基于地理加权回归克里格模型的帽儿山地区森林碳储量空间分布
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摘要
森林碳储量对于全球气候变化具有重要影响,以往的模型估算未考虑到模型残差的空间相关性和碳储量数据的非平稳性,影响模型的预测精度.本研究基于东北林业大学帽儿山实验林场的ETM+遥感影像数据和193块固定样地,利用地理加权克里格回归(GWRK)建立森林碳储量与遥感和地形因子的回归模型,同时对比最小二乘模型(OLS)、地理加权回归模型(GWR)的预测精度.结果表明:对于帽儿山地区的森林碳储量估算,GWRK的平均绝对误差(MAE)、均方根误差(RMSE)低于OLS模型和GWR模型,GWRK模型的平均误差(ME)低于GWR模型,与OLS模型相近.GWRK模型的预测精度为83.2%,较OLS模型(73.7%)和GWR模型(77.3%)分别提高6%和10%,拟合精度明显提高,说明GWRK模型是森林碳储量估算的有效方法.利用GWRK模型预测的研究区森林碳储量平均值为70.31 t·hm~(-2),在海拔较高的地区,森林碳储量值相对较高,说明海拔对其有较大影响.
Forest carbon storage has important impacts on climate change. The previous models do not take into consideration of the inherent spatial correlation structure of residual and non-stationary of forest carbon storage which limits the prediction accuracy. Based on ETM+ remotely sensed imagery and 193 fixed plots of Maoershan Experimental Forest Farm of Northeast Forestry University, we established the geographically weighted regression kriging(GWRK) model between forest carbon storage and extracted factors from remotely sensed imagery and topographic factors. The prediction accuracy of GWRK, ordinary least square(OLS) model and geographically weighted regression(GWR) were compared. The results showed that the mean absolute error(MAE) and root mean square error(RMSE) of GWRK were lower than those of OLS and GWR models, while the mean error(ME) of GWRK model was lower than that of GWR model and was close to that of OLS model. The prediction accuracy of GWRK model was 83.2%, which was 6% and 10% higher than that of OLS model(73.7%) and GWR model(77.3%). Therefore, the GWRK model was more effective in estimating forest carbon storage than the others. The mean value of forest carbon storage predicted by GWRK model was 70.31 t·hm~(-2). The relatively high values presented in high altitude area, indicating that altitude had a great impact on forest carbon storage.
Forest carbon storage has important impacts on climate change. The previous models do not take into consideration of the inherent spatial correlation structure of residual and non-stationary of forest carbon storage which limits the prediction accuracy. Based on ETM+ remotely sensed imagery and 193 fixed plots of Maoershan Experimental Forest Farm of Northeast Forestry University, we established the geographically weighted regression kriging(GWRK) model between forest carbon storage and extracted factors from remotely sensed imagery and topographic factors. The prediction accuracy of GWRK, ordinary least square(OLS) model and geographically weighted regression(GWR) were compared. The results showed that the mean absolute error(MAE) and root mean square error(RMSE) of GWRK were lower than those of OLS and GWR models, while the mean error(ME) of GWRK model was lower than that of GWR model and was close to that of OLS model. The prediction accuracy of GWRK model was 83.2%, which was 6% and 10% higher than that of OLS model(73.7%) and GWR model(77.3%). Therefore, the GWRK model was more effective in estimating forest carbon storage than the others. The mean value of forest carbon storage predicted by GWRK model was 70.31 t·hm~(-2). The relatively high values presented in high altitude area, indicating that altitude had a great impact on forest carbon storage.
引文
[1] Fang J,Guo Z,Hu H,et al.Forest biomass carbon sinks in East Asia,with special reference to the relative contributions of forest expansion and forest growth.Global Change Biology,2014,20:2019-2030
[2] Li H-K (李海奎),Lei Y-C (雷渊才),Zeng W-S (曾伟生).Forest carbon storage in China estimated using forestry inventory data.Scientia Silvae Sinicae (林业科学),2011,47(7):7-12 (in Chinese)
[3] Cao J-X (曹吉鑫),Tian Y (田赟),Wang X-P (王小平),et al.Estimation methods of forest sequestration and their prospects.Ecology and Environmental Sciences (生态环境学报),2009,18(5):2001-2005 (in Chinese)
[4] Xing S-L (邢素丽),Zhang G-L (张广录),Liu H-T (刘慧涛),et al.The estimating model of Larix gmelinii forests biomass using Landsat ETM data.Journal of Fujian College of Forestry (福建林学院学报),2004(2):153-156 (in Chinese)
[5] Li M-Z (李明泽).Estimation and Analysis of Forest Biomass in Northeast Forest Region using Remote Sensing Technology.PhD Thesis.Harbin:Northeast Forestry University,2010 ( in Chinese)
[6] Yu Y (于颖),Fan W-Y (范文义),Li M-Z (李明泽).Forest carbon rates at different scales in Northeast China forest area.Chinese Journal of Applied Ecology (应用生态学报),2012,23(2):341-346 (in Chinese)
[7] Fotheringham AS.Spatial variations in school perfor-mance:A local analysis using geographically weighted regression.Geographical and Environmental Modelling,2001,5:43-66
[8] Zhang L,Bi H,Cheng P,et al.Modeling spatial variation in tree diameter-height relationships.Forest Ecology and Management,2004,189:317-329
[9] Zhang L,Shi H.Local modeling of tree growth by geographically weighted regression.Forest Science,2004,50:225-244
[10] Liu C (刘畅).Spatial Distribution of Forest Carbon Storage in Heilongjiang Province.PhD Thesis.Harbin:Northeast Forestry University,2014 (in Chinese)
[11] Liu C.Spatial modeling of the carbon stock of forest trees in Heilongjiang Province,China.Journal of Forestry Research,2014,25:269-280
[12] Sun W,Zhu Y,Huang S,et al.Mapping the mean annual precipitation of China using local interpolation techniques.Theoretical and Applied Climatology,2015,119:171-180
[13] Harris P,Fotheringham AS,Crespo R,et al.The use of geographically weighted regression for spatial prediction:An evaluation of models using simulated data sets.Mathematical Geosciences,2011,43:399-399
[14] Wasko C,Sharma A,Rasmussen P.Improved spatial prediction:A combinatorial approach.Water Resources Research,2013,49:3927-3935
[15] Kumar,S.Estimating spatial distribution of soil organic carbon for the Midwestern United States using historical database.Chemosphere,2015,127:49-57
[16] Kumar S,Lal R,Liu D.A geographically weighted regression kriging approach for mapping soil organic carbon stock.Geoderma,2012,189-190:627-634
[17] Kumari M,Singh C K,Basistha A,et al.Non-stationary modelling framework for rainfall interpolation in complex terrain.International Journal of Climatology,2017,37:4171-4185
[18] Wang,C.Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests.Forest Ecology and Management,2006,222:9-16
[19] Dong L,Zhang L,Li F.A three-step proportional weighting system of nonlinear biomass equations.Forest Science,2015,61:35-45
[20] Dong L,Zhang L,Li F.A compatible system of biomass equations for three conifer species in Northeast,China.Forest Ecology and Management,2014,329:306-317
[21] Dong L,Zhang L,Li F.Developing additive systems of biomass equations for nine hardwood species in Northeast China.Trees,2015,29:1-15
[22] Liu F (刘帆),Wang C-K (王传宽),Wang X-C (王兴昌) ,et al.Spatial patterns of biomass in the temperate broadleaved deciduous forest within the fetch of the Maoershan flux tower.Acta Ecologica Sinica (生态学报),2016,36(20):1-14 (in Chinese)
[23] Yang G-T (杨国亭),Li F-R (李凤日),Yin T (殷彤),et al.Estimation and Analysis of Spatial Distribution of Forest Carbon Storage in Heilongjiang Province.Harbin:Northeast Forestry University Press,2017 (in Chinese)
[24] Li M-Z (李明泽),Mao X-G (毛学刚),Fan W-Y (范文义).Forest biomass estimation using remote sensing based on canopy density simultaneous equations model.Scientia Silvae Sinicae (林业科学),2014,50(2):85-91(in Chinese)
[25] Liu Z-X (刘正显).Research on the Spatial Distribution of Liangshui Nature Reserve Forest Biomass Based on Geographically Weighted Regression.Master Thesis.Harbin:Northeast Forestry University,2015 (in Chinese)
[26] Christakos G.Random Field Models in Earth Sicences.New York:Academic Press,2012
[27] Zhen Z (甄贞),Guo Z-Y (郭志英),Zhao Y-H (赵颖慧),et al.Spatial distribution of soil total nitrogen in Liangshui National Nature Reserve based on local model.Chinese Journal of Applied Ecology (应用生态学报),2016,27(2):549-558 (in Chinese)
[28] Wang W-F (王维芳),Song L-N (宋丽楠),Sui X (隋欣).Estimation of forest biomass and its temporal and spatial distribution patterns in Maoershan Forest Farm.Journal of Northeast Forestry University (东北林业大学学报),2010,38(1):47-49 (in Chinese)
[29] Réjouméchain M,Mullerlandau HC,Detto M,et al.Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks.Biogeosciences,2014,11:6827-6840
[30] Ciotoli G,Voltaggio M,Tuccimei P,et al.Geographically weighted regression and geostatistical techniques to construct the geogenic radon potential map of the Lazio region:A methodological proposal for the European Atlas of Natural Radiation.Journal of Environmental Radioactivity,2016,166:355-375
[31] Song L-N (宋丽楠).Estimation of Forest Biomass and Its Temporal and Spatial Distribution Patterns Analysis in Maoershan Forest Farm.Master Thesis.Harbin:Northeast Forestry University,2010 ( in Chinese)
[32] Yang S-H (杨顺华),Zhang H-T (张海涛),Guo L (郭龙),et al.Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging.Chinese Journal of Applied Ecology (应用生态学报),2015,26(6):1649-1656 (in Chinese)
[2] Li H-K (李海奎),Lei Y-C (雷渊才),Zeng W-S (曾伟生).Forest carbon storage in China estimated using forestry inventory data.Scientia Silvae Sinicae (林业科学),2011,47(7):7-12 (in Chinese)
[3] Cao J-X (曹吉鑫),Tian Y (田赟),Wang X-P (王小平),et al.Estimation methods of forest sequestration and their prospects.Ecology and Environmental Sciences (生态环境学报),2009,18(5):2001-2005 (in Chinese)
[4] Xing S-L (邢素丽),Zhang G-L (张广录),Liu H-T (刘慧涛),et al.The estimating model of Larix gmelinii forests biomass using Landsat ETM data.Journal of Fujian College of Forestry (福建林学院学报),2004(2):153-156 (in Chinese)
[5] Li M-Z (李明泽).Estimation and Analysis of Forest Biomass in Northeast Forest Region using Remote Sensing Technology.PhD Thesis.Harbin:Northeast Forestry University,2010 ( in Chinese)
[6] Yu Y (于颖),Fan W-Y (范文义),Li M-Z (李明泽).Forest carbon rates at different scales in Northeast China forest area.Chinese Journal of Applied Ecology (应用生态学报),2012,23(2):341-346 (in Chinese)
[7] Fotheringham AS.Spatial variations in school perfor-mance:A local analysis using geographically weighted regression.Geographical and Environmental Modelling,2001,5:43-66
[8] Zhang L,Bi H,Cheng P,et al.Modeling spatial variation in tree diameter-height relationships.Forest Ecology and Management,2004,189:317-329
[9] Zhang L,Shi H.Local modeling of tree growth by geographically weighted regression.Forest Science,2004,50:225-244
[10] Liu C (刘畅).Spatial Distribution of Forest Carbon Storage in Heilongjiang Province.PhD Thesis.Harbin:Northeast Forestry University,2014 (in Chinese)
[11] Liu C.Spatial modeling of the carbon stock of forest trees in Heilongjiang Province,China.Journal of Forestry Research,2014,25:269-280
[12] Sun W,Zhu Y,Huang S,et al.Mapping the mean annual precipitation of China using local interpolation techniques.Theoretical and Applied Climatology,2015,119:171-180
[13] Harris P,Fotheringham AS,Crespo R,et al.The use of geographically weighted regression for spatial prediction:An evaluation of models using simulated data sets.Mathematical Geosciences,2011,43:399-399
[14] Wasko C,Sharma A,Rasmussen P.Improved spatial prediction:A combinatorial approach.Water Resources Research,2013,49:3927-3935
[15] Kumar,S.Estimating spatial distribution of soil organic carbon for the Midwestern United States using historical database.Chemosphere,2015,127:49-57
[16] Kumar S,Lal R,Liu D.A geographically weighted regression kriging approach for mapping soil organic carbon stock.Geoderma,2012,189-190:627-634
[17] Kumari M,Singh C K,Basistha A,et al.Non-stationary modelling framework for rainfall interpolation in complex terrain.International Journal of Climatology,2017,37:4171-4185
[18] Wang,C.Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests.Forest Ecology and Management,2006,222:9-16
[19] Dong L,Zhang L,Li F.A three-step proportional weighting system of nonlinear biomass equations.Forest Science,2015,61:35-45
[20] Dong L,Zhang L,Li F.A compatible system of biomass equations for three conifer species in Northeast,China.Forest Ecology and Management,2014,329:306-317
[21] Dong L,Zhang L,Li F.Developing additive systems of biomass equations for nine hardwood species in Northeast China.Trees,2015,29:1-15
[22] Liu F (刘帆),Wang C-K (王传宽),Wang X-C (王兴昌) ,et al.Spatial patterns of biomass in the temperate broadleaved deciduous forest within the fetch of the Maoershan flux tower.Acta Ecologica Sinica (生态学报),2016,36(20):1-14 (in Chinese)
[23] Yang G-T (杨国亭),Li F-R (李凤日),Yin T (殷彤),et al.Estimation and Analysis of Spatial Distribution of Forest Carbon Storage in Heilongjiang Province.Harbin:Northeast Forestry University Press,2017 (in Chinese)
[24] Li M-Z (李明泽),Mao X-G (毛学刚),Fan W-Y (范文义).Forest biomass estimation using remote sensing based on canopy density simultaneous equations model.Scientia Silvae Sinicae (林业科学),2014,50(2):85-91(in Chinese)
[25] Liu Z-X (刘正显).Research on the Spatial Distribution of Liangshui Nature Reserve Forest Biomass Based on Geographically Weighted Regression.Master Thesis.Harbin:Northeast Forestry University,2015 (in Chinese)
[26] Christakos G.Random Field Models in Earth Sicences.New York:Academic Press,2012
[27] Zhen Z (甄贞),Guo Z-Y (郭志英),Zhao Y-H (赵颖慧),et al.Spatial distribution of soil total nitrogen in Liangshui National Nature Reserve based on local model.Chinese Journal of Applied Ecology (应用生态学报),2016,27(2):549-558 (in Chinese)
[28] Wang W-F (王维芳),Song L-N (宋丽楠),Sui X (隋欣).Estimation of forest biomass and its temporal and spatial distribution patterns in Maoershan Forest Farm.Journal of Northeast Forestry University (东北林业大学学报),2010,38(1):47-49 (in Chinese)
[29] Réjouméchain M,Mullerlandau HC,Detto M,et al.Local spatial structure of forest biomass and its consequences for remote sensing of carbon stocks.Biogeosciences,2014,11:6827-6840
[30] Ciotoli G,Voltaggio M,Tuccimei P,et al.Geographically weighted regression and geostatistical techniques to construct the geogenic radon potential map of the Lazio region:A methodological proposal for the European Atlas of Natural Radiation.Journal of Environmental Radioactivity,2016,166:355-375
[31] Song L-N (宋丽楠).Estimation of Forest Biomass and Its Temporal and Spatial Distribution Patterns Analysis in Maoershan Forest Farm.Master Thesis.Harbin:Northeast Forestry University,2010 ( in Chinese)
[32] Yang S-H (杨顺华),Zhang H-T (张海涛),Guo L (郭龙),et al.Spatial interpolation of soil organic matter using regression Kriging and geographically weighted regression Kriging.Chinese Journal of Applied Ecology (应用生态学报),2015,26(6):1649-1656 (in Chinese)