MSA: Integrating multi-scale remote sensing and mechanistic modeling to assess riparian ecosystem dynamics and feedbacks to hydroclimate variability

MSA：整合多尺度遥感和机械建模来评估河岸生态系统动态和对水文气候变化的反馈

• 批准号: 2106030
• 负责人: Xiaonan Tai
• 金额: $ 29.96万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2106030&HistoricalAwards=false
• 关键词: MSA; Integrating; multi; scale; remote

Forested river and stream ecosystems provide critical services of water and carbon, biological habitats, and recreational activities. These ecosystems are temporally and spatially dynamic in response to changes in climate, streamflow, water management, and biological invasions. Currently, our knowledge of how riparian ecosystems respond to and in turn influence environmental change remains considerably limited, although such knowledge is critical for effective conservation and management. This project aims to study the changes in riparian (i.e., streamside) vegetation over time and evaluate the role of various driving factors underlying this change. This research will provide a new basis for understanding how riparian vegetation has changed during recent decades, and for predicting how it is likely to change in the future. Further, this project will evaluate the contribution of riparian vegetation to large-scale fluxes of water and carbon. The goal is to use cutting-edge techniques of mapping and modeling to identify where riparian zones are likely to have greater impacts on ecosystem function and stability. This information will be useful in developing restoration priorities and facilitating decisions about intervention and management under changing climate and altered streamflow conditions. The team, composed of ecohydrological modeling and remote sensing experts, will publicly disseminate research tools, datasets, and results to the research community and general public. In the process, the team will conduct interdisciplinary undergraduate and graduate training to prepare diverse, next-generation scientists to tackle environmental and data science challenges.The objective of this project is to mechanistically link riparian vegetation dynamics to hydroclimate variations in order to assess the functional importance of riparian ecosystems to macrosystem fluxes of carbon and water. Specifically, this project will leverage high-resolution data from NEON’s airborne observational platform surveys, long-term records of satellite imagery, and deep learning techniques to characterize the dynamics of riparian vegetation cover over the past several decades. This information will be combined with process-based modeling to explore mechanisms underlying changes in riparian vegetation and quantify the relative importance of different hydroclimate factors. Finally, multiple data and modeling products will be synthesized to assess the role of riparian vegetation in contributing to and stabilizing macrosystem fluxes of water and carbon at regional watershed and global model scales. This work will generate new datasets of riparian vegetation dynamics across the continental U.S. during the past several decades, providing a useful baseline for predicting how these systems are likely to change in the future. Intensive data-model comparisons across NEON domains will enable the evaluation of multiple hypotheses related to the spatial-temporal dynamics in riparian ecosystem structure and function, while generating more predictive macroscale understandings that can be broadly applied to a range of riparian conditions. The research team will engage undergraduate and high school researchers from local communities and underrepresented STEM groups in project activities. The team will also reach public audiences through an annual lecture series at local museums to increase the awareness of environmental change and ecosystem sustainability.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.

森林河流和溪流生态系统提供水和碳，生物栖息地和娱乐活动的关键服务。这些生态系统在时间和空间上都是动态的，以应对气候、径流、水资源管理和生物入侵的变化。目前，我们对河岸生态系统如何应对并进而影响环境变化的了解仍然相当有限，尽管这种知识对于有效的保护和管理至关重要。本项目旨在研究河岸的变化（即，河岸）植被随时间的变化，并评估这种变化背后的各种驱动因素的作用。这项研究将为了解近几十年来河岸植被的变化以及预测未来的变化提供新的基础。此外，该项目将评估河岸植被对大规模水和碳通量的贡献。其目标是使用最先进的绘图和建模技术，以确定河岸带可能对生态系统功能和稳定性产生更大影响的地方。这一信息将有助于制定恢复的优先事项，并促进在气候变化和水流条件改变的情况下作出有关干预和管理的决定。该小组由生态水文建模和遥感专家组成，将向研究界和公众公开传播研究工具、数据集和结果。在此过程中，该团队将开展跨学科的本科生和研究生培训，培养多样化的下一代科学家，以应对环境和数据科学挑战。该项目的目标是将河岸植被动态与水文气候变化联系起来，以评估河岸生态系统对碳和水宏观系统通量的功能重要性。具体来说，该项目将利用来自氖空中观测平台调查的高分辨率数据、卫星图像的长期记录和深度学习技术来描述过去几十年河岸植被覆盖的动态特征。这些信息将与基于过程的建模相结合，以探索河岸植被变化的机制，并量化不同水文气候因素的相对重要性。最后，多个数据和建模产品将被合成，以评估河岸植被在促进和稳定区域流域和全球模型尺度的水和碳的宏观系统通量的作用。这项工作将在过去几十年中产生美国大陆河岸植被动态的新数据集，为预测这些系统未来可能如何变化提供有用的基线。密集的数据模型比较跨氖域将使多个假设相关的河岸生态系统的结构和功能的时空动态的评价，同时产生更多的预测宏观尺度的理解，可以广泛应用于一系列的河岸条件。研究团队将邀请来自当地社区和代表性不足的STEM团体的本科生和高中研究人员参与项目活动。该团队还将通过在当地博物馆举办的年度系列讲座接触公众，以提高对环境变化和生态系统可持续性的认识。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。

项目成果

Fusion of optical, radar and waveform LiDAR observations for land cover classification

• DOI: 10.1016/j.isprsjprs.2022.03.010
• 发表时间: 2022-03-17
• 期刊: ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
• 影响因子: 12.7
• 作者: Jin, Huiran;Mountrakis, Giorgos
• 通讯作者: Mountrakis, Giorgos

Linking remotely sensed ecosystem resilience with forest mortality across the continental United States

• DOI: 10.1111/gcb.16529
• 发表时间: 2022-12
• 期刊: Global Change Biology
• 影响因子: 11.6
• 作者: X. Tai;A. Trugman;W. Anderegg