归一化差异植被指数
环境科学
植被(病理学)
足迹
草原
遥感
增强植被指数
涡度相关法
湿地松
水文学(农业)
叶面积指数
自然地理学
地理
生态系统
地质学
生态学
植被指数
考古
岩土工程
病理
生物
医学
植物
松属
作者
J. Kim,Qinghua Guo,Dennis Baldocchi,Monique Y. Leclerc,Liang Xu,Hans Peter Schmid
标识
DOI:10.1016/j.agrformet.2004.11.015
摘要
In this paper, we describe the process of assessing tower footprint climatology, spatial variability of site vegetation density based on satellite image analysis, and sensor location bias in scaling up to 1 km × 1 km patch. Three flat sites with different vegetation cover and surface heterogeneity were selected from AmeriFlux tower sites: the oak/grass site and the annual grassland site in a savannah ecosystem in northern California and a slash pine forest site in Florida, USA. The site vegetation density was expressed in terms of normalized difference vegetation index (NDVI) and crown closure (CC) by analyzing the high-resolution IKONOS satellite image. At each site, the spatial structure of vegetation density was characterized using semivariogram and window size analyses. Footprint maps were produced by a simple model based on the analytical solution of the Eulerian advection–diffusion equation. The resulting horizontal arrays of footprint functions were then superimposed with those of NDVI and CC. Annual sensor location biases for the oak/grass and the pine forest sites were <4% for both NDVI and CC, requiring no flux corrections in scaling from tower to landscape of 1 km2. Although the annual grassland site displayed much larger location biases (28% for NDVI, 94% for CC), their temporal changes associated with averaging time showed a real potential to develop algorithms aimed at upscaling tower fluxes to the landscape in an effort to provide validation data for MODIS products.
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