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。     

基于中、高分辨率遥感数据的铜多金属矿化信息提取与找矿预测——以西藏别若则错地区为例

基于中、高分辨率遥感数据,对西藏别若则错地区开展了蚀变信息提取与找矿预测。首先利用ASTER中等分辨率遥感数据,快速获取成矿地质背景信息及遥感蚀变信息,优选找矿有利区。其次利用World View-2高分辨率遥感数据开展找矿有利区的岩性构造解译,重点解译中酸性侵入岩体、火山机构、断裂、地层岩性等控矿要素。最后在野外查证成果基础上,圈定了3处找矿靶区,取得了良好的应用效果。     

光谱匹配技术应用于ASTER数据中提取蚀变异常

选择朱拉扎嘎金矿为试验区,ASTER数据为数据源,利用光谱匹配技术进行蚀变异常提取实验。重点阐述光谱匹配技术(SM)应用于ASTER数据提取蚀变异常工作中的两项关键技术:内部平均相对反射率法(IARR)大气校正和典型蚀变矿物波谱对应于ASTER数据波段重采样。最终通过光谱匹配技术提取了4组蚀变异常,分别为褐铁矿化蚀变异常、高岭土化蚀变异常、碳酸盐化蚀变组合异常和含OH-蚀变组合异常。     

Comparison and analysis of bare soil evaporation models combined with ASTER data in Heihe River Basin

Based on ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) remote sensing data, bare soil evaporation was estimated with the Penman-Monteith model, the Priestley-Taylor model, and the aerodynamics model. Evaporation estimated by each of the three models was compared with actual evaporation, and error sources of the three models were analyzed. The mean absolute relative error was 9% for the Penman-Monteith model, 14% for the Priestley-Taylor model, and 32% for the aerodynamics model; the Penman-Monteith model was the best of these three models for estimating bare soil evaporation. The error source of the Penman-Monteith model is the neglect of the advection estimation. The error source of the Priestley-Taylor model is the simplification of the component of aerodynamics as 0.72 times the net radiation. The error source of the aerodynamics model is the difference of vapor pressure and neglect of the radiometric component. The spatial distribution of bare soil evaporation is evident, and its main factors are soil water content and elevation.     

利用ASTERGlobalDEM和GoogleEarth卫星遥感影像辅助电力工程选址选线

介绍了ASTER Global DEM数据和Google Earth影像特点,以及阐述通过定制与检查DEM数据、卫星影像下载与纠正、数据基准转换与投影变换、等高线生成与影像叠加四个环节来制作遥感影像地图的方法.结合工程实践经验,说明该方法适合于难以获取基础地理信息的区域,能辅助电力工程的选址选线.     

ASTER卫星影像在太湖水质空间分异分析中的应用

利用ASTER卫星影像针对太湖的部分水域水质进行研究,首先根据夏季与影像同期太湖水体主要为竺山湖水域和梅梁湖水域中的水质实测数据,进行聚类分析和主成分分析,发现太湖水体主要受到悬浮物和藻类物质的污染,其他污染指标与它们之间存在着紧密的联系,所以针对水质的遥感分析也以这两类污染指标为主。对太湖的部分水域水质的遥感影像进行处理,用水体指数掩膜将水体从背景中分离,监督分类将水体按污染物成分与含量不同分成6类:近岸水(相对干净水体)、泥沙污染(泥沙较多)、泥沙和藻类混合、混沙水(泥沙少量)、混藻水(藻类少量)和藻类污染(藻类较多)。分类的总精度为84.796 5%,Kappa系数为0.817 4,统计出各污染类型水域的面积,发现太湖的污染物主要为泥沙类,其次为藻类。在太湖沿岸水域受泥沙污染较严重,且具有一定的扩散趋势;太湖中、东部受藻类的污染较严重。用NDVI提取藻类污染区,结果与监督分类的相符。最后结合遥感图像水体周围状况以及实际统计资料对太湖水质的污染成因作了分析。     

基于ASTER数据的地物光谱比辐射率的反演研究

比辐射率光谱表征着一个物体的内部物理和化学特征,是定量化遥感的一个关键参数.本文利用ASTER数据的第10至14波段,根据其数据的特点,基于温度比辐射率分离算法的思想,提出了将比辐射率标准化法(Normalize Emissivity Method,NEM)、经验公式、比值法(Ratio Method)这几个模块结合起来,在迭代的基础上计算出比辐射率的新算法.本文简要分析了模型误差的主要来源,并且把本文的算法与简化的ASTER的TES算法进行了比较.分析表明本文的算法是可行、有效的,而且该算法简单,易于实现,在能够保证精度的情况下运算速度快;同时也说明ASTER遥感数据用于反演地物的比辐射率可以得到比较理想的结果,具有良好的应用前景.     

Mapping variations in weight percent silica measured from multispectral thermal infrared imagery—Examples from the Hiller Mountains, Nevada, USA and Tres Virgenes-La Reforma, Baja California Sur, Mexico

Remotely sensed multispectral thermal infrared (8-13 μm) images are increasingly being used to map variations in surface silicate mineralogy. These studies utilize the shift to longer wavelengths in the main spectral feature in minerals in this wavelength region (reststrahlen band) as the mineralogy changes from felsic to mafic. An approach is described for determining the amount of this shift and then using the shift with a reference curve, derived from laboratory data, to remotely determine the weight percent SiO₂ of the surface. The approach has broad applicability to many study areas and can also be fine-tuned to give greater accuracy in a particular study area if field samples are available. The approach was assessed using airborne multispectral thermal infrared images from the Hiller Mountains, Nevada, USA and the Tres Virgenes-La Reforma, Baja California Sur, Mexico. Results indicate the general approach slightly overestimates the weight percent SiO₂ of low silica rocks (e.g. basalt) and underestimates the weight percent SiO₂ of high silica rocks (e.g. granite). Fine tuning the general approach with measurements from field samples provided good results for both areas with errors in the recovered weight percent SiO₂ of a few percent. The map units identified by these techniques and traditional mapping at the Hiller Mountains demonstrate the continuity of the crystalline rocks from the Hiller Mountains southward to the White Hills supporting the idea that these ranges represent an essentially continuous footwall block below a regional detachment. Results from the Baja California data verify the most recent volcanism to be basaltic-andesite.     

Object-based crop identification using multiple vegetation indices, textural features and crop phenology

Crop identification on specific parcels and the assessment of soil management practices are important for agro-ecological studies, greenhouse gas modeling, and agrarian policy development. Traditional pixel-based analysis of remotely sensed data results in inaccurate identification of some crops due to pixel heterogeneity, mixed pixels, spectral similarity, and crop pattern variability. These problems can be overcome using object-based image analysis (OBIA) techniques, which incorporate new spectral, textural and hierarchical features after segmentation of imagery. We combined OBIA and decision tree (DT) algorithms to develop a methodology, named Object-based Crop Identification and Mapping (OCIM), for a multi-seasonal assessment of a large number of crop types and field status.In our approach, we explored several vegetation indices (VIs) and textural features derived from visible, near-infrared and short-wave infrared (SWIR) bands of ASTER satellite scenes collected during three distinct growing-season periods (mid-spring, early-summer and late-summer). OCIM was developed for 13 major crops cultivated in the agricultural area of Yolo County in California, USA. The model design was built in four different scenarios (combinations of three or two periods) by using two independent training and validation datasets and the best DTs resulted in an error rate of 9% for the three-period model and between 12 and 16% for the two-period models. Next, the selected DT was used for the thematic classification of the entire cropland area and mapping was then evaluated applying the confusion matrix method to the independent testing dataset that reported 79% overall accuracy. OCIM detected intra-class variations in most crops attributed to variability from local crop calendars, tree-orchard structures and land management operations. Spectral variables (based on VIs) contributed around 90% to the models, although textural variables were necessary to discriminate between most of the permanent crop-fields (orchards, vineyard, alfalfa and meadow). Features extracted from late-summer imagery contributed around 60% in classification model development, whereas mid-spring and early-summer imagery contributed around 30 and 10%, respectively. The Normalized Difference Vegetation Index (NDVI) was used to identify the main groups of crops based on the presence and vigor of green vegetation within the fields, contributing around 50% to the models. In addition, other VIs based on SWIR bands were also crucial to crop identification because of their potential to detect field properties like moisture, vegetation vigor, non-photosynthetic vegetation and bare soil. The OCIM method was built using interpretable rules based on physical properties of the crops studied and it was successful for object-based feature selection and crop identification.     

Spectral characterization and ASTER-based lithological mapping of an ophiolite complex: A case study from Neyriz ophiolite, SW Iran

The Neyriz ophiolite occurs along the Zagros suture zone in SW Iran, and is part of a 3000-km obduction belt thrusting over the edge of the Arabian continent during the late Cretaceous. This complex typically consists of altered dunites and peridotites, layered and massive gabbros, sheeted dykes and pillow lavas, and a thick sequence of radiolarites. Reflectance and emittance spectra of Neyriz ophiolite rock samples were measured in the laboratory and their spectra were used as endmembers in a spectral feature fitting (SFF) algorithm. Laboratory spectral reflectance measurements of field samples showed that in the visible through shortwave infrared (VNIR-SWIR) wavelength region the ultramafic and gabbroic rocks are characterized by ferrous-iron and Fe, MgOH spectral features, and the pillow lavas and radiolarites are characterized by spectral features of ferric-iron and AlOH. The laboratory spectral emittance spectra also revealed a wide wavelength range of SiO spectral features for the ophiolite rock units. After continuum removal of the spectra, the SFF classification method was applied to the VNIR + SWIR 9-band stack, and to the 11-band data set of SWIR and TIR data sets of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor, using field spectra as training sets for evaluating the potential of these data sets in discriminating ophiolite rock units. Output results were compared with the geological map of the area and field observations, and were assessed by the use of confusion matrices. The assessment showed, in terms of kappa coefficient, that the SFF classification method with continuum removal applied to the SWIR data achieved excellent results, which were distinctively better than those obtained using VNIR + SWIR data and TIR data alone.