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| RII TRACK-4: Multiple Global Change Factors Control Forest Nitrogen Cycling - Remote Sensing and Machine Learning Identify Forest Function Across Developed Landscapes | |
| 项目编号 | 1832882 |
| Tara Trammell | |
| 项目主持机构 | University of Delaware |
| 开始日期 | 2018-10-01 |
| 结束日期 | 2020-09-30 |
| 英文摘要 | Nontechnical Description Nitrogen (N), an essential element required by all life, moves through our environment in a very tight cycle. Human activity has more than doubled the amount of nitrogen that cycles through our environment, and yet our understanding of the global nitrogen cycle is about 50 years behind our understanding of the global carbon cycle. While large intact forests have the capacity to store excess N in substantial quantities, the majority of temperate deciduous forests in the U.S. are small forest patches due to expanding urban and suburban development. Small forests are more susceptible to consequences from human activities, such as excess N inputs and non-native invasive plant spread. This research seeks to understand the N sink potential of small forests embedded across developed landscapes that receive excess N from human activities and that experience altered N availability from non-native plant invasion. The fellowship makes new collaborations possible between the early-career PI at the University of Delaware and a senior scientist at the University of Wisconsin-Madison to utilize novel remote sensing techniques that enable large-scale study of forest N dynamics in the face of multiple global change factors, such as excess N inputs and non-native plant invasion. The outcome of this work will provide novel understanding of the source/sink potential of an important air/water pollutant (nitrogen). Technical Description The global nitrogen (N) cycle has changed dramatically over the last century through increases in reactive N worldwide due to anthropogenic activities. Temperate deciduous forests are an important sink for reactive N deposition unless N inputs exceed N demand and forests become an N source. Multiple facets of global change, such as invasive species spread and altered nutrient cycling can interact and feedback to alter forest structure and function, ultimately determining the ability of forests to act as an N sink. This project seeks to ascertain how multiple, co-occurring global changes alter N cycling in forests by utilizing innovative remote sensing and machine learning techniques to integrate forest canopy chemistry and hyperspectral imaging to determine forest health and nutrient status across heterogeneous landscapes. The research will leverage remote sensing techniques to assess how urbanization and invasion impact forest canopy N content and resorption, which are indicators of forest N availability. Forest canopy N dynamics will be evaluated across an urbanization gradient to determine N available for tree uptake in high N-deposition environments. Additionally, forest canopy N will be compared in invaded and uninvaded forests to determine whether plant invaders outcompete native trees for soil N. We will extend beyond the results of this work to forests in different regions across the US using a macroecology approach. The research from this fellowship will enhance our capability to estimate the N source vs sink potential of temperate deciduous forests, and will improve our predictive ability on the fate of global reactive N in forests. 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. |
| 学科分类 | 1303 - 宏观管理与政策;13 - 管理科学 |
| 资助机构 | US-NSF |
| 项目经费 | 203346 |
| 项目类型 | Standard Grant |
| 国家 | US |
| 语种 | 英语 |
| 文献类型 | 项目 |
| 条目标识符 | http://gcip.llas.ac.cn/handle/2XKMVOVA/69280 |
| 推荐引用方式 GB/T 7714 | Tara Trammell.RII TRACK-4: Multiple Global Change Factors Control Forest Nitrogen Cycling - Remote Sensing and Machine Learning Identify Forest Function Across Developed Landscapes.2018. |
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