Scaling ecosystem function: Novel approaches from MaxEnt and Multiresolution

扩展生态系统功能：MaxEnt 和 Multiresolution 的新方法

• 批准号: 1021095
• 负责人: Paul Stoy
• 金额: $ 13.58万
• 依托单位: Montana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-01-01 至 2013-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1021095&HistoricalAwards=false
• 关键词: Scaling; ecosystem; function; Novel; approaches

Upscaling measurements of ecosystem function from leaf to region to globe, and downscaling remote sensing observations and global model output to spatial scales of meters to kilometers, remain grand challenges in Earth system science. The overarching goal of this project is to improve estimates of carbon sequestration and surface-atmosphere water flux at higher spatial resolutions than have been possible in the past, namely the spatial scales at which land surface processes vary on the order of a tree or vegetation patch. In this project, satellite remote sensing data, measured carbon fluxes, and earth system models will be scaled to higher resolutions using techniques from the field of information theory. The approach will apply acknowledged mathematical approaches that have been used successfully in disciplines that range from astronomy to population ecology in a novel way to address questions of global importance: how are vegetation, carbon and water cycles linked across landscapes? High-resolution data sets from arctic and Great Plains ecosystems will be used to validate the scaling techniques and link high-resolution vegetation function to coarser-resolution satellite imagery.The results of this project can be used by scientists from a wide range of disciplines. The resultant algorithms, code and analysis programs will be made publicly available via university websites and open-source code repositories for broad dissemination. Lesson plans will be built into existing upper-level undergraduate and graduate courses. These teaching tools will likewise be made available over the web for students and practitioners. An overarching goal of this research is to improve estimates of carbon and water cycling from space.

将生态系统功能从叶到区域到地球的尺寸测量，以及将遥感的观测和全球模型输出降低到米至公里的空间尺度，仍然是地球系统科学的巨大挑战。该项目的总体目标是在更高的空间分辨率下提高碳固换和表面 - 大气水通量的估计，而不是过去的可能性，即，土地表面过程在树或植被斑块的顺序上变化的空间尺度。 在这个项目中，使用信息理论领域的技术将卫星遥感数据，测量的碳通量和地球系统模型缩放到更高的分辨率。该方法将采用公认的数学方法，这些方法已成功地用于从天文学到人口生态学的学科中，以一种新颖的方式来解决全球重要性问题：植被，碳和水周期如何联系在一起？ 来自北极和大平原生态系统的高分辨率数据集将用于验证缩放技术，并将高分辨率植被功能与更粗的分辨率卫星图像联系起来。该项目的结果可用于广泛学科的科学家。由此产生的算法，代码和分析程序将通过大学网站和开源代码存储库公开提供，以进行广泛传播。课程计划将内置在现有的高级本科生和研究生课程中。这些教学工具同样将通过网络为学生和从业人员提供。这项研究的总体目的是改善对空间的碳和水循环的估计。