Doctoral Dissertation Research: Remote Sensing of Urban Tree Species and Tree Stress

本博士论文研究项目将发展一套稳健的方法来绘制北美城市中单个阔叶落叶乔木的种类和胁迫症状水平。该项目将提供新的见解，将遥感图像的光谱-时间变异性信息与树种和应激症状水平的野外清单相结合。通过进一步减少劳动密集型田野调查的潜力，使用新的高分辨率遥感图像来评估树种和应激症状水平，该项目有助于推进不同因素对城市森林健康的相互作用研究。该项目将有助于推进城市森林管理实践，这具有重大的社会价值，因为健康的城市森林可以改善城市空气质量，加强地下水的维护，并调节城市气温。虽然该项目将侧重于华盛顿特区的一个案例研究，但开发一种准确、可靠和可重复的方法，利用广泛可用的、最先进的遥感数据来更新森林树种和树木压力的清单，将在更广泛的城市和农村环境中发挥作用。作为博士论文研究改进奖，该奖项还将提供支持，使有前途的学生建立一个强大的独立研究生涯。世界上一半以上的人口现在居住在城市，树木的生物多样性和健康已被广泛认为是城市环境可持续性的核心。主持该项目的博士生将利用优化的遥感数据和机器学习算法，开发一种强大而准确的方法来识别树种和压力症状水平，以提高城市的可持续性。她将寻求两个与不同树种在一年中不同时间的叶片物候变化有关的问题的答案：(1)随着叶片色素引起的可见光光谱带的变化，春季叶片出现和秋季衰老的物候期是否最能预测树种？(2)夏末近红外反射光谱带的衰减幅度是否能作为预测某树种胁迫症状水平变化的最具预测性成分？该项目将把哥伦比亚特区交通部的大型实地调查资料与一套WorldView-3卫星图像相结合，绘制华盛顿街道上7000多棵树木的树种和应激症状水平。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This doctoral dissertation research project will develop a robust methodology to map the species and stress symptom level of individual broadleaf deciduous trees in North American cities. The project will provide new insights regarding ways to integrate information about spectral-temporal variability from remotely sensed images with field inventories of tree species and stress symptom levels. By furthering the potential to reduce labor-intensive fieldwork and instead use new, high-resolution remote sensing imagery to assess tree species and stress symptom levels, this project can help advance research on the interplay of different factors on the health of urban forests. The project will help advance the practice of urban forest management, which has significant societal value because healthy urban forests improve urban air quality, enhance ground water maintenance, and moderate urban air temperatures. Although this project will focus on a case study in Washington, D.C., the development of an accurate, robust, and repeatable methodology for using widely available, state-of-the-art remote sensing data to update forest inventories of tree species and tree stress will have utility in a much broader set of urban and rural settings. As a Doctoral Dissertation Research Improvement award, this award also will provide support to enable a promising student to establish a strong independent research career.More than half of the world's population now lives in cities, and the biodiversity and health of trees has been widely recognized as central to the sustainability of urban environments. The doctoral student conducting this project will develop a robust and accurate method to identify tree species and stress symptom level to improve urban sustainability using optimized suite of remote sensing data and machine-learning algorithms. She will seek answers to two questions related to phenological variations in the foliage of different kinds of trees at different times of the year: (1) With the leaf pigment-induced changes in the visible-light spectral bands, will spring leaf emergence and fall senescence phenology periods be most informative to predict tree species? (2) Will the magnitude of decline in near infrared reflectance spectral bands during late summer be the most predictive component to predict changes in stress symptom level within a certain tree species? This project will pair large field inventories from the District of Columbia Department of Transportation with a suite of WorldView-3 satellite images to map the tree species and stress symptom levels of more than 7,000 trees along Washington streets.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.