生态学报2017，Vol.37Issue(10)：3459-3470,12.DOI:10.5846/stxb201603010352
基于人工神经网络与空间仿真模拟的区域森林碳估算比较——以龙泉市为例
Comparison of regional forest carbon estimation methods based on back-propagation neural network and spatial simulation: A case study in Longquan County
摘要
Abstract
Quantifying the carbon stocks of forest is critical for understanding the dynamics of carbon fluxes in terrestrial ecosystems and the atmosphere as well as monitoring ecosystem responses to environmental changes.However,due to the lack of methods and data,results of forest carbon estimation from different studies shown large difference,which presents a great uncertainty in the evaluation of forest carbon sink.Different methods can be used to estimate the carbon storage in the same study area,which can be compared with the advantages and disadvantages of each method and provides guidance for forest carbon estimation.On the basis of National Forest Inventory (NFI) data and the Land-sat TM image data collected in Longquan County,Zhejiang Province in 2009,we applied two methods,namely error back-propagation neural network (BPNN) and sequential Gaussian co-simulation (SGCS) to reproduce the distribution of above-ground forest carbon.We randomly divided plots into two sets,a 70-plot set for modeling and a 29-plot set for testing.For the model test,the correlation coefficient of predictive value and the plot data was 0.67 and 0.68 for BPNN and SGCS,respectively.Both of the two methods have the same RRMSE value (0.63).The predictive ability of SGCS was slightly higher than that of BPNN.The estimation results using BPNN showed that the sum of above-ground carbon is 11042990 Mg and the mean carbon density was 36.10 Mg/hm2 which was higher than the average from the sample plots with a relative error of 8.82％.The SGCS showed that the sum of above-ground carbon was 11388657 Mg with a mean carbon density 37.23 Mg/hm2 which was higher than the average from the sample plots with a relative error of 9.4％.Comparative analysis showed the carbon densities estimated using these two methods are both close to that calculated from the NFI data.However,there were some differences between the two methods with respect to the estimation of the frequency distribution and the carbon distribution in the study area.Predictive value of sample plot obtained using the SGCS method was closer to the plot data value than that obtained using the BPNN.And the correlation between predictive value and the plot data was 0.75,which proved that there were obvious advantages in estimating the spatial distribution of forest carbon.In addition,in terms of carbon density range and frequency distribution,SGCS was more reliable.This study further verifies the effectiveness of the SGSC which could provide effective methods for the estimation of regional forest carbon storage.关键词
森林碳储量/高斯协同仿真模拟/BP神经网络/森林资源清查数据/TM影像Key words
forest carbon storage/sequential Gaussian co-simulation/back-propagation neural network/National Forest Inventory/TM image引用本文复制引用
秦立厚,张茂震,袁振花,杨海宾..基于人工神经网络与空间仿真模拟的区域森林碳估算比较——以龙泉市为例[J].生态学报,2017,37(10):3459-3470,12.基金项目
国家自然科学基金项目(30972360,41201563) （30972360,41201563）
浙江农林大学农林碳汇与生态环境修复研究中心预研基金 （）
浙江省林业碳汇与计量创新团队项目(2012R10030-01) （2012R10030-01）
浙江省林学一级重中之重学科学生创新计划项目资助(201515) （201515）
