中文摘要：

城市森林发挥着改善和维护城市生态环境质量的作用,研究城市森林生物量和分布特点对其生态系统服务评价和林分经营均具有重要意义。该文根据上海城市森林的种植分布和经营状况利用2011年6月-（–2）012年6月样地实测森林生物量数据和同期Landsat ETM＋遥感图像,在基于逐步回归分析建立森林生物量反演模型的基础上,引入回归残差及空间分析,研究了城市森林及其主要优势树种樟（Cinnamomum camphora）林分的生物量分布特征,探讨了区域尺度森林生物量的遥感估测方法。结果表明：（1）上海城市森林生物量密度总体呈现中心城区（静安区、黄浦区等）较高,生物量密度集中在35–70 t·hm^-2之间,郊区（嘉定区、青浦区等）空间分布状况相对较低,生物量密度介于15–50 t·hm^-2之间的变化特征。上海优势树种樟林分生物量密度范围为20–110 t·hm^-2;空间上呈现出东北部较高、西南部较低的变化特征。（2）上海城市森林及樟林分的生物量总量分别为3.57 Tg和1.33 Tg。林地面积小,具有较高森林生物量密度的上海中心城区,其森林生物量占总量的6.1%,其中林地面积最小的静安区生物量最低,仅占总量的0.11%。在所有区县中,林地面积最大的崇明县、浦东新区具有较高的森林生物量,分别占总量的20.08%和19.18%。（3）所建立的基于回归反距离插值的城市森林生物量估测模型,其标准误差、平均绝对误差、平均相对误差分别为8.39、6.86、24.22%,较回归模型分别降低了57.69%、55.43%、64.00%,较空间插值的方法分别降低了62.21%、58.50%、65.40%。残差的引入减少了由于空间变异引发的城市森林生物量遥感估测的不确定性。相比基于实测数据通过空间插值的估测,遥感为快速便捷、客观高效的森林生物量监测提供了可能,更加完善的结果和模型的优化有待引入其他信息源如高分高光?

英文摘要：

Aims Monitoring and quantifying the biomass and its distribution in urban trees and forests are crucial to understanding the role of vegetation in an urban environment. In this paper, an estimation method for biomass of urban forests was developed for the Shanghai metropolis, China, based on spatial analysis and a wide variety of data from field inventory and remote sensing.Methods An optimal regression model between forest biomass and auxiliary variables was established by stepwise regression analysis. The residual value of regression model was computed for each of the sites sampled and interpolated by Inverse-distance weighting（IDW） to predict residual errors of other sites not subjected to sampling. Forest biomass in the study area was estimated by combining the regression model based on remote sensing image data and residual errors of spatial distribution map. According to the distribution of plantations andmanagement practices, a total of 93 sample plots were established between June 2011 and June 2012 in the Shanghai metropolis. To determine a suitable model, several spectral vegetation indices relating to forest biomass and structure such as normalized difference vegetation index（NDVI）, ratio vegetation index（RVI）, difference vegetation index（DVI）, soil-adjusted vegetation index（SAVI）, and modified soil-adjusted vegetation index（MSAVI）, and new images synthesized through band combinations such as the sum of TM2, TM3 and TM4（denoted Band 234）, and the sum of TM3, TM4 and TM5（denoted Band 345） were used as alternative auxiliary parameters. Important findings The biomass density in urban forests of the Shanghai metropolis varied from 15 to 120 t·hm^-2. The higher densities of forest biomass concentrated mostly in the urban areas, e.g. in districts of Jing＇an and Huangpu, mostly ranging from 35 to 70 t·hm^-2. Suburban localities such as the districts of Jiading and Qingpu had lower biomass densities at around 15 to 50 t·hm^-2. The biomass density of Cinnamomum camphora tree