Elements: A workflow for efficient and reproducible permafrost geomorphology analysis

该项目的重点是开发软件来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。在这个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连接起来，以促进对永久冻土景观的理解。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。这项工作训练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还促进了永久冻土研究的跨学科研究。该项目促进了我们对气候变化及其对景观的影响的理解，同时通过开发教材和研究工具提供了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，限制了了解永久冻土景观动态的进展。为了克服这个问题，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计算地貌学软件系统（PyCoGSS）能够对景观形态、地形变化和生态水文指标进行可复制和可扩展的分析。该软件便于地形和多光谱数据的采集、分析和可视化，允许进行空间和时间趋势分析。它可以将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进行快速实验。软件开发优先考虑用户友好性和可访问性，迎合不同职业阶段的研究人员。该项目聘请本科生研究人员测试软件，以确保编码新手可以访问内容，并对本科生进行可扩展和可重复的景观分析培训。PyCoGSS的发展有潜力培育一个更具包容性的表面过程社区，并促进开源工具和数据集。该奖项由美国国家科学基金会高级网络基础设施办公室颁发，由极地项目办公室（OPP）、研究、创新、协同和教育部门（RISE）以及美国国家科学基金会地球科学理事会的地貌和土地利用动态（GLD）项目共同支持。该奖项反映了美国国家科学基金会的法定使命，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。

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.