Lithological Discrimination of the Mafic-Ultramafic Complex, Huitongshan, Beishan, China: Using ASTER Data

The Beishan area has more than seventy mafic-ultramafic complexes sparsely distributed in the area and is of a big potential in mineral resources related to mafic-ultramafic intrusions. Many mafic-ultramafic intrusions which are mostly in small sizes have been omitted by previous works. This research takes Huitongshan as the study area, which is a major district for mafic-ultramafic occurrences in Beishan. Advanced spaceborne thermal emission and reflection radiometer（ASTER） data have been processed and interpreted for mapping the mafic-ultramafic complex. ASTER data were processed by different techniques that were selected based on image reflectance and laboratory emissivity spectra. The visible near-infrared（VNIR） and short wave infrared（SWIR） data were transformed using band ratios and minimum noise fraction（MNF）, while the thermal infrared（TIR） data were processed using mafic index（MI） and principal components analysis（PCA）. ASTER band ratios（6/8, 5/4, 2/1） in RGB image and MNF（1, 2, 4） in RGB image were powerful in distinguishing the subtle differences between the various rock units. PCA applied to all five bands of ASTER TIR imagery highlighted marked differences among the mafic rock units and was more effective than the MI in differentiating mafic-ultramafic rocks. Our results were consistent with information derived from local geological maps. Based on the remote sensing results and field inspection, eleven gabbroic intrusions and a pyroxenite occurrence were recognized for the first time. A new geologic map of the Huitongshan area was created by integrating the results of remote sensing, previous geological maps and field inspection. It is concluded that the workflow of ASTER image processing, interpretation and ground inspection has great potential for mafic-ultramafic rocks identifying and relevant mineral targeting in the sparsely vegetated arid region of northwestern China.

Extraction of altered minerals from Aster remote sensing data in Gongchangling iron deposit of Liaoning, China

The precision of Aster data is higher than that of Landsat series of multispectral remote sensing data,which can more accurately reveal the distribution of altered minerals.It plays an important role in prospecting,but it is rarely used in areas with complex terrain and high vegetation coverage.Based on this purpose,this study used Aster remote sensing data,and took Gongchangling iron deposit as a case study.It combined the mineral spectrum theory and the basic geologic data of the study area,using the model of principal component analysis(PCA)and color synthesis to extract abnormal altered minerals.The results show that the distribution of identified anomalies is basically consistent with the existing geological data in this study area,which provides a reliable reference for the mineral resources ex-ploration and delineation of mining areas.

Self-organizing feature map neural network classification of the ASTER data based on wavelet fusion

Most methods for classification of remote sensing data are based on the statistical parameter evaluation with the assumption that the samples obey the normal distribution. How-ever, more accurate classification results can be obtained with the neural network method through getting knowledge from environments and adjusting the parameter (or weight) step by step by a specific measurement. This paper focuses on the double-layer structured Kohonen self-organizing feature map (SOFM), for which all neurons within the two layers are linked one another and those of the competition layers are linked as well along the sides. Therefore, the self-adapting learning ability is improved due to the effective competition and suppression in this method. The SOFM has become a hot topic in the research area of remote sensing data classi-fication. The Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) is a new satellite-borne remote sensing instrument with three 15-m resolution bands and three 30-m resolution bands at the near infrared. The ASTER data of Dagang district, Tianjin Munici-pality is used as the test data in this study. At first, the wavelet fusion is carried out to make the spatial resolutions of the ASTER data identical; then, the SOFM method is applied to classifying the land cover types. The classification results are compared with those of the maximum likeli-hood method (MLH). As a consequence, the classification accuracy of SOFM increases about by 7% in general and, in particular, it is almost as twice as that of the MLH method in the town.

Remote Gully Erosion Mapping Using ASTER Data and Geomorphologic Analysis in the Main Ethiopian Rift

The Main Ethiopian Rift(MER)is an area of extreme topography underlain by post-Miocene volcanic rocks,Jurassic limestone and a Precambrian basement.A prime concern is the rapid expansion of wide gullies that are impinging on agricultural land.We investigate the potential contribution of Advanced Space-borne Thermal Emission and Reflection Radiometer(ASTER)data and geomorphologic parameters to discern patterns and features of gully erosion in the MER.Maximum Likelihood Classifica-tion(MLC),Support Vector Machine(SVM),and Minimum Distance(MD)classifiers are used to extract different gully shapes and patterns.Several spatial textures based on Grey Level Co-occurrence Matrices(GLCMs)are then generated.Afterwards,the same classifiers are applied to the ASTER data combined with the spatial texture information.We used geomorphologic parameters ex-tracted from SRTM and ASTER DEMs to describe the geomorphologic setting and the gullies' shapes.The classifications show accuracies varying between 67% and 89%.Maps derived from this quantitative analysis allow the monitoring and mapping of land degradation as a direct result of gully-widening.This study reveals the utility of combining ASTER data and spatial textural infor-mation in discerning areas affected by gully erosion.

Spatial Characteristics of Ancient Villages in Western Beijing Based on ASTER GDEM Data

There are hundreds of villages in the western mountainous area of Beijing,of which quite a few have a profound history and form the settlement culture in the western part of Beijing.Taking dozens of ancient villages in Mentougou District as the research sample,the village space as the research object,based on ASTER GDEM database and quantitative analysis tools such as Global Mapper and ArcGIS,this study analyzed from the perspectives of altitude,topography,slope direction,and building density distribution,made a quantitative study on the spatial distribution and plane structure of ancient villages so that the law of village space with the characteristics of western Beijing was summarized to supplement and improve the relevant achievements in the research field of ancient villages in western Beijing.

SRTM DEM与ASTER GDEM精度对比分析与验证

数字高程模型(Digital Elevation Model,DEM)在生产中有着重要作用。文章分析了目前两种全球大范围覆盖的免费DEM产品(ASTER GDEM和SRTM DEM)的精度,并使用大同地区合成孔径雷达差分干涉数据结果展现了两者的差异。结果表明:SRTM DEM 30m分辨率的DEM数据精度优于ASTER GDEM。

基于ASTER数据的地表温度遥感反演与产品检验——以黑河流域为例

针对黑河流域的地表类型特点和大气特征,基于ASTER发射率产品和植被覆盖度法(vegetation cover method,VCM)计算了研究区地表发射率,并利用改进的多层前馈神经网络(multilayer feedforword neural network,MFNN)算法估算了区域大气水汽含量,通过对输入参数分组构建系数查找表,发展了适用于ASTER数据遥感反演地表温度的分裂窗算法。为检验算法的适应性和精度,利用黑河流域2019年的地表温度实测数据和MODIS温度产品对算法进行评价。结果表明,与站点数据相比,均方根误差在1.81~3.01 K之间;在与MODIS数据产品交叉验证中,本文提出的算法误差和偏差相对较小,均方根误差在1.11~1.75 K之间。总体来说,利用本算法反演得到的温度产品精度可满足气象气候学研究的需要,算法的构建思路也可为类似的热红外传感器提供借鉴。

基于FLAASH模型的ASTER卫星影像大气校正

主要介绍了FLAASH大气校正模型的主要原理及算法,并运用FLAASH大气校正模型对泸沽湖地区的ASTER卫星影像进行大气校正,对校正前后的影像进行对比分析。研究表明,ASTER卫星影像经过FLAASH大气校正后,较好地消除了大气影响。

用ASTER数据提取植被覆盖区遥感铁矿化蚀变信息

以植被覆盖度较大的山东省兰陵县凤凰山铁矿区为例,选取覆盖该区的ASTER数据作为遥感数据源,首先进行了几何精纠正、大气校正和水体、阴影等干扰去除;然后在充分了解岩石波谱特征和ASTER数据波段特征的基础上选择了提取矿物蚀变信息的最优波段组合,采用主成分分析(Principal component analysis,PCA)法对研究区的铁染蚀变和羟基蚀变信息进行了提取,并对蚀变异常强度进行了标准化等级划分;最后通过分析蚀变信息与已知矿床的关系圈定了遥感异常区,并在矿化蚀变较强的地段选取8处铁染蚀变异常点和6处羟基蚀变异常点,通过布设踏勘路线进行了采样和验证。结果表明:从ASTER数据中提取的铁染和羟基蚀变信息的分布与实际情况吻合较好,其高值区分别对应着铁矿化和高岭土化强烈的地区,验证精度分别达到87.5%、83.3%。可见,在植被覆盖度较大的地区,ASTER数据的短波红外波段内仍包含丰富的矿物蚀变信息,可为地质找矿提供重要依据。

ASTER遥感数据解读与应用

ASTER遥感数据可以在地球表面自然过程与全球变化等多个研究领域得到广泛应用。本文首先对ASTER数据格式进行了分析,并对解读关键技术进行了研究,在此基础上,通过计算机编程实现了ASTER数据的读取;最后结合ASTER数据的特点给出了ASTER数据在几个主要研究领域的应用。

基于分层分析的ASTER影像土地利用/覆盖遥感监测研究

以龙海市为实验区,利用A STER遥感数据,在研究区典型地物光谱特征系统分析的基础上,进行基于分层分类思想的地物分类提取方法研究。首先将影像划分为独立的子区(水体、植被覆盖区和非植被覆盖区域)以避免分类过程中光谱的互相影响;然后在每个独立的子区基础上根据各类地物的不同光谱特征和空间特征,对各类地物进行逐层掩模、分层提取。结果表明该方法优于传统的监督和非监督分类效果。

ASTER数据矿化蚀变异常信息提取方法研究

选取云南省保山市西邑矿区作为示范区,利用USGS和AUS常见矿物矿化蚀变光谱数据库,采用ASTER遥感数据,依据干扰信息分布状况将研究区划分为高度、中度和相对无干扰区,并在相对无干扰区开展了基于主成分分析的铁染、羟基、碳酸盐化及硅化蚀变异常信息的量化提取,在高度和中度干扰区采用比值运算和主成分分析提取铁染蚀变异常信息,提高了遥感矿化蚀变信息识别精度,方法示范效果好。

ASTER数据在北方铝土矿预普查中的应用——以豫西渑池地区为例

ASTER数据的光谱和空间分辨率均具有一定优势,适合于中等比例尺的矿产预普查。为了探讨ASTER数据应用于铝土矿预普查的效果,通过对ASTER图像彩色合成、地质解译及异常信息提取,对豫西渑池地区的铝土矿进行了预普查试验。结果表明,在北方基岩裸露地区,应用ASTER数据圈定"赋矿影像单元"和提取铝土矿遥感找矿信息能获得较好的效果。

利用ASTER遥感数据反演陆面温度的算法及应用研究

陆面温度是地气交换过程的一个重要参数,在生态环境研究中应用很广。传统方法只能进行点上的观测和计算,遥感的出现则使得计算区域陆面温度成为可能。ASTER遥感数据具有较高的空间分辨率和光谱分辨率,能够提供比NOAA/AVHRR和Landsat等遥感数据更丰富的陆面信息,有助于提高反演陆面温度的精度。本文以新疆自治区阿瓦提县典型研究区域为例,根据ASTER遥感数据的特点,基于温度/比辐射率分离算法的思想,运用ADE(AlphaDerivedEmissivity)、比值法和MMD(Maximum-MinimumDiffer-ence)三个模块计算陆面温度,并简要分析了模型的主要误差来源。分析结果表明本文所采用的算法是可行的,ASTER遥感数据用于反演陆面温度可以取得比较理想的结果,具有良好的应用前景。

ASTER数据蚀变矿物信息提取技术研究及其应用综述

由于ASTER卫星数据具有6个短波红外(SWIR)波段,对粘土矿物和碳酸盐化类矿物具有一定的识别能力,在岩浆热液作用密切相关的铜、金等多金属矿找矿中得到了广泛应用。总结了常见蚀变矿物波谱曲线特征,特别是其吸收谷和反射峰对应ASTER卫星数据波段的特征,同时对比分析了常用的基于ASTER数据开展蚀变矿物信息提取的主成分分析法、光谱角法和比值分析法,分析其各自的优缺点及其主要适用区域,并简要介绍了2个典型蚀变矿物组合信息提取的实例。认为ASTER数据蚀变矿物信息提取方法的选择应结合研究区具体情况而定,即根据研究区地理地貌、植被覆盖度、蚀变类型及其蚀变矿物特征选择最优的蚀变矿物信息提取方法。

基于ASTER VNIR-SWIR多光谱遥感数据识别与提取干旱地区岩性信息——以西南天山柯坪隆起东部为例

ASTER多光谱遥感数据目前可以用于岩石矿物资源信息的识别和提取。本研究尝试利用ASTER可见光近红外(VNIR)和短波红外(SWIR)的多光谱遥感数据提取干旱地区的岩石与矿物信息。基于新疆天山西南缘柯坪隆起东部不同地层单元岩石的化学组成和矿物成份以及VNIR-SWIR谱域光谱吸收特征的分析,我们采用相关吸收波段深度(RBD)和波段比值(BR)方法对研究区的多光谱遥感数据进行图像处理,有效区分和识别了白云岩、石灰岩、砂岩以及阿克苏群的蓝片岩—绿片岩和砂质片岩。白云岩的CO_3^(2-)吸收谱带中心波长位于2.32μm,与灰岩的CO_3^(2-)吸收谱带中心波长位置2.35μm相比,具有向短波长方向移动的特点,据此可以利用RBD7、RBD8分别有效的识别白云岩和灰岩;长英质岩石显示Al-OH和Fe^(3+)VNIR-SWIR吸收特征,而基性-超基性岩石显示Fe^(2+)和Fe、Mg-OH特征,利用不同的铁价态和次要矿物可以区分它们:ASTER band2/band1代表了含Fe^(3+)矿物分布信息、ASTER band5/band4代表了含Fe^(2+)矿物分布信息、RBD6可以估计Al-OH矿物的丰度;砂质/泥质片岩含较多的多硅白云母、绿泥石、黑硬绿泥石以及风化后表面覆盖的其它粘土矿物,在2.21μm(band6)存在有特征的吸收谱带,并且在1.65μm(band 4)具有较高的反射率,而蓝片/绿片岩在2.21μm(band 6)反射率较高,不具有明显特征吸收谱带,同时其在1.65μm(band 4)反射率较低,因此蓝片/绿片岩ASTER band4/band6比值低。应用ASTER band4/band6波段比值可以有效的区分开砂质/泥质片岩与蓝片岩/绿片岩。

利用ASTER提取德兴斑岩铜矿遥感蚀变分带信息

在遥感光谱分析的基础上,结合德兴斑岩铜矿矿床地质特征,利用ASTER遥感数据提取了德兴斑岩铜矿的遥感蚀变分带信息。德兴斑岩铜矿蚀变分带根据蚀变矿物特征来分析,主要的蚀变矿物为白云母、伊利石、绿泥石、方解石、白云石;根据光谱特征,采用比值、斜率、相关吸收和主成分分析4种提取方法提取各蚀变矿物的信息,并采用叠加法对各蚀变矿物信息进行叠加,从而形成各个蚀变分带的遥感信息。各蚀变分带的遥感信息总体特征比较明显。最后,根据矿床成矿模式对蚀变分带信息进行了分析。

基于ASTER遥感数据的西藏多龙矿集区示矿信息的提取

矿化蚀变信息的提取对圈定成矿区具有重要意义。近年来,运用多光谱遥感数据进行示矿信息提取得到了长足的发展。常用的示矿信息提取技术方法包括优化的主成分分析方法、比值算法、光谱角算法等。选取西藏改则县多龙地区为研究区,以区内多不杂斑岩型铜矿为典型的已知矿床,基于ASTER遥感数据,运用多种示矿信息提取方法进行靶区圈定综合研究,多种技术方法相互印证,避免了单一算法的局限性,为成矿靶区的圈定提供了更为准确的遥感依据。

ASTER遥感数据在矿区生态损害现状监测中的应用

德兴铜矿矿山多年开采对矿区生态环境造成极大的损害,为维护矿区可持续发展,生态损害现状调查尤为重要。利用ASTER数据结合野外实测各类地物反射光谱曲线,利用矿物指数提取矿区重金属和粘土污染;采用差值法提取水体污染状况;采用NDVI彩色密度分割法提取矿区植被污染信息。研究表明,铜矿区生态损害现状甚为严重,利用遥感手段监测矿区生态环境是完全可行的。这为矿区土地复垦与生态修复提供了技术支持和决策依据。

ASTER GDEM垂直精度评价及其在重力地形改正中的适用性

利用重力剖面的GPS数据对ASTER GDEM模型进行垂直精度评估,并对其在重力地形改正中的适用性进行讨论。结果表明,ASTER GDEM模型在贵阳-维西和金川-犍为两个重力剖面中,与实测GPS高程相比标准差分别为14.93m和18.02m,模型起伏与实测数据具有较好的一致性。在小比例尺中、远区及大比例尺远区重力地形改正中,ASTER GDEM模型数据的适用性较好。