复杂地形地区月平均气温(混合)地理加权回归克里格插值
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摘要
以地形地貌特征复杂、观测站点分布稀疏不均匀的四川省为研究区,引入地形因子(坡度和坡向)和植被指数,采用顾及空间关系非平稳性的(混合)地理加权回归克里格模型((mixed)geographically weighted regression Kriging,(m)GWRK)进行月尺度平均气温插值方法及精度分析研究。针对不同季节和不同地区,将(m)GWRK插值结果与基于全局回归的回归克里格(regression Kriging,RK)插值结果进行对比。结果表明,RK、GWRK、mGWRK回归关系的决定系数R2分别为0.795、0.922、0.911,均方根误差分别为0.83℃、0.64℃、0.55℃,表明GWRK、mGWRK对目标变量的解释能力以及插值精度都优于RK;GWRK、mGWRK相对于RK对月平均气温插值的改进具有季节与地区差异,冬半年的改进大于夏半年,在地形地貌变化大的地区改进大于地形地貌变化小的地区。
Based on the complex topographical features and sparse uneven observation sites in Sichuan Province,terrain factors(slope and aspect)and vegetation index were introduced in this paper.The method of(mixed)geographically weighted regression kriging((m)GWRK)which took into account the non-stationary of spatial relationship was adopted to study the interpolation method of monthly mean temperature and the precision analysis of the estimation results.In different seasons and different regions,the estimation results of(m)GWRK and regression Kriging(RK)based on global regression were compared.The results indicate that the coefficient of determination(R2)of regression relationship of RK,GWRK and mGWRK are 0.795,0.922 and 0.911,respectively,and root meansquare error of these three methods are 0.83℃,0.64℃,0.55℃,respectively.This implies(m)GWRK is better than RK in ability to interpret the target variable and estimation accuracy.Compared with RK,the improvements of(m)GWRK on estimating monthly average temperature have the characteristics of seasonal and regional differences.The improvement is more significant in winter half year than in summer half year.And in northwest and southwest Sichuan,where topography changes acutely,the improvement is greater than in basin where topography changes gently.
Based on the complex topographical features and sparse uneven observation sites in Sichuan Province,terrain factors(slope and aspect)and vegetation index were introduced in this paper.The method of(mixed)geographically weighted regression kriging((m)GWRK)which took into account the non-stationary of spatial relationship was adopted to study the interpolation method of monthly mean temperature and the precision analysis of the estimation results.In different seasons and different regions,the estimation results of(m)GWRK and regression Kriging(RK)based on global regression were compared.The results indicate that the coefficient of determination(R2)of regression relationship of RK,GWRK and mGWRK are 0.795,0.922 and 0.911,respectively,and root meansquare error of these three methods are 0.83℃,0.64℃,0.55℃,respectively.This implies(m)GWRK is better than RK in ability to interpret the target variable and estimation accuracy.Compared with RK,the improvements of(m)GWRK on estimating monthly average temperature have the characteristics of seasonal and regional differences.The improvement is more significant in winter half year than in summer half year.And in northwest and southwest Sichuan,where topography changes acutely,the improvement is greater than in basin where topography changes gently.
引文
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[14]Ma Xiaobin.Sichuan Yearbook,2015[M].Chengdu:Sichuan Yearbook Press,2015(马小彬.四川年鉴,2015[M].成都:四川年鉴社,2015)
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[16]Brunsdon C,Fotheringham S,Charlton M.Geographically Weighted Regression:A Method for Exploring Spatial Nonstationarity[J].Geographical Analysis,1996,28(4):281-298
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[19]Tang Guoan,Yang Xin.ArcGIS Geographic Information System Spatial Analysis the Experimental Tutorial[M].2nd ed Beijing:Science Press,2012(汤国安,杨昕.ArcGIS地理信息系统空间分析实验教程[M].2版.北京:科学出版社,2012)
[20]Hu Dangui,Shu Hong,Hu Hongda.Varlogram Modeling in Space-time CoKriging[J].Journal of Central China Normal University(Natural Sciences),2015,49(4):596-602(胡丹桂,舒红,胡泓达.时空CoKriging的变异函数建模[J].华中师范大学学报(自然科学版),2015,49(4):596-602)
[21]Zhang Jianping,Liu Shuzhen.A Preliminary Study on Land Degradation in Panzhihua City on Jinsha River Day-Hot Valley Area[J].Journal of Desert Research,1998,18(2):149-153(张建平,刘淑珍.金沙江干热河谷区攀枝花市土地退化初探[J].中国沙漠,1998,18(2):149-153)
[2]Xie Yunfeng,Zhang Shuwen.Spatial Interpolation of Mean Temperature of Heilongjiang Province Based on Digital Elevation Model[J].Chinese Journal of Agrometeorology,2007,28(2):205-211(谢云峰,张树文.基于数字高程模型的复杂地形下的黑龙江平均气温空间插值[J].中国农业气象,2007,28(2):205-211)
[3]You Songcai,Li Jun.Study on Error and Its Pervasion of Temperature Estimation[J].Journal of Natural Resources,2005,20(1):140-144(游松财,李军.海拔误差影响气温空间插值误差的研究[J].自然资源学报,2005,20(1):140-144)
[4]Li Sha,Shu Hong,Xu Zhengquan.Interpolation of Temperature Based on Spatial-temporal Kriging[J].Geomatics and Information Science of Wuhan University,2012,37(2):237-241(李莎,舒红,徐正全.利用时空Kriging进行气温插值研究[J].武汉大学学报·信息科学版,2012,37(2):237-241)
[5]Cai Dihua,Guo Ni,Li Chongwei.Interpolation of Air Temperature Based on DEM over Eastern Region of Gansu[J].Journal of Arid Meteorology,2009,27(1):10-17(蔡迪花,郭铌,李崇伟.基于DEM的气温插值方法研究[J].干旱气象,2009,27(1):10-17)
[6]Szymanowski M,Kryza M.The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland[J].Pure&Applied Geophysics,2017,174(2):1-27
[7]Xu Min,Wang Yan,Zhou Zhaoye,et al.Discussion of Methods of Spatial Interpolation for Monthly Temperature Data in the Yangtze River Basin[J].Resources&Environment in the Yangtze Basin,2012,21(3):327-334(许民,王雁,周兆叶,等.长江流域逐月气温空间插值方法的探讨[J].长江流域资源与环境,2012,21(3):327-334)
[8]Tan Wenzhong.The Basic Theoretics and Application Research on Geographically Weighted Regression[D].Shanghai:Tongji University,2007(覃文忠.地理加权回归基本理论与应用研究[D].上海:同济大学,2007)
[9]Yang Shunhua,Zhang Haitao,Guo Long,et al.Spatial Interpolation of Soil Organic Matter Using Regression Kriging and Geographically Weighted Regression Kriging[J].Chinese Journal of Applied Ecology,2015,26(6):1 649-1 656(杨顺华,张海涛,郭龙,等.基于回归和地理加权回归Kriging的土壤有机质空间插值[J].应用生态学报,2015,26(6):1 649-1 656)
[10]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
[11]Szymanowski M,Kryza M,Spallek W.Regressionbased Air Temperature Spatial Prediction Models:An Example from Poland[J].Meteorologische Zeitschrift,2013,22(5):577-585
[12]Donkelaar A V,Martin R V,Spurr R J D,et al.High-Resolution Satellite-Derived PM2.5from Optimal Estimation and Geographically Weighted Regression over North America[J].Environmental Science&Technology,2015,49(17):10 482-10 491
[13]Zhang Guofeng,Yang Lirong,Qu Mingkai,et al.Interpolation of Daily Mean Temperature by Using Geographically Weighted Regression-Kriging[J].Chinese Journal of Applied Ecology,2015,26(5):1 531-1 536(张国峰,杨立荣,瞿明凯,等.基于地理加权回归克里格的日平均气温插值[J].应用生态学报,2015,26(5):1 531-1 536)
[14]Ma Xiaobin.Sichuan Yearbook,2015[M].Chengdu:Sichuan Yearbook Press,2015(马小彬.四川年鉴,2015[M].成都:四川年鉴社,2015)
[15]Hengl T,Heuvelink G B M,Rossiter D G.About Regression-Kriging:From Equations to Case Studies[J].Computers&Geosciences,2007,33(10):1 301-1 315
[16]Brunsdon C,Fotheringham S,Charlton M.Geographically Weighted Regression:A Method for Exploring Spatial Nonstationarity[J].Geographical Analysis,1996,28(4):281-298
[17]Brunsdon C,Fotheringham S,Charlton M.Geographically Weighted Regression[J].Journal of the Royal Statistical Society,1998,47(3):431-443
[18]Brunsdon C,Fotheringham A S,Charlton M.Some Notes on Parametric Significance Tests for Geographically Weighted Regression[J].Journal of Regional Science,1999,39(3):497-524
[19]Tang Guoan,Yang Xin.ArcGIS Geographic Information System Spatial Analysis the Experimental Tutorial[M].2nd ed Beijing:Science Press,2012(汤国安,杨昕.ArcGIS地理信息系统空间分析实验教程[M].2版.北京:科学出版社,2012)
[20]Hu Dangui,Shu Hong,Hu Hongda.Varlogram Modeling in Space-time CoKriging[J].Journal of Central China Normal University(Natural Sciences),2015,49(4):596-602(胡丹桂,舒红,胡泓达.时空CoKriging的变异函数建模[J].华中师范大学学报(自然科学版),2015,49(4):596-602)
[21]Zhang Jianping,Liu Shuzhen.A Preliminary Study on Land Degradation in Panzhihua City on Jinsha River Day-Hot Valley Area[J].Journal of Desert Research,1998,18(2):149-153(张建平,刘淑珍.金沙江干热河谷区攀枝花市土地退化初探[J].中国沙漠,1998,18(2):149-153)
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