西南岩溶地区植被覆盖条件下的碳酸盐岩岩性遥感识别研究
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摘要
西南岩溶地区,由于茂密的植被覆盖,在遥感图象上直接的岩性光谱信息很弱,而反映的大部分是植被覆盖层的光谱信息。因而利用遥感图像识别岩性,尤其是碳酸盐岩岩性的识别难度很大。
本论文以广西灌江流域为主研究区,在对研究区各种碳酸盐岩进行野外光谱测试的基础上,首次探讨利用多源遥感数据-ETM、SPOT、ASTER数据在植被茂密的中国西南岩溶地区进行碳酸盐岩的计算机岩性遥感自动识别。以广西东兰县、巴马瑶族自治县岩溶山地为辅助研究区开展不同类型碳酸盐岩之岩性与土壤、植被之间的相关关系研究,揭示碳酸盐岩岩性、土壤和植被之间的元素迁移规律。据此,将不同岩性分布区连同其上覆的土壤、植被作为一个完整的、相互关联的系统,研究遥感数据处理与信息提取技术,并进行遥感图像的岩性分类与岩性填图。
通过研究,取得如下认识与结论：
一、岩性、土壤和植被间存在着密切的相关关系,三者之间的元素迁移遵循一定的规律。岩性作为土壤、植被的基底起着土壤元素和植物元素“源”的作用,制约着土壤元素和植物元素的成分和构成。土壤和植物元素成分和构成的不同在遥感图像上便会表现出不同的光谱特征。
二、基于上述认识,本次研究将同一岩性分布区内的岩性、土壤和植被作为一个有机关联的系统,进而从整体上探索并提取植被覆盖下岩性的遥感光谱信息和纹理信息,作为碳酸盐岩岩性遥感自动识别分类的依据。
三、岩性遥感自动识别分类的探索研究表明：单一的遥感数据类型在岩性的自动识别分类中,分类精度都不高。以单独遥感数据类型的岩性识别分类效果来比较,ASTER数据的识别精度最高,TM/ETM数据次之,SPOT数据最低,这应该归因于它们的波段范围及波段设置的差异；由于单一类型的遥感数据无法取得较高的岩性识别精度,采用多源遥感数据的组合来进行岩性识别分类便是合乎逻辑的途径,不同的遥感数据有着不同的特征,这些不同的特征在岩性识别时可以起到互补的作用,如本次研究,ASTER数据较宽的光谱范围和较高的光谱分别率与SPOT数据较高的空间分别率的结合使得岩性识别效果大为改善；在充分利用光谱信息的基础上,加入纹理信息参与分类大大地提高了岩性的识别程度和分类精度。提取纹理信息时关键在于确定窗口的大小,窗口大小的确定可以从两个方面进行考虑：一是研究区域遥感图象上纹理的复杂程度和纹理条带的宽度；二是岩性地层单位的出露宽度。尤其是后者对岩性识别精度影响很大,对于薄层、地面出露宽度小的地层,提取纹理信息的窗口大小以不超过其出露宽度为宜。窗口过大,反而降低了这类薄层地层的识别精度。
最后以SPOT的4个多光谱波段、ASTER所有14个波段、TM的6个波段共24个波段加上SPOT的4个光谱波段和ASTER的3个近红外波段分别以11×11窗口提取的纹理波段的组合共31个波段组合进行岩性的自动识别分类,取得了82.01%的自动识别分类总精度。
岩性遥感自动识别分类研究的最终目的是利用遥感技术进行岩性填图,自动分类结果即使理论精度很高,但错分、混分难免,只有经过屏幕目视判别修改才能形成最终的遥感岩性地质图。目视判别修改遵循两条原则：(1)优势原则,(2)岩性地层单位分布的规律性原则。
本次研究表明,即使是在植被覆盖、岩层出露条件不好的地区,利用遥感技术进行岩性识别进而开展遥感岩性填图也是可行的。以岩性、土壤和植被三者之间的关系的分析研究为基础,将不同岩性单元及其上覆的土壤和植被作为有机关联的系统,从整体上提取不同岩性单元的光谱信息和纹理信息,进行岩性遥感识别分类和岩性遥感填图,是本次研究的独特之处,可以推广到类似植被覆盖地区的岩性遥感识别研究。
Southwestern China karst area covers an about 907,000 km2 area and mainly exposed carbonate rocks. Characteristics of karst area are that soil is difficult to develop and the karst entironment, if the vegetation was felled and destroyed, is very difficult to be resumed or renewed. Therefore, karst rocky desertification is often occur in karst area, causing serious environment issues and leading to progressive impoverishment of local residents. In addition to people's irrational activity, the speed or course of karst rocky desertification is specially influenced by the lithology of carbonate rocks. For example, karst rocky desertification is very easy to occur in pure limestone area. To prevent and recover from karst rocky desertification, it is necessary to know lithological distribution of carbonate rocks.
Lithological mapping using remote sensing data is fast and cheap. Since remote sensing come out, a number of researches have been done in lithological mapping or lithological discrimination using remote sensing data in two aspects:First, spectroscopy, lithological discrimination by studying the spectral property of different rocks; Second, technology or method of information extracting. Many of these studies are focused in arid region with thin soil, little vegetation, and well-outcropped strata. However, in southwestern China karst area, rocks were covered by flourishing vegetation and deep soils. The spectral information of the remote sensing images mainly reflects vegetation and soils. So, lithological discrimination by using remote sensing becomes very difficult.
In this thesis, the use of multi-source remote sensing data, such as TM, SPOT and ASTER, for lithological discrimination was evaluated in the major test area covered a 900 km2 karst area located in southeastern of Quanzhou County, Guangxi. The spectra of carbonate rocks were measured in field using portable spectrum meter. In the assistant study area, which is located in Donglan County, Guangxi and is a typical karst area too, the relationship among lithology, soil and vegetation was studied by measuring thire chemical element. The rock and its soil and vegetation was conside as a whole system in remote sensing image, lithology was classified by using remote sensing data in the major test area.
Through research, following knowledge and conclusions can be reached:
There is a close correlation betreen lithology, soil and vegetation. The elements migration follows certain patterns from Lithology to soil and soil to vegetation. Lithology, as the basement of soil and vegetation, play a "source" role in elements migration, which restricts the component and composition of soils elements and plant elements. Different spectral characteristics will be showed on remote sensing image because of different Soil and plant elemental composition.
Based on the above understanding, in the area of the same lithology, the lithology, soil and vegetation were consided as a whole system in the present study, and thus the spectral information and texture information of different lithology unit were explored and extracted from remote sensing data under vegetation cover, and was used in lithology automatic identification and classification.
The process and results of lithological classification indicate that the accuracy of lithological discrimination using single RS data was 69.36% for ASTER data,64.37% for TM data and 54.41% for SPOT data. The reason should due to different spectral range and band settings of each RS data.
Because satisfying classification accuracy using single RS data can't be acquired, rational strategy is to use multi-sources RS data. Each RS data have its own character and bands setting. By combining of multi-sources RS data, those different characters of different kinds of data can reinforce each other, and supplementary information helped to increase accuracy of lithological discrimination may be obtained. For instance, ASTER data have more wide spectral range and more bands than SPOT data, but SPOT data have higher resolution. These two kinds of data reinforce each other in spectral range and resolution. Usually, the more sorts of RS data were used for classification, the higher accuracy was obtained.
In classification, in addition to spectral bands, the inclusion of variogram texture images in spectral classification could considerably improves the classification accuracy. The key for pick-up texture image is the size of window. Two factors decided the size of window are:first, texture complexity on remote sensing image; secondly, the exposed width of lithological unit, especially the latter. For the lithological unit which is thin and narrow exposed on the surface, it is reasonable that the window size do not exceed the exposed width of the thin unit.
Finally,4 SPOT spectral bands and its 4 texture images,6 TM spectral bands,14 ASTER spectral bands and its 3 texture images extracted from 3 ASTER VNIR spectral bands was used for classification. The final overall classification accuracy is 82.01%.
本论文以广西灌江流域为主研究区,在对研究区各种碳酸盐岩进行野外光谱测试的基础上,首次探讨利用多源遥感数据-ETM、SPOT、ASTER数据在植被茂密的中国西南岩溶地区进行碳酸盐岩的计算机岩性遥感自动识别。以广西东兰县、巴马瑶族自治县岩溶山地为辅助研究区开展不同类型碳酸盐岩之岩性与土壤、植被之间的相关关系研究,揭示碳酸盐岩岩性、土壤和植被之间的元素迁移规律。据此,将不同岩性分布区连同其上覆的土壤、植被作为一个完整的、相互关联的系统,研究遥感数据处理与信息提取技术,并进行遥感图像的岩性分类与岩性填图。
通过研究,取得如下认识与结论：
一、岩性、土壤和植被间存在着密切的相关关系,三者之间的元素迁移遵循一定的规律。岩性作为土壤、植被的基底起着土壤元素和植物元素“源”的作用,制约着土壤元素和植物元素的成分和构成。土壤和植物元素成分和构成的不同在遥感图像上便会表现出不同的光谱特征。
二、基于上述认识,本次研究将同一岩性分布区内的岩性、土壤和植被作为一个有机关联的系统,进而从整体上探索并提取植被覆盖下岩性的遥感光谱信息和纹理信息,作为碳酸盐岩岩性遥感自动识别分类的依据。
三、岩性遥感自动识别分类的探索研究表明：单一的遥感数据类型在岩性的自动识别分类中,分类精度都不高。以单独遥感数据类型的岩性识别分类效果来比较,ASTER数据的识别精度最高,TM/ETM数据次之,SPOT数据最低,这应该归因于它们的波段范围及波段设置的差异；由于单一类型的遥感数据无法取得较高的岩性识别精度,采用多源遥感数据的组合来进行岩性识别分类便是合乎逻辑的途径,不同的遥感数据有着不同的特征,这些不同的特征在岩性识别时可以起到互补的作用,如本次研究,ASTER数据较宽的光谱范围和较高的光谱分别率与SPOT数据较高的空间分别率的结合使得岩性识别效果大为改善；在充分利用光谱信息的基础上,加入纹理信息参与分类大大地提高了岩性的识别程度和分类精度。提取纹理信息时关键在于确定窗口的大小,窗口大小的确定可以从两个方面进行考虑：一是研究区域遥感图象上纹理的复杂程度和纹理条带的宽度；二是岩性地层单位的出露宽度。尤其是后者对岩性识别精度影响很大,对于薄层、地面出露宽度小的地层,提取纹理信息的窗口大小以不超过其出露宽度为宜。窗口过大,反而降低了这类薄层地层的识别精度。
最后以SPOT的4个多光谱波段、ASTER所有14个波段、TM的6个波段共24个波段加上SPOT的4个光谱波段和ASTER的3个近红外波段分别以11×11窗口提取的纹理波段的组合共31个波段组合进行岩性的自动识别分类,取得了82.01%的自动识别分类总精度。
岩性遥感自动识别分类研究的最终目的是利用遥感技术进行岩性填图,自动分类结果即使理论精度很高,但错分、混分难免,只有经过屏幕目视判别修改才能形成最终的遥感岩性地质图。目视判别修改遵循两条原则：(1)优势原则,(2)岩性地层单位分布的规律性原则。
本次研究表明,即使是在植被覆盖、岩层出露条件不好的地区,利用遥感技术进行岩性识别进而开展遥感岩性填图也是可行的。以岩性、土壤和植被三者之间的关系的分析研究为基础,将不同岩性单元及其上覆的土壤和植被作为有机关联的系统,从整体上提取不同岩性单元的光谱信息和纹理信息,进行岩性遥感识别分类和岩性遥感填图,是本次研究的独特之处,可以推广到类似植被覆盖地区的岩性遥感识别研究。
Southwestern China karst area covers an about 907,000 km2 area and mainly exposed carbonate rocks. Characteristics of karst area are that soil is difficult to develop and the karst entironment, if the vegetation was felled and destroyed, is very difficult to be resumed or renewed. Therefore, karst rocky desertification is often occur in karst area, causing serious environment issues and leading to progressive impoverishment of local residents. In addition to people's irrational activity, the speed or course of karst rocky desertification is specially influenced by the lithology of carbonate rocks. For example, karst rocky desertification is very easy to occur in pure limestone area. To prevent and recover from karst rocky desertification, it is necessary to know lithological distribution of carbonate rocks.
Lithological mapping using remote sensing data is fast and cheap. Since remote sensing come out, a number of researches have been done in lithological mapping or lithological discrimination using remote sensing data in two aspects:First, spectroscopy, lithological discrimination by studying the spectral property of different rocks; Second, technology or method of information extracting. Many of these studies are focused in arid region with thin soil, little vegetation, and well-outcropped strata. However, in southwestern China karst area, rocks were covered by flourishing vegetation and deep soils. The spectral information of the remote sensing images mainly reflects vegetation and soils. So, lithological discrimination by using remote sensing becomes very difficult.
In this thesis, the use of multi-source remote sensing data, such as TM, SPOT and ASTER, for lithological discrimination was evaluated in the major test area covered a 900 km2 karst area located in southeastern of Quanzhou County, Guangxi. The spectra of carbonate rocks were measured in field using portable spectrum meter. In the assistant study area, which is located in Donglan County, Guangxi and is a typical karst area too, the relationship among lithology, soil and vegetation was studied by measuring thire chemical element. The rock and its soil and vegetation was conside as a whole system in remote sensing image, lithology was classified by using remote sensing data in the major test area.
Through research, following knowledge and conclusions can be reached:
There is a close correlation betreen lithology, soil and vegetation. The elements migration follows certain patterns from Lithology to soil and soil to vegetation. Lithology, as the basement of soil and vegetation, play a "source" role in elements migration, which restricts the component and composition of soils elements and plant elements. Different spectral characteristics will be showed on remote sensing image because of different Soil and plant elemental composition.
Based on the above understanding, in the area of the same lithology, the lithology, soil and vegetation were consided as a whole system in the present study, and thus the spectral information and texture information of different lithology unit were explored and extracted from remote sensing data under vegetation cover, and was used in lithology automatic identification and classification.
The process and results of lithological classification indicate that the accuracy of lithological discrimination using single RS data was 69.36% for ASTER data,64.37% for TM data and 54.41% for SPOT data. The reason should due to different spectral range and band settings of each RS data.
Because satisfying classification accuracy using single RS data can't be acquired, rational strategy is to use multi-sources RS data. Each RS data have its own character and bands setting. By combining of multi-sources RS data, those different characters of different kinds of data can reinforce each other, and supplementary information helped to increase accuracy of lithological discrimination may be obtained. For instance, ASTER data have more wide spectral range and more bands than SPOT data, but SPOT data have higher resolution. These two kinds of data reinforce each other in spectral range and resolution. Usually, the more sorts of RS data were used for classification, the higher accuracy was obtained.
In classification, in addition to spectral bands, the inclusion of variogram texture images in spectral classification could considerably improves the classification accuracy. The key for pick-up texture image is the size of window. Two factors decided the size of window are:first, texture complexity on remote sensing image; secondly, the exposed width of lithological unit, especially the latter. For the lithological unit which is thin and narrow exposed on the surface, it is reasonable that the window size do not exceed the exposed width of the thin unit.
Finally,4 SPOT spectral bands and its 4 texture images,6 TM spectral bands,14 ASTER spectral bands and its 3 texture images extracted from 3 ASTER VNIR spectral bands was used for classification. The final overall classification accuracy is 82.01%.
引文
[1]袁道先,蔡桂鸿.岩溶环境学.重庆：重庆出版社,1988
[2]张寿越,袁道先.岩溶研究进展.地质论评.1982,(3)：281-285
[3]袁道先等.中国岩溶动力系统.北京：地质出版社,2002
[4]袁道先.岩溶动力学理论的发展与国际岩溶研究中心的成立.中国岩溶,2009,28(2)：Ⅷ
[5]袁道先, 章程. 岩溶动力学的理论探索与实践.地球学报.2008,29(3)：355-365
[6]袁道先、曹建华.岩溶动力学的理论与实践.北京,科学出版社,2008：121-126
[7]袁道先.新形式下我国岩溶研究面临的机遇与挑战.中国岩溶,2009,28(4)：329-331
[8]袁道先等.中国岩溶动力系统.北京.地质出版社.2002
[9]Yuan Daoxian. Rocky desertification in the subtropical karst of south China. Z.Geomorph.N.F.,1997,108:81-90
[10]王世杰.喀斯特石漠化概念演绎及其科学内涵的探讨.中国岩溶,2002,21 (20)： 101-105
[11]莫源富,奚小双,夏日元.广西都安县三只羊地区与贵州罗甸县大关地区岩溶石漠化进程对比分析.中国岩溶,2007,26(4)：291-296
[12]李瑞玲.贵州岩溶地区土地石漠化形成的自然背景及其空间地域分异.[博士学位论文].贵阳：中国科学院地球化学研究所,2004
[13]Hunt G R. Spectroscopic properties of rocks and minerals in handbook of physical properties of rocks. Volume Ⅰ. Boca Raton:CRC Press,1982
[14]甘甫平,王润生.遥感岩矿信息提取基础与技术方法研究.北京：地质出版社,2004
[15]王青华,王润生,等.高光谱遥感技术在岩石识别中的应用.国土资源遥感,2000,4：39-43
[16]甘甫平,王润生,等.光谱遥感岩矿识别基础与技术研究进展.遥感技术与应用,2002,17(3)：140-147
[17]甘甫平,王润生,等.基于完全谱形特征的成像光谱遥感岩矿识别技术及其应用.地质科学,2000,35(3)：376-385
[18]Clark R N. Spectroscopy of rocks and minerals, and principles of spectroscopy (1999-06-25) 2006-11-08. http://speclab.Cr. usgs. Gov
[19]Clark R N, Roush T L. Reflectance spectroscopy:quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research,1984, 89(7):6329-6340
[20]Clark R N, Swayze G A,Ericlivo K, et al. Imaging spectroscopy:earth and planetary remote sensing with USGS tetracorder and expert systems. Journal of Geophysical Research,2003,108(E12):5131
[21]Clark R N, Swayze G A. Digital spectral library:version 1(0.2-3.0um). Denver: US Geological Survey,1993
[22]舒守荣.最佳遥感波段选择的概率统计方法及其在碳酸盐岩石鉴别中的应用.中国岩溶,1982(1)：66-76
[23]舒守荣.碳酸盐岩石最佳遥感波段选择的叠合光谱图方法.中国岩溶,1982(2)：152-158
[24]傅碧宏.遥感岩石学的研究及其进展.地球科学进展,1996,11(3)：252-258.
[25]丑晓伟,傅碧宏.干旱区TM图像岩石地层信息提取与分析方法研究.沉积学报,1995,13 (supp):164-170
[26]Nahid D S, Mohamed G A. Remote sensing analysis of the gorge of the nile, ethiopia with emphasis on dejen-gohatsion region. Journal of African Earth Sciences, 2006,44(2):135-150
[27]丑晓伟,傅碧宏,郑建京.沉积岩石信息的热红外多光谱遥感探测及有效性评价.遥感技术与应用,1996,11(1)：7-13
[28]金浩,童庆禧,郑兰芬等.成像光谱和热红外多光谱技术地质制图研究.环境遥感,1994,(2)：138-144
[29]Mars J C. Geologic mapping of the sierra sanjose mountain range mexico using ASTER data:a remote sensing tool to assist geologic mapping in the field. The U S Colorado Convention Center:Remotely Sensed Data for Geologic and Environmental Studies,2002
[30]黄颖端,李培军,李争晓.基于地统计学的图像纹理在岩性分类中的应用.国土资源遥感.2003,3：45-49
[31]李培军.用ASTER图像和地统计学纹理进行岩性分类.矿物岩石,2004,24(3)：116-120
[32]赵建华,杨树锋,陈汉林.基于分形纹理的遥感图像岩性识别方法.遥感信息理论研究,2004,2：2-4.
[33]Rowan L C, Mars J C. Lithologic mapping in the mountain pass California area using ASTER data. Remote Sensing of Environment,2003,84(3):350-366
[34]张万良.遥感岩性识别的发展趋势-遥感与航空放射性信息集成.矿物岩石地球化学通报,2005,24(1)：88-91
[35]R.A.肖温格.遥感中的图象处理和分类技术.北京：科学出版社,1991
[36]Bolstad P V & Lillesand T M. Semi-automated traning approaches for spectral class definition. INT.J.REMOTE SENSING,1991,13(16):3157-3166
[37]Maselli F, Conese C, Peikov L, et al. Inclusion of prior probabilities derived from a nonparametric process into the maximum-likelihood classifier. Photogrammetric engineering and Remote Sensing,1992,58(2):201-207
[38]Conese C, Maracchi G and Maselli F. Improvement in maximumlikelihood classification performance on highly rugged using principal components analysis. INT.J.REMOTE SENSING,1993,14(7):1371-1382
[39]Arai K. A. Classification method with aspatial-spectral variability. INT.J.REMOTE SENSING.,1993,14(4):699-709
[40]Conese C & Maselli F. Use of error matrices to improve area estimates with maximum-likelihood classification procedures. Remote Sensing of Environment, 1992,40:113-124
[41]Kerl I. Multiple probability comparison-A method for enhancement of maximumlikelihood classification accuracy. In:Progress in Environmental Remote Sensing Research and Applications. Parlow(eds.). Balkema, Rotterdam,1996, ISBN9054105984:101-105
[42]薛重生.遥感图象定量分析方法及应用.地质科技情报,1987,6(4)：142-157
[43]Kellenberger T W and Itten K. Limitations of multispectral classification of temperate forests-scaling problems of ground truth and satellite data. IEEE,1996: 2326-2328
[44][日]遥感研究会编.刘勇卫,贺雪鸿译.遥感精解.北京：测绘出版社,1993
[45]Wang Fangju. Fuzzy supervised classification of remote sensing images. IEEE Trans. On Geosci.and Remote Sensing,1990,28(2):194-201
[46]Gamba P, Marazzi A, Mecocci F, et al. A completely fuzzy classification chain for mutispectral remote sensing images. IEEE,1996:2071-2073
[47]陈述彭,童庆禧,郭华东,遥感信息机理研究.北京：科学出版社.1998
[48]McClellan G E, DeWitt R N, Hemmer T H, et al. Mutispectral image processing with a three-layer backpropagation network. In Proceedings of International Joint Conference on Neural Network'89,1989:151-153
[49]Benediktssn J A, Swain P H and Ersoy O K. Neural network approches versus statistical methods in classification of multisource remote sensing data. IEEE Trans. On Geosci. and Remote Sensing,1990,28(4):540-552
[50]Kanellopoulos I, Varfis A, Wilkinson G G, et al. Neural network classification of multidate satellite imagery. In proceedings of IGARSS'91,1991:2215-2218
[51]Heermann P D and hazenie N K. Classification of multispectral remote sensing data using a back-propogation neural network. IEEETrans.On Geosci.and Remote Sensing,1992,30(1):81-88
[52]Bischof H, Schneider W and Pintz A J. Multispectral classification of Landsat-images using neural networks. IEEETrans.On Geosci.and Remote Sensing, 1992,30(3):482-490
[53]Sanjo K. Robustness of image classification usin gneural network techniques. Presented at the Eleventh Thematic Conference and Workshops on Applied Geologic Remote Sensing. Las Vegas, Nevada,27-29 February,1996, (2):159-167
[54]Cetin H & Levanddowski D W. Interactive classification and mapping of multi-dimensional remotely sensed data using n-Dimensional Probability Density Functions(nPDF). Photogrammetric engineering and Remote Sensing,1991,57(12): 1579-1587
[55]Cetin H, Warner T A and Levanddowski D W. Data classification, visualization and enhancement using n-Dimensional Probability Density Functions(nPDF). AVIRIS, TIMS, TM and Geophysical Applications. Presented at the Ninth Thematic Conference on Geologic Remote Sensing. Pasadena, California, USA,8-11 February, 1993:331-342
[56]Venkateswarlu N B & Raju P S V S K. Technical note, A new fast classifier for remotely sensed imagery. INT.J.REMOTE SENSING,1993,14(2):383-389
[57]Cetin H. An absolute classification technique:Interclass Distance Frequency Distribution (IDFD), Presented at the Eleventh Thematic Conference and Workshops on Applied Geologic Remote Sensing. Las Vegas, Nevada, USA.27-29 February,1996, (2):129-132
[58]Franklin S E & Wilson B A. A three-stage classifier for remote sensing of mountain environments. Photogrammetric engineering and Remote Sensing,1992, 58(4):449-454
[59]宋永昌.植被生态学.上海.华东师范大学出版社,2001：595-596
[60]戴广凯、杨锋杰等.基于遥感技术的泰山植被覆盖和岩性关系研究.山东科技大学学报(自然科学版),2005,24(1)：62-65.
[61]何政伟、孙传敏.岩石-土壤-植被信息系统建立探讨.矿物岩石,2002.22(2)：100-104.
[62]李正积.时代前缘的全息探索-岩土植物大系统研究.地质论评,1996,42(4)： 369-372
[63]李洁维,蒋桥生.广西岩溶山区栽培果树生态适应性初步研究.广西农业科学,2006.37(2)：176.
[64]李阳兵.西南岩溶山地生态环境研究进展与若干问题.云南地理环境研究,2006.18(2)：61.
[65]马超飞、蔺启忠、马建文等,定量消除植被影响的补偿置换方法研究。中国图象图形学报,1999,4(A,7)：553-556
[66]马建文.利用TM数据快速提取含矿蚀变带方法研究.遥感学报,1997,1(3)：208-213.
[67]马超飞,马建文等.应用多源数据提取高植被覆盖地区岩性信息-以湖南黔阳地区为例.地质科学.2002,37(3)：365-371
[68]王贤敏、牛瑞卿.三峡库区基于概念格算法的遥感岩性分析,遥感技术与应用,2009,24(1)：32-39.
[69]王贤敏、牛瑞卿.三峡库区遥感岩性分类规则挖掘.计算机工程与应用,2008,44(35)：13-16.
[70]王贤敏、牛瑞卿.三峡库区岩性植被关联规则挖掘.计算机工程与应用,2008,44(31)：8-11.
[71]程瑞梅,肖文发等.三峡库区灌丛群落多样性的研究.林业科学研究,2000,13(2)：129-133
[72]Pielou E C. Ecological diversity. New York:John Wiley & Sons Inc.1975
[73]Whittaker R H. Evolution of species diversity in land communities. In:Hecht M K, Steere W C, Wallace B eds. Evolutionary biology. New York:Plenum.1977
[74]曹建华,袁道先,潘根兴.岩溶生态系统中的土壤.地球科学进展,2003, 18(1)： 37-44
[75]李小方.岩溶环境中土壤-植物系统钙元素形态分析及其生态意义.桂林：广西师范大学出版社,2004
[76]蒋忠诚.岩溶动力系统中的元素迁移.地理学报,1999,54(5)：438-444
[77]邓艳,蒋忠诚等.广西木论两种珍惜濒危植物的元素背景值比较.中国岩溶,2002,21(4)：299-302
[78]龚子同.土壤圈生命元素的空间分异及其生态效应.见：中科院土壤圈物质循环开放实验室.土壤圈物质循环研究导向会论文集,1989
[79]蒋忠诚.广西弄拉白云岩环境元素的岩溶地球化学迁移.中国岩溶,1997,16(4)：304-312
[80]郑颖吾.木论喀斯特林区概论.科学出版社,1999：124-127
[81]李曙光,聂永红等.俯冲陆壳与上地幔的相互作用——Ⅰ.大别山同碰撞镁铁-超镁铁岩的主要元素及痕量元素地球化学.中国科学D辑：地球科学,1997,27(6)：488-493
[82]吕晓男,孟赐福等.重金属与土壤环境质量及食物安全问题研究.中国生态农业学报,2007,15(2)：197-200
[83]黄建辉,陈灵芝.北京百花山附近-灌丛的化学元素含量特征.植物生态学与地植物学学报,1991,15(3)：224—233
[84]王晋年,郑兰芬,童庆禧.成像光谱图像光谱吸收鉴别模型与矿物填图研究.环境遥感,1996,11(1)：20-31
[85]郑兰芬,王晋年.成像光谱遥感技术及其图像光谱信息提取分析研究.环境遥感,1992,7(1)：49-58
[2]张寿越,袁道先.岩溶研究进展.地质论评.1982,(3)：281-285
[3]袁道先等.中国岩溶动力系统.北京：地质出版社,2002
[4]袁道先.岩溶动力学理论的发展与国际岩溶研究中心的成立.中国岩溶,2009,28(2)：Ⅷ
[5]袁道先, 章程. 岩溶动力学的理论探索与实践.地球学报.2008,29(3)：355-365
[6]袁道先、曹建华.岩溶动力学的理论与实践.北京,科学出版社,2008：121-126
[7]袁道先.新形式下我国岩溶研究面临的机遇与挑战.中国岩溶,2009,28(4)：329-331
[8]袁道先等.中国岩溶动力系统.北京.地质出版社.2002
[9]Yuan Daoxian. Rocky desertification in the subtropical karst of south China. Z.Geomorph.N.F.,1997,108:81-90
[10]王世杰.喀斯特石漠化概念演绎及其科学内涵的探讨.中国岩溶,2002,21 (20)： 101-105
[11]莫源富,奚小双,夏日元.广西都安县三只羊地区与贵州罗甸县大关地区岩溶石漠化进程对比分析.中国岩溶,2007,26(4)：291-296
[12]李瑞玲.贵州岩溶地区土地石漠化形成的自然背景及其空间地域分异.[博士学位论文].贵阳：中国科学院地球化学研究所,2004
[13]Hunt G R. Spectroscopic properties of rocks and minerals in handbook of physical properties of rocks. Volume Ⅰ. Boca Raton:CRC Press,1982
[14]甘甫平,王润生.遥感岩矿信息提取基础与技术方法研究.北京：地质出版社,2004
[15]王青华,王润生,等.高光谱遥感技术在岩石识别中的应用.国土资源遥感,2000,4：39-43
[16]甘甫平,王润生,等.光谱遥感岩矿识别基础与技术研究进展.遥感技术与应用,2002,17(3)：140-147
[17]甘甫平,王润生,等.基于完全谱形特征的成像光谱遥感岩矿识别技术及其应用.地质科学,2000,35(3)：376-385
[18]Clark R N. Spectroscopy of rocks and minerals, and principles of spectroscopy (1999-06-25) 2006-11-08. http://speclab.Cr. usgs. Gov
[19]Clark R N, Roush T L. Reflectance spectroscopy:quantitative analysis techniques for remote sensing applications. Journal of Geophysical Research,1984, 89(7):6329-6340
[20]Clark R N, Swayze G A,Ericlivo K, et al. Imaging spectroscopy:earth and planetary remote sensing with USGS tetracorder and expert systems. Journal of Geophysical Research,2003,108(E12):5131
[21]Clark R N, Swayze G A. Digital spectral library:version 1(0.2-3.0um). Denver: US Geological Survey,1993
[22]舒守荣.最佳遥感波段选择的概率统计方法及其在碳酸盐岩石鉴别中的应用.中国岩溶,1982(1)：66-76
[23]舒守荣.碳酸盐岩石最佳遥感波段选择的叠合光谱图方法.中国岩溶,1982(2)：152-158
[24]傅碧宏.遥感岩石学的研究及其进展.地球科学进展,1996,11(3)：252-258.
[25]丑晓伟,傅碧宏.干旱区TM图像岩石地层信息提取与分析方法研究.沉积学报,1995,13 (supp):164-170
[26]Nahid D S, Mohamed G A. Remote sensing analysis of the gorge of the nile, ethiopia with emphasis on dejen-gohatsion region. Journal of African Earth Sciences, 2006,44(2):135-150
[27]丑晓伟,傅碧宏,郑建京.沉积岩石信息的热红外多光谱遥感探测及有效性评价.遥感技术与应用,1996,11(1)：7-13
[28]金浩,童庆禧,郑兰芬等.成像光谱和热红外多光谱技术地质制图研究.环境遥感,1994,(2)：138-144
[29]Mars J C. Geologic mapping of the sierra sanjose mountain range mexico using ASTER data:a remote sensing tool to assist geologic mapping in the field. The U S Colorado Convention Center:Remotely Sensed Data for Geologic and Environmental Studies,2002
[30]黄颖端,李培军,李争晓.基于地统计学的图像纹理在岩性分类中的应用.国土资源遥感.2003,3：45-49
[31]李培军.用ASTER图像和地统计学纹理进行岩性分类.矿物岩石,2004,24(3)：116-120
[32]赵建华,杨树锋,陈汉林.基于分形纹理的遥感图像岩性识别方法.遥感信息理论研究,2004,2：2-4.
[33]Rowan L C, Mars J C. Lithologic mapping in the mountain pass California area using ASTER data. Remote Sensing of Environment,2003,84(3):350-366
[34]张万良.遥感岩性识别的发展趋势-遥感与航空放射性信息集成.矿物岩石地球化学通报,2005,24(1)：88-91
[35]R.A.肖温格.遥感中的图象处理和分类技术.北京：科学出版社,1991
[36]Bolstad P V & Lillesand T M. Semi-automated traning approaches for spectral class definition. INT.J.REMOTE SENSING,1991,13(16):3157-3166
[37]Maselli F, Conese C, Peikov L, et al. Inclusion of prior probabilities derived from a nonparametric process into the maximum-likelihood classifier. Photogrammetric engineering and Remote Sensing,1992,58(2):201-207
[38]Conese C, Maracchi G and Maselli F. Improvement in maximumlikelihood classification performance on highly rugged using principal components analysis. INT.J.REMOTE SENSING,1993,14(7):1371-1382
[39]Arai K. A. Classification method with aspatial-spectral variability. INT.J.REMOTE SENSING.,1993,14(4):699-709
[40]Conese C & Maselli F. Use of error matrices to improve area estimates with maximum-likelihood classification procedures. Remote Sensing of Environment, 1992,40:113-124
[41]Kerl I. Multiple probability comparison-A method for enhancement of maximumlikelihood classification accuracy. In:Progress in Environmental Remote Sensing Research and Applications. Parlow(eds.). Balkema, Rotterdam,1996, ISBN9054105984:101-105
[42]薛重生.遥感图象定量分析方法及应用.地质科技情报,1987,6(4)：142-157
[43]Kellenberger T W and Itten K. Limitations of multispectral classification of temperate forests-scaling problems of ground truth and satellite data. IEEE,1996: 2326-2328
[44][日]遥感研究会编.刘勇卫,贺雪鸿译.遥感精解.北京：测绘出版社,1993
[45]Wang Fangju. Fuzzy supervised classification of remote sensing images. IEEE Trans. On Geosci.and Remote Sensing,1990,28(2):194-201
[46]Gamba P, Marazzi A, Mecocci F, et al. A completely fuzzy classification chain for mutispectral remote sensing images. IEEE,1996:2071-2073
[47]陈述彭,童庆禧,郭华东,遥感信息机理研究.北京：科学出版社.1998
[48]McClellan G E, DeWitt R N, Hemmer T H, et al. Mutispectral image processing with a three-layer backpropagation network. In Proceedings of International Joint Conference on Neural Network'89,1989:151-153
[49]Benediktssn J A, Swain P H and Ersoy O K. Neural network approches versus statistical methods in classification of multisource remote sensing data. IEEE Trans. On Geosci. and Remote Sensing,1990,28(4):540-552
[50]Kanellopoulos I, Varfis A, Wilkinson G G, et al. Neural network classification of multidate satellite imagery. In proceedings of IGARSS'91,1991:2215-2218
[51]Heermann P D and hazenie N K. Classification of multispectral remote sensing data using a back-propogation neural network. IEEETrans.On Geosci.and Remote Sensing,1992,30(1):81-88
[52]Bischof H, Schneider W and Pintz A J. Multispectral classification of Landsat-images using neural networks. IEEETrans.On Geosci.and Remote Sensing, 1992,30(3):482-490
[53]Sanjo K. Robustness of image classification usin gneural network techniques. Presented at the Eleventh Thematic Conference and Workshops on Applied Geologic Remote Sensing. Las Vegas, Nevada,27-29 February,1996, (2):159-167
[54]Cetin H & Levanddowski D W. Interactive classification and mapping of multi-dimensional remotely sensed data using n-Dimensional Probability Density Functions(nPDF). Photogrammetric engineering and Remote Sensing,1991,57(12): 1579-1587
[55]Cetin H, Warner T A and Levanddowski D W. Data classification, visualization and enhancement using n-Dimensional Probability Density Functions(nPDF). AVIRIS, TIMS, TM and Geophysical Applications. Presented at the Ninth Thematic Conference on Geologic Remote Sensing. Pasadena, California, USA,8-11 February, 1993:331-342
[56]Venkateswarlu N B & Raju P S V S K. Technical note, A new fast classifier for remotely sensed imagery. INT.J.REMOTE SENSING,1993,14(2):383-389
[57]Cetin H. An absolute classification technique:Interclass Distance Frequency Distribution (IDFD), Presented at the Eleventh Thematic Conference and Workshops on Applied Geologic Remote Sensing. Las Vegas, Nevada, USA.27-29 February,1996, (2):129-132
[58]Franklin S E & Wilson B A. A three-stage classifier for remote sensing of mountain environments. Photogrammetric engineering and Remote Sensing,1992, 58(4):449-454
[59]宋永昌.植被生态学.上海.华东师范大学出版社,2001：595-596
[60]戴广凯、杨锋杰等.基于遥感技术的泰山植被覆盖和岩性关系研究.山东科技大学学报(自然科学版),2005,24(1)：62-65.
[61]何政伟、孙传敏.岩石-土壤-植被信息系统建立探讨.矿物岩石,2002.22(2)：100-104.
[62]李正积.时代前缘的全息探索-岩土植物大系统研究.地质论评,1996,42(4)： 369-372
[63]李洁维,蒋桥生.广西岩溶山区栽培果树生态适应性初步研究.广西农业科学,2006.37(2)：176.
[64]李阳兵.西南岩溶山地生态环境研究进展与若干问题.云南地理环境研究,2006.18(2)：61.
[65]马超飞、蔺启忠、马建文等,定量消除植被影响的补偿置换方法研究。中国图象图形学报,1999,4(A,7)：553-556
[66]马建文.利用TM数据快速提取含矿蚀变带方法研究.遥感学报,1997,1(3)：208-213.
[67]马超飞,马建文等.应用多源数据提取高植被覆盖地区岩性信息-以湖南黔阳地区为例.地质科学.2002,37(3)：365-371
[68]王贤敏、牛瑞卿.三峡库区基于概念格算法的遥感岩性分析,遥感技术与应用,2009,24(1)：32-39.
[69]王贤敏、牛瑞卿.三峡库区遥感岩性分类规则挖掘.计算机工程与应用,2008,44(35)：13-16.
[70]王贤敏、牛瑞卿.三峡库区岩性植被关联规则挖掘.计算机工程与应用,2008,44(31)：8-11.
[71]程瑞梅,肖文发等.三峡库区灌丛群落多样性的研究.林业科学研究,2000,13(2)：129-133
[72]Pielou E C. Ecological diversity. New York:John Wiley & Sons Inc.1975
[73]Whittaker R H. Evolution of species diversity in land communities. In:Hecht M K, Steere W C, Wallace B eds. Evolutionary biology. New York:Plenum.1977
[74]曹建华,袁道先,潘根兴.岩溶生态系统中的土壤.地球科学进展,2003, 18(1)： 37-44
[75]李小方.岩溶环境中土壤-植物系统钙元素形态分析及其生态意义.桂林：广西师范大学出版社,2004
[76]蒋忠诚.岩溶动力系统中的元素迁移.地理学报,1999,54(5)：438-444
[77]邓艳,蒋忠诚等.广西木论两种珍惜濒危植物的元素背景值比较.中国岩溶,2002,21(4)：299-302
[78]龚子同.土壤圈生命元素的空间分异及其生态效应.见：中科院土壤圈物质循环开放实验室.土壤圈物质循环研究导向会论文集,1989
[79]蒋忠诚.广西弄拉白云岩环境元素的岩溶地球化学迁移.中国岩溶,1997,16(4)：304-312
[80]郑颖吾.木论喀斯特林区概论.科学出版社,1999：124-127
[81]李曙光,聂永红等.俯冲陆壳与上地幔的相互作用——Ⅰ.大别山同碰撞镁铁-超镁铁岩的主要元素及痕量元素地球化学.中国科学D辑：地球科学,1997,27(6)：488-493
[82]吕晓男,孟赐福等.重金属与土壤环境质量及食物安全问题研究.中国生态农业学报,2007,15(2)：197-200
[83]黄建辉,陈灵芝.北京百花山附近-灌丛的化学元素含量特征.植物生态学与地植物学学报,1991,15(3)：224—233
[84]王晋年,郑兰芬,童庆禧.成像光谱图像光谱吸收鉴别模型与矿物填图研究.环境遥感,1996,11(1)：20-31
[85]郑兰芬,王晋年.成像光谱遥感技术及其图像光谱信息提取分析研究.环境遥感,1992,7(1)：49-58