基于QuickBird影像的城市森林碳储量遥感估测

城市森林是增加城市碳吸收的积极因素之一，为全球碳循环作出了重要贡献.本文基于Quickbird高分辨率遥感影像，以浙江省义乌市环城路以内区域为研究区，将市区的森林分为公园森林、防护森林、单位附属森林和其他森林4种类型.以实地样地调查碳储量为因变量，利用逐步线性回归的分析方法从遥感影像中的波段灰度值、植被指数、纹理信息等50个因子中选取自变量因子，最终建立不同森林类型的遥感碳储量估算模型.结果表明： 研究区4种森林类型的模型精度都在70%左右.公园森林、防护森林、单位附属森林和其他森林的碳储量分别为3623.80、5245.78、5284.84、5343.65 t.该区域碳密度主要集中在25～35 t·hm^-2.在今后的城市森林规划中，可通过提高绿化率以及乔木与低矮灌木的套种来进一步加强城市森林碳吸收的功能.

Remote sensing estimation of urban forest carbon stocks based on QuickBird images.

Urban forest is one of the positive factors that increase urban carbon sequestration, which makes great contribution to the global carbon cycle. Based on the high spatial resolution imagery of QuickBird in the study area within the ring road in Yiwu, Zhejiang, the forests in the area were divided into four types, i.e., park forest, shelter forest, company forest and others. With the carbon stock from sample plot as dependent variable, at the significance level of 0.01, the stepwise linear regression method was used to select independent variables from 50 factors such as band grayscale values, vegetation index, texture information and so on. Finally, the remote sensing based forest carbon stock estimation models for the four types of forest were established. The estimation accuracies for all the models were around 70%, with the total carbon reserve of each forest type in the area being estimated as 3623.80, 5245.78, 5284.84, 5343.65 t, respectively. From the carbon density map, it was found that the carbon reserves were mainly in the range of 25-35 t·hm^-2. In the future, urban forest planners could further improve the ability of forest carbon sequestration through afforestation and interplanting of trees and low shrubs.