Scaling Up Forest Ecosystem Carbon Budget from Stand to Landscape: Impacts of Forest Structures

该项目的总体目标是通过综合利用遥感、生态模型和地面观测，提高我们对多维森林结构对从林分到景观的森林生态系统碳循环的影响的认识。虽然资金只要求为查佩尔山，这是一个合作研究之间的地理系在查查佩尔山和环境学院在杜克大学。两个校区的专业知识和研究设施是互补的。拟议的研究将扩大杜克森林现有项目的范围和深度。该项目将采取必要步骤，将对杜克森林的森林生态系统与大气之间的物质和能量交换的理解转变为对区域的理解。该项目将利用杜克森林的AmeriFlux和FACE站点作为通过综合利用遥感和生态系统模型扩大规模的锚点。目前，全球FLUXNET在全球范围内拥有200多个通量塔，本项目中研究的放大策略将深入了解如何从FLUXNET测量中放大，以提高我们对全球碳循环的理解。该项目提议利用多传感器遥感数据绘制多维森林结构图，包括树木大小和密度、林龄、叶面积指数和亚像素树木覆盖率。遥感数据包括高分辨率（= 1米）的数字正射影像四边形和来自Ikonos/QuickBird的空间图像，中等分辨率（30米）的陆地卫星图像，以及粗分辨率（250米）的MODIS/MISR图像。该项目将开发利用遥感数据的空间、光谱/时间和方向域信息的算法。该项目可大大加强利用遥感提取详细的空间植被信息。该项目将使用一系列成熟的生态模型，每个模型将采用适当尺度的森林结构来模拟陆地生态系统碳循环。除了量化在模拟碳循环时忽略森林结构所造成的误差外，该项目还将对这些模型进行重大改进。

The overall goal of this project is to improve our understanding of the impacts of the multi-dimensional forest structures on scaling up forest ecosystem carbon cycle from stand to landscape through the integrative use of remote sensing, ecological models and ground observations. Though funding is requested for UNC Chapel Hill only, this is a collaborative research between the Department of Geography at UNC Chapel Hill and the School of Environment at Duke University. The expertise and research facilities from the two campuses are complementary. The proposed research will expands the scope and depth of the existing projects in Duke Forest. The project will take the necessary steps to transform the understanding of mass and energy exchange between forest ecosystems and the atmosphere at Duke Forest to a regional understanding. The project will use the AmeriFlux and FACE sites in the Duke Forest as the anchor points for scaling up through the integrative use of remote sensing and ecosystem models. Currently the global FLUXNET has over 200-flux towers world wide, and the up-scaling strategy investigated in this project will provide in-depth understanding of how to scale up from FLUXNET measurements to improve our understanding in global carbon cycle. The project proposes to use multisensor remotely sensed data to map multi-dimensional forest structures, including tree size and density, stand ages, leaf area index, and subpixel tree cover. The remotely sensed data include high-resolution (=1m) digital orthophoto quads and space-born images from Ikonos/QuickBird, medium resolution (30 m) Landsat images, and coarse-resolution (250m) MODIS/MISR images. The project will develop algorithms to use information from spatial, spectral/temporal and directional domains of remotely sensed data. The project can substantially enhance the use of remote sensing to extract detailed spatial vegetation information. A series of well-established ecological models will be used in the project, each of which will take forest structure at the appropriate scale to simulate terrestrial ecosystem carbon cycle. In addition to quantifying the errors caused by omitting forest structures in simulating carbon cycle, the project will lead to major improvements to the these models.