植被净初级生产力估算及与城市化水平的关系研究
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摘要
植被净初级生产力(Net Primary Productivity,NPP)的估算是碳循环研究的基础和重要内容。本文在对SPOT5遥感影像进行几何校正、辐射校正以及土地覆盖分类等预处理的基础上,利用植被、气象等地面空间数据,应用CASA模型对2005年6月份上海市东南区域NPP进行了估算;在此基础上,对研究区域NPP空间分布特征、NPP与城市化水平之间的关系以及不同土地覆盖类型NPP之间的差异性进行了分析;最后依据分析结果对上海市的绿化布局发展方向提出了一些可行性建议。
本文主要的研究工作和结论如下:
(1)采用基于大气辐射理论的FLAASH模型和6S模型分别对SPOT5遥感影像进行大气校正,通过比较校正前后地物光谱曲线和利用NDVI分析两种模型的实际校正效果,证明FLAASH模型能较好地消除大气的影响,对SPOT5影像具有更好的校正作用。
(2)通过支持向量机分类方法和最大似然法分类方法分别提取了研究区域的土地覆被信息。分析分类结果图和Kappa系数,对两种方法的分类结果进行评价,结果表明:支持向量机分类准确度高,地物错分现象相对较少。
(3)通过对研究区NPP空间分布分析发现: NPP在各区内均出现了高值,总体上市区高值出现的概率相对较低,浦东、南汇、奉贤、金山等郊区高值出现的概率相对较高。这与市区存在林地及农田较少,郊区存在林地和农田较多的现状还是比较吻合的。
(4)NPP与城市化水平之间的关系研究表明: NPP与城市化水平是密切相关的,总体上呈现出随城市化水平提高,NPP逐渐降低的趋势。对各区域内不同土地覆盖类型NPP比较分析发现:林地NPP最大,固碳能力最高,草地次之,农田植被固碳能力相对较低。
Estimate of NPP is the basis and main content of carbon cycle research. On the basis of the Vegetation Classification of SPOT5 remote sensing image,Remote sensing estimation of vegetation NPP of parts of Shanghai in June 2005 was realized.The estimate made use of CASA model,weather and other ground data. The thesis has analysised the relationship between NPP and the level of urbanization and NPP of various land cover types in various regions.Finally, the study put forward advice for the sustainable development plan of Shanghai .
In this paper, the research work and conclusions are as follows:
(1)The FLAASH and 6S are used as the atmospheric correction models.In order to evaluate and validate the correction results, The object spectral reponse curve and NDVI profile curve were used to analyse the images after atmospheric correction.As a result, the FLAASH atmospheric correction model is better to SPOT5 remote sensing images than 6S model.
(2) The land cover information was extracted from the study area making use of the support vector machine classification and maximum likelihood classification. Evaluating the classification results of two methods evaluate, the results show that the accuracy of Support vector machine classification is higher and the phenomenon of surface features misclassification is less.
(3)According to NPP density distribution map, High value of NPP is in the various regions of the study area. Generally, the probability of high value appears relatively low in urban area, and the probability of high-value appeared relatively high in Pudong, Nanhui, Fengxian, Jinshan and other suburbs. The phenomenon consistent with the facts that woodland and farmland are less in urban, and woodland and agricultural land are relatively more.
(4) NPP is closely related to the level of urbanization.With the level of urbanization increased, NPP is decreased. NPP of different land cover types are comparatively analyzed in each region.The results show that the forest NPP is the largest;carbon sequestration capacity is the highest, followed by grassland, and cropland carbon sequestration capacity is relatively low.
本文主要的研究工作和结论如下:
(1)采用基于大气辐射理论的FLAASH模型和6S模型分别对SPOT5遥感影像进行大气校正,通过比较校正前后地物光谱曲线和利用NDVI分析两种模型的实际校正效果,证明FLAASH模型能较好地消除大气的影响,对SPOT5影像具有更好的校正作用。
(2)通过支持向量机分类方法和最大似然法分类方法分别提取了研究区域的土地覆被信息。分析分类结果图和Kappa系数,对两种方法的分类结果进行评价,结果表明:支持向量机分类准确度高,地物错分现象相对较少。
(3)通过对研究区NPP空间分布分析发现: NPP在各区内均出现了高值,总体上市区高值出现的概率相对较低,浦东、南汇、奉贤、金山等郊区高值出现的概率相对较高。这与市区存在林地及农田较少,郊区存在林地和农田较多的现状还是比较吻合的。
(4)NPP与城市化水平之间的关系研究表明: NPP与城市化水平是密切相关的,总体上呈现出随城市化水平提高,NPP逐渐降低的趋势。对各区域内不同土地覆盖类型NPP比较分析发现:林地NPP最大,固碳能力最高,草地次之,农田植被固碳能力相对较低。
Estimate of NPP is the basis and main content of carbon cycle research. On the basis of the Vegetation Classification of SPOT5 remote sensing image,Remote sensing estimation of vegetation NPP of parts of Shanghai in June 2005 was realized.The estimate made use of CASA model,weather and other ground data. The thesis has analysised the relationship between NPP and the level of urbanization and NPP of various land cover types in various regions.Finally, the study put forward advice for the sustainable development plan of Shanghai .
In this paper, the research work and conclusions are as follows:
(1)The FLAASH and 6S are used as the atmospheric correction models.In order to evaluate and validate the correction results, The object spectral reponse curve and NDVI profile curve were used to analyse the images after atmospheric correction.As a result, the FLAASH atmospheric correction model is better to SPOT5 remote sensing images than 6S model.
(2) The land cover information was extracted from the study area making use of the support vector machine classification and maximum likelihood classification. Evaluating the classification results of two methods evaluate, the results show that the accuracy of Support vector machine classification is higher and the phenomenon of surface features misclassification is less.
(3)According to NPP density distribution map, High value of NPP is in the various regions of the study area. Generally, the probability of high value appears relatively low in urban area, and the probability of high-value appeared relatively high in Pudong, Nanhui, Fengxian, Jinshan and other suburbs. The phenomenon consistent with the facts that woodland and farmland are less in urban, and woodland and agricultural land are relatively more.
(4) NPP is closely related to the level of urbanization.With the level of urbanization increased, NPP is decreased. NPP of different land cover types are comparatively analyzed in each region.The results show that the forest NPP is the largest;carbon sequestration capacity is the highest, followed by grassland, and cropland carbon sequestration capacity is relatively low.
引文
[1]Houghton.全球变暖[M].北京:气象出版社,1998.
[2]Keeling C.D.T.P. Whorf .Atmospheric CO2 records from sites in the SIO air samplingNetwork .In Trends: A Compendium of Data on Global Change. Oak Ridge, TN: Carbon Dioxide Information Analysis Center, 2004.
[3]IPCC, Climate Change 2001: Impaction and vulnerability[M].Cambridge University Press,2001.
[4]http://gcmd.nasa.gov.
[5]Tao S, Chen P, Riches M R, Wang W-C. Greenhouse Effect and Climate Change [M].Beijing: China Ocean Press,1999.
[6]Riches M.R, Wei-Chyung Wang,Panqin Chen,et al.Recent Progress in the joint agreements On“Global and Regional Climate Change”study between the United States and the PeoPle’s Republic of China[J].Bulletin of the American Meteorological Society, 2000, 81(3):491-499.
[7]IPCC, Climate Change 1995, The science of climate change[M]. Cambridge University Press, 1996.
[8]龚道溢.大尺度大气环流变化及其对北半球冬季温度的影响[J].地学前缘,2000,7(增刊): 203-208.
[9]周广胜.全球碳循环[M].北京:气象出版社,2003.
[10]周广胜,王玉辉,蒋延玲,等.陆地生态系统转变与碳循环[J].植物生态学报,2002, 26(2):250-254.
[11]陈正华.基于CASA和多光谱遥感数据的黑河流域NPP研究[D].甘肃:兰州大学,2006.
[12]张冬有.黑龙江省森林植被净初级生产力遥感估算研究[D].北京:北京林业大学,2009.
[13]上海人均GDP突破7000美元大关[N].中国经济时报,2007-02-26.
[14]节能减排让上海经济更“绿”[N].文汇报,2008-01-12.
[15]方精云,柯金虎,唐志尧,等.生物生产力的“4P”概念、估算及相互关系[J].植物生态学报,2001,25(4):414-419.
[16]Holt,Rinehart and Winston.Odum E P.Basic ecology 3rd[M].CBS College Publishing. 1983,98-99.
[17]陈国南.用迈阿密模型测算我国生物生产量的初步尝试[J].自然资源学报,1987,2(3):270-277.
[18]Braswell B H,Schimel D S,Linder E,et al.The response of global terrestrial ecosystems to interannual temperature variability[J].science,1997,28:870-872.
[19]埃塞林顿. J. R.环境和植物生态学[M].曲仲湘译.北京:科学出版社,1989.
[20]Wittaker RH,Likens GE. The biosphere and man[A].In:Lieth H,eds.Primary Productivity of the Biosphere [C].New York:Springer-Verlag, 1975,55-115.
[21]Lieth,H.F.H.Modeling thePrimary Productivity of the world.Nature andResoucres,1972,8(2):5-10.
[22]赵士洞,罗天祥.区域尺度陆地生态系统生物生产力研究方法[J].资源科学,1998,20(1):23-34.
[23]李文华.森林生物生产力的概念及其研究的基本途径[J].自然资源,1978,(l):71-92.
[24]胡自治,孙吉雄,李洋,等.甘肃天祝主要高山草地生物量及光能转化率[J].植物生态学报, 1994,18(2):121-131.
[25]方精云,刘国华,徐嵩龄.我国森林植被的生物量和净生产量[J].生态学报,1996,16(5):497-508.
[26]任海,彭少麟,向言词.鹤山马占相思人工林的生物量和净初级生产力[J].植物生态学报,2000,24(1):18-21.
[27]Ruimy,A.,Saugier,B.Methodology for the estimation of terrestrial net Primary Production from remotely sensed data[J].Journal of Geophysical Research, 1994, 99: 5263 -5283.
[28]Uchijima Z.Seino H.Agroclimatic evaluation of net primary productivity of natural vegetationⅠ.Chikugo model for evaluating productivity [J].J.Agric.Meteor.,1985,40:343-353.
[29]Lieth,H,Whittaker,R.H.,Modeling the Primary Productivity of the world[A].Primary Productivity of the Biosphere[M].New York:SPringer-Verlag ,1975.
[30]龙爱华,王浩,程国栋,等.黑河流域中游地区净初级生产力的人类占用[J].应用生态学报,2008,19(4):853-858.
[31] Lieth H,Whittaker,R.H,王业蓬,等译.生物圈的第一性生产力[M].北京:科学出社,1985.
[32]杜军,胡军,张勇,等.西藏植被净初级生产力对气候变化的响应[J].南京气象学院学报,2008,31,(5)738-743.
[33]Efimova N A. Plant productivity and radiation factor[M].Gidrometeoizdat: Leingrad, 1977.
[34]Cannell M G R. World forest biomass and primary productivity data[M]. London: Academic Press,1982.
[35]王宗明,梁银丽.植被净第一性生产力模型研究进展[J].西北林学院学报,2002,17(2):22-25.
[36]朱志辉.自然植被净第一性生产力估计模型[J].科学通报,1993,38(15):1422-1426.
[37]周广胜,张新时.自然植被第一性生产力模型初探[J].植物生态学报,1995,19(3):193-200.
[38] De Wit C T.农作物同化、呼吸和蒸腾的模拟[M].北京农业大学生理生化组译.北京:科学出版社,1987.
[39]冯险峰,刘高焕,陈述彭,等.陆地生态系统净第一性生产力过程模型研究综述[J].自然资源学报,2004,19(3)369-375.
[40]朱文泉,陈云浩,徐丹,等.陆地植被净初级生产力计算模型研究进展[J].生态学杂志,2005,24(3):296-300.
[41]肖向明,王义凤,陈佐忠.内蒙古锡林河流域典型草原初级生产力和土壤有机质的动态及其对气候变化的反应[J].植物学报,1996,38(l):45-52.
[42]陶波,李克让,邵雪梅,等.中国陆地净初级生产力时空特征模拟[J].地理学报,2003,58(3):372-380.
[43]蔡睿,徐瑞松.广东省植被NPP时空特征变化分析[J].农机化研究,2009,2:9-11.
[44]周才平,欧阳华,王勤学,等.青藏高原主要生态系统净初级生产力的估算[J].地理学报,2004,59(1):74-79.
[45]松下文经,杨翠芬,陈晋,等.广域空间尺度上植被净初级生产力的精确推算[J].地理学报,2004,59(1):80-87.
[46]潘愉德,Melillo,J.M.,Kicklighter,D.w.,等.大气CO2升高及气候变化对中国陆地生态系统结构与功能的制约和影响[J].植物生态学报,2001,25(2).175-189.
[47]Field CB,Randerson JT,Malmstrom CM.Global net primary production:Combining ecology and remote sensing [J].Remote Sensing Environ.,1995,51:74-88.
[48]Bloom AJ,Chapin FSⅢ,Mooney HA.Resource limitation in plants-An economic analogy [J].Ann. Rev. Ecol. Syst.,1985,16:363-392.
[49]张峰,周广胜,王玉辉.基于CASA模型的内蒙古典型草原植被净初级生产力动态模拟[J].植物生态学报,2008,32(4)786-797.
[50]周才平,欧阳华,曹宇,等.“一江两河”中部流域植被净初级生产力估算[J].应用生态学报,2008,19(5):1071-1076.
[51]高志强,刘纪远,曹明奎,等.土地利用和气候变化对区域净初级生产力的影响[J].地理学报,2004,59(4)581-591.
[52]王军邦,刘纪远,邵全琴,等.基于遥感-过程耦合模型的1988～2004年青海三江源区净初级生产力模拟[J].植物生态学报,2009,33(2)254-269.
[53]刘卫国,魏文寿,刘志辉.新疆气候变化下植被净初级生产力格局分析[J].干旱区研究,2009,26(2):206-211.
[54]丁志,童庆禧,郑兰芬,等.应用气象卫星图像资料进行草场生物量测量方法的初步研究[J].干旱区研究,1986,(2):8-13.
[55]肖乾广,陈维英.用NOAA气象卫星的AVHRR遥感资料估算中国的第一性生产力[J].植物学报,1996,38(1):45-52.
[56]柯金虎,朴世龙,方精云.长江流域植被净第一性生产力及其时空格局研究[J].植物生态学报,2003,27(6):764-770.
[57]http://www.21cma.net/thread-3624-1-3.html
[58]林文鹏,赵敏,张翼飞,等.基于SPOT5遥感影像的城市森林叶面积指数反演[J].测绘科学,2008,33(2):57-59.
[59]尹占娥.现代遥感导论[M].北京:科学出版社,2008.
[60]SPOT IMAGE.SPOT to direet reeeiving station interface doeument, S-IF-O/E-10-SI, Ed.2-Rev. 01997.
[61]Monteith,J.L.,Solar radiation and Productivity in tropical ecosystem[J].Journal of Application Ecology,1927,9:747-766.
[62]Heiman,M.,Keeling,C.D.,A three-dimensional model of atmospheric CO2 transport based on observed winds:2.Model description and simulated tracer experiments,in Climate Variability in the Pacificand the Western Americas (D.H.Peterson,Ed.).American Geophysical Union,1989,237-275.
[63]Potter,C.S.,Randerson,J.T.,Field,C.B.,Matson,P.A.,Vitousek,P.M.,Mooney,H.A.,and Klooster, S.A.,Terrestrial ecosystem production:A process model based on global satellite and surface data[J].Global Biogeochemical Cycles.1993,7(4):811-841.
[64]Field C.B.,Randerson,J.T.,Malmstrom C.M.,Global net primary production:combining ecology and remote sensing[J].Remote Sensing of Environment,1995,51:74-88.
[65]Potter C.S.,Klooster,S.A.,Global model estimates of carbon and nitrogen storage in litter and soil pools:response to changes in vegetation quality and biomass allocation[J].Tellus, 1997, 49B:1-17.
[66]Lobell D.B.,Hicke J.A.,Asner G.P.,Field C.B.,Tucker C.J.,Los S.O.,Satellite estimates of productivity and light use efficiency in United States agriculture,1982-1998[J].Global Change Biology,2002,8:722-735.
[67]Bradford J.B.,Hicke J.A.,Lauenroth W.K.,The relative importance of light-use efficiency modifications from environmental conditions and cultivation for estimation of large-scale net primary productivity[J].Remote Sensing of Environment,2005,96:246-255.
[68]孙睿,朱启疆.中国陆地植被净第一性生产力及季节变化研究[J].地理学报,2000,55(1):36-45.
[69]朴世龙,方精云,郭庆华.利用CASA模型估算我国净第一性生产力[J].植物生态学报.2001,25(5):603-608.
[70]Huemmrich K.F,Goward S.N.Spectral vegetation indices and the remote sensing of biophysical parameters[A].Proceeding of International Geoscience and Remote Sensing Symposium(IGARSS)[C].Institute of Electrical and Electronics Engineers,Houston Texas,1992,1017-1019.
[71]Seller P.J.,Berry J.A.,Collatz G.J.,Field C.B.,Hall F.G.,Canopy reflectance,photosynthesis,and transpiration:Ⅲ.A reanalysis using improved leaf models and a new canopy integration scheme.Remote Sensing of Environment,1992,42:187-216.
[72]侯光良,李继由,张谊光.中国农业气候资源[M].北京:中国人民大学出版社.1993.
[73]彭少麟,郭志华,王伯荪.利用GIS和RS估算广东植被光利用率[J].生态学报,2000,20(6):903-909.
[74]Running S.W,Thornton P.E,Nemani R,el al.Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System[A].Sala O,Jackson R,Mooney H.Methods in Ecosystem Science[C].New York:Springer Verlag,2000,44-57.
[75]牛建明.内蒙古主要植被类型与气候因子关系的研究[J].应用生态学报,2000,11(1):47-52.
[76]张志明.计算蒸发量的原理与方法[M].成都:成都科技大学出版社,1990.
[77]周广胜,张新时.全球变化的中国气候一植被分类研究[J].植物学报,1996,38(1):8-17.
[78]张占睦,芮杰.遥感技术基础[M].北京:科学出版社,2007.
[79]Vermote E, Tanre D, Deuze JL,et al. Second simulation of the satellite signal in the solar spectrum, 6S: Anoverview[J]..IEEE Transactions on Geoscience and RemoteSensing, 1997,35(3): 675-686.
[80]CHEN Xinfang,CHEN Jingming,AN Shuqing,et al.Comparison of different atmospheric correction models in their effects on Landsat TM estimation of forest leaf area index[J].Chinese Journal of Ecology,2006,25(7):769-773.
[81]ZHENG Wei,ZENG Zhi-yuan.A Review on Methods of Atmospheric Correction for Remote Sensing Images[J].Remote Sensing Information, 2004,(4):66-67.
[82]Tian Qingjiu,Zheng Lanfen,Tong Qingxi. Image-based atmospheric radiation correction and reflectance retrieval methods[J].Quarterly Journal of Applied Meteorology,1998,9(4):456-461.
[83]SONG Xiao-yu, WANG Ji-hua, LIU Liang-yun,et al.Atmospheric Correction of Hyper-spectral Imagery:Evaluation of the FLAASH Algorithm with AVRIS Data[J].Remote Sensing Technology and Application, 2005,20(4):393-398.
[84]SPOT IMAGE. SPOT to direetreeeiving station interface doeument,S-IF-O/E-10-SI, Ed.2-Rev. 01997.
[85]邓锟.基于多源信息的TM遥感影像分类[D].陕西:西北农林科技大学,2008.
[86]常庆瑞.遥感技术导论[M].北京:科学出版社,2004.
[87]李颖,赵文吉等.遥感影像的分类与识别技术在土地资源调查中的应用[J].长春科技大学学报,2001,31(3):261-264.
[88]李小娟,刘晓萌,胡德勇,等编译.ENVI遥感影像处理教程[M].北京:中国环境科学出版社,2007.
[89]梅安新,彭望碌等.遥感导论[J].北京:北京高等教育出版社,2002.198-199.
[90]Bezdek J C. Pattern recognition with fuzzy objective function algorithms[M].NewYork: Plenum Press,1981 .
[91]马建文等.遥感数据智能处理方法与程序设计[M].北京:科学出版社,2005.
[92]李霞,卢宗庆,谢维信.基于退火的状态转移式模糊C-均值聚类算法[J].系统工程与电子技术,2002,24(1):86-88.
[93]黎夏.形状信息的提取与计算机自动分类[J].环境遥感,1995,10(4):279-287.
[94]席学强,王润生.一个针对遥感图像特定目标的自动识别系统[J].遥感技术与应用,2000,15(3):179-183.
[95]文贡坚,王润生.从航空遥感图像中自动提取主要道路[J].软件学报,2000,11(7):957-964.
[96]沈清,汤霖.模式识别导论[M].长沙:国防科技大学出版社,1991.
[97]王喜鹏,张养贞.应用神经网络模型分解AVHRR混合像元[J].遥感学报,1998,2(1):51-56.
[98]贾永红,张春森,王爱平.基于BP网络的多源遥感图像分类[J].西安科技院学报报,2001,21(1):58-60.
[99]王耀南,毛建旭.基于模糊高斯基函数神经网络的遥感图象分类[J].遥感技术与应用,2001,16(1):62-65.
[100]B.E.Boser,I. M. Guyon,and V. N. Vapnik.A training algorithm for optimal magin classifiers.In D.Haussler,editor,Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory[M].ACM Press,1992.
[101]张勇.基于SVM遥感图像专题信息提取研究[D].湖南:中南大学,2005.
[102]Foody G M, MathurA·A Relative Evaluation ofMulti-class Image Classification by Support Vector Machines[J].IEEE Trans·Geo-science and Remote Sensing,2004, 42: 1335-1343.
[103]刘治国.支持向量机算法在TM多光谱图像分类中的应用与实现[D].北京:中国地质大学,2006.
[104]李淼.基于支持向量机的MODIS数据土地覆盖分类研究[D].辽宁:辽宁工程技术大学,2007.
[105]吴建平,杨星卫.遥感数据监督分类中训练样本的纯化[J].国土资源遥感,1996,1:36-41.
[106]傅文杰,洪金益,林明森.基于光谱相似尺度的支持向量机遥感土地利用分类[J].遥感技术与应用,2006,21(1):25-30.
[107]傅伯杰,陈利顶,蔡运龙,等.环渤海地区土地利用变化及可持续利用研究[M].北京:北京科学出版社,2004.
[108]许学强,周一星,宁越敏.城市地理学[M].北京:高等教育出版社,1996.
[109]师谦友,范化冬,张敏娜,等.西安城市化进程与人居环境协调发展研究[J].西北大学学报(自然科学版),2008,38(4):673-679.
[110]卢新卫,陈鹏.西安城市化进程与环境生态问题研究[J].干旱区资源与环境,2006,20(1):7-12.
[111]安祥生.山西省城市化类型的划分[J].经济地理,200,20(4):50-54.
[112]欧向军,甄峰,秦永东,等.区域城市化水平综合测度及其理想动力分析[J].地理研究,2008,27(5):994-1002.
[113]徐建华.现代地理学中的数学方法[M].高等教育出版社,2002.
[114]耿修林,谢兆茹.应用统计学[M].北京:科学出版社,2002.
[2]Keeling C.D.T.P. Whorf .Atmospheric CO2 records from sites in the SIO air samplingNetwork .In Trends: A Compendium of Data on Global Change. Oak Ridge, TN: Carbon Dioxide Information Analysis Center, 2004.
[3]IPCC, Climate Change 2001: Impaction and vulnerability[M].Cambridge University Press,2001.
[4]http://gcmd.nasa.gov.
[5]Tao S, Chen P, Riches M R, Wang W-C. Greenhouse Effect and Climate Change [M].Beijing: China Ocean Press,1999.
[6]Riches M.R, Wei-Chyung Wang,Panqin Chen,et al.Recent Progress in the joint agreements On“Global and Regional Climate Change”study between the United States and the PeoPle’s Republic of China[J].Bulletin of the American Meteorological Society, 2000, 81(3):491-499.
[7]IPCC, Climate Change 1995, The science of climate change[M]. Cambridge University Press, 1996.
[8]龚道溢.大尺度大气环流变化及其对北半球冬季温度的影响[J].地学前缘,2000,7(增刊): 203-208.
[9]周广胜.全球碳循环[M].北京:气象出版社,2003.
[10]周广胜,王玉辉,蒋延玲,等.陆地生态系统转变与碳循环[J].植物生态学报,2002, 26(2):250-254.
[11]陈正华.基于CASA和多光谱遥感数据的黑河流域NPP研究[D].甘肃:兰州大学,2006.
[12]张冬有.黑龙江省森林植被净初级生产力遥感估算研究[D].北京:北京林业大学,2009.
[13]上海人均GDP突破7000美元大关[N].中国经济时报,2007-02-26.
[14]节能减排让上海经济更“绿”[N].文汇报,2008-01-12.
[15]方精云,柯金虎,唐志尧,等.生物生产力的“4P”概念、估算及相互关系[J].植物生态学报,2001,25(4):414-419.
[16]Holt,Rinehart and Winston.Odum E P.Basic ecology 3rd[M].CBS College Publishing. 1983,98-99.
[17]陈国南.用迈阿密模型测算我国生物生产量的初步尝试[J].自然资源学报,1987,2(3):270-277.
[18]Braswell B H,Schimel D S,Linder E,et al.The response of global terrestrial ecosystems to interannual temperature variability[J].science,1997,28:870-872.
[19]埃塞林顿. J. R.环境和植物生态学[M].曲仲湘译.北京:科学出版社,1989.
[20]Wittaker RH,Likens GE. The biosphere and man[A].In:Lieth H,eds.Primary Productivity of the Biosphere [C].New York:Springer-Verlag, 1975,55-115.
[21]Lieth,H.F.H.Modeling thePrimary Productivity of the world.Nature andResoucres,1972,8(2):5-10.
[22]赵士洞,罗天祥.区域尺度陆地生态系统生物生产力研究方法[J].资源科学,1998,20(1):23-34.
[23]李文华.森林生物生产力的概念及其研究的基本途径[J].自然资源,1978,(l):71-92.
[24]胡自治,孙吉雄,李洋,等.甘肃天祝主要高山草地生物量及光能转化率[J].植物生态学报, 1994,18(2):121-131.
[25]方精云,刘国华,徐嵩龄.我国森林植被的生物量和净生产量[J].生态学报,1996,16(5):497-508.
[26]任海,彭少麟,向言词.鹤山马占相思人工林的生物量和净初级生产力[J].植物生态学报,2000,24(1):18-21.
[27]Ruimy,A.,Saugier,B.Methodology for the estimation of terrestrial net Primary Production from remotely sensed data[J].Journal of Geophysical Research, 1994, 99: 5263 -5283.
[28]Uchijima Z.Seino H.Agroclimatic evaluation of net primary productivity of natural vegetationⅠ.Chikugo model for evaluating productivity [J].J.Agric.Meteor.,1985,40:343-353.
[29]Lieth,H,Whittaker,R.H.,Modeling the Primary Productivity of the world[A].Primary Productivity of the Biosphere[M].New York:SPringer-Verlag ,1975.
[30]龙爱华,王浩,程国栋,等.黑河流域中游地区净初级生产力的人类占用[J].应用生态学报,2008,19(4):853-858.
[31] Lieth H,Whittaker,R.H,王业蓬,等译.生物圈的第一性生产力[M].北京:科学出社,1985.
[32]杜军,胡军,张勇,等.西藏植被净初级生产力对气候变化的响应[J].南京气象学院学报,2008,31,(5)738-743.
[33]Efimova N A. Plant productivity and radiation factor[M].Gidrometeoizdat: Leingrad, 1977.
[34]Cannell M G R. World forest biomass and primary productivity data[M]. London: Academic Press,1982.
[35]王宗明,梁银丽.植被净第一性生产力模型研究进展[J].西北林学院学报,2002,17(2):22-25.
[36]朱志辉.自然植被净第一性生产力估计模型[J].科学通报,1993,38(15):1422-1426.
[37]周广胜,张新时.自然植被第一性生产力模型初探[J].植物生态学报,1995,19(3):193-200.
[38] De Wit C T.农作物同化、呼吸和蒸腾的模拟[M].北京农业大学生理生化组译.北京:科学出版社,1987.
[39]冯险峰,刘高焕,陈述彭,等.陆地生态系统净第一性生产力过程模型研究综述[J].自然资源学报,2004,19(3)369-375.
[40]朱文泉,陈云浩,徐丹,等.陆地植被净初级生产力计算模型研究进展[J].生态学杂志,2005,24(3):296-300.
[41]肖向明,王义凤,陈佐忠.内蒙古锡林河流域典型草原初级生产力和土壤有机质的动态及其对气候变化的反应[J].植物学报,1996,38(l):45-52.
[42]陶波,李克让,邵雪梅,等.中国陆地净初级生产力时空特征模拟[J].地理学报,2003,58(3):372-380.
[43]蔡睿,徐瑞松.广东省植被NPP时空特征变化分析[J].农机化研究,2009,2:9-11.
[44]周才平,欧阳华,王勤学,等.青藏高原主要生态系统净初级生产力的估算[J].地理学报,2004,59(1):74-79.
[45]松下文经,杨翠芬,陈晋,等.广域空间尺度上植被净初级生产力的精确推算[J].地理学报,2004,59(1):80-87.
[46]潘愉德,Melillo,J.M.,Kicklighter,D.w.,等.大气CO2升高及气候变化对中国陆地生态系统结构与功能的制约和影响[J].植物生态学报,2001,25(2).175-189.
[47]Field CB,Randerson JT,Malmstrom CM.Global net primary production:Combining ecology and remote sensing [J].Remote Sensing Environ.,1995,51:74-88.
[48]Bloom AJ,Chapin FSⅢ,Mooney HA.Resource limitation in plants-An economic analogy [J].Ann. Rev. Ecol. Syst.,1985,16:363-392.
[49]张峰,周广胜,王玉辉.基于CASA模型的内蒙古典型草原植被净初级生产力动态模拟[J].植物生态学报,2008,32(4)786-797.
[50]周才平,欧阳华,曹宇,等.“一江两河”中部流域植被净初级生产力估算[J].应用生态学报,2008,19(5):1071-1076.
[51]高志强,刘纪远,曹明奎,等.土地利用和气候变化对区域净初级生产力的影响[J].地理学报,2004,59(4)581-591.
[52]王军邦,刘纪远,邵全琴,等.基于遥感-过程耦合模型的1988～2004年青海三江源区净初级生产力模拟[J].植物生态学报,2009,33(2)254-269.
[53]刘卫国,魏文寿,刘志辉.新疆气候变化下植被净初级生产力格局分析[J].干旱区研究,2009,26(2):206-211.
[54]丁志,童庆禧,郑兰芬,等.应用气象卫星图像资料进行草场生物量测量方法的初步研究[J].干旱区研究,1986,(2):8-13.
[55]肖乾广,陈维英.用NOAA气象卫星的AVHRR遥感资料估算中国的第一性生产力[J].植物学报,1996,38(1):45-52.
[56]柯金虎,朴世龙,方精云.长江流域植被净第一性生产力及其时空格局研究[J].植物生态学报,2003,27(6):764-770.
[57]http://www.21cma.net/thread-3624-1-3.html
[58]林文鹏,赵敏,张翼飞,等.基于SPOT5遥感影像的城市森林叶面积指数反演[J].测绘科学,2008,33(2):57-59.
[59]尹占娥.现代遥感导论[M].北京:科学出版社,2008.
[60]SPOT IMAGE.SPOT to direet reeeiving station interface doeument, S-IF-O/E-10-SI, Ed.2-Rev. 01997.
[61]Monteith,J.L.,Solar radiation and Productivity in tropical ecosystem[J].Journal of Application Ecology,1927,9:747-766.
[62]Heiman,M.,Keeling,C.D.,A three-dimensional model of atmospheric CO2 transport based on observed winds:2.Model description and simulated tracer experiments,in Climate Variability in the Pacificand the Western Americas (D.H.Peterson,Ed.).American Geophysical Union,1989,237-275.
[63]Potter,C.S.,Randerson,J.T.,Field,C.B.,Matson,P.A.,Vitousek,P.M.,Mooney,H.A.,and Klooster, S.A.,Terrestrial ecosystem production:A process model based on global satellite and surface data[J].Global Biogeochemical Cycles.1993,7(4):811-841.
[64]Field C.B.,Randerson,J.T.,Malmstrom C.M.,Global net primary production:combining ecology and remote sensing[J].Remote Sensing of Environment,1995,51:74-88.
[65]Potter C.S.,Klooster,S.A.,Global model estimates of carbon and nitrogen storage in litter and soil pools:response to changes in vegetation quality and biomass allocation[J].Tellus, 1997, 49B:1-17.
[66]Lobell D.B.,Hicke J.A.,Asner G.P.,Field C.B.,Tucker C.J.,Los S.O.,Satellite estimates of productivity and light use efficiency in United States agriculture,1982-1998[J].Global Change Biology,2002,8:722-735.
[67]Bradford J.B.,Hicke J.A.,Lauenroth W.K.,The relative importance of light-use efficiency modifications from environmental conditions and cultivation for estimation of large-scale net primary productivity[J].Remote Sensing of Environment,2005,96:246-255.
[68]孙睿,朱启疆.中国陆地植被净第一性生产力及季节变化研究[J].地理学报,2000,55(1):36-45.
[69]朴世龙,方精云,郭庆华.利用CASA模型估算我国净第一性生产力[J].植物生态学报.2001,25(5):603-608.
[70]Huemmrich K.F,Goward S.N.Spectral vegetation indices and the remote sensing of biophysical parameters[A].Proceeding of International Geoscience and Remote Sensing Symposium(IGARSS)[C].Institute of Electrical and Electronics Engineers,Houston Texas,1992,1017-1019.
[71]Seller P.J.,Berry J.A.,Collatz G.J.,Field C.B.,Hall F.G.,Canopy reflectance,photosynthesis,and transpiration:Ⅲ.A reanalysis using improved leaf models and a new canopy integration scheme.Remote Sensing of Environment,1992,42:187-216.
[72]侯光良,李继由,张谊光.中国农业气候资源[M].北京:中国人民大学出版社.1993.
[73]彭少麟,郭志华,王伯荪.利用GIS和RS估算广东植被光利用率[J].生态学报,2000,20(6):903-909.
[74]Running S.W,Thornton P.E,Nemani R,el al.Global Terrestrial Gross and Net Primary Productivity from the Earth Observing System[A].Sala O,Jackson R,Mooney H.Methods in Ecosystem Science[C].New York:Springer Verlag,2000,44-57.
[75]牛建明.内蒙古主要植被类型与气候因子关系的研究[J].应用生态学报,2000,11(1):47-52.
[76]张志明.计算蒸发量的原理与方法[M].成都:成都科技大学出版社,1990.
[77]周广胜,张新时.全球变化的中国气候一植被分类研究[J].植物学报,1996,38(1):8-17.
[78]张占睦,芮杰.遥感技术基础[M].北京:科学出版社,2007.
[79]Vermote E, Tanre D, Deuze JL,et al. Second simulation of the satellite signal in the solar spectrum, 6S: Anoverview[J]..IEEE Transactions on Geoscience and RemoteSensing, 1997,35(3): 675-686.
[80]CHEN Xinfang,CHEN Jingming,AN Shuqing,et al.Comparison of different atmospheric correction models in their effects on Landsat TM estimation of forest leaf area index[J].Chinese Journal of Ecology,2006,25(7):769-773.
[81]ZHENG Wei,ZENG Zhi-yuan.A Review on Methods of Atmospheric Correction for Remote Sensing Images[J].Remote Sensing Information, 2004,(4):66-67.
[82]Tian Qingjiu,Zheng Lanfen,Tong Qingxi. Image-based atmospheric radiation correction and reflectance retrieval methods[J].Quarterly Journal of Applied Meteorology,1998,9(4):456-461.
[83]SONG Xiao-yu, WANG Ji-hua, LIU Liang-yun,et al.Atmospheric Correction of Hyper-spectral Imagery:Evaluation of the FLAASH Algorithm with AVRIS Data[J].Remote Sensing Technology and Application, 2005,20(4):393-398.
[84]SPOT IMAGE. SPOT to direetreeeiving station interface doeument,S-IF-O/E-10-SI, Ed.2-Rev. 01997.
[85]邓锟.基于多源信息的TM遥感影像分类[D].陕西:西北农林科技大学,2008.
[86]常庆瑞.遥感技术导论[M].北京:科学出版社,2004.
[87]李颖,赵文吉等.遥感影像的分类与识别技术在土地资源调查中的应用[J].长春科技大学学报,2001,31(3):261-264.
[88]李小娟,刘晓萌,胡德勇,等编译.ENVI遥感影像处理教程[M].北京:中国环境科学出版社,2007.
[89]梅安新,彭望碌等.遥感导论[J].北京:北京高等教育出版社,2002.198-199.
[90]Bezdek J C. Pattern recognition with fuzzy objective function algorithms[M].NewYork: Plenum Press,1981 .
[91]马建文等.遥感数据智能处理方法与程序设计[M].北京:科学出版社,2005.
[92]李霞,卢宗庆,谢维信.基于退火的状态转移式模糊C-均值聚类算法[J].系统工程与电子技术,2002,24(1):86-88.
[93]黎夏.形状信息的提取与计算机自动分类[J].环境遥感,1995,10(4):279-287.
[94]席学强,王润生.一个针对遥感图像特定目标的自动识别系统[J].遥感技术与应用,2000,15(3):179-183.
[95]文贡坚,王润生.从航空遥感图像中自动提取主要道路[J].软件学报,2000,11(7):957-964.
[96]沈清,汤霖.模式识别导论[M].长沙:国防科技大学出版社,1991.
[97]王喜鹏,张养贞.应用神经网络模型分解AVHRR混合像元[J].遥感学报,1998,2(1):51-56.
[98]贾永红,张春森,王爱平.基于BP网络的多源遥感图像分类[J].西安科技院学报报,2001,21(1):58-60.
[99]王耀南,毛建旭.基于模糊高斯基函数神经网络的遥感图象分类[J].遥感技术与应用,2001,16(1):62-65.
[100]B.E.Boser,I. M. Guyon,and V. N. Vapnik.A training algorithm for optimal magin classifiers.In D.Haussler,editor,Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory[M].ACM Press,1992.
[101]张勇.基于SVM遥感图像专题信息提取研究[D].湖南:中南大学,2005.
[102]Foody G M, MathurA·A Relative Evaluation ofMulti-class Image Classification by Support Vector Machines[J].IEEE Trans·Geo-science and Remote Sensing,2004, 42: 1335-1343.
[103]刘治国.支持向量机算法在TM多光谱图像分类中的应用与实现[D].北京:中国地质大学,2006.
[104]李淼.基于支持向量机的MODIS数据土地覆盖分类研究[D].辽宁:辽宁工程技术大学,2007.
[105]吴建平,杨星卫.遥感数据监督分类中训练样本的纯化[J].国土资源遥感,1996,1:36-41.
[106]傅文杰,洪金益,林明森.基于光谱相似尺度的支持向量机遥感土地利用分类[J].遥感技术与应用,2006,21(1):25-30.
[107]傅伯杰,陈利顶,蔡运龙,等.环渤海地区土地利用变化及可持续利用研究[M].北京:北京科学出版社,2004.
[108]许学强,周一星,宁越敏.城市地理学[M].北京:高等教育出版社,1996.
[109]师谦友,范化冬,张敏娜,等.西安城市化进程与人居环境协调发展研究[J].西北大学学报(自然科学版),2008,38(4):673-679.
[110]卢新卫,陈鹏.西安城市化进程与环境生态问题研究[J].干旱区资源与环境,2006,20(1):7-12.
[111]安祥生.山西省城市化类型的划分[J].经济地理,200,20(4):50-54.
[112]欧向军,甄峰,秦永东,等.区域城市化水平综合测度及其理想动力分析[J].地理研究,2008,27(5):994-1002.
[113]徐建华.现代地理学中的数学方法[M].高等教育出版社,2002.
[114]耿修林,谢兆茹.应用统计学[M].北京:科学出版社,2002.
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