Internet of Things (IoT) devices such as CCTV cameras, dashcams, and traffic cameras will be obtained as sources of urban tree imagery. Computer vision techniques and deep learning algorithms will be applied to detect and segment urban trees from the data, and subsequently map the seasonal phenology of trees through obtaining metrics and indices including: greenness, leaf area, and vertical foliage profiles. Information on urban tree phenology is a valuable input variable in modelling carbon sequestration, urban hydrology, and the urban heat island effect. Phenology is also an important ecological indicator for understanding the feedback between climate change and vegetation productivity. Existing remote sensing and field observation approaches for monitoring phenology, in particular for trees in urban environments, have pitfalls in their resolutions which consequently limit our understanding of this natural phenomena. Urban trees play a key role in the functionality of city environments; ecosystem services have numerous benefits for cities and their citizens. This plethora of regulative, provisional, and cultural services to both society and the natural environment include: improving air quality, air temperature cooling, carbon sequestration, stormwater management, habitat provision, with urban trees are also thought to reduce stress and improve mental wellbeing. High resolution data on the phenology of urban trees will result in a better understanding of climate change impacts on tree productivity, and subsequently the actual value of ecosystem services. When also considering the potential to improve various environmental models, this project's outputs will be able to better inform the decision making and management of urban trees, such as for future planting plans, working towards a data driven, smart green city.

这个博士项目旨在从地面、高空间和高时间数据集提供对城市树木的自动洞察。项目产出将用于支持可持续智慧绿色城市绿色基础设施的决策、规划和管理。来自新型传感器网络和物联网（IoT）设备（如闭路电视摄像机、行车记录仪和交通摄像机）的数据将作为城市树木图像的来源获得。计算机视觉技术和深度学习算法将应用于从数据中检测和分割城市树木，并随后通过获取指标和指数绘制树木的季节物候图，包括：绿化率、叶面积和垂直叶剖面。城市树木物候信息是模拟碳封存、城市水文和城市热岛效应的一个有价值的输入变量。物候也是了解气候变化与植被生产力之间反馈关系的重要生态指标。监测物候学，特别是监测城市环境中的树木的现有遥感和实地观测方法在其分辨率方面存在缺陷，因此限制了我们对这一自然现象的理解。城市树木在城市环境功能中发挥着关键作用；生态系统服务对城市及其市民有许多好处。这种对社会和自然环境的大量监管、临时和文化服务包括：改善空气质量、冷却气温、碳封存、雨水管理、栖息地提供，城市树木也被认为可以减轻压力和改善心理健康。高分辨率的城市树木物候数据将有助于更好地了解气候变化对树木生产力的影响，进而了解生态系统服务的实际价值。同时考虑到改善各种环境模型的潜力，该项目的产出将能够更好地为城市树木的决策和管理提供信息，例如未来的种植计划，努力实现数据驱动的智能绿色城市。