基于RS和GIS的敦煌市土地沙漠化研究
|
摘要
沙漠化是当今世界面临的最大的环境一社会经济问题之一。我国是世界上受沙漠化影响最严重的国家之一,给生态环境和社会经济带来极大危害。遥感技术为沙漠化监测提供了一种全新的手段。由于土地沙漠化的复杂性,目视解译仍然是当前沙漠化监测的主要手段。但目视解译存在定位不准、时效性差、周期长、工作量大等缺陷,因此,建立一种监测土地沙漠化的计算机自动分类方法十分必要且有意义。
敦煌是历史文化名城、全国优秀旅游城市,且在国际上具有一定影响。市域内文物古迹众多,自然景观独特。然而,近几十年来,敦煌境内土地沙漠化日趋加重,对当地的生态环境和人民生产生活等各方面造成严重影响。对此,温家宝作出批示,必须加快治理沙漠化。所以,了解和掌握敦煌市的土地沙漠化发展变化情况,对当地沙漠化治理和环境保护具有重要的现实意义。
决策树分类法能够有机地组合多种信息,在遥感分类问题上表现出巨大的优势。植被覆盖度是本文在土地沙漠化程度分类中的主要参考指标,而NDVI是植被覆盖度遥感估算中最常用的植被指数。在植被覆盖度基础上,引入地表温度和纹理特征可提高各级别沙漠化土地的分类精度,对主要地类也有更好的区分性。因此,本文以敦煌为研究区,基于TM遥感影像数据,采用决策树分类方法,试图建立一种将遥感定量反演结果(植被覆盖度和地表温度)和纹理分析结果引入沙漠化监测自动分类过程的新方法,并分析敦煌市20年来的土地沙漠化态势,探索其分布规律和发展变化,主要结论有:
(1)敦煌市的土地沙漠化情况已相当严重。据目视解译结果,敦煌市2004年的沙漠化土地总面积为7551km~2,沙漠化程度以中度和重度为主,土地沙漠化情况已相当严重;沙漠化土地主要分布在疏勒河以南的地区,且沙漠化土地的分布与特定的土地利用方式相关联,成因也有所不同。
(2)敦煌市近20年来的沙漠化土地一直处于扩张趋势,且沙漠化程度也有所转变。1987—2006年,敦煌市的沙漠化土地不断扩张,且后10年的扩张速度明显大于前10。同时,沙漠化程度也从1987年的中度占优势过渡到1996年的严重占优势和2006年的中度和严重共同主导沙漠化进程的局面。
(3)沙漠化的成因主要有地理位置和气候因素、水文因素、植被因素和人为因素;治理对策主要是加大依法治沙力度,发展沙区产业,促进沙区科学发展、遵循自然规律和经济规律,采取综合措施防止沙漠化、政策上采取鼓励措施,提高公众的防治意识、加强部门协调和监督检查、合理解决水资源利用问题和加强沙漠化动态监测。
(4)将遥感定量反演结果(植被覆盖度、地表温度)和纹理分析结果引入决策树分类,实现土地沙漠化监测的自动分类是可行的。决策树分类方法的结果可比性强,但精度明显低于人机交互目视解译。
Desertification is one of the most serious environmental and social problems all over the world which causes great damage to the eco-environment and social economy. China is one of the countries that are severely threatened by desertification in the world. Remote sensing provides a fire-new measure for monitoring desertification. Due to the complexity of desertification, interactive interpretation is still the principal method even though it has several limitations, such as orientation error, low time-effectiveness, long cycle time, and huge workload etc. So it is very necessary and meaningful to establish an automatic classifying method for monitoring desertification.
As one of the famous national historical city and the attractive tourist destiny in China, Dunhuang possesses a great number of cultural relics and historic sites which are noted all around the world and it has unique nature scenes. However, in recent decades, the rapid development of land desertification has threatened the local environment and people's livelihood. As for this, WEN Jiaobao, the premier of our country, pointed out that we must advance the desertification prevention in Dunhuang. Therefore, to know current states of desertification in Dunhuang has a very important practical significance for desertification prevention and protection of environment.
Decision tree has great advantages in remote sensing image classification, which can organize multi-information efficaciously. In this study, vegetation fraction is the main index used in the classification system of desertification, and NDVI (normalized difference vegetation index) is the mostly used vegetation index in the estimation of vegetation fraction. Land surface temperature and texture features are introduced in classifying process to increase the precision of classification. Based on Landsat TM images, using decision tree, this study tries to establish an automatic classifying method for monitoring desertification, which has integrated the quantitative estimation result, namely vegetation fraction and land surface temperature, and the outcome of texture analysis. Finally, the development of desertification in Dunhuang in recent two decades and the distribution characteristics of desertification land are analyzed. The main conclusions are as follows:
(1) The desertification in Dunhuang is very serious. According to the result of interactive interpretation, the area of desertification land in Dunhuang in 2004 reaches 7551km~2 , dominated by moderate and severe levels. Desertification land appears mainly in the south of Shule River and the spatial distribution pattern of desertification is related to the special types of land use for different reasons.
(2)The desertification land in Dunhuang is expanding in recent two decades, and the degree of desertification changes. From 1987 to 2006, desertification land in Dunhuang is expanding and aggravating with the expanding speed of the latter decade is significantly faster than the former one. In 1987, it is dominated by the moderate desertification, in 1996 the severe desertification and in 2006 combination of moderate and severe desertification.
(3)The major caused of desertification are location and climate background, hydrological, vegetation and humanity factors. The ways to control desertification are to enhance the power of curing the desertification according to the law, to develop sandy industry and manage it scientifically, to comply with natural and economic law to control desertification, to encourage people to take action in improving desertification policy and improve the public consciousness in policy, to intensify departmental cooperation and monitoring, to solve the water utilization problems reasonably and to strengthen dynamic monitoring of desertification.
(4) This automatic classifying method for monitoring desertification, which has integrated the quantitative estimation result, namely vegetation fraction and land surface temperature, and the outcome of texture analysis, is feasible. The result of decision tree is high in comparability, but the precision is obviously lower than that of interactive interpretation.
敦煌是历史文化名城、全国优秀旅游城市,且在国际上具有一定影响。市域内文物古迹众多,自然景观独特。然而,近几十年来,敦煌境内土地沙漠化日趋加重,对当地的生态环境和人民生产生活等各方面造成严重影响。对此,温家宝作出批示,必须加快治理沙漠化。所以,了解和掌握敦煌市的土地沙漠化发展变化情况,对当地沙漠化治理和环境保护具有重要的现实意义。
决策树分类法能够有机地组合多种信息,在遥感分类问题上表现出巨大的优势。植被覆盖度是本文在土地沙漠化程度分类中的主要参考指标,而NDVI是植被覆盖度遥感估算中最常用的植被指数。在植被覆盖度基础上,引入地表温度和纹理特征可提高各级别沙漠化土地的分类精度,对主要地类也有更好的区分性。因此,本文以敦煌为研究区,基于TM遥感影像数据,采用决策树分类方法,试图建立一种将遥感定量反演结果(植被覆盖度和地表温度)和纹理分析结果引入沙漠化监测自动分类过程的新方法,并分析敦煌市20年来的土地沙漠化态势,探索其分布规律和发展变化,主要结论有:
(1)敦煌市的土地沙漠化情况已相当严重。据目视解译结果,敦煌市2004年的沙漠化土地总面积为7551km~2,沙漠化程度以中度和重度为主,土地沙漠化情况已相当严重;沙漠化土地主要分布在疏勒河以南的地区,且沙漠化土地的分布与特定的土地利用方式相关联,成因也有所不同。
(2)敦煌市近20年来的沙漠化土地一直处于扩张趋势,且沙漠化程度也有所转变。1987—2006年,敦煌市的沙漠化土地不断扩张,且后10年的扩张速度明显大于前10。同时,沙漠化程度也从1987年的中度占优势过渡到1996年的严重占优势和2006年的中度和严重共同主导沙漠化进程的局面。
(3)沙漠化的成因主要有地理位置和气候因素、水文因素、植被因素和人为因素;治理对策主要是加大依法治沙力度,发展沙区产业,促进沙区科学发展、遵循自然规律和经济规律,采取综合措施防止沙漠化、政策上采取鼓励措施,提高公众的防治意识、加强部门协调和监督检查、合理解决水资源利用问题和加强沙漠化动态监测。
(4)将遥感定量反演结果(植被覆盖度、地表温度)和纹理分析结果引入决策树分类,实现土地沙漠化监测的自动分类是可行的。决策树分类方法的结果可比性强,但精度明显低于人机交互目视解译。
Desertification is one of the most serious environmental and social problems all over the world which causes great damage to the eco-environment and social economy. China is one of the countries that are severely threatened by desertification in the world. Remote sensing provides a fire-new measure for monitoring desertification. Due to the complexity of desertification, interactive interpretation is still the principal method even though it has several limitations, such as orientation error, low time-effectiveness, long cycle time, and huge workload etc. So it is very necessary and meaningful to establish an automatic classifying method for monitoring desertification.
As one of the famous national historical city and the attractive tourist destiny in China, Dunhuang possesses a great number of cultural relics and historic sites which are noted all around the world and it has unique nature scenes. However, in recent decades, the rapid development of land desertification has threatened the local environment and people's livelihood. As for this, WEN Jiaobao, the premier of our country, pointed out that we must advance the desertification prevention in Dunhuang. Therefore, to know current states of desertification in Dunhuang has a very important practical significance for desertification prevention and protection of environment.
Decision tree has great advantages in remote sensing image classification, which can organize multi-information efficaciously. In this study, vegetation fraction is the main index used in the classification system of desertification, and NDVI (normalized difference vegetation index) is the mostly used vegetation index in the estimation of vegetation fraction. Land surface temperature and texture features are introduced in classifying process to increase the precision of classification. Based on Landsat TM images, using decision tree, this study tries to establish an automatic classifying method for monitoring desertification, which has integrated the quantitative estimation result, namely vegetation fraction and land surface temperature, and the outcome of texture analysis. Finally, the development of desertification in Dunhuang in recent two decades and the distribution characteristics of desertification land are analyzed. The main conclusions are as follows:
(1) The desertification in Dunhuang is very serious. According to the result of interactive interpretation, the area of desertification land in Dunhuang in 2004 reaches 7551km~2 , dominated by moderate and severe levels. Desertification land appears mainly in the south of Shule River and the spatial distribution pattern of desertification is related to the special types of land use for different reasons.
(2)The desertification land in Dunhuang is expanding in recent two decades, and the degree of desertification changes. From 1987 to 2006, desertification land in Dunhuang is expanding and aggravating with the expanding speed of the latter decade is significantly faster than the former one. In 1987, it is dominated by the moderate desertification, in 1996 the severe desertification and in 2006 combination of moderate and severe desertification.
(3)The major caused of desertification are location and climate background, hydrological, vegetation and humanity factors. The ways to control desertification are to enhance the power of curing the desertification according to the law, to develop sandy industry and manage it scientifically, to comply with natural and economic law to control desertification, to encourage people to take action in improving desertification policy and improve the public consciousness in policy, to intensify departmental cooperation and monitoring, to solve the water utilization problems reasonably and to strengthen dynamic monitoring of desertification.
(4) This automatic classifying method for monitoring desertification, which has integrated the quantitative estimation result, namely vegetation fraction and land surface temperature, and the outcome of texture analysis, is feasible. The result of decision tree is high in comparability, but the precision is obviously lower than that of interactive interpretation.
引文
A.Baraldi,F.Parmigianni.An investigation of the Textural Characteristics Associated with Gray Level Co-occurrence Matrix Statistical Parameters[J].IEEE Trans.Geosci.Remore Sens,1995,32(2):293-304
Burner C,Blaschke T..A Multi-scale segmentation/object relationship modeling methodology for landscape analysis[J].Ecological Modeling,2003,233-249
Dymond J R,Stephens P R,Newsome P F et al.Percent vegetation cover of a degrading rangeland from SPOT[J].IN T.J.Remote Sensing,1992,13(11):1999-2007
Friedl M A,et al.Maximizing land cover classification accuracies produced by decision trees at continental to global scales[J].IEEE Trans.Geosci.Remote Sens,1999,37(2):969-977
Hanson M C,DubayahR,Deities R S.Classification trees:an alternative to traditional land cover classifiers [J].Int.J.Remote Sens,1996,17:1075-1081
Haralick R.M.,Shanmugam K.K.,Dinstein I.Texture features for image classification[J].IEEE Trans.Syst.Man Cyb,1973,8(6):610-621
Inzana J,Kusky T,Higgs G,Tucker R.Supervised Classifications of Landsat TM Band Ratio Images and Landsat TM Band Ratio Image with Radar for Geological Interpretations of Central Madagascar[J].Journal of African Earth Sciences,2003,37(1-2):59-72
J.Zhang,G.M.Foody.A Fuzzy classification of sub-urban land cover form remotely sensed imagery[J].Int.J.Remote Sens,1998,19(14):2721-2738
Jim(e|¨)nez-Mu(?)oz J C,Sobrino,J A.A generalized single channel method for retrieving land surface temperature from remote sensing data[J].Journal of Geophysical Research,2003
Jochen Grandell,et al.Sub-pixel Land Use Classification and Retrieval of Forest Steminthe Boreal Forest Zone by Employing SSM/I Data[J].Remote Sens.Environ,1998(63):140-154
John R.Jensen.Introductory Digital Image Processing:A Remote Sensing Perspective(Third Edition)(陈晓玲等中文导读)[M].北京:科学出版社,2007
Kavzoglu T,Mather P M.The use of back propagating artificial neural networks in land cover classification [J].Int.J.Remote Sens,2003,24(23):4907-4938
Murthy C.S.,Raju P V,et al.Classification of wheat crop with multi-temperoral images:performance of maximum likelihood and artificial neural networks[J].Int.J.Remote Sens,2003,24(23):4871-4890
Ohanian P P,Dubes R C.Performance evaluation for four class of texture features[J].Patern Recognition,1992,25(8):819-833
Olivier Debeir,Isabelle Vandensteen,et al.Texturaland Contextual Land Cover Classification Using Single and Multiple Classifier System[J].Photogrammetric Engineering & Remote Sensing,2002(7):597-605
Qi J,Marsett R C,Moran M.S.,et al.Spatial and temporal dynamics of vegetation in the San Pedro River basin area[J].Agricultural and Forest Meteorology,2000(105):55-68
Qin Z,et al.A Mono-window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region[J].Int.J.Remote Sens,2001,22(18):3719-3746
R.M.Haralick.Statistical and structural approaches to Texture[J].Proc.of IEEE,1979,67:786-804
Valor E V,et al.Mapping Land Surface Emissivity from NDVI:Application to European,African,and South American areas[J].Remote Sens.Environ,1996,57(3):167-184
Van,et al.On the Relationship Between Thermal Emissivity and the Normalized Difference Vegetation Index for Natural Surface[J].Int.J.Remote Sens,1993,14(6):1119-1131
陈俊,王文,李子扬等.Landsat-5卫星数据产品[J].遥感数据,2007:85-88
陈良富,庄家礼,徐希孺.热红外遥感中通道信息相关性及其对陆面温度反演的影响fJl.科学通报,1999,44(19):2122-2127
杜凤兰等.遥感图像分类方法评析与展望[J].遥感技术与应用,2004,19(6):521-525
杜明义.决策树方法在土地荒漠化分类中的应用研究[J].测绘科学,2006,31(2):81-82
段晓芹.对Landsat7 ETM+遥感图像分类识别技术的研究与应用[D].内蒙古大学硕士学位论文,2006
敦煌市统计局.敦煌市国民经济统计资料(1980—2005年,2007年)
敦煌市志编纂委员会.敦煌市志[M].北京:新华出版社,1994
高志海,魏怀东等.基于3S技术的荒漠化监测技术系统研究[J].遥感技术与应用,2002,17(6):330-336
何英彬.基于MODIS与TM的冷害影响水稻产量评估研究[D].中国农科院硕士学位论文,2007
侯敏.基于人工神经网络的湿地遥感分类研究[D].中国地质大学硕士学位论文,2006
胡召玲,郭达志等.基于小波分解的星载SAR图像纹理信息提取[J].遥感学报,2001,5(6):423-427
黄端琼.多适度纹理特征分析及其在遥感影像分类中的应用[D].福州大学硕士学位论文,2006
黄桂兰.影像纹理分类的马尔可夫随机场法与实验研究[J].中国图形图像学报,1999,(4):317-320
黄妙芬,刘绍民等.地表温度和地表辐射温度差值分析.地球科学进展[J].2005,20(10):1075-1081
黄妙芬,邢旭峰,王培娟等.利用LANDSAT/TM热红外通道反演地表温度的三种方法比较[J].干旱区地理,2006,29(1):132-137
黄妙芬等.反演地表温度三要素获取途径研究及其研究价值[J].干旱区地理.2005,28(4):541-547
霍艾迪.基于MODIS数据的沙漠化遥感监测技术研究[D].西北农林科技大学博士学位论文,2008
贾永红,李德仁.多源遥感影像象素级融合分类与决策级分类融合法的研究[J].武汉大学学报(信息科学版),2001,5:430-434
匡霞等.专家系统在TM图像分类中的应用[J].环境遥感,1986,4(4):257-266
李爱华.基于遥感影像认知理解的干旱半干旱地区土地利用覆盖自动分类方法[D].兰大硕士论文,2008
李粉玲.高山高原地区地表温度遥感反演研究[D].兰州:兰州大学硕士学位论文.2005
李厚强,刘政凯等.基于分形理论的航空图像分类方法[J].遥感学报,2001,5(5):353-357
李厚强,王宜主,刘政凯.一种适用于多类别遥感图像分类的新方法——复合神经网络分类方法[J].遥感学报,1997,1(4):257-261
李净.基于Landsat-5 TM估算地表温度[J].遥感技术与应用,2006,21(4):322-326
李苗苗.植被覆盖度的遥感估算方法研究[D].北京:中科院研究生院,2003
李全等.基于最大似然分类算法的土地覆盖分类精度控制研究[J].国土资源科技管理,2005,22(4):42-45
李爽等.决策树分类方法及其在土地覆盖分类中的应用[J].遥感技术与应用,2002,17(1):6-11
李召良等.一种从中红外和热红外数据中反演地表比辐射率的物理算法[J].中国科学,2000,30(18):26
刘勇洪,牛铮等.基于MODIS数据的决策树分类方法研究与应用[J].遥感学报,2005,9(4):405-412
刘正军,王长耀,延昊等.基于Fuzzy ARTMAP神经网络的高分辨率图象土地覆盖分类及其评价[J].中国图象图形学报,2003,8(2):151-154
刘志刚.支撑向量机在光谱遥感影像分类中的若干问题研究[D].武汉大学硕士论文,2004
骆剑承,王钦敏等.遥感图像晟大似然分类方法的EM改进算法[J].测绘学报,2002,31(2):234-239
骆剑承等.具有部分监督的遥感影像模糊聚类方法研究及应用[J].遥感技术与应用,1999,14(4):37-43
骆剑承等.人工神经网络遥感影像分类模型及其与知识集成方法研究[J].遥感学报,2001,5(2):122-129
毛建旭,王耀南等.基于模糊小脑模型神经网络的遥感图像分类算法[J].测绘学报,2002,31(4):327-332
毛克彪,覃志豪,王建明等.针对MODIS数据的大气水汽含量反演及31和32波段透过率计算fJ].国土资源遥感,2005,1:26-30
毛克彪等.大气辐射传输模型及MODTRAN中透过率计算[J].测绘与空间地理信息,2004,27(4):1-3
梅安新,秦其明,刘慧平等.遥感导论[M].北京:高等教育出版社,2001
齐家国.环境遥感课件.兰州大学,2008
覃志豪,Li Wenjuan等.单窗算法的大气参数估计方法[J].国土资源遥感,2003,2:37-43
覃志豪,李文娟,徐斌等.利用Landsat TM6反演地表温度所需地表辐射率参数的估计方法[J].海洋科学进展,2004,22(1):129-137
覃志豪,李文娟等.陆地卫星TM6波段范围内地表比辐射率的估算[J].国土资源遥感,2004,3:28-41
覃志豪等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报.2001,7(4):456-466
苏理宏,黄裕霞.基于知识的空间决策支持模型集成[J].遥感学报,2000,4(2):151-156
王葆芳.国内外沙漠化监测评价指标体系概述[J].林业科技通讯,1997,(7):4-8
王葆芳等.沙质荒漠化土地评价指标体系研究[J].干旱区资源与环境,2004,18(4):23-28
王建,董光荣,李文君.利用遥感信息决策树方法分层提取荒漠化土地类型的研究探讨[J].中国沙漠,2000,20(3):243-247
王涛.中国沙漠与沙漠化[M].河北科技出版社,2003
韦玉春,黄家柱.Landsat5图像的增益、偏置取值及其对行星反射率计算分析[J].地球信息科学,2006,8(1):110-113
吴薇,王熙章,姚发芬.毛乌素沙地沙漠化的遥感监测[J].中国沙漠,1997,17(4):415-420
吴学军.城市TM遥感图像分类方法研究[D].广西师范大学硕士学位论文,2007
徐希孺.遥感物理[M].北京:北京大学出版社,2005
颜长珍.1996-2000年陕甘宁青沙地遥感宏观动态研究[J].遥感学报,2002,6(5):376-381
杨何群.基于遥感和DEM的山区-平原地表水热通量估算及对比分析研究[D].兰大硕士学位论文,2005
曾文华.基于灰度共生法和小波变换的遥感影像纹理信息提取[D].东北师范大学硕士学位论文,2006
曾志远.卫星遥感图像计算机分类与地学应用研究[M].北京:科学出版社,2004
张肃.地物频谱在解译遥感图像中的应用研究[J].成都理工大学学报(自然科学版),2006,33(2):198-202
张友静等.基于SVM决策支持树的城市植被类型遥感分类研究[J].遥感学报,2006,10(2):191-196
张友水等.基于GIS的BP神经网络遥感影像分类研究[J].南京大学学报(自科版),2003,39(6):806-813
张友水,冯学智等.Kohonen神经网络在遥感影像分类中的应用研究[J].遥感学报,2004,8(2):178-184
张友水等.多时相TM影像相对辐射校正研究[J].测绘学报,2006,35(2):123-127
赵萍,冯学智等.SPOT卫星影像居民地信息自动提取的决策树方法研究[J].遥感学报,2003,7(4):309-315
赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003
朱长青,杨晓梅.具有更佳分辨率小波分解的遥感影像纹理分类[J].地理研究,1997,16(1):53-59
朱述龙,张古睦.遥感图像获取与分析[M].北京:科学出版社,2000
朱震达等.中国荒漠化(土地退化)防治研究[M].北京:中国环境出版社,1998
Burner C,Blaschke T..A Multi-scale segmentation/object relationship modeling methodology for landscape analysis[J].Ecological Modeling,2003,233-249
Dymond J R,Stephens P R,Newsome P F et al.Percent vegetation cover of a degrading rangeland from SPOT[J].IN T.J.Remote Sensing,1992,13(11):1999-2007
Friedl M A,et al.Maximizing land cover classification accuracies produced by decision trees at continental to global scales[J].IEEE Trans.Geosci.Remote Sens,1999,37(2):969-977
Hanson M C,DubayahR,Deities R S.Classification trees:an alternative to traditional land cover classifiers [J].Int.J.Remote Sens,1996,17:1075-1081
Haralick R.M.,Shanmugam K.K.,Dinstein I.Texture features for image classification[J].IEEE Trans.Syst.Man Cyb,1973,8(6):610-621
Inzana J,Kusky T,Higgs G,Tucker R.Supervised Classifications of Landsat TM Band Ratio Images and Landsat TM Band Ratio Image with Radar for Geological Interpretations of Central Madagascar[J].Journal of African Earth Sciences,2003,37(1-2):59-72
J.Zhang,G.M.Foody.A Fuzzy classification of sub-urban land cover form remotely sensed imagery[J].Int.J.Remote Sens,1998,19(14):2721-2738
Jim(e|¨)nez-Mu(?)oz J C,Sobrino,J A.A generalized single channel method for retrieving land surface temperature from remote sensing data[J].Journal of Geophysical Research,2003
Jochen Grandell,et al.Sub-pixel Land Use Classification and Retrieval of Forest Steminthe Boreal Forest Zone by Employing SSM/I Data[J].Remote Sens.Environ,1998(63):140-154
John R.Jensen.Introductory Digital Image Processing:A Remote Sensing Perspective(Third Edition)(陈晓玲等中文导读)[M].北京:科学出版社,2007
Kavzoglu T,Mather P M.The use of back propagating artificial neural networks in land cover classification [J].Int.J.Remote Sens,2003,24(23):4907-4938
Murthy C.S.,Raju P V,et al.Classification of wheat crop with multi-temperoral images:performance of maximum likelihood and artificial neural networks[J].Int.J.Remote Sens,2003,24(23):4871-4890
Ohanian P P,Dubes R C.Performance evaluation for four class of texture features[J].Patern Recognition,1992,25(8):819-833
Olivier Debeir,Isabelle Vandensteen,et al.Texturaland Contextual Land Cover Classification Using Single and Multiple Classifier System[J].Photogrammetric Engineering & Remote Sensing,2002(7):597-605
Qi J,Marsett R C,Moran M.S.,et al.Spatial and temporal dynamics of vegetation in the San Pedro River basin area[J].Agricultural and Forest Meteorology,2000(105):55-68
Qin Z,et al.A Mono-window Algorithm for Retrieving Land Surface Temperature from Landsat TM Data and Its Application to the Israel-Egypt Border Region[J].Int.J.Remote Sens,2001,22(18):3719-3746
R.M.Haralick.Statistical and structural approaches to Texture[J].Proc.of IEEE,1979,67:786-804
Valor E V,et al.Mapping Land Surface Emissivity from NDVI:Application to European,African,and South American areas[J].Remote Sens.Environ,1996,57(3):167-184
Van,et al.On the Relationship Between Thermal Emissivity and the Normalized Difference Vegetation Index for Natural Surface[J].Int.J.Remote Sens,1993,14(6):1119-1131
陈俊,王文,李子扬等.Landsat-5卫星数据产品[J].遥感数据,2007:85-88
陈良富,庄家礼,徐希孺.热红外遥感中通道信息相关性及其对陆面温度反演的影响fJl.科学通报,1999,44(19):2122-2127
杜凤兰等.遥感图像分类方法评析与展望[J].遥感技术与应用,2004,19(6):521-525
杜明义.决策树方法在土地荒漠化分类中的应用研究[J].测绘科学,2006,31(2):81-82
段晓芹.对Landsat7 ETM+遥感图像分类识别技术的研究与应用[D].内蒙古大学硕士学位论文,2006
敦煌市统计局.敦煌市国民经济统计资料(1980—2005年,2007年)
敦煌市志编纂委员会.敦煌市志[M].北京:新华出版社,1994
高志海,魏怀东等.基于3S技术的荒漠化监测技术系统研究[J].遥感技术与应用,2002,17(6):330-336
何英彬.基于MODIS与TM的冷害影响水稻产量评估研究[D].中国农科院硕士学位论文,2007
侯敏.基于人工神经网络的湿地遥感分类研究[D].中国地质大学硕士学位论文,2006
胡召玲,郭达志等.基于小波分解的星载SAR图像纹理信息提取[J].遥感学报,2001,5(6):423-427
黄端琼.多适度纹理特征分析及其在遥感影像分类中的应用[D].福州大学硕士学位论文,2006
黄桂兰.影像纹理分类的马尔可夫随机场法与实验研究[J].中国图形图像学报,1999,(4):317-320
黄妙芬,刘绍民等.地表温度和地表辐射温度差值分析.地球科学进展[J].2005,20(10):1075-1081
黄妙芬,邢旭峰,王培娟等.利用LANDSAT/TM热红外通道反演地表温度的三种方法比较[J].干旱区地理,2006,29(1):132-137
黄妙芬等.反演地表温度三要素获取途径研究及其研究价值[J].干旱区地理.2005,28(4):541-547
霍艾迪.基于MODIS数据的沙漠化遥感监测技术研究[D].西北农林科技大学博士学位论文,2008
贾永红,李德仁.多源遥感影像象素级融合分类与决策级分类融合法的研究[J].武汉大学学报(信息科学版),2001,5:430-434
匡霞等.专家系统在TM图像分类中的应用[J].环境遥感,1986,4(4):257-266
李爱华.基于遥感影像认知理解的干旱半干旱地区土地利用覆盖自动分类方法[D].兰大硕士论文,2008
李粉玲.高山高原地区地表温度遥感反演研究[D].兰州:兰州大学硕士学位论文.2005
李厚强,刘政凯等.基于分形理论的航空图像分类方法[J].遥感学报,2001,5(5):353-357
李厚强,王宜主,刘政凯.一种适用于多类别遥感图像分类的新方法——复合神经网络分类方法[J].遥感学报,1997,1(4):257-261
李净.基于Landsat-5 TM估算地表温度[J].遥感技术与应用,2006,21(4):322-326
李苗苗.植被覆盖度的遥感估算方法研究[D].北京:中科院研究生院,2003
李全等.基于最大似然分类算法的土地覆盖分类精度控制研究[J].国土资源科技管理,2005,22(4):42-45
李爽等.决策树分类方法及其在土地覆盖分类中的应用[J].遥感技术与应用,2002,17(1):6-11
李召良等.一种从中红外和热红外数据中反演地表比辐射率的物理算法[J].中国科学,2000,30(18):26
刘勇洪,牛铮等.基于MODIS数据的决策树分类方法研究与应用[J].遥感学报,2005,9(4):405-412
刘正军,王长耀,延昊等.基于Fuzzy ARTMAP神经网络的高分辨率图象土地覆盖分类及其评价[J].中国图象图形学报,2003,8(2):151-154
刘志刚.支撑向量机在光谱遥感影像分类中的若干问题研究[D].武汉大学硕士论文,2004
骆剑承,王钦敏等.遥感图像晟大似然分类方法的EM改进算法[J].测绘学报,2002,31(2):234-239
骆剑承等.具有部分监督的遥感影像模糊聚类方法研究及应用[J].遥感技术与应用,1999,14(4):37-43
骆剑承等.人工神经网络遥感影像分类模型及其与知识集成方法研究[J].遥感学报,2001,5(2):122-129
毛建旭,王耀南等.基于模糊小脑模型神经网络的遥感图像分类算法[J].测绘学报,2002,31(4):327-332
毛克彪,覃志豪,王建明等.针对MODIS数据的大气水汽含量反演及31和32波段透过率计算fJ].国土资源遥感,2005,1:26-30
毛克彪等.大气辐射传输模型及MODTRAN中透过率计算[J].测绘与空间地理信息,2004,27(4):1-3
梅安新,秦其明,刘慧平等.遥感导论[M].北京:高等教育出版社,2001
齐家国.环境遥感课件.兰州大学,2008
覃志豪,Li Wenjuan等.单窗算法的大气参数估计方法[J].国土资源遥感,2003,2:37-43
覃志豪,李文娟,徐斌等.利用Landsat TM6反演地表温度所需地表辐射率参数的估计方法[J].海洋科学进展,2004,22(1):129-137
覃志豪,李文娟等.陆地卫星TM6波段范围内地表比辐射率的估算[J].国土资源遥感,2004,3:28-41
覃志豪等.用陆地卫星TM6数据演算地表温度的单窗算法[J].地理学报.2001,7(4):456-466
苏理宏,黄裕霞.基于知识的空间决策支持模型集成[J].遥感学报,2000,4(2):151-156
王葆芳.国内外沙漠化监测评价指标体系概述[J].林业科技通讯,1997,(7):4-8
王葆芳等.沙质荒漠化土地评价指标体系研究[J].干旱区资源与环境,2004,18(4):23-28
王建,董光荣,李文君.利用遥感信息决策树方法分层提取荒漠化土地类型的研究探讨[J].中国沙漠,2000,20(3):243-247
王涛.中国沙漠与沙漠化[M].河北科技出版社,2003
韦玉春,黄家柱.Landsat5图像的增益、偏置取值及其对行星反射率计算分析[J].地球信息科学,2006,8(1):110-113
吴薇,王熙章,姚发芬.毛乌素沙地沙漠化的遥感监测[J].中国沙漠,1997,17(4):415-420
吴学军.城市TM遥感图像分类方法研究[D].广西师范大学硕士学位论文,2007
徐希孺.遥感物理[M].北京:北京大学出版社,2005
颜长珍.1996-2000年陕甘宁青沙地遥感宏观动态研究[J].遥感学报,2002,6(5):376-381
杨何群.基于遥感和DEM的山区-平原地表水热通量估算及对比分析研究[D].兰大硕士学位论文,2005
曾文华.基于灰度共生法和小波变换的遥感影像纹理信息提取[D].东北师范大学硕士学位论文,2006
曾志远.卫星遥感图像计算机分类与地学应用研究[M].北京:科学出版社,2004
张肃.地物频谱在解译遥感图像中的应用研究[J].成都理工大学学报(自然科学版),2006,33(2):198-202
张友静等.基于SVM决策支持树的城市植被类型遥感分类研究[J].遥感学报,2006,10(2):191-196
张友水等.基于GIS的BP神经网络遥感影像分类研究[J].南京大学学报(自科版),2003,39(6):806-813
张友水,冯学智等.Kohonen神经网络在遥感影像分类中的应用研究[J].遥感学报,2004,8(2):178-184
张友水等.多时相TM影像相对辐射校正研究[J].测绘学报,2006,35(2):123-127
赵萍,冯学智等.SPOT卫星影像居民地信息自动提取的决策树方法研究[J].遥感学报,2003,7(4):309-315
赵英时.遥感应用分析原理与方法[M].北京:科学出版社,2003
朱长青,杨晓梅.具有更佳分辨率小波分解的遥感影像纹理分类[J].地理研究,1997,16(1):53-59
朱述龙,张古睦.遥感图像获取与分析[M].北京:科学出版社,2000
朱震达等.中国荒漠化(土地退化)防治研究[M].北京:中国环境出版社,1998
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |