荒漠区人工林NPP估算模型比较研究
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摘要
利用LANDSAT8遥感影像数据、结合气象数据和野外调查数据,采用CASA模型、C-FIX模型和GLO-PEM模型,分别估算和分析了2014年墨玉县森林植被净初生产力时空分布特征,并进行验证和比较。结果表明:植被净初生产力空间分布差异比较明显,主要集中于研究区中部和东南部;CASA模型的NPP平均值为70gC·m~(-2)、C-FIX模型的为56C·m~(-2),GLO-PEM模型的为28C·m~(-2),其中CASA模型模拟结果最接近于前人研究成果;通过实测值验证,各模型实测值和模拟值相当吻合,其中CASA模型的平均值最接近,相关系数R2=0.994 15,大部分样本点的误差小于5%。
By Using LANDSAT8 remote sensing image data,combined with the meteorological data &field survey data,using CASA model,C-FIX model &using GLO-PEM model,forest vegetation net primary productivity spatial &temporal distribution characteristics in Moyu County of 2014 were estimated,and were validated & compared.Result shows that three NPP model calculation result shows that vegetation net primary productivity spatial distribution differences,mainly concentrated in the central &southeast of the study area.The average NPP of each model is 70 g C·m~(-2),C-FIX model respectively.The model is 56 C·m~(-2),a GLO-PEM model 28 c·m~(-2) which CASA model simulation result is most close to the former research results;measured value of verification,the model test value and the simulated values are in optimal agreement,which CASA model average closest,correlation coefficient R2=0.994 15.Most of the samples of the error is less than 5%.
By Using LANDSAT8 remote sensing image data,combined with the meteorological data &field survey data,using CASA model,C-FIX model &using GLO-PEM model,forest vegetation net primary productivity spatial &temporal distribution characteristics in Moyu County of 2014 were estimated,and were validated & compared.Result shows that three NPP model calculation result shows that vegetation net primary productivity spatial distribution differences,mainly concentrated in the central &southeast of the study area.The average NPP of each model is 70 g C·m~(-2),C-FIX model respectively.The model is 56 C·m~(-2),a GLO-PEM model 28 c·m~(-2) which CASA model simulation result is most close to the former research results;measured value of verification,the model test value and the simulated values are in optimal agreement,which CASA model average closest,correlation coefficient R2=0.994 15.Most of the samples of the error is less than 5%.
引文
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[29]刘建锋,肖文发,郭明春,等.基于3-PGS模型的中国陆地植被格局[J].林业科学,2011(5):16-22
[30]程春晓,徐宗学,王志慧,等.2001-2010年东北三省植被净初级生产力模拟与时空变化分析[J].资源科学,2014,36(11):2401-2412
[2]李鑫,欧阳勋志,刘琪璟.江西省2001-2005年森林植被碳储量及区域分布特征[J].自然资源学报,2011,26(4):655-665
[3]Grubb M,Vrolijk C,Brack D.The kyoto Protocol:A Guide and Assessment[M].London:Royal institute of international affairs London,1999
[4]潘帅,于澎涛,王彦辉,等.六盘山森林植被碳密度空间分布特征及其成因[J].生态学报,2014,34(22):6666-6677
[5]刘伟玲,张林波,叶有华.深圳市森林植被碳储量特征及其空间分布[J].生态科,2012,31(2):144-154
[6]Ceulemans R,Janssens I A,Jach M E.Effects of CO2Enrichment on Trees and Forests:Lessons to Be Learned in View of Future Ecosystem Studies[J].Annuals of Botany,1999,84:577-590
[7]李士美,杨传强,王红年,等.基于森林资源清查资料分析山东省森林立木碳储量[J].应用生态学报,2014,25(8):2215-2220
[8]Fang J Y,Chen A P,Peng CH,et al.Changes in forest biomass carbon storage in China between 1994and 1998.science,2001,292,2320-2322
[9]Liu SN,Zhou T,Wei LY,et al.The spatial distribution of forest carbon sinks and sources in China.Chinese science Bulletin,2012,57:1699-1707
[10]Keeling R F,PiperS C,HeimannM.Global and hemispheric CO2sinks deduced from changes in atmospheric CO2concentration.Nature,1996,381:218-2211
[11]Tans P P,Fung I Y,Takahashi T.Observational constraints on the global atmospheric CO2budget[J].Science,1990,247:1431-1438
[12]黄从德,张健,杨万勤,等.四川省森林植被碳储量的空间分异特征[J].生态学报,2009,29(9)5115-5121
[13]Su HX,Sang WG,Wang QF,et al.Simulation study of CO2and moisture and heat flux in Chang bai Mountain forest ecosystem[J].China Science:Ser.D,2004,34(Suppl.):131-140
[14]Crame W,Bondeau A,Wood ward F I,et a.l.Global response of terrestrial ecosystem structure and function to CO2and climate change:results from six dynamic global vegetation models[J].Global Change Biology,2001(7):357-373
[15]Kimmins J P.Predicting sustainability of forest bioenergy production in the face of changing paradigms[J].Biomass and Bio-energy,1997(13):201-212
[16]Christopher J,Kucharik,Jonathan A,et al.Testing the performance of a dynamic global ecosystem model:Water balance,carbon balance,and vegetation structure[J].Global Biogeochemical Cycles,2000,14(3):795-825
[17]刘国华,傅伯杰,方精云.中国森林碳动态及其对全球碳平衡的贡献[J].生态学报,2000,20(5):733-740
[18]赵敏,周广胜.中国森林生态系统的植物碳贮量及其影响因子分析[J].地理科学,2004,24(1):50-54
[19]方岳,刘华,白志强,等.新疆喀纳斯保护区森林碳储量及碳密度研究[J].南京林业大学学报,2014,38(6):17-22
[20]郑朝晖,马江林,孙守文,等.新疆8年生银新杨热值与含碳率特征分析研究[J].2011,38(4):21-25
[21]吕研,宁虎森,王让会,等.人工防护林碳储量估算-以新疆墨玉为例[J].环境科学与技术,2010,33(7):27-30
[22]凌加富.墨玉县荒漠区人工林生态效益评价[D].乌鲁木齐:新疆农业大学,2016
[23]张福平,冯起,李旭谱,等.黑河流域NPP遥感估算及其时空变化特征[J].中国沙漠,2014,34(6):1657-1664
[24]张冬有,冯仲科,李亦秋,等.基于C-FIX模型的黑龙江省森林植被净初级生产力遥感估算[J].林业科学,2011,47(7):13-19
[25]姜群鸥,邓祥征,战金艳,等.黄淮海平原耕地转移对植被碳储量的影响[J].地理研究,2008,27(4):839-846
[26]Goetz S J,Prince S D,Goward S N,et al.Mapping net primary production and related biophysical variables with remote sensing:Application to the BOREAS region.Journal of Geophysical Research.1999,104(22):27719-27733
[27]Yang J L,Prince S D.A theoretical assessment of the relation between woody canopy cover and red reflectance.Remote Sensing of Environment.1997,59(3):428-439
[28]梁妙玲,谢正辉.我国气候对植被分布和净初级生产力影响的数值模拟[J].气候与环境研究,2006(5):582-592
[29]刘建锋,肖文发,郭明春,等.基于3-PGS模型的中国陆地植被格局[J].林业科学,2011(5):16-22
[30]程春晓,徐宗学,王志慧,等.2001-2010年东北三省植被净初级生产力模拟与时空变化分析[J].资源科学,2014,36(11):2401-2412
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