阿拉套山南坡山地植被类型遥感识别模型研究
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摘要
山地植被由于其特殊的分布位置,加大了信息提取的难度。探究山区森林植被信息识别的有效方法,是生产实践活动和科研工作有待解决的问题。本研究将数字高程模型(DEM)和纹理信息引入决策树分类方法,利用C4.5数据挖掘方法,生成分类规则,对阿拉套山南坡植被类型进行识别研究,得到如下主要结论:
(1)DEM和纹理特征成为山地植被类型识别的重要变量,改变了常规的单一依赖于遥感影像光谱特征的局面。
(2)基于C4.5数据挖掘技术的决策树分类提高了山地植被类型识别的精度。
(3)提出了具有广泛适宜性的山地植被类型识别方法。
The accurate extraction of forest vegetation information is one of the most important problem in vegetation resource investigation and management,especially for mountain areas.Information extraction of vegetation in mountain areas become more difficult because of the location and topography.Therefore,searehing for a effetive methodes of information extraction is a urgent problem.In this paper,the site was located in southern area of Alatao mountain. DEM and texture were imported into the decision tree.By using C4.5 agorithm method to build the rules of classification,following aspects were concluded.Firstly,DEM and texture are very important features in classifying in mountain areas.The seconde conclusion is that the decision tree based on C4.5 agorithm can improve the accuracy of classifying vegetation in mountain areas.This paper also showes that the methodes of classifying in mountain areas can be widely used in others in Xinjiang.
(1)DEM和纹理特征成为山地植被类型识别的重要变量,改变了常规的单一依赖于遥感影像光谱特征的局面。
(2)基于C4.5数据挖掘技术的决策树分类提高了山地植被类型识别的精度。
(3)提出了具有广泛适宜性的山地植被类型识别方法。
The accurate extraction of forest vegetation information is one of the most important problem in vegetation resource investigation and management,especially for mountain areas.Information extraction of vegetation in mountain areas become more difficult because of the location and topography.Therefore,searehing for a effetive methodes of information extraction is a urgent problem.In this paper,the site was located in southern area of Alatao mountain. DEM and texture were imported into the decision tree.By using C4.5 agorithm method to build the rules of classification,following aspects were concluded.Firstly,DEM and texture are very important features in classifying in mountain areas.The seconde conclusion is that the decision tree based on C4.5 agorithm can improve the accuracy of classifying vegetation in mountain areas.This paper also showes that the methodes of classifying in mountain areas can be widely used in others in Xinjiang.
引文
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[21]何祺胜,塔西甫拉提·特依拜,丁建丽.基于决策树方法的干旱区盐渍地信息提取_以渭干河_库车河三角洲绿洲为例[J].资源科学,2006,28(6):134-140.
[22]程彬,姜琦刚,周云轩,湛邵斌.基于ASTER数据遥感影像的决策树分类[J].吉林大学学报,2007,37(1):179-184.
[23]张百平,谭娅,武红智.中国山地垂直带信息系统的设计与开发[J].地球信息科学,2005,7(1):21-24.
[24]Junichi Susakil,Ryosuke Shibasaki.Fusion of AVHRR and TM data for vegetation classification based on unmixing technique[C]. Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2000,265-277.
[25]Mayaux, P.,De Grandi, G. F.,Malingreau, J. P. Central African forest cover revisited: a multisatellite analysis [J].Remote Sensing of Environment, 2000, 71(2):183一196.
[26] ZHU Guo-bin, Blumberg D G. Classification using ASTER data and SVM algorithms: The case study of BeerSheva,Israel[J].Remote Sensing of Environment, 2002, 80 (1):233-240.
[27]Richard O.Duda, Peter E.Hart, David G.Stork. Pattern Classification [M].Beijing: China Machine Press, 2004.
[28] Breiman,Leo(2001)."Decision Tree Forests". Machine Learning 45(1):5一32, October 2001.
[2]Carpenter,Gaila.ART neural networks for remote sensing vegetation classification from landsat TM andterrain data[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(2):308-325.
[3]Zarco-Tejada,Pable Miller.Land cover mapping at BOREAS using red edge spectral parameters from CASI imagery[J].Journal of Geophysical Research,1999,104(D22):27921-27933.
[4]D.AMARSAIKHAN,T.DOUGLAS,(2004),Data fusion and multisource image classification.INT.J.Remote Sensing,25(17):3529-3539.
[5]段四波,山区遥感图像地形校正模型研究综述[J].北京师范大学学报43(3)362-366
[6]李明诗,基于ASTER遥感数据的建湖县杨树信息提取的研究[D],博士论文,南京林业大学,2005
[7]郭航,北京山区森林植被分类技术研究[硕士论文].首都师范大学,2007
[8]赵英时等编著,遥感应用分析原理与方法[M],北京:科学出版社,2003,251-278.
[9]陈鑫,基于决策树技术的遥感影像分类研究[硕士论文].南京林业大学,2006
[10]方精云,沈泽昊,崔海亭.试论山地的生态特征及山地生态学的研究内容[J].生物多样性2004,12(1):10-12.
[11]梅安新等编著,遥感导论[M],北京:高等教育出版社,2001.
[12]甘淑,袁希平,何大明.遥感专家分类系统在滇西北植被信息提取中的应用试验研究[J].云南大学学报,2003,25(6):553-557.
[13]黄建文,鞠洪波.山区林地多季相TM影像综合分类研究[J].福建林学院学报,2004,24(2):136-139.
[14]骆剑乘,周成虎,粱怡.支持向量机及其遥感影像空间特征提取和分类的应用研究[J].遥感学报,2002,6(1):50-55.
[15]Sherfali Agrawal,P. K.Ioshi, Yogita Shukla, P. S.multi-temporal SPOT-VEGETATION data for classifying vegetation in south central AsiaCurrent Sciences Asia[J], 2003, 84:1440-1448.
[16] ZHU Guo-bin, Blumberg D G. Classification using ASTER data and SVM algorithms: The case study of BeerSheva,Israel[J]. Remote Sensing of Environment,2002, 80 (1):233-240.
[17]浦瑞良,宫鹏.高光谱遥感及应用fMl.北京:高等教育出版社,2000.
[18]梅安新等编著,遥感导论[M],北京:高等教育出版社,2001.
[19]常庆瑞,蒋平安,周勇等.遥感技术导论[M].北京:科学出版社,2004.
[20]邓书斌,林冠状态变化遥感监测方法研究[硕士论文].山东科技大学,2006.
[21]何祺胜,塔西甫拉提·特依拜,丁建丽.基于决策树方法的干旱区盐渍地信息提取_以渭干河_库车河三角洲绿洲为例[J].资源科学,2006,28(6):134-140.
[22]程彬,姜琦刚,周云轩,湛邵斌.基于ASTER数据遥感影像的决策树分类[J].吉林大学学报,2007,37(1):179-184.
[23]张百平,谭娅,武红智.中国山地垂直带信息系统的设计与开发[J].地球信息科学,2005,7(1):21-24.
[24]Junichi Susakil,Ryosuke Shibasaki.Fusion of AVHRR and TM data for vegetation classification based on unmixing technique[C]. Proceedings of IEEE International Geoscience and Remote Sensing Symposium, 2000,265-277.
[25]Mayaux, P.,De Grandi, G. F.,Malingreau, J. P. Central African forest cover revisited: a multisatellite analysis [J].Remote Sensing of Environment, 2000, 71(2):183一196.
[26] ZHU Guo-bin, Blumberg D G. Classification using ASTER data and SVM algorithms: The case study of BeerSheva,Israel[J].Remote Sensing of Environment, 2002, 80 (1):233-240.
[27]Richard O.Duda, Peter E.Hart, David G.Stork. Pattern Classification [M].Beijing: China Machine Press, 2004.
[28] Breiman,Leo(2001)."Decision Tree Forests". Machine Learning 45(1):5一32, October 2001.