Abstract: Estimation of urban forest leaf biomass at regional scale plays a significant role in understanding plant growth, carbon assimilation processes and forest ecosystems. In this study, an urban forest leaf biomass estimation method which combined regression analysis and spatial analysis in Shanghai, China was explored. Based on the measured data of leaf biomass from June 2011 to June 2012 and a variety of remote sensing data, an analysis of the distribution characteristics of urban forest leaf biomass was also carried out. The results showed that (1) The higher leaf biomass densities concentrated mainly in the urban areas like Jing’an District and the Huangpu District, while suburban localities like Songjiang District and Jinshan District presented lower biomass densities, which were around 4 to 10 and 1 to 6 t·hm^-2, respectively. (2) The density and the amount of urban forest leaf biomass in Shanghai were 2.55 t·hm^-2 and 300.81×10³ t, respectively. The overall leaf biomass was also found to be distributed mainly in the suburban areas with a fraction of 94.16%, whereas the urban areas shared a little fraction of 5.84%. Among the administrative districts of Shanghai, Chongming County and Pudong New District owned the highest and second highest leaf biomass, altogether reaching 34.82% of the total, however, Jing’an District occupied only 0.11%, which was in accordance with its area proportion. (3) The rootmeansquare error (RMSE), mean absolute error (MAE) and mean relative error (MRE) of the regressionIDW model for urban forest leaf biomass in this study were respectively 0.81%, 0.62% and 29.33%, which were decreased by 58.46%, 48.76% and 48.71% respectively than those of the original simple regression model and by 47.74%, 38% and 49.24% respectively than those of the spatial analysis method. The combination of spatial analysis and regression analysis provided a quick, convenient and efficient method for estimating the urban forest leaf biomass and monitoring upscaled forest inventory data at a regional scale.

Key words: straw-returning to field, soil total organic carbon, carbon pool management index, carbon stock.