Combining Satellite-Derived Estimates of Canopy Properties with Monthly Mean Weather Data to Model Regional Variation in Forest Productivity

摘要

Potential forest productivity was predicted at 1 km~2 resolution with a simplified physiologically-based model (3-PGS) across an 54,000 km~2 area in southwestern Oregon (Lat 43. N, Long. 123. W). Spatial estimates of temperature extremes were converted to provide monthly estimates of incoming shortwave radiation, daytime vapor pressure deficits, and the frequency of subfreezing events, all variables critical to the estimation of photosynthesis and transpiration. Monthly coverage of satellite imagery provided seasonal estimates of the vegetation's capacity to intercept solar radiation and soil survey maps provided estimates of spatial variation in soil fertility and soil water holding capacity. Above-ground growth estimates, summed for the year, were compared with field estimates of site productivity. We found considerable variation existed within each 1km~2 pixel which was only partly attributed to variation in canopy properties. Local variation in climate and soils played an equal if not greater role. When the sample plots were stratified into 14 broad forest types, within which growth potential varied similarly (coefficient of variation for each of the 14 types averaged 6%), a good relation between predicted and measured forest growth capacity resulted (r~2= 0.82). This paper describes methods used to develop the spatial databases and presents the comparison between modelled and measured forest growth.