CAREER: Peatland Geomorphology: Quantifying Geomorphological Changes across SE Asia Peatlands

该项目将确定人类活动(毁林导致的泥炭退化)对自然灾害(洪水)和气候变化(二氧化碳排放)的影响。泥炭地是一种湿地，在那里，淹水的条件会减缓植物的分解速度，以至于枯死的植物堆积成泥炭。在自然条件下，泥炭穹顶会随着时间的推移而生长，将碳储存在地下，并将沿海洪水的风险降至最低。相比之下，受损的泥炭地正在变得紧凑，这导致地面高度降低，温室气体排放增加。该项目提议将多个卫星和地面数据集与人工智能工具相结合，以解决一个具有高度社会重要性的问题：“几千年积累的泥炭碳将以多快的速度释放到大气中？东南亚沿海大片地区将在多长时间内被永久淹没？”回顾路易斯安那州的情况表明，如果没有之前几十年的泥炭退化，卡特里娜飓风的影响就不会那么严重。该项目将主动量化东南亚洪灾将在何时何地变得最严重，并提高我们对全球泥炭地动态的了解。从这项工作中获得的知识将能够预测未来的泥炭腐解率和洪水危险，并改进对二氧化碳通量的估计。这些成果将为旨在通过教育增强当地社区能力的外联工作提供信息，促进科学家参与公共交流，并通过及早接触亲身实践研究来增加本科生的多样性。该项目的目标是建立预测热带泥炭地命运及其碳的能力，以应对环境变化，并开发有效的方法，让人们参与到泥炭地的重要性中来。该项目的中心假设是泥炭退化速度受景观形态和土地利用历史的控制。为了测试它，该项目将依靠三项主要任务：1)通过结合形态(光探测和测距数据)、形变(经实地工作验证的干涉合成孔径雷达数据)和土地利用时间序列(多光谱光学数据)，确定对整个东南亚泥炭退化速度的控制；2)开发一个机器学习模型，说明对退化速度的控制，以预测未来退化、二氧化碳排放以及相关洪灾的时间和幅度；3)通过分享对社会响应的外联材料，提高对泥炭重要性的认识。开发的技术将为世界范围内的泥炭地貌研究提供一个重要的框架。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This project will determine how a process resulting from human activities (peat degradation due to deforestation) influences natural hazards (flooding) and climate change (carbon dioxide emissions). Peatlands are wetlands where waterlogged conditions slow plant decomposition to such an extent that dead plants accumulate to form peat. Under natural conditions peat domes grow over time, storing carbon underground and minimizing the risk of coastal flooding. In contrast, damaged peatlands are compacting, which results in a lowering of the ground elevation and in greenhouse gas emissions. This project proposes to integrate multiple satellite- and ground-based datasets with artificial intelligence tools to address a question of high societal importance: “At what rates will thousands of years of accumulated peat carbon be released to the atmosphere and on what timescale will large coastal areas in southeast Asia become permanently flooded?” Retrospectively, in Louisiana, it was shown that the impact of hurricane Katrina would have been less severe without the preceding decades of peat degradation. This project will proactively quantify when and where flooding will become the most severe in southeast Asia and improve our understanding of peatland dynamics worldwide. Knowledge from this work will enable forecasting future peat decomposition rates and flooding hazards, and refine estimations of carbon dioxide fluxes. These results will inform outreach efforts aimed to empower local communities through education, boost engagement of scientists in public communication, and increase undergraduate diversity through early exposure to hands on research. The goals of this project are to establish the capability to predict the fate of tropical peatlands and their carbon in response to environmental changes and to develop effective ways to engage the population on the importance of peatlands. The central hypothesis of this project is that peat degradation rates are controlled by the landscape morphology and land use history. To test it, this project will rely on three main tasks: 1) identify the controls on peat degradation rates across southeast Asia by combining morphology (Light Detection and Ranging data), deformation (Interferometric Synthetic Aperture Radar data validated by fieldwork), and land use timeseries (multi-spectral optical data); 2) develop a machine learning model accounting for the controls on degradation rates to predict the timing and amplitude of future degradation, carbon dioxide emissions, and the associated flooding; and 3) increase awareness on the importance of peatlands by sharing socially responsive outreach material. The developed techniques will provide a critical framework for worldwide peatland geomorphology studies.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.