Distribution of hydrothermally altered rocks in the Reko Diq, Pakistan mineralized area based on spectral analysis of ASTER data

The Reko Diq, Pakistan mineralized study area, approximately 10 km in diameter, is underlain by a central zone of hydrothermally altered rocks associated with Cu-Au mineralization. The surrounding country rocks are a variable mixture of unaltered volcanic rocks, fluvial deposits, and eolian quartz sand. Analysis of 15-band Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data of the study area, aided by laboratory spectral reflectance and spectral emittance measurements of field samples, shows that phyllically altered rocks are laterally extensive, and contain localized areas of argillically altered rocks.In the visible through shortwave-infrared (VNIR + SWIR) phyllically altered rocks are characterized by Al-OH absorption in ASTER band 6 because of molecular vibrations in muscovite, whereas argillically altered rocks have an absorption feature in band 5 resulting from alunite. Propylitically altered rocks form a peripheral zone and are present in scattered exposures within the main altered area. Chlorite and muscovite cause distinctive absorption features at 2.33 and 2.20 μm, respectively, although less intense 2.33 μm absorption is also present in image spectra of country rocks.Important complementary lithologic information was derived by analysis of the spectral emittance data in the 5 thermal-infrared (TIR) bands. Silicified rocks were not distinguished in the 9 VNIR + SWIR bands because of the lack of diagnostic spectral absorption features in quartz in this wavelength region. Quartz-bearing surficial deposits, as well as hydrothermally silicified rocks, were mapped in the TIR bands by using a band 13/band 12 ratio image, which is sensitive to the intensity of the quartz reststrahlen feature. Improved distinction between the quartzose surficial deposits and silicified bedrock was achieved by using matched-filter processing with TIR image spectra for reference.     

Targeting key alteration minerals in epithermal deposits in Patagonia,Argentina, using ASTER imagery and principal component analysis

Principal component analysis (PCA) is an image processing technique that has been commonly applied to Landsat Thematic Mapper (TM) data to locate hydrothermal alteration zones related to metallic deposits. With the advent of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), a 14-band multispectral sensor operating onboard the Earth Observation System (EOS)-Terra satellite, the availability of spectral information in the shortwave infrared (SWIR) portion of the electromagnetic spectrum has been greatly increased. This allows detailed spectral characterization of surface targets, particularly of those belonging to the groups of minerals with diagnostic spectral features in this wavelength range, including phyllosilicates (‘clay’ minerals), sulphates and carbonates, among others. In this study, PCA was applied to ASTER bands covering the SWIR with the objective of mapping the occurrence of mineral endmembers related to an epithermal gold prospect in Patagonia, Argentina. The results illustrate ASTER's ability to provide information on alteration minerals which are valuable for mineral exploration activities and support the role of PCA as a very effective and robust image processing technique for that purpose.     

Lithological discrimination of the Phenaimata felsic–mafic complex,Gujarat, India,using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

This study deals with an evaluation of the efficacy of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image for lithological mapping. ASTER level-1B data in the visible near-infrared (VNIR), short wave infrared (SWIR) and thermal infrared (TIR) regions have been processed to generate a lithological map of the study area in and around the Phenaimata igneous complex, in mainland Gujarat, India. ASTER band combinations, band ratio images and spectral angle mapper (SAM) processing techniques were evaluated for mapping various lithologies. The reflectance and emissivity spectra of rock samples collected from the study area were obtained in the laboratory and were used as reference spectra for ASTER image analysis. The original data in the scaled digital number (DN) values were converted to radiance and then to relative reflectance by using a scene-derived correction technique prior to SAM classification. The SAM classification in the VNIR–SWIR region is found to be effective in differentiating felsic and mafic lithologies. The relative band depth (RBD) images were generated from the continuum-removed images of ASTER VNIR–SWIR bands. Four RBD combinations (3, 5, 6 and 8) were used to identify Al-OH (aluminium hydroxide), Fe-OH (iron hydroxide), Mg-OH (magnesium hydroxide) and CO₃ (carbonate) absorption from various lithological components. ASTER TIR spectral emittance data and the laboratory emissivity measurements show the presence of a number of discrete Si-O spectral features that can differentiate mafic and felsic rock types reflecting the lithological diversity around the regions of Phenaimata igneous complex. SAM classification using emittance data failed to distinguish the felsic and mafic lithology due to the wider spectral bandwidth. The felsic class comprises the granitoid composition of rocks. RBD12 and 13 images in the TIR region were used to derive the mafic index (MI) and the silica index (SI). The MI shows the highest value in regions of gabbro–basalt occurrence, while the SI indicates regions of high silica content. The MI is lowest in regions where granophyres occur. The complimentary attributes based on the spectral reflectance and emittance data resulted in the discrimination of silica-rich and silica-poor lithologies.     

Lithologic mapping in the Oscar II Coast area,Graham Land,Antarctic Peninsula using ASTER data

The results of the first attempt to use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for the purposes of lithologic mapping on the Antarctic Peninsula are presented for an area on the Oscar II Coast, eastern Graham Land. This study included undertaking laboratory reflectance spectroscopy of ～70 rock samples from the study area and spectral lithologic analysis of two ASTER scenes. Spectra of the granitoids, silicic volcanic/volcaniclastic and terrestrial sedimentary rocks in the study area display a limited range of absorption features associated with muscovite, smectite and chlorite that are generally present as the alteration products of regional metamorphism. ASTER data analysis was undertaken using the reflective bands of the Level 1B registered radiance at-sensor data and the standard thermal infrared (TIR) emissivity product (AST05). For both wavelength regions, standard qualitative image processing methods were employed to define image end-members that were used as reference within Matched Filter (MF) processing procedures. The results were interpreted with reference to existing field observations, and photogeologic analysis of the ASTER visible to near-infrared (VNIR)/shortwave infrared (SWIR) data was used to resolve ambiguities in the spectral mapping results. The results have enabled the discrimination of most of the major lithologic groups within the study area as well as delineation of hydrothermal alteration zones of propylitic, and argillic grades associated with the Mesozoic Mapple Formation volcanics. The results have extended the mapped coverage of the Mapple Formation into un-investigated regions further north and validated previously inferred geological observations concerning other rocks throughout the study area. The outcomes will enable important revisions to be made to the existing geological map of the Oscar II Coast and demonstrate that ASTER data offers potential for improving geological mapping coverage across the Antarctic Peninsula.     

Spectral indices for lithologic discrimination and mapping by using the ASTER SWIR bands

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a research facility instrument launched on NASA's Terra spacecraft in December 1999. Spectral indices, a kind of orthogonal transformation in the five-dimensional space formed by the five ASTER short-wave-infrared (SWIR) bands, were proposed for discrimination and mapping of surface rock types. These include Alunite Index, Kaolinite Index, Calcite Index, and Montmorillonite Index, and can be calculated by linear combination of reflectance values of the five SWIR bands. The transform coefficients were determined so as to direct transform axes to the average spectral pattern of the typical minerals. The spectral indices were applied to the simulated ASTER dataset of Cuprite, Nevada, USA after converting its digital numbers to surface reflectance. The resultant spectral index images were useful for lithologic mapping and were easy to interpret geologically. An advantage of this method is that we can use the pre-determined transform coefficients, as long as image data are converted to surface reflectance.     

ASTER Level 1A数据产品文件解析及应用

深入解析了ASTER数据的结构,研究如何正确读取数据中卫星的位置、速度、时间、姿态角、姿态变化率等与影像定位有关的数据,并研究了这些数据的变化规律。根据ASTER数据的特点,给出了该数据在辐射校正及影像定位方面的应用方法,使得影像能更好的在这些方面得到应用。     

Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data

Wildland fires are an annually recurring phenomenon in many terrestrial ecosystems. Accurate burned area estimates are important for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. High spatial and spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over three 2007 southern California burns were used to evaluate the sensitivity of different spectral indices at discriminating burned land shortly after a fire. The performance of the indices, which included both traditional and new band combinations, was assessed by means of a separability index that provides an assessment of the effectiveness of a given index at discriminating between burned and unburned land. In the context of burned land applications results demonstrated (i) that the highest sensitivity of the longer short wave infrared (SWIR) spectral region (1.9 to 2.5 μm) was found at the band interval from 2.31 to 2.36 μm, (ii) the high discriminatory power of the mid infrared spectral domain (3 to 5.5 μm) and (iii) the high potential of emissivity data. As a consequence, a newly proposed index which combined near infrared (NIR), longer SWIR and emissivity outperformed all other indices when results were averaged over the three fires. Results were slightly different between land cover types (shrubland vs. forest-woodland). Prior to use in the indices the thermal infrared data were separated into temperature and emissivity to assess the benefits of using both temperature and emissivity. Currently, the only spaceborne sensor that provides moderate spatial resolution (< 100 m) temperature and emissivity data is the Advanced Spaceborne and Thermal Emission Radiometer (ASTER). Therefore, our findings can open new perspectives for the utility of future sensors, such as the Hyperspectral Infrared (HyspIRI) sensor. However, further research is required to evaluate the performance of the newly proposed band combinations in other vegetation types and different fire regimes.     

Detection of sandy soil surfaces using ASTER‐derived reflectance,emissivity and elevation data: potential for the identification of land degradation

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) reflectance and emissivity data were used to discriminate nonphotosynthetic vegetation (NPV) from exposed soils, to produce a topsoil texture image, and to relate sand fraction estimates with elevation data in an agricultural area of central Brazil. The results show that the combination of the shortwave infrared (SWIR) bands 5 and 6 (hydroxyl absorption band) and thermal infrared (TIR) bands 10 and 14 (quartz reststrahlen feature) discriminated dark red clayey soils and bright sandy soils from NPV (crop litter), respectively. The ratio of the bands 10 and 14 was correlated with laboratory measured total sand fraction. When applied to the image and associated with topography, a predominance of sandy soil surfaces at lower elevations and clayey soil surfaces at higher elevations was observed. Areas presenting the largest sand fraction values, identified from ASTER band 10/14 emissivity ratio, were coincident with land degradation processes.     

Regional scale prospecting for non-sulphide zinc deposits using ASTER data and different spectral processing methods

ABSTRACT

The Ravar-Kuhbanan-Bahabad belt (RKBB) in Central Iran contains several carbonate-hosted non-sulphide Zn (zinc)-Pb (lead) deposits. The Gujer Zn mine area located in the middle of the RKBB was selected as the case study. Due to its large extent, dolomitic envelope in carbonate host rocks can be considered as a more appropriate exploratory target than small Zn-rich gossans or blind karst filling ore. Based on previous studies, the occurrence of red sandstone as a candidate of supplying metal for mineralization and evaporate as sulphate source for mineralized liquids in the vicinity of carbonate rocks can be important exploratory key in the RKBB. Non-sulphide Zn deposits were formed through oxidation of primary Mississippi Valley-type (MVT) deposits in the study area. Remote sensing studies were undertaken using visible to near-infrared (VNIR) and shortwave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with the objective of lithological mapping. Five traverse lines containing a total of 81 samples were designed and followed with subsequent chemical analysis, thin section studies, and spectroscopy to verify the results. Two types of carbonates, namely, magnesian dolomite as host rock and surrounding calcitic limestone, were realized through using magnesium oxide (MgO) to calcium oxide (CaO) ratio. Based on spectroscopy studies, calcite and dolomite showed distinct absorption features at 2.35 µm and 2.32 µm, respectively, in ASTER band 8 while a shoulder at 2.25 µm was seen in ASTER band 7 for dolomite. Three image processing methods including spectral angle mapper (SAM), linear spectral unmixing (LSU), and mixture-tuned matched-filtering (MTMF) were applied to separate dolomite and limestone. The accuracy of image classification was numerically estimated using a confusion matrix. Limestone with the accuracy of 95.83% was more precisely enhanced using MTMF method compared to SAM and LSU methods. Highest accuracy of 75% for dolomite was obtained through using LSU method. Red sandstone and evaporate units were classified using MTMF and SAM/LSU methods, respectively. Rock units with the highest accuracy were selected and simply overlain on an image of ASTER in a GIS platform to create the potential map of the study area. Results showed that ASTER data can be successfully used to prepare a potential map for regional scale prospecting for carbonate-hosted non-sulphide Zn-Pb deposits in geological setting and climate condition similar to the RKBB.     

基于蚁群优化算法的遥感器波段设置探究

卫星载荷研制发射后其光谱和空间观测模式固定,无法根据复杂地表的多样化需求进行实时灵活调整,且目前遥感器波段设置尚不完善还存在优化空间.引进基于蚁群优化算法的波段选择方法(AntColonyOptimization basedBandSelection,ACOBS),结合北美区域３３景AVIRIS航空高光谱图像,开展了不同区域、不同地表覆盖类型的高光谱波段优选研究,发现各地表类型优选波段组合存在一定差异,其中４波段组合中红光、近红外波段为２个共同入选波段,６波段组合中绿光、红光、短波红外波段为３个共有波段,８波段组合中紫光、绿光、红光、红边、近红外１、近红外２、短波红外１、短波红外２为８个共有入选波段,其他入选波段与地表覆盖类型有关.在此基础上,进一步开展了多光谱卫星波段设置评价研究,发现:４波段优化方案中,绿光、红光、近红外波段１ (７７０~８９５nm)、近红外波段２(９００~１３５０nm)为最优波段组合;６波段优化方案中,绿、红、红边、近红外１(７７０~８９５nm)、近红外２(９００~１３５０nm)、短波红外１(１５６０~１６６０nm)为最优波段组合;８波段优化方案中,蓝、绿、红、红边、近红外１(７７０~８９５nm)、近红外２(９００~１３５０nm)、短波红外１(１５６０~１６６０nm)和短波红外２(２１００~２３００nm)为最优波段组合.研究结果表明Land satTM OLI、SPOT等陆地资源遥感器波段设置还存在一定优化调整空间,特别是红边波段在目前传感器波段设置中没有得到足够重视.     

Feasibility of estimating leaf water content using spectral indices from WorldView-3’s near-infrared and shortwave infrared bands

A recently-launched high-resolution commercial satellite, DigitalGlobe’s WorldView-3, has 8 bands in the shortwave infrared (SWIR) wavelength region, which may be capable of estimating canopy water content at 3.7-m spatial resolution. WorldView-3 also has 8 multispectral bands at 1.24-m resolution with two bands in the near-infrared (NIR). The relative spectral response functions for WorldView-3 were provided by DigitalGlobe, Inc., and band reflectances were determined for reflectance spectra of PROSPECT model simulations and leaf data from maize, trees, grasses, and broadleaf herbaceous eudicots. For laboratory measurements, the range of leaf water contents was extended by including drying leaves and leaf stacks of corn, soybean, oaks, and maples. Correlations between leaf water content and spectral indices from model simulations suggested that indices using SWIR band 1 (center wavelength 1210 nm) had low variability with respect to leaf water content, but also low sensitivity. Other indices using SWIR band 5 (2165 nm) had the highest sensitivity, but also had high variability caused by different values of the leaf structure parameter in PROSPECT. Indices using SWIR bands 2, 3 and 4 (1570, 1660, and 1730 nm, respectively) had high correlations and intermediate variability from the leaf structure parameter. Spectral indices calculated from the leaf data had the same overall patterns as the simulations for variation and sensitivity; however, indices using SWIR band 1 had low correlations, and the best correlations were from indices that used SWIR bands 2, 3 and 4. Spectral indices for maize, grasses, and herbaceous crops and weeds had similar responses to leaf water content; tree leaves had higher index values and saturated at lower leaf water contents. The specified width of NIR band 2 (860–1040 nm) overlaps the water absorption feature at 970 nm wavelength; however, the normalized difference of NIR band 1 and 2 was insensitive to water content because NIR band 2’s spectral response was most heavily weighted to wavelengths less than 930 nm. The high spatial resolution of the WorldView-3 SWIR data will help analyze how variation among plant species and functional groups affects spectral responses to differences in canopy water content.     

Identification of mineral components in tropical soils using reflectance spectroscopy and advanced spaceborne thermal emission and reflection radiometer (ASTER) data

Soil characteristics provide important support for understanding transformations that occur in environmental systems. Physical characteristics and chemical compositions of soils controlled by pedogenetic processes, climatic changes and land use imply different types of environmental transformations. Reflectance spectroscopy is an alternative soil mapping technique that uses spectral absorption features between visible (VIS) and short-wave infrared (SWIR) wavelengths (0.3-2.5 μm) for determining soil mineralogy. Soil analysis by means of reflectance spectroscopy and orbital optical sensors have provided favorable results in mapping transformation processes in environmental systems, particularly in arid and semiarid climates in extra-tropical terrains. In the case of inter-tropical environments, these methods cannot be readily applied due to local factors such as lack of exposed regolith, high amounts of soil moisture and the presence of dense vegetation. This study uses Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and reflectance spectroscopy data to map mineral components of soils covering a part of the state of São Paulo, Brazil, which could be linked to key aspects of environmental transformations in this tropical area (e.g., climate change, shifts in agriculture fronts, ph, and soil characteristics). We collected forty-two (42) soil samples at a depth of 0-20 cm, considering that this superficial layer corresponds to the highest correlation with soil properties detected by the ASTER sensor. These samples were measured using a FieldSpec FR spectrometer, and the derived spectra were interpreted for mineral composition. Interpretation was supported by X-ray diffraction analysis on the same samples. The spectral signatures were re-sampled to ASTER VNIR (AST1-4: 0.52-0.86 μm) and SWIR (AST5-9: 1.60-2.43 μm) spectral bandwidths and validated by comparing reflectance spectra of field samples with those extracted from atmospherically corrected and calibrated ASTER pixels. The agreement between spectral signatures measured from soil samples and those derived from ASTER imagery pixels proved plausible, with R² correlation values ranging from 0.6493 to 0.7886. This signifies that diagnostic spectral features of key minerals in tropical soils can be mapped at the spectral resolution of 9-band ASTER VNIR through SWIR reflectance. We used these spectral signatures as end-members in hyperspectral routine classifications adapted for use with ASTER data. Results proved possible the identification and remote mapping of minerals such as kaolinite, montmorillonite and gibbsite, as well as the distinction between iron-rich and iron-poor soils.     

Spectral mixture analysis for mapping abundance of urban surface components from the Terra/ASTER data

Using a linear unconstrained least squares (LSS) method and a non-linear artificial neural network (ANN) algorithm, we conducted a spectral mixture analysis to the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) image data in Yokohama city, Japan, for mapping the abundance of the urban surface components. ASTER is a newly developed research facility instrument. The regions of interest of four endmembers (Vegetation, Soil, High/Low albedo impervious surfaces) were determined in Maximum Noise Fraction (MNF) feature spaces. The spectral signatures of the four endmembers were then extracted from the ASTER VNIR (15-m resolution) and SWIR (30-m resolution) imagery by referring to high spatial resolution airborne imagery (The Airborne Imaging Spectrometer, AISA, with 2-m resolution) and land use/land cover map for training and testing the LSS and ANN algorithms. Experimental results indicate that ASTER VNIR and SWIR image data are capable of mapping the abundances of urban surface components with a reasonable accuracy and that the ANN outperforms the unconstrained LSS in this spectral mixture analysis.     

Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery

Remote sensing represents a powerful tool to derive quantitative and qualitative information about ecosystem biodiversity. In particular, since plant species richness is a fundamental indicator of biodiversity at the community and regional scales, attempts were made to predict species richness (spatial heterogeneity) by means of spectral heterogeneity. The possibility of using spectral variance of satellite images for predicting species richness is known as Spectral Variation Hypothesis. However, when using remotely sensed data, researchers are limited to specific scales of investigation. This paper aims to investigate the effects of scale (both as spatial and spectral resolution) when searching for a relation between spectral and spatial (related to plant species richness) heterogeneity, by using satellite data with different spatial and spectral resolution. Species composition was sampled within square plots of 100 m² nested in macroplots of 10,000 m². Spectral heterogeneity of each macroplot was calculated using satellite images with different spatial and spectral resolution: a Quickbird multispectral image (4 bands, spatial resolution of 3 m), an Aster multispectral image (first 9 bands used, spatial resolution of 15 m for bands 1 to 3 and 30 m for bands 4 to 9), an ortho-Landsat ETM+ multispectral image (bands 1 to 5 and band 7 used; spatial resolution, 30 m), a resampled 60 m Landsat ETM+ image.Quickbird image heterogeneity showed a statistically highly significant correlation with species richness (r = 0.69) while coarse resolution images showed contrasting results (r = 0.43, r = 0.67, and r = 0.69 considering the Aster, Landsat ETM+, and the resampled 60 m Landsat ETM+ images respectively). It should be stressed that spectral variability is scene and sensor dependent. Considering coarser spatial resolution images, in such a case even using SWIR Aster bands (i.e. the additional spectral information with respect to Quickbird image) such an image showed a very low power in catching spectral and thus spatial variability with respect to Landsat ETM+ imagery. Obviously coarser resolution data tend to have mixed pixel problems and hence less sensitive to spatial complexity. Thus, one might argue that using a finer pixel dimension should inevitably result in a higher level of detail. On the other hand, the spectral response from different land-cover features (and thus different species) in images with higher spectral resolution should exhibit higher complexity.Spectral Variation Hypothesis could be a basis for improving sampling designs and strategies for species inventory fieldwork. However, researchers must be aware on scale effects when measuring spectral (and thus spatial) heterogeneity and relating it to field data, hence considering the concept of scale not only related to a spatial framework but even to a spectral one.     

Satellite observation of active carbonatite volcanism at Ol Doinyo Lengai,Tanzania

Natrocarbonatite lavas extruded by Ol Doinyo Lengai volcano, Tanzania, exhibit the lowest known magmatic eruption temperatures, ranging between 500 and 600 C. Nevertheless, as shown here, the near-infrared bands 5 and 7 of the Landsat Thematic Mapper (TM), and the mid-infrared channel 3 of the spaceborne Advanced Very High Resolution Radiometer (AVHRR) are able to detect thermal emission from active carbonatites. Laboratory-measured visible-to-near-infrared reflectance spectra of both silicate and carbonatite rocks from Ol Doinyo Lengai are used to infer spectral emissivities, enabling interpretation of satellite measurements. Given the remote location of this unique volcano, satellite remote sensing could play a valuable role in its future surveillance, and offers a potential means for distinguishing between silicate and carbonatite eruptions.     

Integrating visible,near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite,Nevada: a rule-based system

Previous research has shown that integrating hyperspectral visible and near-infrared (VNIR) / short-wave infrared (SWIR) with multispectral thermal infrared (TIR) data can lead to improved mineral and rock identification. However, inconsistent results were found regarding the relative accuracies of different classification methods for dealing with the integrated data set. In this study, a rule-based system was developed for integration of VNIR/SWIR hyperspectral data with TIR multispectral data and evaluated using a case study of Cuprite, Nevada. Previous geological mapping, supplemented by field work and sample spectral measurements, was used to develop a generalized knowledge base for analysis of both spectral reflectance and spectral emissivity. The characteristic absorption features, albedo and the location of the spectral emissivity minimum were used to construct the decision rules. A continuum removal algorithm was used to identify absorption features from VNIR/SWIR hyperspectral data only; spectral angle mapper (SAM) and spectral feature fitting (SFF) algorithms were used to estimate the most likely rock type. The rule-based system was found to achieve a notably higher performance than the SAM, SFF, minimum distance and maximum likelihood classification methods on their own.     

Evaluating Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for alteration zone enhancement in a semi‐arid area,northern Shahr‐e‐Babak,SE Iran

The visible–near infrared (VNIR) and short wave infrared (SWIR) spectral bands of both the level 1B, radiance at sensor, and level 2, AST_07 surface reflectance data products of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument were evaluated and compared for mapping the alteration zones around porphyry copper deposits and occurrences at the northern Shahr‐e‐Babak, SE Iran. The level 1B data were converted to reflectance using internal average relative reflectance (IARR) method whereas the AST_07 dataset was processed as delivered. The porphyry copper mineralization occurs in Eocene, andesitic and basaltic rocks with zonal alteration patterns that are concentric and almost symmetrically arranged. The spectral signatures of alteration index minerals collected from field samples and the United States Geological Survey (USGS) spectral reference library, were considered in directed principal component analysis (DPCA) and spectral angle mapping (SAM) algorithms. Carrying out the DPCA method on three spectral bands enhanced the alteration haloes in the last principal component (PC) images. Generating RGB colour composite images using these PC images differentiated three alteration zones from the host rocks. The SAM results of the IARR calibrated dataset discriminated the propylitic, argillic and phyllic alteration zones. It is concluded that the higher spectral resolution of ASTER instrument is effective for mineral mapping. However, the method of conversion from radiance to reflectance is critical to the validity of the outputs and that the pseudo‐reflectance method using the IARR process may be more reliable than the standard reflectance product.     

Spectral unmixing of vegetation,soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations

Remote measurements of the fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare soil are critical to understanding climate and land-use controls over the functional properties of arid and semi-arid ecosystems. Spectral mixture analysis is a method employed to estimate PV, NPV and bare soil extent from multispectral and hyperspectral imagery. To date, no studies have systematically compared multispectral and hyperspectral sampling schemes for quantifying PV, NPV and bare soil covers using spectral mixture models. We tested the accuracy and precision of spectral mixture analysis in arid shrubland and grassland sites of the Chihuahuan Desert, New Mexico, USA using the NASA Airborne Visible and Infrared Imaging Spectrometer (AVIRIS). A general, probabilistic spectral mixture model, Auto-MCU, was developed that allows for automated sub-pixel cover analysis using any number or combination of optical wavelength samples. The model was tested with five different hyperspectral sampling schemes available from the AVIRIS data as well as with data convolved to Landsat TM, Terra MODIS, and Terra ASTER optical channels. Full-range (0.4-2.5 w m) sampling strategies using the most common hyperspectral or multispectral channels consistently over-estimated bare soil extent and under-estimated PV cover in our shrubland and grassland sites. This was due to bright soil reflectance relative to PV reflectance in visible, near-IR, and shortwave-IR channels. However, by utilizing the shortwave-IR2 region (SWIR2; 2.0-2.3 w m) with a procedure that normalizes all reflectance values to 2.03 w m, the sub-pixel fractional covers of PV, NPV and bare soil constituents were accurately estimated. AVIRIS is one of the few sensors that can provide the spectral coverage and signal-to-noise ratio in the SWIR2 to carry out this particular analysis. ASTER, with its 5-channel SWIR2 sampling, provides some means for isolating bare soil fractional cover within image pixels, but additional studies are needed to verify the results.     

ASTER and field observations of the 24 December 2006 eruption of Bezymianny Volcano, Russia

An explosive eruption occurred at Bezymianny Volcano (Kamchatka Peninsula, Russia) on 24 December 2006 at 09:17 (UTC). Seismicity increased three weeks prior to the large eruption, which produced a 12–15 km above sea level (ASL) ash column. We present field observations from 27 December 2006 and 2 March 2007, combined with satellite data collected from 8 October 2006 to 11 April 2007 by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), as part of the instrument's rapid-response program to volcanic eruptions. Pixel-integrated brightness temperatures were calculated from both ASTER 90 m/pixel thermal infrared (TIR) data as well as 30 m/pixel short-wave infrared (SWIR) data. Four days prior to the eruption, the maximum TIR temperature was 45 °C above the average background temperature (− 33 °C) at the dome, which we interpret was a precursory signal, and had dropped to 8 °C above background by 18 March 2007. On 20 December 2006, there was also a clear thermal signal in the SWIR data of 128 °C using ASTER Band 7 (2.26 μm). The maximum SWIR temperature was 181 °C on the lava dome on 4 January 2007, decreasing below the detection limit of the SWIR data by 11 April 2007. On 4 January 2007 a hot linear feature was observed at the dome in the SWIR data, which produced a maximum temperature of 700 °C for the hot fraction of the pixel using the dual band technique. This suggests that magmatic temperatures were present at the dome at this time, consistent with the emplacement of a new lava lobe following the eruption. The eruption also produced a large, 6.5 km long by up to 425 m wide pyroclastic flow (PF) deposit that was channelled into a valley to the south–southeast. The PF deposit cooled over the following three months but remained elevated above the average background temperature. A second field investigation in March 2007 revealed a still-warm PF deposit that contained fumaroles. It was also observed that the upper dome morphology had changed in the past year, with a new lava lobe having in-filled the crater that formed following the 9 May 2006 eruption. These data provide further information on effusive and explosive activity at Bezymianny using quantitative remote sensing data and reinforced by field observations to assist in pre-eruption detection as well as post-eruption monitoring.     

Classification of soils based on engineering indices and spectral data

This study sought to establish diagnostic spectral characteristics in the short-wave infrared (SWIR) that could be used to classify soils in terms of their swelling potential. Three widely accepted soil-swelling indices, i.e. Atterberg limits, cation exchange capacity (CEC) and coefficient of linear extensibility (COLE), were used as controlling parameters to identify these spectral parameters. The results show that several spectral absorption feature parameters, namely position, depth and asymmetry, can be used in the classification on the basis of the discrete thresholds of these indices. The results show potential application of soil spectral characteristics in the construction industry and add a physical basis to the identification of clay mineral types dominant in engineering soils from currently used indices.