期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B7
页码：31-34
出版社：Copernicus Publications
摘要：Vegetation plays an important role in the exchange of carbon dioxide, water, and energy between the land surface and the atmosphere. LAI, defined as one-half the total green leaf area per unit of ground surface area, drives the within and the below canopy microclimate, determines canopy water interception, radiation extinction, and water and carbon gas exchange. Therefore, accurate LAI is a key parameter in all models describing the exchange of fluxes of energy, mass (e.g., water and CO 2 ), and momentum between the surface and the planetary boundary layer. Unfortunately, LAI is very difficult to quantify accurately due to its spatial heterogeneity and temporal dynamics. The long-term objectives of this study are 1) to improve LAI estimation accuracy with considerations of scale, heterogeneity (spatial, vertical, and temporal), and land cover type, 2) to develop a better approach to LAI parameterization for models in hydrology, climatology, and ecosystem, and 3) to investigate the effects of land cover and land use changes on LAI dynamics, which can cause massive hydrological change. In this paper, we aim to 1) characterize the spatial scale of LAI and normalized difference of vegetation index (NDVI) in a Canadian prairie using a wavelet approach based on field measured LAI and reflectance data, and 2) to simulate the temporal dynamics of LAI variation intra-annually with both ground measured LAI and satellite derived NDVI values. The study area is in St. Denis Wildlife Reserve Area, 40km east of Saskatoon, Saskatchewan, Canada. Results indicated that the spatial variation of LAI and NDVI is maximized at 22.5 meters and with several small scale variations (4.5m, 12m, and 18m). The temporal LAI dynamics indicated that the native prairie greens up in May and senescent in September, and the maximum growing season is in July for the Canadian prairie
关键词：Leaf Area Index; Remote Sensing; Variability; Wavelet
