基于地理加权回归模型的亚热带地区乔木林生物量估算
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摘要
基于浙江省碳汇样地调查数据,以乔木林生物量(含地上和地下生物量)为因变量,将筛选的与因变量相关性较高的因子作为解释变量,采用地理加权回归和协同克里格方法对乔木林生物量进行估算,对比分析两种估测方法的精度。结果表明:基于地理加权回归方法构建的乔木林生物量估算模型(R2adj=0.820 4,RMSE=23.021 5 t/hm2)精度优于协同克里格方法(R2adj=0.726 3,RMSE=28.054 9 t/hm2),同时使用地理加权回归方法的乔木林生物量预测值的变异系数(Cv=0.618 9)高于协同克里格法(Cv=0.585 4),由此可知地理加权回归方法因考虑了待估变量的局部变异,比协同克里格方法具有更好的拟合结果,预测精度较高。
Accurate estimation of arbor forest biomass is of great significance for the study of forest ecological function and carbon storage. Because of the spatial heterogeneity of the survey factors,the geographically weighted regression method can estimate the local regression of variables and show a good application advantage. Based on the survey data of carbon sinks in Zhejiang Province,taking the biomass of arbor forest(including aboveground and belowground biomass) as dependent variable and factors with high correlation with dependent variable as the explanatory variables,the biomass of arbor forest was estimated by using the geographically weighted regression and co-Kriging methods and compared the accuracy of the two estimation methods. The results showed that the accuracy of arbor forest biomass estimation model(R2 adjwas 0. 820 4,RMSE was 23. 021 5 t/hm2) constructed by geographically weighted regression method was better than that of co-Kriging method(R2 adjwas 0. 726 3,RMSE was 28. 054 9 t/hm2).The coefficient of variation(Cvwas 0. 618 9) of the prediction value of biomass of arbor forest using geographically weighted regression method was higher than that of the co-Kriging method(Cvwas 0. 585 4).Because of considering the local variation of the estimated variables, the geographically weighted regression method had better fitting results than co-Kriging method,and the prediction accuracy was high. This study can provide a reference for estimating the forest biomass and other forest parameters in a wide range of tree stands by using the geographically weighting regression method.
Accurate estimation of arbor forest biomass is of great significance for the study of forest ecological function and carbon storage. Because of the spatial heterogeneity of the survey factors,the geographically weighted regression method can estimate the local regression of variables and show a good application advantage. Based on the survey data of carbon sinks in Zhejiang Province,taking the biomass of arbor forest(including aboveground and belowground biomass) as dependent variable and factors with high correlation with dependent variable as the explanatory variables,the biomass of arbor forest was estimated by using the geographically weighted regression and co-Kriging methods and compared the accuracy of the two estimation methods. The results showed that the accuracy of arbor forest biomass estimation model(R2 adjwas 0. 820 4,RMSE was 23. 021 5 t/hm2) constructed by geographically weighted regression method was better than that of co-Kriging method(R2 adjwas 0. 726 3,RMSE was 28. 054 9 t/hm2).The coefficient of variation(Cvwas 0. 618 9) of the prediction value of biomass of arbor forest using geographically weighted regression method was higher than that of the co-Kriging method(Cvwas 0. 585 4).Because of considering the local variation of the estimated variables, the geographically weighted regression method had better fitting results than co-Kriging method,and the prediction accuracy was high. This study can provide a reference for estimating the forest biomass and other forest parameters in a wide range of tree stands by using the geographically weighting regression method.
引文
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9戚玉娇,李凤日.基于KNN方法的大兴安岭地区森林地上碳储量遥感估算[J].林业科学,2015,51(5):46-55.QI Yujiao,LI Fengri.Remote sensing estimation of aboveground forest carbon storage in Daxing'an mountains based on KNNmethod[J].Scientia Silvae Sinicae,2015,51(5):46-55.(in Chinese)
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11 FOTHERINGHAM A S,BRUNSDON C,CHARLTON M.Geographically weighted regression:the analysis of spatially varying relationships[M].Chester UK:International Union of Crystallography,2002.
12 WANG Quan,NI Jian,TENHUNEN John.Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems[J].Global Ecology and Biogeography,2005,14:379-393.
13郭含茹.基于地理加权回归的区域森林碳储量估计[D].杭州:浙江农林大学,2015.GUO Hanru.Geographically weighted regression based estimation of regional forest carbon storage[D].Hangzhou:Zhejiang A&FUniversity,2015.(in Chinese)
14郭龙,张海涛,陈家赢,等.基于协同克里格插值和地理加权回归模型的土壤属性空间预测比较[J].土壤学报,2012,49(5):1037-1042.GUO Long,ZHANG Haitao,CHEN Jiaying,et al.Comparison between co-Kriging model and geographically weighted regression model in spatial prediction of soil attributes[J].Acta Pedologica Sinia,2012,49(5):1037-1042.(in Chinese)
15袁玉芸,瓦哈甫·哈力克,关靖云,等.基于GWR模型的于田绿洲土壤表层盐分空间分异及其影响因子[J].应用生态学报,2016,27(10):3273-3282.YUAN Yuyun,HALIKE Waha,GUAN Jingyun,et al.Spatial differentiation and impact factors of Yutian Oasis's soil surface salt based on GWR model[J].Chinese Journal of Applied Ecology,2016,27(10):3273-3282.(in Chinese)
16戚玉娇.大兴安岭森林地上碳储量遥感估算与分析[D].哈尔滨:东北林业大学,2014.QI Yujiao.Estimation of forest above ground carbon storage using remote sensing in Daxing'anmountains[D].Harbin:Northeast Forestry University,2014.(in Chinese)
17 MENG Q,CIESZEWSKI C,MADDEN M.Large area forest inventory using Landsat ETM+:a geostatistical approach[J].ISPRS Journal of Photogrammetry&Remote Sensing,2009,64(1):27-36.
18沈楚楚.浙江省主要树种(组)生物量转换因子研究[D].杭州:浙江农林大学,2013.SHEN Chuchu.The research on biomass expansion factors of the dominant tree species in Zhejiang Province[D].Hangzhou:Zhejiang A&F University,2013.(in Chinese)
19 DU H,ZHOU G,FAN W,et al.Spatial heterogeneity and carbon contribution of aboveground biomass of moso bamboo by using geostatistical theory[J].Plant Ecology,2010,207(1):131-139.
20 KUMAR S,LAL R,LIU D.A geographically weighted regression Kriging approach for mapping soil organic carbon stock[J].Geoderma,2012,189-190(6):627-634.
2毛学刚,王静文,范文义.基于遥感与地统计的森林生物量时空变异分析[J].北京林业大学学报,2016,38(2):10-19.MAO Xuegang,WANG Jingwen,FAN Wenyi.Spatial and temporal variation of forest biomass based on remote sensing and geostatistics[J].Journal of Beijing Forestry University,2016,38(2):10-19.(in Chinese)
3杨传强,李士美.2012年山东省乔木林碳储量研究[J].资源科学,2015,37(8):1661-1667.YANG Chuanqiang,LI Shimei.Carbon storage of arboreal forests in 2012 in Shandong Province,China[J].Resources Science,2015,37(8):1661-1667.(in Chinese)
4 PROPASTIN P.Modifying geographically weighted regression for estimating aboveground biomass in tropical rainforests by multispectral remote sensing data[J].International Journal of Applied Earth Observation and Geoinformation,2012,18:82-90.
5贺鹏,张会儒,雷相东,等.基于地统计学的森林地上生物量估计[J].林业科学,2013,49(5):101-109.HE Peng,ZHANG Huiru,LEI Xiangdong,et al.Estimation of forest aboved-ground biomass based on geotatistics[J].Scientia Silvae Sinicae,2013,49(5):101-109.(in Chinese)
6王海宾,彭道黎,范应龙,等.基于辅助信息的森林蓄积量空间模拟[J/OL].农业机械学报,2016,47(6):283-289.http:∥www.j-csam.org/jcsam/ch/reader/view_abstract.aspx?file_no=20160637&flag=1&journal_id=jcsam.DOI:10.6041/j.issn.1000-1298.2016.06.037.WANG Haibin,PENG Daoli,FAN Yinglong,et al.Spatial modeling of forest stock volume based on auxiliary information[J/OL].Transactions of the Chinese Society for Agricultural Machinery,2016,47(6):283-289.(in Chinese)
7涂云燕.森林蓄积量遥感估测研究[D].北京:北京林业大学,2013.TU Yunyan.The research of estimating forest volume based on remote sensing[D].Beijing:Beijing Forestry University,2013.(in Chinese)
8范文义,张海玉,于颖,等.三种森林生物量估测模型的比较分析[J].植物生态学报,2011,35(4):402-410.FAN Wenyi,ZHANG Haiyu,YU Ying,et al.Comparison of three models of forest biomass estimation[J].Chinese Journal of Plant Ecology,2011,35(4):402-410.(in Chinese)
9戚玉娇,李凤日.基于KNN方法的大兴安岭地区森林地上碳储量遥感估算[J].林业科学,2015,51(5):46-55.QI Yujiao,LI Fengri.Remote sensing estimation of aboveground forest carbon storage in Daxing'an mountains based on KNNmethod[J].Scientia Silvae Sinicae,2015,51(5):46-55.(in Chinese)
10郑刚,彭世揆,戎慧,等.基于KNN方法的森林蓄积量遥感估计和反演概述[J].遥感技术与应用,2010,25(3):430-437.ZHENG Gang,PENG Shikui,RONG Hui,et al.A general introduction to estimation and retrieval of forest volume with remote sensing based on KNN[J].Remote Sensing Technology&Application,2010,25(3):430-437.(in Chinese)
11 FOTHERINGHAM A S,BRUNSDON C,CHARLTON M.Geographically weighted regression:the analysis of spatially varying relationships[M].Chester UK:International Union of Crystallography,2002.
12 WANG Quan,NI Jian,TENHUNEN John.Application of a geographically-weighted regression analysis to estimate net primary production of Chinese forest ecosystems[J].Global Ecology and Biogeography,2005,14:379-393.
13郭含茹.基于地理加权回归的区域森林碳储量估计[D].杭州:浙江农林大学,2015.GUO Hanru.Geographically weighted regression based estimation of regional forest carbon storage[D].Hangzhou:Zhejiang A&FUniversity,2015.(in Chinese)
14郭龙,张海涛,陈家赢,等.基于协同克里格插值和地理加权回归模型的土壤属性空间预测比较[J].土壤学报,2012,49(5):1037-1042.GUO Long,ZHANG Haitao,CHEN Jiaying,et al.Comparison between co-Kriging model and geographically weighted regression model in spatial prediction of soil attributes[J].Acta Pedologica Sinia,2012,49(5):1037-1042.(in Chinese)
15袁玉芸,瓦哈甫·哈力克,关靖云,等.基于GWR模型的于田绿洲土壤表层盐分空间分异及其影响因子[J].应用生态学报,2016,27(10):3273-3282.YUAN Yuyun,HALIKE Waha,GUAN Jingyun,et al.Spatial differentiation and impact factors of Yutian Oasis's soil surface salt based on GWR model[J].Chinese Journal of Applied Ecology,2016,27(10):3273-3282.(in Chinese)
16戚玉娇.大兴安岭森林地上碳储量遥感估算与分析[D].哈尔滨:东北林业大学,2014.QI Yujiao.Estimation of forest above ground carbon storage using remote sensing in Daxing'anmountains[D].Harbin:Northeast Forestry University,2014.(in Chinese)
17 MENG Q,CIESZEWSKI C,MADDEN M.Large area forest inventory using Landsat ETM+:a geostatistical approach[J].ISPRS Journal of Photogrammetry&Remote Sensing,2009,64(1):27-36.
18沈楚楚.浙江省主要树种(组)生物量转换因子研究[D].杭州:浙江农林大学,2013.SHEN Chuchu.The research on biomass expansion factors of the dominant tree species in Zhejiang Province[D].Hangzhou:Zhejiang A&F University,2013.(in Chinese)
19 DU H,ZHOU G,FAN W,et al.Spatial heterogeneity and carbon contribution of aboveground biomass of moso bamboo by using geostatistical theory[J].Plant Ecology,2010,207(1):131-139.
20 KUMAR S,LAL R,LIU D.A geographically weighted regression Kriging approach for mapping soil organic carbon stock[J].Geoderma,2012,189-190(6):627-634.