Elements: A workflow for efficient and reproducible permafrost geomorphology analysis

该项目的重点是开发软件，研究永久冻土景观的动态，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连接起来，以促进对永久冻土景观的了解。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明智的减缓努力至关重要。这项工作培训下一代地球科学家使用先进的数据和计算工具，确保他们有能力应对复杂的环境挑战，并培养一个更具包容性和多样性的科学界。通过对开源工具和互操作性的承诺，PyCoGSS还促进了冻土研究的跨学科研究。该项目推进了我们对气候变化及其对景观影响的理解，同时通过开发教学材料和研究工具提供地理空间科学方面的实践学习经验，卫星技术和软件的最新进展改善了对永久冻土景观的研究，但缺乏适当的网络基础设施和培训阻碍了广泛采用，限制了对永久冻土景观动态的理解。为了克服这一点，该项目将过程地貌学与先进的计算工具相结合，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复和可扩展的分析。该软件有助于地形和多光谱数据的获取、分析和可视化，从而可以进行空间和时间趋势分析。它可以快速实验形态测量数据和多光谱产品的不同组合作为机器学习算法的输入。软件开发优先考虑用户友好性和可访问性，以满足不同职业阶段的研究人员。该项目雇用本科研究人员测试软件，以确保内容可供编码新手访问，并导致本科生在可扩展和可复制的景观分析方面的培训。PyCoGSS的发展有可能促进更具包容性的表面过程社区，并促进开源工具和数据集。NSF高级网络基础设施办公室的这一奖项由极地计划办公室（Office of Polar Programs），研究，创新，协同和教育部门（RISE），地貌与土地利用动态（GLD）该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

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.