基于遥感与GIS的城市绿地研究
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摘要
随着遥感技术的发展,高分辨率遥感影像在城市研究中得到广泛应用。高分辨率遥感影像由于其本身的优点为研究城市环境提供了重要的信息来源。
利用高分辨率遥感影像,获取城市植被类型、分布及其结构,可以为改善城市环境质量、优化植被布局、改进城市绿地系统规划提供客观的现时依据,对于城市未来的绿化建设和总体规划具有重要意义。
本文以重庆市北碚区为例,应用高空间分辨率QuickBird影像,采用面向对象的遥感解译方法,对城市地物进行分类,提取植被信息。建立基于高空间分辨率卫星影像的城市绿地解译模式,提高城市植被分类精度,定量分析城市植被数量、对城市植被进行评价和分析、为城市绿地规划相关决策部门提供客观依据。
主要研究内容与研究成果如下：
(1)影像预处理：通过定量分析各波段之间的相互关系,进行波段选择；几何校正；对高空间分辨率影像的全色波段与多光谱波段进行像素级融合。
(2)利用面向对象的方法进行遥感解译：利用多尺度分割的方法,进行影像分割；构建解译地物的知识库；提取地物信息。
(3)利用传统的分类方法(监督分类法与非监督分类法)进行解译,通过多种评价指标进行评价传统解译与面向对象解译方法的差异。
(4)在遥感与GIS支持下,运用景观生态学原理,结合多种绿地评价指标对北碚区植被进行生态评价和景观分析,分析北碚区绿化现状、探讨北碚区城市绿地规划布局并提出合理性建议。
With the development of remote sensing, high spatial resolution remote sensing images are extensively used in the studing of city. High spatial resolution remote sensing images can provide important information sources.
To abtain the kinds, distribution and structure of urban vegetation by using high spatial resolution remote sensing images can provide objective foundation data. The results are very important to virescence construction and general planning.
The paper takes the Beibei Chongqing for example. The aim is the information extraction of vegetation and the other things in the city by using the object-oriented method. The central contents of the paper are model struction, high classes precision and estimation and ananlysis about ubran vegetation, which are the foundations for government decision-making.
The main harvests of studing:
(1)Image pretreatment:the choice of available bands by quantitative analysis about all bands, Ortho-rectification, then by making use of high spatial resolution remote sensing images and pan image resolve images in the level of pix.
(2)Oriented-object method which includes multi-scale segment, repository construction and information extraction is made use of extracting information about city.
(3)The traditional classifications (supervised and unsuperviesed) are used extracton information. The methods are compared to the oriented-object method by many indexes.
(4)On the bases of remote sensing and GIS, making use of landscape ecology theory and indexes about greenbelt estimation analysis greenbelt actuality and discuss the greenbelt distribution and provide reasonable suggestions.
利用高分辨率遥感影像,获取城市植被类型、分布及其结构,可以为改善城市环境质量、优化植被布局、改进城市绿地系统规划提供客观的现时依据,对于城市未来的绿化建设和总体规划具有重要意义。
本文以重庆市北碚区为例,应用高空间分辨率QuickBird影像,采用面向对象的遥感解译方法,对城市地物进行分类,提取植被信息。建立基于高空间分辨率卫星影像的城市绿地解译模式,提高城市植被分类精度,定量分析城市植被数量、对城市植被进行评价和分析、为城市绿地规划相关决策部门提供客观依据。
主要研究内容与研究成果如下：
(1)影像预处理：通过定量分析各波段之间的相互关系,进行波段选择；几何校正；对高空间分辨率影像的全色波段与多光谱波段进行像素级融合。
(2)利用面向对象的方法进行遥感解译：利用多尺度分割的方法,进行影像分割；构建解译地物的知识库；提取地物信息。
(3)利用传统的分类方法(监督分类法与非监督分类法)进行解译,通过多种评价指标进行评价传统解译与面向对象解译方法的差异。
(4)在遥感与GIS支持下,运用景观生态学原理,结合多种绿地评价指标对北碚区植被进行生态评价和景观分析,分析北碚区绿化现状、探讨北碚区城市绿地规划布局并提出合理性建议。
With the development of remote sensing, high spatial resolution remote sensing images are extensively used in the studing of city. High spatial resolution remote sensing images can provide important information sources.
To abtain the kinds, distribution and structure of urban vegetation by using high spatial resolution remote sensing images can provide objective foundation data. The results are very important to virescence construction and general planning.
The paper takes the Beibei Chongqing for example. The aim is the information extraction of vegetation and the other things in the city by using the object-oriented method. The central contents of the paper are model struction, high classes precision and estimation and ananlysis about ubran vegetation, which are the foundations for government decision-making.
The main harvests of studing:
(1)Image pretreatment:the choice of available bands by quantitative analysis about all bands, Ortho-rectification, then by making use of high spatial resolution remote sensing images and pan image resolve images in the level of pix.
(2)Oriented-object method which includes multi-scale segment, repository construction and information extraction is made use of extracting information about city.
(3)The traditional classifications (supervised and unsuperviesed) are used extracton information. The methods are compared to the oriented-object method by many indexes.
(4)On the bases of remote sensing and GIS, making use of landscape ecology theory and indexes about greenbelt estimation analysis greenbelt actuality and discuss the greenbelt distribution and provide reasonable suggestions.
引文
[1]张春鹏.基于高分辨率遥感影像的城市环境结构信息提取研究[D].上海：华东师范大学.2007,6-7.
[2]李德仁.“论21世纪遥感与GIS的发展”[J].武汉大学学报(信息科学版),1995,28(2)：127-131.
[3]王佳,刘召伟等.“我国遥感科学事业发展的论战-辩题5：当前遥感应用发展的瓶颈是数据获取而不是数据应用”[J].国土资源遥感,2005,(3)：88-91.
[4]王圆圆,李京.遥感影像土地利用覆盖分类方法研究综述[J].遥感信息,2004,(1)：53-59.
[5]霍宏涛.三维及相关辅助信息在提高图像分类精度中的研究[J].2001,23：(2)：28-31.
[6]Duncan J Stow D, Franklin J et al. Assessing the relationship between spectral vegetation indices and shrub cover in the Jornada Basin, New Mexico[j].International Journal Remote Sensing, 1993,14 (18):3395-3416.
[7]Defries, R. et al Global discrimination of land cover types from metrics derived from AVHRR path finder data[J].Remote Sens Environ,1995,54:209-222.
[8]Gaston, G. G. et al Identification of carbon quantifiable regions in the former Soviet Union using unsupervised classification of AVHRR global vegetation index images. Int. J. Remote Sens, 1994,(15):3199-3221.
[9]池红康.沙地油蒿群落覆盖度的遥感定量化研究[J].植物生态学报,2000,24(4)：494-497.
[10]李俊祥.基于NOAA/AVHRR数据的中国东部地区植被遥感分类研究[J].植物生态学报,2005,29(3)：436-443.
[11]Baatz M, Schape A. Object-Oriented and muti-scale image analysis in semantic net works [A].In:Proc of the 2ad International Symposium on Operational of Remote Sensing[C].August 16-20th 1999.Enschede ITC.
[12]Bauer, T. and Ste inn ocher, K. Per-parcel land use classification in urban areas applying a rule-based technique[J],GeoBIT/GIS,2001,6:12-27.
[13]Willhauck, G. Benz, U. C. and Siege rt. F. Semi automatic classification proced-ures for fire monitoring using multi-temporal SAR images and NOAA-AVHRR hotspot data [C]. Proceedings of the 4th European Conference on Synthetic Aperture Rader, Cologne, Germany, 2002,4-6.
[14]Shackelford A K, Davis C H.A combined fuzzy pixel-based and object-based approach for classification of high-resolution multi-spectral data over urban areas. IEEE Transaction on Geo Science and Remote sensing 2003,41 (10):2354-2363.
[15]Hofmann, P. Detecing informal settlements from IKONOS image data using methods of object-oriented image analysis an example from Cape Town (South Africa), Remote Sensing of Urban Areas/Femerkudung in urbanen Raumen. Regens-burg, ed. by JURGENS, C.,2001:41-42.
[16]Renaud Mathieu, Jagannath Aryal. Object-oriented classification and Ikonos multi-spectral imagery for mapping vegetation communities in urban areas.The 17th Annual Colloquium of the Spatial Information Research Center,2005
[17]黄慧萍.面向对象影像分析中的尺度问题研究[D].北京：北京林业大学,2003.
[18]杜凤兰,田庆久,夏学齐等.面向对象的地物分类法分析与评价[J].遥感技术应用,2004：19(1)：20-23.
[19]颜梅春.基于IKONOS卫星数据的植被类型信息提取研究——以南京市宣武区为例[D].北京：北京林业大学,2005.
[20]钱巧静,谢瑞,张磊等.面向对象的土地覆盖信息提取方法研究[J].遥感技术与应用,2005,20(3)：338-342.
[21]蒲智.面向对象的技术在城市绿地信息提取中的应用[D].北京：北京林业大学,2006.
[22]崔林丽.一种基于对象和多种特征整合的分类识别方法研究[J].遥感学报.2006,11(1)：104-106.
[23]孔春芳,吴冲龙,徐凯等.基于多特征的城市用地高分辨率影像分类与提取[J].数据采集与处理,2007,22(3),315-320.
[24]周春艳,王萍,张振勇.基于面向对象信息提取技术的城市用地分类[J].遥感技术与应用,2008,23(1),32-36.
[25]王启田,林祥国,王志军等.利用面向对象分类方法提取冬小麦种植面积的研究[J].测绘科学,2008,33(2)：143-146.
[26]Digtial Globe 公司.网址:http://www.digitalglobe.com/products/basic.html.
[27]赵英时.遥感应用分析原理与方法[M].北京：科学出版社.2003：172-208.
[28]张超.地理信息系统实习教程[M].北京：高等教育出版社.2000：182-186.
[29]赵文吉.ENVI遥感影像处理专题与实践[M].北京：中国环境科学出版社.2007：86-96.
[30]党安荣.]ERDAS IMAGINE遥感图像处理方法[M].北京：清华大学出版社2003：107-108,
[31]Chavez P.S.J, Berlin GL., Sowers L.B., Statistical Methods for Selecting Landsat MSS Rations [J].Journal of Applied Photographic Engineering,1982,8(l),23-30.
[32]李小娟等.ENVI遥感影像处理教程[M].北京：中国环境科学出版.2007社.2007：343-344.
[33]陈启浩.面向对象的多源遥感数据分类技术研究与实现[D].北京：中国地质大学,2007.
[34]Baatz, M and A. Schape.Multi-resolution Segmentation an optimization Approach for High Quality Multi-Scale Image Segmentation. In:Strobl, J.Blaschke, T.and G Griesebner(Eds), Angewandte Geographische Information sverarbeitung Ⅶ, Heidel-berg wichmann-Verlag,2000,12-23.
[35]Kuehn, S.,Benz, U. and J. Hurley, Efficient flood Monitoring Based on RADARAAT- Images Data and Information Fusion with Object-Oriented Technology. Proceedings, Igarss,2002,3 Definien,2003, eCognition Professional, Version3.0,Munich:Definiens Imaging GmbH,www.define-ns-imaging.com
[36]Definien,2003, eCognition Professional, Version 3.0,Munich:Definiens ImagingGmbH, www. Definiens imaging.com.
[37]http://www.definiens imaging.com.
[38]http://www.bsei.com.cn/.
[39]颜宏娟.面向对象的遥感影像模糊分类方法研究[D].西安：西安科技大学,2008.
[40]Definiens Image gmbH. eCognition User Guide[M].German,2004,60-65.
[41]方圣辉,朱武.辅以纹理特征的SAR图像分类研究[J].测绘通报,2001(10),12-14.
[42]Huete A.R.,1988, A soil-Adjusted vegetation index (SAVI).Remote Sens. Environ.25:295-309.
[43]梅安新.遥感导论[M].北京：高等教育出版社.2001.193～202.
[44]黄浩.基于高分辨率卫星影像的城市植被信息提取与分析研究[D].上海：同济大学,2005.
[45]周廷刚.基于遥感于GIS的城市绿量研究[M].重庆：西南师范大学出版社.2002.142-144.
[46]陈利项.黄河三角洲地区人文活动对景观结构的影像分析[J].生态学报,1996,16(1)：37-42.
[2]李德仁.“论21世纪遥感与GIS的发展”[J].武汉大学学报(信息科学版),1995,28(2)：127-131.
[3]王佳,刘召伟等.“我国遥感科学事业发展的论战-辩题5：当前遥感应用发展的瓶颈是数据获取而不是数据应用”[J].国土资源遥感,2005,(3)：88-91.
[4]王圆圆,李京.遥感影像土地利用覆盖分类方法研究综述[J].遥感信息,2004,(1)：53-59.
[5]霍宏涛.三维及相关辅助信息在提高图像分类精度中的研究[J].2001,23：(2)：28-31.
[6]Duncan J Stow D, Franklin J et al. Assessing the relationship between spectral vegetation indices and shrub cover in the Jornada Basin, New Mexico[j].International Journal Remote Sensing, 1993,14 (18):3395-3416.
[7]Defries, R. et al Global discrimination of land cover types from metrics derived from AVHRR path finder data[J].Remote Sens Environ,1995,54:209-222.
[8]Gaston, G. G. et al Identification of carbon quantifiable regions in the former Soviet Union using unsupervised classification of AVHRR global vegetation index images. Int. J. Remote Sens, 1994,(15):3199-3221.
[9]池红康.沙地油蒿群落覆盖度的遥感定量化研究[J].植物生态学报,2000,24(4)：494-497.
[10]李俊祥.基于NOAA/AVHRR数据的中国东部地区植被遥感分类研究[J].植物生态学报,2005,29(3)：436-443.
[11]Baatz M, Schape A. Object-Oriented and muti-scale image analysis in semantic net works [A].In:Proc of the 2ad International Symposium on Operational of Remote Sensing[C].August 16-20th 1999.Enschede ITC.
[12]Bauer, T. and Ste inn ocher, K. Per-parcel land use classification in urban areas applying a rule-based technique[J],GeoBIT/GIS,2001,6:12-27.
[13]Willhauck, G. Benz, U. C. and Siege rt. F. Semi automatic classification proced-ures for fire monitoring using multi-temporal SAR images and NOAA-AVHRR hotspot data [C]. Proceedings of the 4th European Conference on Synthetic Aperture Rader, Cologne, Germany, 2002,4-6.
[14]Shackelford A K, Davis C H.A combined fuzzy pixel-based and object-based approach for classification of high-resolution multi-spectral data over urban areas. IEEE Transaction on Geo Science and Remote sensing 2003,41 (10):2354-2363.
[15]Hofmann, P. Detecing informal settlements from IKONOS image data using methods of object-oriented image analysis an example from Cape Town (South Africa), Remote Sensing of Urban Areas/Femerkudung in urbanen Raumen. Regens-burg, ed. by JURGENS, C.,2001:41-42.
[16]Renaud Mathieu, Jagannath Aryal. Object-oriented classification and Ikonos multi-spectral imagery for mapping vegetation communities in urban areas.The 17th Annual Colloquium of the Spatial Information Research Center,2005
[17]黄慧萍.面向对象影像分析中的尺度问题研究[D].北京：北京林业大学,2003.
[18]杜凤兰,田庆久,夏学齐等.面向对象的地物分类法分析与评价[J].遥感技术应用,2004：19(1)：20-23.
[19]颜梅春.基于IKONOS卫星数据的植被类型信息提取研究——以南京市宣武区为例[D].北京：北京林业大学,2005.
[20]钱巧静,谢瑞,张磊等.面向对象的土地覆盖信息提取方法研究[J].遥感技术与应用,2005,20(3)：338-342.
[21]蒲智.面向对象的技术在城市绿地信息提取中的应用[D].北京：北京林业大学,2006.
[22]崔林丽.一种基于对象和多种特征整合的分类识别方法研究[J].遥感学报.2006,11(1)：104-106.
[23]孔春芳,吴冲龙,徐凯等.基于多特征的城市用地高分辨率影像分类与提取[J].数据采集与处理,2007,22(3),315-320.
[24]周春艳,王萍,张振勇.基于面向对象信息提取技术的城市用地分类[J].遥感技术与应用,2008,23(1),32-36.
[25]王启田,林祥国,王志军等.利用面向对象分类方法提取冬小麦种植面积的研究[J].测绘科学,2008,33(2)：143-146.
[26]Digtial Globe 公司.网址:http://www.digitalglobe.com/products/basic.html.
[27]赵英时.遥感应用分析原理与方法[M].北京：科学出版社.2003：172-208.
[28]张超.地理信息系统实习教程[M].北京：高等教育出版社.2000：182-186.
[29]赵文吉.ENVI遥感影像处理专题与实践[M].北京：中国环境科学出版社.2007：86-96.
[30]党安荣.]ERDAS IMAGINE遥感图像处理方法[M].北京：清华大学出版社2003：107-108,
[31]Chavez P.S.J, Berlin GL., Sowers L.B., Statistical Methods for Selecting Landsat MSS Rations [J].Journal of Applied Photographic Engineering,1982,8(l),23-30.
[32]李小娟等.ENVI遥感影像处理教程[M].北京：中国环境科学出版.2007社.2007：343-344.
[33]陈启浩.面向对象的多源遥感数据分类技术研究与实现[D].北京：中国地质大学,2007.
[34]Baatz, M and A. Schape.Multi-resolution Segmentation an optimization Approach for High Quality Multi-Scale Image Segmentation. In:Strobl, J.Blaschke, T.and G Griesebner(Eds), Angewandte Geographische Information sverarbeitung Ⅶ, Heidel-berg wichmann-Verlag,2000,12-23.
[35]Kuehn, S.,Benz, U. and J. Hurley, Efficient flood Monitoring Based on RADARAAT- Images Data and Information Fusion with Object-Oriented Technology. Proceedings, Igarss,2002,3 Definien,2003, eCognition Professional, Version3.0,Munich:Definiens Imaging GmbH,www.define-ns-imaging.com
[36]Definien,2003, eCognition Professional, Version 3.0,Munich:Definiens ImagingGmbH, www. Definiens imaging.com.
[37]http://www.definiens imaging.com.
[38]http://www.bsei.com.cn/.
[39]颜宏娟.面向对象的遥感影像模糊分类方法研究[D].西安：西安科技大学,2008.
[40]Definiens Image gmbH. eCognition User Guide[M].German,2004,60-65.
[41]方圣辉,朱武.辅以纹理特征的SAR图像分类研究[J].测绘通报,2001(10),12-14.
[42]Huete A.R.,1988, A soil-Adjusted vegetation index (SAVI).Remote Sens. Environ.25:295-309.
[43]梅安新.遥感导论[M].北京：高等教育出版社.2001.193～202.
[44]黄浩.基于高分辨率卫星影像的城市植被信息提取与分析研究[D].上海：同济大学,2005.
[45]周廷刚.基于遥感于GIS的城市绿量研究[M].重庆：西南师范大学出版社.2002.142-144.
[46]陈利项.黄河三角洲地区人文活动对景观结构的影像分析[J].生态学报,1996,16(1)：37-42.