Elements: A workflow for efficient and reproducible permafrost geomorphology analysis

Elements：高效且可重复的永久冻土地貌分析的工作流程

• 批准号: 2311319
• 负责人: Joanmarie Del Vecchio
• 金额: $ 39.38万
• 依托单位: Dartmouth College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311319&HistoricalAwards=false
• 关键词: Elements; workflow; efficient; reproducible; permafrost

The project is focused on developing software to study the dynamics of permafrost landscapes, with the aim of quantifying and predicting landscape changes and carbon fluxes. The software developed in this project connects the ever-growing volume of environmental data being collected from high latitudes datasets with cutting-edge software to advance understanding of permafrost landscapes. These analyses will help understand permafrost landscape dynamics and their influence on carbon release, which is crucial for accurate climate projections and informed mitigation efforts. This work trains the next generation of geoscientists in the use of advanced data and computational tools, ensuring they are well-equipped to tackle complex environmental challenges and fostering a more inclusive and diverse scientific community. Through its commitment to open-source tools and interoperability, PyCoGSS also promotes interdisciplinary investigations in permafrost research. This project advances our understanding of climate change and its effects on landscapes while providing hands-on learning experiences in geospatial science through the development of teaching materials alongside research tools.Recent advancements in satellite technology and software have improved the study of permafrost landscapes, but the lack of appropriate cyberinfrastructure and training hinders widespread adoption, limiting progress in understanding permafrost landscape dynamics. To overcome this, the project combines process geomorphology with advanced computational tools for acquiring, analyzing, and visualizing large interdisciplinary datasets. The Python Computational Geomorphology Software System (PyCoGSS) enables reproducible and scalable analyses of landscape morphology, topographic change, and ecohydrological indicators. The software facilitates the acquisition, analysis, and visualization of topographic and multispectral data, allowing for spatial and temporal trend analyses. It enables quick experimentation with different combinations of morphometric data and multispectral products as inputs to machine learning algorithms. The software development prioritizes user-friendliness and accessibility, catering to researchers at different career stages. The project employs undergraduate researchers to test software to ensure the content is accessible to coding novices and lead to the training of undergraduates in scalable and reproducible landscape analysis. The development of PyCoGSS has the potential to foster a more inclusive surface processes community and promote open-source tools and datasets.This award by the NSF Office of Advanced Cyberinfrastructure is jointly supported by the Office of Polar Programs (OPP), the Division of Research, Innovation, Synergies, and Education (RISE), and the Geomorphology and Land-use Dynamics (GLD) Program within the NSF Directorate for Geosciences.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.

该项目的重点是开发软件来研究多年冻土景观的动态，目的是量化和预测景观变化和碳通量。该项目中开发的软件将从高纬度数据集收集的不断增长的环境数据与尖端软件相连，以提高人们对多年冻土景观的了解。这些分析将有助于了解永久冻土景观动态及其对碳释放的影响，这对于准确的气候预测和知情的缓解工作至关重要。这项工作培训了下一代地球科学家在使用高级数据和计算工具方面，确保他们有能力应对复杂的环境挑战并培养更具包容性和多样化的科学界。通过致力于开源工具和互操作性，Pycogs还促进了多年冻土研究中的跨学科研究。该项目通过发展教学材料以及研究工具以及卫星技术和软件的进步改善了永久冻土景观的研究，在提供地理空间科学的动手学习经验的同时，提高了我们对气候变化及其对景观的影响的理解，同时提供了动手的学习经验，但是缺乏适当的小链结构和培训的培训，并培训了较高的限制性的限制性，以限制了限制性的进步，这是不足的。为了克服这一点，该项目将流程的地貌与高级计算工具相结合，用于获取，分析和可视化大型跨学科数据集。 Python计算地貌软件系统（Pycogss）可以对景观形态，地形变化和生态水文指标进行可重现和可扩展的分析。该软件促进了地形和多光谱数据的获取，分析和可视化，从而进行了空间和时间趋势分析。它可以通过形态计量数据和多光谱产品组合作为机器学习算法的输入进行快速实验。软件开发优先考虑用户友好性和可访问性，可在不同职业阶段迎合研究人员。该项目雇用本科研究人员测试软件，以确保编码新手可以访问内容，并在可扩展且可重复的景观分析中对本科生进行培训。 The development of PyCoGSS has the potential to foster a more inclusive surface processes community and promote open-source tools and datasets.This award by the NSF Office of Advanced Cyber​​infrastructure is jointly supported by the Office of Polar Programs (OPP), the Division of Research, Innovation, Synergies, and Education (RISE), and the Geomorphology and Land-use Dynamics (GLD) Program within the NSF Directorate for地球科学。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准的评估来支持的。