中国西北地区NPP模拟及其时空格局
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摘要
中国西北地区MOD17A3 NPP数据产品缺失严重,影响了该区域植被净初级生产力的进一步研究。本研究利用气象数据、高程数据、NDVI和质量好的MOD17A3 NPP,构建BP神经网络模型,模拟2000—2014年西北地区植被NPP,填补数据缺失区域。利用一元线性回归分析法、R/S分析法、偏相关分析法等,分析了植被NPP的时空变化特征及其与气象要素的关系。结果表明:(1) MODIS NPP产品值与BP神经网络模拟值的决定系数R~2、平均绝对误差MAE、平均相对误差MRE、均方根误差RMSE分别在0.833～0.906、25.84～40.10、0.16～0.23和34.57～59.36,满足精度要求,BP神经网络模型适用于模拟西北地区植被NPP。(2)植被年均NPP具有较强的空间差异,呈现出由东南向西北递减,而新疆西北部地区出现"条块状"高值区特征。(3) 2000—2014年西北地区植被年均NPP在106.64～156.17 g C·m~(-2)·a~(-1),年际变化上呈现波动下降趋势。(4) 2000—2014年西北地区植被NPP变化具有空间异质性,以减少为主,仅10.79%的区域通过了显著性检验。植被NPP变化具有较弱的持续性特征,未来发展方向以不确定为主,有利和不利为辅,其中有利区域面积大于不利区域。(5)植被NPP对气温和降水的响应具有空间差异,总体上与降水关系更密切。
The data production of MOD17 A3 NPP in northwest China is seriously deficient,with consequences on further research on vegetation net primary productivity( NPP) in this region.Based on the meteorological data,DEM,NDVI,and MOD17 A3 NPP with high quality,the BP neural network model was constructed to simulate vegetation NPP and fill the areas without NPP data in northwest China from 2000 to 2014. Its spatio-temporal variations and relationships with meteorological factors were analyzed using unitary linear regression,R/S analysis,and partial correlation analysis. The results showed that:( 1) Coefficients of determination( R~2),mean absolute error( MAE),mean relative error( MRE) and root mean square error( RMSE)between MODIS NPP and simulated NPP were 0.833-0.906,25.84-40.1,0.16-0.23,34.57-59.36,respectively,which well met the accuracy requirements. The BP neural network model was suitable for vegetation NPP simulation in northwest China.( 2) The annual mean NPP had a strong spatial variation,showing a gradual decline from southeast to northwest and a high-value block area in the northwest of Xinjiang.( 3) The annual mean NPP ranged between 106.64 and156.17 g C · m~(-2)·a~(-1) from 2000 to 2014,with a slightly fluctuating downward trend in the interannual variation.( 4) From 2000 to 2014,the change of NPP in northwest China had spatial heterogeneity,which was mainly reduced. Only 10.79% of the areas passed the significance test.NPP change was weakly persistent. The future change trend of NPP is mainly uncertain,supplemented by improved and declined areas,with the improved area being larger than the declined area.( 5) Responses of vegetation NPP to temperature and precipitation varied spatially,which were generally more closely related to precipitation.
The data production of MOD17 A3 NPP in northwest China is seriously deficient,with consequences on further research on vegetation net primary productivity( NPP) in this region.Based on the meteorological data,DEM,NDVI,and MOD17 A3 NPP with high quality,the BP neural network model was constructed to simulate vegetation NPP and fill the areas without NPP data in northwest China from 2000 to 2014. Its spatio-temporal variations and relationships with meteorological factors were analyzed using unitary linear regression,R/S analysis,and partial correlation analysis. The results showed that:( 1) Coefficients of determination( R~2),mean absolute error( MAE),mean relative error( MRE) and root mean square error( RMSE)between MODIS NPP and simulated NPP were 0.833-0.906,25.84-40.1,0.16-0.23,34.57-59.36,respectively,which well met the accuracy requirements. The BP neural network model was suitable for vegetation NPP simulation in northwest China.( 2) The annual mean NPP had a strong spatial variation,showing a gradual decline from southeast to northwest and a high-value block area in the northwest of Xinjiang.( 3) The annual mean NPP ranged between 106.64 and156.17 g C · m~(-2)·a~(-1) from 2000 to 2014,with a slightly fluctuating downward trend in the interannual variation.( 4) From 2000 to 2014,the change of NPP in northwest China had spatial heterogeneity,which was mainly reduced. Only 10.79% of the areas passed the significance test.NPP change was weakly persistent. The future change trend of NPP is mainly uncertain,supplemented by improved and declined areas,with the improved area being larger than the declined area.( 5) Responses of vegetation NPP to temperature and precipitation varied spatially,which were generally more closely related to precipitation.
引文
艾则孜提约麦尔·麦麦提,玉素甫江·如素力,姜红,等.2018.2000-2014年博斯腾湖流域NPP时空变化特征及影响因子分析.草业科学,35(7):1743-1753.
程曼,王让会,薛红喜,等.2012.干旱对我国西北地区生态系统净初级生产力的影响.干旱区资源与环境,26(6):1-7.
杜加强,舒俭民,张林波.2010.基于NPP的黄南州自然植被对气候变化的响应.生态学杂志,29(6):1094-1102.
洪长桥,金晓斌,陈昌春,等.2017.集成遥感数据的陆地净初级生产力估算模型研究综述.地理科学进展,36(8):924-939.
侯英雨,毛留喜,李朝生,等.2008.中国植被净初级生产力变化的时空格局.生态学杂志,27(9):1455-1460.
焦伟,陈亚宁,李稚,等.2017a.基于多种回归分析方法的西北干旱区植被NPP遥感反演研究.资源科学,39(3):545-556.
焦伟,陈亚宁,李稚.2017b.西北干旱区植被净初级生产力的遥感估算及时空差异原因.生态学杂志,36(1):181-189.
李登科,范建忠,王娟.2011.基于MOD17A3的陕西省植被NPP变化特征.生态学杂志,30(12):2776-2782.
李登科,王钊.2018.基于MOD17A3的中国陆地植被NPP变化特征分析.生态环境学报,27(3):397-405.
林慧龙,王军,徐震.2005.草地净第一性生产力与≥0℃年积温、湿润度指标间的关系.草业科学,22(6):8-10.
卢玲,李新,Veroustraete F.2005.中国西部地区植被净初级生产力的时空格局.生态学报,25(5):1026-1032.
罗敏,古丽·加帕尔,郭浩,等.2017.2000-2013年塔里木河流域生长季NDVI时空变化特征及其影响因素分析.自然资源学报,32(1):50-63.
陶波,李克让,邵雪梅,等.2003.中国陆地净初级生产力时空特征模拟.地理学报,58(3):372-380.
王娟,何慧娟,卓静,等.2017.基于MODIS数据的陕西省植被净初级生产力与实际蒸散的变化关系分析.水土保持通报,37(3):264-269.
沃笑,吴良才,张继平,等.2014.基于CASA模型的三江源地区植被净初级生产力遥感估算研究.干旱区资源与环境,28(9):45-50.
王新闯,王世东,张合兵.2013.基于MOD17A3的河南省NPP时空格局.生态学杂志,32(10):2797-2805.
徐建华.2002.现代地理学中的数学方法(第2版).北京:高等教育出版社.
许莉,赵嵩正,杨海光.2008.水资源承载力的BP神经网络评价模型研究.计算机工程与应用,44(8):217-219.
荀其蕾,董乙强,安沙舟,等.2018.基于MOD09GA数据的新疆草地生长状况遥感监测研究.草业学报,27(4):10-26.
胥晓.2004.四川植被净第一性生产力(NPP)对全球气候变化的响应.生态学杂志,23(6):19-24.
袁文平,蔡文文,刘丹,等.2014.陆地生态系统植被生产力遥感模型研究进展.地球科学进展,29(5):541-550.
周才平,欧阳华,王勤学,等.2004.青藏高原主要生态系统净初级生产力的估算.地理学报,59(1):74-79.
周广胜,张新时.1995.自然植被净第一性生产力模型初探.植物生态学报,19(3):193-200.
赵育民,牛树奎,王军邦,等.2007.植被光能利用率研究进展.生态学杂志,26(9):1471-1477.
Field CB,Behrenfeld MJ,Randerson JT,et al.1998.Primary production of the biosphere:Integrating terrestrial and oceanic components.Science,281:237-240.
Lieth H,Whittaker RH.1975.Primary Productivity of the Biosphere.Berlin:Springer-Verlag,14:274.
Ma D,Zhou T,Chen J,et al.2017.Supercritical water heat transfer coefficient prediction analysis based on BP neural network.Nuclear Engineering and Design,320:400-408.
Mao J,Wang B,Dai Y,et al.2007.Improvements of a dynamic global vegetation model and simulations of carbon and water at an upland-oak forest.Advances in Atmospheric Sciences,24:311-322.
Melillo JM,McGuire AD,Kicklighter DW,et al.1993.Global climate change and terrestrial net primary production.Nature,363:234-240.
Piao SL.2005.NDVI-indicated decline in desertification in China in the past two decades.Geophysical Research Letters,32:L06402.
Potter CS,Randerson JT,Field CB,et al.1993.Terrestrial ecosystem production:A process model based on global satellite and surface data.Global Biogeochemical Cycles,7:811-841.
Ruimy A,Kergoat L,Bondeau A.2010.Comparing global models of terrestrial net primary productivity(NPP):Analysis of differences in light absorption and light-use efficiency.Global Change Biology,5:56-64.
Rumelhart DE,Hinton GE,Williams RJ.1986.Learning representations by back-propagating errors.Nature,323:533-536.
Stow D,Daeschner S,Hope A,et al.2003.Variability of the seasonally integrated normalized difference vegetation index across the north slope of Alaska in the 1990s.International Journal of Remote Sensing,24:1111-1117.
Wang Y,Lu C,Zuo C.2015.Coal mine safety production forewarning based on improved BP neural network.International Journal of Mining Science and Technology,25:319-324.
Zhao M,Heinsch FA,Nemani RR,et al.2005.Improvements of the MODIS terrestrial gross and net primary production global data set.Remote Sensing of Environment,95:164-176.
Zhuang Q,He J,Lu Y,et al.2010.Carbon dynamics of terrestrial ecosystems on the Tibetan Plateau during the 20th century:An analysis with a process-based biogeochemical model.Global Ecology and Biogeography,19:649-662.
程曼,王让会,薛红喜,等.2012.干旱对我国西北地区生态系统净初级生产力的影响.干旱区资源与环境,26(6):1-7.
杜加强,舒俭民,张林波.2010.基于NPP的黄南州自然植被对气候变化的响应.生态学杂志,29(6):1094-1102.
洪长桥,金晓斌,陈昌春,等.2017.集成遥感数据的陆地净初级生产力估算模型研究综述.地理科学进展,36(8):924-939.
侯英雨,毛留喜,李朝生,等.2008.中国植被净初级生产力变化的时空格局.生态学杂志,27(9):1455-1460.
焦伟,陈亚宁,李稚,等.2017a.基于多种回归分析方法的西北干旱区植被NPP遥感反演研究.资源科学,39(3):545-556.
焦伟,陈亚宁,李稚.2017b.西北干旱区植被净初级生产力的遥感估算及时空差异原因.生态学杂志,36(1):181-189.
李登科,范建忠,王娟.2011.基于MOD17A3的陕西省植被NPP变化特征.生态学杂志,30(12):2776-2782.
李登科,王钊.2018.基于MOD17A3的中国陆地植被NPP变化特征分析.生态环境学报,27(3):397-405.
林慧龙,王军,徐震.2005.草地净第一性生产力与≥0℃年积温、湿润度指标间的关系.草业科学,22(6):8-10.
卢玲,李新,Veroustraete F.2005.中国西部地区植被净初级生产力的时空格局.生态学报,25(5):1026-1032.
罗敏,古丽·加帕尔,郭浩,等.2017.2000-2013年塔里木河流域生长季NDVI时空变化特征及其影响因素分析.自然资源学报,32(1):50-63.
陶波,李克让,邵雪梅,等.2003.中国陆地净初级生产力时空特征模拟.地理学报,58(3):372-380.
王娟,何慧娟,卓静,等.2017.基于MODIS数据的陕西省植被净初级生产力与实际蒸散的变化关系分析.水土保持通报,37(3):264-269.
沃笑,吴良才,张继平,等.2014.基于CASA模型的三江源地区植被净初级生产力遥感估算研究.干旱区资源与环境,28(9):45-50.
王新闯,王世东,张合兵.2013.基于MOD17A3的河南省NPP时空格局.生态学杂志,32(10):2797-2805.
徐建华.2002.现代地理学中的数学方法(第2版).北京:高等教育出版社.
许莉,赵嵩正,杨海光.2008.水资源承载力的BP神经网络评价模型研究.计算机工程与应用,44(8):217-219.
荀其蕾,董乙强,安沙舟,等.2018.基于MOD09GA数据的新疆草地生长状况遥感监测研究.草业学报,27(4):10-26.
胥晓.2004.四川植被净第一性生产力(NPP)对全球气候变化的响应.生态学杂志,23(6):19-24.
袁文平,蔡文文,刘丹,等.2014.陆地生态系统植被生产力遥感模型研究进展.地球科学进展,29(5):541-550.
周才平,欧阳华,王勤学,等.2004.青藏高原主要生态系统净初级生产力的估算.地理学报,59(1):74-79.
周广胜,张新时.1995.自然植被净第一性生产力模型初探.植物生态学报,19(3):193-200.
赵育民,牛树奎,王军邦,等.2007.植被光能利用率研究进展.生态学杂志,26(9):1471-1477.
Field CB,Behrenfeld MJ,Randerson JT,et al.1998.Primary production of the biosphere:Integrating terrestrial and oceanic components.Science,281:237-240.
Lieth H,Whittaker RH.1975.Primary Productivity of the Biosphere.Berlin:Springer-Verlag,14:274.
Ma D,Zhou T,Chen J,et al.2017.Supercritical water heat transfer coefficient prediction analysis based on BP neural network.Nuclear Engineering and Design,320:400-408.
Mao J,Wang B,Dai Y,et al.2007.Improvements of a dynamic global vegetation model and simulations of carbon and water at an upland-oak forest.Advances in Atmospheric Sciences,24:311-322.
Melillo JM,McGuire AD,Kicklighter DW,et al.1993.Global climate change and terrestrial net primary production.Nature,363:234-240.
Piao SL.2005.NDVI-indicated decline in desertification in China in the past two decades.Geophysical Research Letters,32:L06402.
Potter CS,Randerson JT,Field CB,et al.1993.Terrestrial ecosystem production:A process model based on global satellite and surface data.Global Biogeochemical Cycles,7:811-841.
Ruimy A,Kergoat L,Bondeau A.2010.Comparing global models of terrestrial net primary productivity(NPP):Analysis of differences in light absorption and light-use efficiency.Global Change Biology,5:56-64.
Rumelhart DE,Hinton GE,Williams RJ.1986.Learning representations by back-propagating errors.Nature,323:533-536.
Stow D,Daeschner S,Hope A,et al.2003.Variability of the seasonally integrated normalized difference vegetation index across the north slope of Alaska in the 1990s.International Journal of Remote Sensing,24:1111-1117.
Wang Y,Lu C,Zuo C.2015.Coal mine safety production forewarning based on improved BP neural network.International Journal of Mining Science and Technology,25:319-324.
Zhao M,Heinsch FA,Nemani RR,et al.2005.Improvements of the MODIS terrestrial gross and net primary production global data set.Remote Sensing of Environment,95:164-176.
Zhuang Q,He J,Lu Y,et al.2010.Carbon dynamics of terrestrial ecosystems on the Tibetan Plateau during the 20th century:An analysis with a process-based biogeochemical model.Global Ecology and Biogeography,19:649-662.
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