【作者】 莫源富；

【导师】 奚小双；

【作者基本信息】 中南大学 ， 矿产普查与勘探， 2010， 博士

【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。更多还原

【Abstract】 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%.更多还原