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

这个博士论文研究项目将开发一个强大的方法来映射北美城市中个别阔叶落叶树的物种和压力症状水平。 该项目将提供新的见解，说明如何将遥感图像的光谱-时间变化信息与树种和压力症状水平的实地盘存相结合。 通过进一步减少劳动密集型实地工作的潜力，而是使用新的高分辨率遥感图像来评估树种和压力症状水平，该项目可以帮助推进对城市森林健康的不同因素之间相互作用的研究。 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改善城市空气质量，加强地下水维护，并调节城市气温。 虽然本项目将侧重于华盛顿，华盛顿特区的案例研究，开发一种准确、可靠和可重复的方法，利用可广泛获得的最新遥感数据来更新树种和树木压力的森林清单，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，该奖项还将为有前途的学生提供支持，使其能够建立强大的独立研究事业。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。 进行该项目的博士生将开发一种强大而准确的方法来识别树种和压力症状水平，以使用优化的遥感数据和机器学习算法来提高城市的可持续性。 她将寻求两个问题的答案，在不同种类的树木在一年中的不同时间的树叶的物候变化：（1）与叶色素诱导的可见光光谱带的变化，春季叶片的出现和秋季衰老的物候期是否是最有信息的预测树种？ (2)夏末近红外反射光谱带的下降幅度是否是预测某一树种内胁迫症状水平变化的最具预测性的组成部分？ 该项目将把来自哥伦比亚特区交通部的大型实地库存与一套WorldView-3卫星图像结合起来，绘制沿着华盛顿街道的7,000多棵树的树种和压力症状水平。该奖项反映了NSF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.