绢云母的光谱特征变异分析及成像光谱地质成因信息提取

鉴于绢云母(和白云母)类粘土矿物对于研究与矿化紧密相关的中低温热液蚀变机制、与温压有关的地质成因过程等的重要意义,开展了绢云母的岩矿光谱特征变异分析和成像光谱矿物识别,并对岩石、矿物的试验室光谱和影像光谱所反映的地质成因信息的提取进行探讨。     

中等程度植被覆盖区岩石蚀变信息提取技术及其应用

各种类型的热液型矿产经常与围岩蚀变相伴生,蚀变岩类是找矿标志之一。陆地卫星TM数据在短波近红外范围内有二个波段,这两个波段的信息可用来进行蚀变岩的制图。可是在有部分或中等密度的植被复盖区,由于植被和蚀变岩类的反射光谱特性比较相似,影响了TM数据的制图能力。笔者开发了三种用来在中等程度植被复盖区进行蚀变岩类信息提取方法。这三种方法是:直接主成分分析法、最小平方拟合法和混合单元分解法。在滇黔桂微细浸染型金矿勘查中,这三种方法被成功地用来提取与金矿化有关的粘土蚀变岩类信息和制图。     

多元数据分析与遥感矿化蚀变信息提取模型

蚀变矿物的光谱行为造成了蚀变岩的光谱特征。利用典型岩石的多元数据分析，可以对蚀变矿物成份和光谱数据之间的关系作出定性和定量评价，进而建立遥感矿化蚀变信息提取模型。笔者利用青海某金多金属矿化区典型岩（矿）石样品的野外实测波谱数据和化学分析数据，进行了基于多元数据分析的遥感矿化蚀变信息提取模型研究，建立的比值组合线性回归模型较之单一比值方法具有更好的应用效果。     

基于矿物光谱指数的蚀变信息提取——以西藏多龙铜金矿集区ASTER数据为例

矿物光谱指数是利用比值的方法增强Fe和含OH矿物在可见光、短波红外区间的吸收特征,进而提取蚀变矿物信息的一种方法。由于斑岩型矿床的蚀变分带特征明显,西藏多龙矿集区中集中了斑岩型、浅成低温热液型等铜、金矿床,通过矿物光谱指数的计算提取相关蚀变信息,对该区域的下一步找矿具有指导意义。本文采用矿物光谱指数(5+7)/6、(6+9)/8等方法提取了镁、铝两类羟基及铝羟基类下的高岭石等蚀变信息,采用5/4、2/1的方法提取了二价铁、三价铁的蚀变信息。经过与OLI影像采用PCA方法及野外实地勘查采样,证实了利用本文方法所提取的蚀变信息有效。     

Landsat8 OLI数据斑岩铜矿遥感蚀变矿物组合提取研究

Landsat8 OLI数据NIR波段(0. 845~0. 885μm)和SWIR2波段(2. 100~2. 300μm)波谱分辨率的提高,已分别成为含Fe~(3+)类、Al-OH类和Mg-OH类蚀变矿物的诊断性谱段。通过利用混合调制匹配滤波(mixture tuned matched filtering,MTMF)方法,开展多龙矿集区铁格隆远景区含Fe~(3+)类、Al-OH类和Mg-OH类蚀变矿物信息提取研究,并与Hyperion数据蚀变矿物信息提取结果进行对比分析。研究结果表明,基于Landsat8 OLI提取的3类蚀变矿物较为可靠;铁格隆远景区蚀变矿物组合的空间分布与斑岩铜矿经典蚀变分带特征基本吻合;遥感蚀变分带由内而外分别为绢英岩化+泥化带(Al-OH类蚀变矿物)和青磐岩化带(Mg-OH类蚀变矿物),Fe~(3+)矿物组合则分布在绢英岩化+泥化带和青磐岩化带之间或过渡带上。     

协同处理方式在遥感蚀变异常成因分析中的应用——以北山方山口地区为例

遥感蚀变异常是重要的示矿指标。但以往研究对遥感蚀变异常的地质成因及其示矿意义探讨不足,导致对遥感蚀变异常的分析往往具有多解性,影响了遥感蚀变异常在地质找矿中的先导作用。为此建立了能定量解释遥感蚀变异常地质成因和示矿意义的多源数据、多方法协同处理方式。该方式基于不同光谱分辨率和空间分辨率的多源遥感数据,采用光谱分析与岩矿测试相融合的方法分析不同尺度(不同矿物类型、不同成矿类型、不同地质剖面、不同空间区域)遥感蚀变异常的特征,即利用多光谱遥感宏观分析遥感蚀变异常区域分布特征;利用高光谱遥感分解典型地质体的遥感蚀变异常信息发育模式;利用X射线衍射分析、岩矿鉴定和光谱解算准确鉴别蚀变矿物类型并解释其地质成因与示矿意义。通过对北山方山口地区的测试,发现遥感蚀变异常的类型与地表发育的蚀变矿物类型基本一致,且不同成矿类型矿床的围岩蚀变可以被特定的遥感蚀变矿物信息组合有效反映,据此规律进行遥感蚀变异常查证,新发现多处矿化线索,较好地实现了遥感信息向地质信息的转化应用。研究表明,本研究中提出的多源数据、多方法协同处理的方式可有效克服单一数据源或单一方法分析结果的不完备性,提高遥感蚀变异常在地质应用中的可信度。     

遥感岩矿信息提取基础与技术方法研究

在遥感地质应用中 ,对岩矿光谱和空间分布精细特征的探测是高空间与高光谱分辨率遥感的优势所在。随着传感器性能的改进 ,尤其是光谱分辨率的提高 ,改善了信息识别与提取的技术环境。本文从分析岩矿的光谱特征与遥感光谱识别规则出发 ,根据不同类型遥感数据的光谱特征 ,尤其是成像光谱数据丰富的光谱信息 ,研究不同尺度下遥感岩矿识别技术 ,建立相应的遥感应用模型 ,讨论了遥感技术集成。具体研究内容有 :(1)分析与总结遥感光谱识别规则。分别从矿物离子、矿物蚀变类型和蚀变矿物组合 3个层次分析了岩矿作用过程中矿物特征光谱的变化与变异…     

应用ASTER数据提取矽卡岩型锡矿蚀变遥感异常信息——以滇东南马关都龙锡矿为例

运用多光谱遥感数据进行矿化蚀变信息提取,对圈定找矿预测区具有重要意义.采用ASTER数据及其矿化蚀变信息提取原理,对滇东南马关都龙锡矿含矿岩体分布区分别提取了方解石化、白云岩化、绿泥石化等3类蚀变遥感异常信息.所提取的蚀变异常与野外调查发现的矿化点吻合性好,蚀变信息可靠,对指导找矿有较好的效果,为该地区矿产资源潜力评价...     

黔桂地区微细浸染型金矿蚀变信息提取机理

本文围绕与微细浸染型金矿有关的硅化、褐铁矿化、粘土矿化、碳酸盐化等蚀变标志，在黔桂地区进行了相关的地球化学和光谱信息研究。通过对岩矿的光谱特征、地球化学因子、ＴＭ波段比值因子与地球化学因子的相关性等研究，探索从室内光谱数据中提取与金矿化密切相关的蚀变信息，并以此为依据做图象处理，取得了满意的结果。     

藏东罗布莎蛇绿岩遥感岩矿信息提取方法研究

为研究如何利用遥感技术进行蛇绿岩岩矿信息识别和提取,以藏东罗布莎蛇绿岩为例,基于ETM和ASTER遥感数据,分别采用比值法和主成分分析法提取羟基类(蛇纹石、绿泥石)和铁染类(磁铁矿、橄榄石)矿物信息;利用纯净像元指数法(pure pixel index,PPI)提取端元波谱,结合地面已知岩性信息,分别采用光谱角分类法(spectral angle mapping,SAM)和波谱特征拟合法(spectral feature fitting,SFF)识别纯橄岩和橄榄岩,研究了罗布莎蛇绿岩及其主要蚀变矿物的空间分布特征.经过对比分析可知,用多种方法、多种数据提取的岩矿信息可以相互验证;提取出的纯橄岩和橄榄岩与所提取的羟基类和铁染类蚀变遥感异常信息有较好的空间重叠,且与地面调查结果基本吻合.研究表明,该方法用于蛇绿岩遥感岩矿信息提取是可行的,并取得较好的地质效果.     

Delineation of coalfield surface fires by thresholding Landsat TM-7 day-time image data

Surface fires are common in coalfields where coal is mined or exposed to sunlight for long durations of time. The heat energy emitted from these fires affects the signal recorded by sensors operating in the shortwave infrared regions of the electromagnetic spectrum. The Landsat TM/ETM+ band-7 is sensitive to solar reflected radiation as well as emitted radiation from a target. The ‘maximum solar reflection threshold’ method proposed in this study uses the highest spectral radiance that can be attributed to solar reflection as the conservative threshold to segregate the pixels with emitted component from those with reflected component of the EM energy. Investigations with Landsat TM/ETM+ data indicate a reflectance value of 0.23–0.25 as the most representative highest reflectance (threshold) in coal mining areas. The method apparently has the advantage that it is based on the reflectance characteristics of materials (sandstone-shale mixtures) typically found in coal mining areas.     

一种拓展的半物理时空融合算法及其初步应用

Landsat 5卫星较低的时间分辨率(16天)使得其很难获得大区域的、时相一致的清晰影像数据集。本文发展了一种基于半物理模型的时空融合算法-即乘性调制融合算法,并借助多时序的MODIS反射率数据来生成多时相的Landsat TM/ETM+反射率合成影像,经镶嵌后得到区域尺度的高时空分辨率地表反射率数据集(Landsat TM/ETM+)。本文利用吉林省2006年—2011年的Landsat 5 TM地表反射率数据以及500 m的MOD09A1反射率产品来生成3个时相的Landsat 5 TM反射率合成数据,从而获得研究区在上述时相下地表反射率数据的镶嵌图。初步分析表明,所生成的Landsat 5 TM反射率数据的光谱分布特征与MOD09A1反射率数据较为一致,且图像在整体上光谱特征的连续性较好。     

Use of Principal Component Analysis (PCA) of Remote Sensing Images in Wetland Change Detection on the Kafue Flats,Zambia

Abstract

The paper describes the use of Principal Component Analysis (PCA) of remote sensing images as a method of change detection for the Kafue Flats, an inland wetland system in southern Zambia. The wetland is under human and natural pressures but is also an important wildlife habitat. A combination of Landsat MSS and TM images were used. The images used were from 24 September 1984 (MSS), 3 September 1988 (MSS), 12 September 1991 (TM) and 20 September 1994 (TM). They were geometrically co‐registered and, in the process, the 80m resolution MSS images were resampled to 30m using nearest neighbour resampling. Preliminary PCA revealed that for the MSS images most of the data variance was in near infrared reflectance while for the TM images it was in mid and thermal infrared bands. Holding sensor type constant, separate inter‐band correlation analysis for each image could indicate whether the wetland was drier or wetter on one date versus another. The 1994 image was made the reference image and equivalent green, red and near infrared bands from the other images were radiometrically normalised with those on the reference image. All the bands, three from each date, were then merged into a twelve‐band image on which PCA for change detection was undertaken. A colour composite of eigen images from the resulting principal components was used in change detection. Hydrological data, indicating long‐term reduced inflow of water into the wetland due to human regulation, help explain some of the wetland change detected. Compared to a classification comparison approach to change detection for this area, PCA was found to be very useful in indicating where change had occurred, though interpretation of the changes was difficult without reference to the input images. The methodology appears to have potential use in habitat monitoring for this wetland area.     

HJ-1A CCD与TM数据及其估算草地LAI和鲜生物量效果比较分析

基于地面实测和PROSAIL模型模拟数据,研究了新型传感器HJ-1ACCD与TM数据一致性问题,分析了传感器天顶角和光谱相应函数差异的影响,对比两种传感器数据估算草地LAI和鲜生物量的效果,得出以下结论:(1)HJ-1ACCD和TM反射率数据的拟合系数R2在0.7322和0.9205左右,在反射率较小时,两种传感器数据一致性较好;随着反射率增大,HJ-1ACCD数值逐渐高于TM。总体而言,在可见光和近红外波段,两种传感器较为接近,其中红波段最接近。(2)两种传感器的NDVI数据一致性非常高,且受传感器天顶角和光谱响应函数影响作用较小(相对误差约为0.34%—0.53%),而反射率的相对差别在3.34%—9.54%。(3)传感器天顶角较光谱响应函数对反射率影响更大。(4)基于HJ-1ACCD反射率数据估算草地LAI和鲜生物量效果较好,其中以CCD2传感器估算效果最好。     

Mapping Oolitic Iron Ore Deposits in the Ashumaysi Formation,Western Saudi Arabia,Using Different Remote Sensing Techniques Applied to Landsat 7 Enhanced Thematic Mapper Plus Data (ETM+)

Mapping the surficial extent of oolitic iron ore deposits hosted in the Oligo–Miocene sedimentary rocks of the Ashumaysi Formation, western Saudi Arabia, was carried out using Landsat 7 Enhanced Thematic Mapper Plus (ETM+) data. Ore samples were collected from four various locations in the study area, and were studied in the laboratory using the GER 3700 Spectroradiometer (0.4–2.5 µm) and X-ray diffraction (XRD). Principal component analysis (PCA), minimum noise fraction (MNF), and minimum distance classification were used and assessed to map mineralization zones in the study area. Good correspondences were observed between the results obtained from the above mentioned techniques, spectral reflectance analyses, and XRD. The confusion matrix results revealed that mapping of iron ores using MNF is better and more accurate than using PCA. Good matching was also observed between the spectral reflectance curves of the collected samples and the corresponding pixels from Landsat 7 ETM+. The results demonstrated the usefulness of the image processing and interpretation of Landsat 7 ETM+ data for the detection and delineation iron ore deposits in arid and semi-arid areas.     

Evaluation of directional normalization methods for Landsat TM/ETM+ over primary Amazonian lowland forests

Biodiversity mapping in extensive tropical forest areas poses a major challenge for the interpretation of Landsat images, because floristically clearly distinct forest types may show little difference in reflectance. In such cases, the effects of the bidirectional reflection distribution function (BRDF) can be sufficiently strong to cause erroneous image interpretation and classification. Since the opening of the Landsat archive in 2008, several BRDF normalization methods for Landsat have been developed. The simplest of these consist of an empirical view angle normalization, whereas more complex approaches apply the semi-empirical Ross–Li BRDF model and the MODIS MCD43-series of products to normalize directional Landsat reflectance to standard view and solar angles. Here we quantify the effect of surface anisotropy on Landsat TM/ETM+ images over old-growth Amazonian forests, and evaluate five angular normalization approaches. Even for the narrow swath of the Landsat sensors, we observed directional effects in all spectral bands. Those normalization methods that are based on removing the surface reflectance gradient as observed in each image were adequate to normalize TM/ETM+ imagery to nadir viewing, but were less suitable for multitemporal analysis when the solar vector varied strongly among images. Approaches based on the MODIS BRDF model parameters successfully reduced directional effects in the visible bands, but removed only half of the systematic errors in the infrared bands. The best results were obtained when the semi-empirical BRDF model was calibrated using pairs of Landsat observation. This method produces a single set of BRDF parameters, which can then be used to operationally normalize Landsat TM/ETM+ imagery over Amazonian forests to nadir viewing and a standard solar configuration.     

A forward/backward principal component analysis of Landsat-7 ETM+ data to enhance the spectral signal of burnt surfaces

In this paper, principal component analysis (PCA), a dimensionality reduction method, has been applied successfully as an image enhancement technique to improve the spectral signal of burnt surfaces. Forward/backward PCA (F/B PCA) and image differencing, which the proposed method consists of, creates a new spectral space that preserves the original spectral patterns while enhancing particular structures of the original satellite data. Burnt surfaces constitute a spectrally enhanced feature after selective removal of spectral information from the original Landsat-7 Enhanced Thematic Mapper data.Improvement of the spectral separability of burnt surfaces is most evident in spectral channels ETM+4 and ETM+7, where burnt surfaces already compose distinct spectral objects, and channels ETM+2 and ETM+5. This improvement is reasonable since the third PC axis, which is not considered in the back-transformation, is composed mainly of the spectral information in these channels. Another benefit of the technique is a reduction of interband correlation in the satellite data.No clear differences between the standardized and non-standardized F/B PCA were identified to recommend the use of one over the other. Both methods show advances in certain aspects. Finally, an increase of the separability value between burnt areas and dry vegetated areas from 0.473 to 1.06 and 1.31 was obtained with the use of the standardized and non-standardized F/B PCA, respectively.     

盐沼植被光谱特征的间接排序识别分析

运用ASD地物光谱仪,采用12个小型机载成像光谱仪(CASI)默认的植被波段组,以上海崇明东滩自然保护区的盐沼植物群落为对象,应用主成分分析法和相关分析研究了不同群落光谱特征与生态环境因子之间的关系。结果表明:运用PCA间接排序法能够识别盐沼植被中光滩、海三棱藨草群落、芦苇群落和互花米草群落等光谱特征;绝大多数盐沼植物的群落组成与所选波段的光谱特征之间有显著的相关关系;可见光和近红外波段数据可以分别识别低盖度的海三棱藨草群落和高盖度的互花米草和芦苇群落;对光谱反射率影响最大的生态环境因子是植物群落的高度和盖度,而高程和其它环境因子的影响次之。     

Discrimination of low-grade magnetite ores using remote sensing techniques

The remote sensing community in geology is widely using the Multispectral Landsat Thematic Mapper (TM) data which has a wider choice of spectral bands (six between 0.45 and 2.35 μm, plus a thermal infrared channel 10.4-12.5 urn). These were evaluated for low-grade magnetite ores mapping over the high-grade granulite region of Kanjamalai area of Tamil Nadu state, India. The Fourier Transform Infrared (FTIR) spectroscopy data (0.4-4.0 μm) for powders of the magnetite ores exposed with granulite rock and published spectral reflectance data were used as guides in selecting TM band reflectance ratios, which maximize discrimination of magnetite ores on the basis of their respective mineralogies. The study shows that the weathering mineralogy of magnetite ores causes absorption features in their reflectance spectra which are particularly characteristic of the near infrared. Comparison of TM data with field and petrographic observations shows the presence of magnetite and aluminosilicate minerals & show strong absorption at 0.7-1 μ.m wavelength spectral region & increase in the product of two TM band ratios: band 5 (1.55-1.75 μm) to band 4 (0.76-0.9 μm) and band 3 (0.63-0.69 μm) to band 4 (0.76-0.9 μm). Various computer image enhancement and data extraction techniques such as interactive digital image classification techniques using color compositing stretched ratio, maximum likelihood and thresholding statistical approaches using Landsat TM data are used to map the low-grade magnetite ores of the granulite region. The field traverses and local verification enhanced to map the other rock types namely granulites and gneisses of the study area.     

Ground validation of an algorithm for estimating surface suspended sediment concentrations from multi-spectral reflectance data

Pollution of water resources by sediments eroded from degraded watersheds is a critical concern around the world. Current methods for locating these eroding areas and off-site damage to water resources through visual observations and field sampling with subsequent laboratory analysis are time consuming and expensive. There is thus, a justified interest in developing algorithms for quick estimation of suspended sediment concentrations in large water-bodies from remotely sensed data. This paper presents the results of a ground validation study on characterization and quantification of surface suspended sediment concentrations (SSC) in sediment laden water bodies through an n-waveband specific numerical index, total information content. A comparison of SSC-predictive potential of the proposed new index, derived from four broad (100–300 nm) Landsat MSS, five broad (40–300 nm) Landsat TM and eight narrow (20–40 nm) IRS-P4 OCM spectral bands, with that of the conventional (NIR-Red and NIR+Red) indices, computed from the same spectral band data, is also presented. The study reveaied that at SSCs 250 mg/1, the proposed index (derived from either broad / narrow landsat MSS/TM or IRS-P4 OCM spectral data) could lead to SSC predictions (with mean errors within 20%) comparable with those obtained with the conventional indices (derived from the same spectral band data). It could further be observed that, in general, lower sediment concentrations (i.e. SSCs 150 mg/1) were associated with higher prediction inaccuracies. A comparison of the mean errors of predictions associated with the proposed and the conventional (NIR-Red and NIR+Red) indices computed from broad and narrow band data for SSCs 150 mg/I, revealed that an increase in number of wavebands (from 4 MSS to 5 TM or 8 OCM bands) and a decrease in the bandwidth of these wavebands (from broad MSS/ TM bands to narrow OCM bands) led to a significant increase in the prediction accuracy of the proposed new index. These prediction accuracies were observed to be the highest with the proposed index calculated from narrow OCM-P4 spectral data. However this could not be observed with the conventional indices at any of the SSC ranges and with the proposed index at SSCs 250 mg/l. This shows that the lower SSC-predictive potential of proposed index was a significant function of both the number and the bandwidth of spectral bands used for its computation. In fact in one of the cases, lower SSC (150 mg/l) -predictive accuracy of the proposed index was found to be significantly higher than that of the conventional (NIR+R) index. The proposed algorithm could thus compress the information contained in the entire reflectance spectrum of the sediment laden water bodies to their sediment type and concentration specific characteristic values. This characteristic of the proposed index was not shared by any of the conventional indices, based on only two waveband data. In fact the proposed index appears to be the only mean of completely compressing and quantifying the information contained in all the information channels of a narrow band spectrometer (consisting of 200 wavebands) to be shortly launched by ISRO for satellite based inventory of natural resources.