基于空间协同仿真模拟的开化县森林碳估计  

A spatial co-simulation based forest carbon estimation for Kaihua County

作　　者：袁振花 张茂震[1,2] 郭含茹[1,2] 秦立厚 

机构地区：[1]浙江农林大学浙江省森林生态系统碳循环与固碳减排重点实验室,浙江临安311300 [2]浙江农林大学环境与资源学院,浙江临安311300

出　　处：《浙江农林大学学报》2016年第3期384-393,共10页Journal of Zhejiang A&F University

基　　金：国家自然科学基金资助项目(30972360);浙江省林业碳汇与计量创新团队资助项目(2010R50030);浙江省林学重中之重一级学科研究生创新项目资助项目(201515)

摘　　要：森林碳储量是反映森林生态系统生产力的重要指标,也是区域森林碳汇计量的基础。以浙江省开化县为研究区,采用2013年资源3号遥感影像与2014年森林资源清查样地数据,结合序列高斯协同模拟方法对全县地上部分森林碳储量及其分布进行估计,并以平均误差、残差平方和、平均相对误差以及均方根误差4个指标为基础对估计结果进行精度评价。结果表明:开化县2014年森林总碳储量空间协同仿真估计结果为7.221 573 Tg,碳密度值分布为0~109.178 0 Mg·hm^(-2),均值为32.376 4 Mg·hm^(-2),基于15%检验样本的平均相对误差为4.565%,仿真估计碳总量在实测样地估算的置信区间内。本研究发现,遥感影像与地面样地森林碳密度的相关性随遥感影像的空间分辨率变化而变化,这对于提高森林碳储量估计精度有着重要意义,也是下一步研究的重点。To estimate the spatial distribution of aboveground forest carbon storage, an important index of forest ecosystem productivity and the foundation of regional forest carbon sink measurements, and carbon density in Kaihua County, Zhejiang Province, ZY-3 image data from 2013, National Forest Inventory data from 2014, and the Sequential Gaussian Co-simulation Method were used. Estimated results of above ground forest carbon were analyzed by four indicators： mean error （ME）, residual square sum （IlSS）, mean relative error （MILE）, and root mean square error （IlMSE）. A correlation analysis between sample plots of remote sensing and forest carbon density was also conducted. Results showed that the above ground carbon was 7.221 573 Tg, estimated values of spatial co-simulation ranged from 0 to 109.178 Mg.hm-2, mean carbon density was 32.376 4 Mg.hm-2, mean relative error based on the 15% test sample was 4.565%, and forest carbon estimated by simulation was in the confidence interval range measured by sample plots. Also, the correlation analysis varied with the resolution of remote sensing image changes. Thus, a key point of further research would be to improve the estimation accuracy of forest carbon reserves. [ Ch, 5 fig. 3 tab. 29 ref. ]

关 键 词：森林生态学 森林碳储量 序列高斯协同模拟 资源3号 森林资源清查 

分 类 号：S718.5[农业科学—林学]