Advanced Spaceborne Thermal Emission And Reflection Radiometer Imagery Research Articles

Machine Learning-Based Lithological Mapping from ASTER Remote-Sensing Imagery

Accurately mapping lithological features is essential for geological surveys and the exploration of mineral resources. Remote-sensing images have been widely used to extract information about mineralized alteration zones due to their cost-effectiveness and potential for being widely applied. Automated methods, such as machine-learning algorithms, for lithological mapping using satellite imagery have also received attention. This study aims to map lithologies and minerals indirectly through machine-learning algorithms using advanced spaceborne thermal emission and reflection radiometer (ASTER) remote-sensing data. The capabilities of several machine-learning (ML) algorithms were evaluated for lithological mapping, including random forest (RF), support vector machine (SVM), gradient boosting (GB), extreme gradient boosting (XGB), and a deep-learning artificial neural network (ANN). These methods were applied to ASTER imagery of the Sar-Cheshmeh copper mining region of Kerman Province, in southern Iran. First, several spectral features that were extracted from ASTER bands were used as input data. Second, correlation coefficients between the original spectral bands and features were extracted. The importance of the random forest features (RF’s feature importance) was subsequently computed, and features with less importance were removed. Finally, the remained features were given to the models as input data in the second scenario. Accuracy assessments were performed for lithological classes in the study region, including Sar-Cheshmeh porphyry, quartz eye, late fine porphyry, hornblende dike, granodiorite, feldspar dike, biotite dike, andesite, and alluvium. The overall accuracy results of lithological mapping showed that ML-based algorithms without feature extraction have the highest accuracy. The overall accuracy percentages for ML-based algorithms without conducting feature extraction were 84%, 85%, 80%, 82%, and 80% for RF, SVM, GB, XGB, and ANN, respectively. The results of this study would be of great interest to geologists for lithological mapping and mineral exploration, particularly for selecting appropriate ML-based techniques to be implemented in similar regions.

Lithological mapping of ophiolitic rocks from southern part of the Sivas Basin (Turkey) using ASTER imagery

The east-west extended Sivas Basin in central-eastern Anatolia is a foreland basin that formed after the obduction of the Tethyan ophiolite during the late Cretaceous and is also a north-verging fold-and-thrust belt. The basement rocks of the basin represent mainly sedimentary (Mesozoic platform-type carbonates) and late Cretaceous Divriği ophiolitic complex. As the ophiolitic rocks are affected by intense tectonic processes, field-based mapping studies require long processes and costs. The study tests to reveal the lithological features of the ophiolitic complex outcropping around the Ulaş district of the Sivas Province using remote sensing methods and techniques. Due to arid climatic conditions and rare vegetation cover in the region, almost all outcrops of the basin rocks can be separated by spectral enhancement methods easily. Band ratio (BR), spectral indices (SI), decorrelation stretch (DS), principal component analysis (PCA), and support vector machine (SVM) on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used in this research. BR, PCA, DC, and SI techniques clearly distinguish the Divriği Ophiolitic Complex from the basement and cover sedimentary rocks. SVM distinguishes the chromite-bearing dunites from the other ophiolite-related rock units. According to image analysis performances, it has been observed that the rocks of the Divriği ophiolitic complex can be differentiated in more detail compared to a 1/100.000 scaled geological map of the region.

Integrating Aster Images Processing and Fieldwork for Identification of Hydrothermal Alteration Zones at the Oumjrane-Boukerzia District, Moroccan Anti-Atlas

Mapping from remote sensing has become more effective in the field of geology, mainly in lithological discrimination and identification of hydrothermal alteration zones. The use of this technique consists in obtaining information about the rock mass and the main ones existing in the inaccessible areas. Satellite data from the ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) sensor represent a favorable potential for detecting the spectral signatures of mineral zones and identifying their nature. These data are more reliable in places where the climate is arid with less abundant vegetation, as at the Oumjrane-Boukerzia mining district. This region which is part of the Eastern Anti-Atlas, is composed of several mineralized veins which still require detailed studies and exploration by the technique of remote sensing. In this work we applied several processing techniques on ASTER imagery such as Colored Composition, Principal Component Analysis and Ratio Bands. The use of the reports of the specialized Bands makes it possible to identify some hydrothermal alteration minerals within the mining district of Oumjrane Boukerzia. These minerals are represented mainly by iron oxides and hydroxides (Hematite, jarosite, limonite and goethite), carbonate minerals (dolomite, calcite), clay minerals (Illite, kaolinite and chlorite) and quartz minerals. This work allows us to produce a map of hydrothermal alteration zones which can be used as a valuable reference in the strategy of mining exploration for the base metals (Cu, Pb, Zn and Ba), in the mining district of Oumjrane-Boukerzia and in the entire Eastern Anti-Atlas.

A New Band Ratio Approach for Discriminating Calcite and Dolomite by ASTER Imagery in Arid and Semiarid Regions

In this article, a new approach is developed for discrimination of calcite (limestone) and dolomite (dolostone) by utilizing data of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The study area is located at the Zagros Mountains, SW Iran, in which outcrops of sedimentary carbonate rocks are abundant. The approach exploits short-wave infrared spectral features of dolomite and calcite and is based on the spectral variations of these minerals in bands 7 and 8 of ASTER imagery. In order to evaluate the efficacy of the method, spectral analysis was carried out on the spectra of dolomite, dolostone, calcite, and limestone, available in ASTER spectral library. The statistical analysis of the spectra showed that dolomite and calcite have distinguishable b8/b7 band ratios. Spectral measurements of rock samples and knowledge of the mineral composition of rock samples attained by staining carbonate rocks in Alizarin red-S solution were used to evaluate the method used in this study. The applied imagery was normalized by the internal average relative reflectance (IARR) method due to the arid and semiarid climate of the region. The results showed that combined with spectral measurements of rock samples, the ((b5 + b9)/b7)/((b6 + b9)/b8), and b8/b7 band ratios can be used to effectively discriminate limestone and dolostone. This approach is completely independent of variations in illumination conditions of image pixels; therefore, topographic correction of the image is not needed. The approach performs better in areas where weathering of rock surfaces and vegetation cover are minimal.

Evapotranspiration in the Tono Reservoir Catchment in Upper East Region of Ghana Estimated by a Novel TSEB Approach from ASTER Imagery

Evapotranspiration (ET) is dynamic and influences water resource distribution. Sustainable management of water resources requires accurate estimations of the individual components that result in evapotranspiration, including the daily net radiation (DNR). Daily ET is more useful than the evaporative fraction (EF) provided by remote sensing ET models, and to account for daily variations, EF is usually combined with the DNR. DNR exhibits diurnal and spatiotemporal variations due to landscape heterogeneity. In the modified Two-Source Energy Balance (TSEB) approach by Zhuang and Wu, 2015, ecophysiological constraint functions of temperature and moisture of plants based on atmospheric moisture and vegetation indices were introduced, but the DNR was not spatially accounted for in the estimation of the daily ET. This research adopted a novel approach that accounts for spatiotemporal variations in estimated daily ET by incorporating the Bisht and Bras DNR model in the modified version of the TSEB model. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery over the Tono irrigation watershed within the Upper East Region of Ghana and Southern Burkina Faso were used. We estimated the energy fluxes of latent and sensible heat as well as the net radiation and soil heat fluxes from the satellite images and compared our results with ground-based measurements from an eddy covariance (EC) station established by the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL) within the watershed area. We noticed a similarity between our model estimated fluxes and ET with the ground-based EC station measurements. Eight different land use/cover types were identified in the study area, and each of these contributed significantly to the overall ET variations between the two study periods: December 2009 and December 2017. For instance, due to a higher leaf area index (LAI) for all vegetation types in December 2009 than in December 2017, the ET for December 2017 was higher than that for December 2009. We also noticed that the land use/cover types within the footprint area of the EC station were only six out of the eight. Generally, all the surface energy fluxes increased from December 2009 to December 2017. Mean ET varied from 3.576 to 4.486 (mm/d) for December 2009 while from 4.502 to 5.280 (mm/d) for December 2017 across the different land use/cover classes. Knowledge of the dynamics of evapotranspiration and adoption of cost-effective methods to estimate its individual components in an effective and efficient way is critical to water resources management. Our findings provide a tool for all water stakeholders within watersheds to manage water resources in an engaging and cost-effective way.

Temporal multispectral and 3D analysis of Cerro de Pasco, Peru

Mining operations across the world often lead to contamination of land, water resources, ecosystems and in some cases, entire communities.Results of recent health and ground sampling studies revealed extensive lead contamination within the populace and around the City of Cerro de Pasco, Peru. Tailings excavated from a large open pit zinc mine in the center of the city have been aggregated in four large stockpiles within close proximity to neighborhoods, schools, and hospitals. Visual comparison of ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery from 2001 and Sentinel-2 imagery from 2018 suggests a size increase in one tailing stockpile in particular near the neighborhood of Paragsha. Due to ongoing mining efforts, the hypothesis motivating the work presented here is that Pb-bearing minerals would be detectable through multispectral analysis, an increase in Pb mineral percent abundance would be observed and tailing stockpile volume would be detectable between 2001 and 2016. This hypothesis is tested using Spectral Angle Mapper (SAM), Adaptive Coherence Estimator (ACE), and Jeffries-Matusita distance calculation on ASTER (2001) and Sentinel-2 (2018) VNIR and SWIR bands. Volume and area estimate of tailing stockpiles were calculated using a photogrammetrically derived point cloud. SAM detected the presence of five Pb-bearing minerals around Cerro de Pasco and Paragsha. The results of the temporal SAM analysis displayed an increase of approximately 17% of Pb-bearing minerals around the greater Cerro de Pasco city area and approximately 11% for the neighborhood of Paragsha. Jeffries-Matusita distance results suggest clear correlation between contamination sources and affected locations. Total tailing stockpile volume was measured to be approximately 200,300,000 m3. Volume for Pile 4 was estimated to have increased by approximately 46,000,000 m3 between 2001 and 2018. These presented results will hopefully inspire and guide future remote sensing campaigns, perhaps involving a UAV or aircraft-based hyperspectral instrument.

Comparative Study of Atmospheric Correction Methods of ASTER Data to Enhance the Delineation of Uranium Mineralized Zones

Multispectral imaging sensors are utilized to discover and identify various surface materials, topographical and geological features. Also it is a used to differentiate lithological units and to provide mineral maps. Although successful processing methodologies have been developed for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery for mineral exploration and geological mapping. Questions still arise regarding the optimal atmospheric correction for these applications. This article provides an outline and an evaluation of existing atmospheric compensation algorithms applied on the visible and shortwave infrared spectral region. This may assist in developing a uranium exploration program using ASTER imagery by delineation of the altered minerals associated with the uranium mineralization

Global 15-Meter Mosaic Derived from Simulated True-Color ASTER Imagery

This work proposes a new methodology to build an Earth-wide mosaic using high-spatial resolution ( 15 m ) Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images in pseudo-true color. As ASTER originally misses a blue visible band, we have designed a cloud of artificial neural networks to estimate the ASTER blue reflectance from Level-1 data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) on the same satellite Terra platform. Next, the granules are radiometrically harmonized with a novel color-balancing method and seamlessly blended into a mosaic. We demonstrate that the proposed algorithms are robust enough to process several thousands of scenes acquired under very different temporal, spatial, and atmospheric conditions. Furthermore, the created mosaic fully preserves the ASTER fine structures across the various building steps. The proposed methodology and protocol are modular so that they can easily be adapted to similar sensors with enormous image libraries.

Integrated Approach for Lithological Classification Using ASTER Imagery in a Shallowly Covered Region—The Eastern Yanshan Mountain of China

This paper studied the applicability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data for lithological classification in the shallowly covered Eastern Yanshan Mountain of Eastern China. The interpreted objects include quartz sandstone, carbonate rock, gneiss, and andesite. An integrated approach was employed to process the remote sensing data: First, the matched filtering method, accompanied by the ASTER library and imagery spectra as reference, was used to enhance the targeted information, namely the remotely visible (typical) rock outcrops. As the andesite is frequently weathered to iron-rich minerals in the field, so the high-resolution SPOT6 3/1 image, instead of the ASTER image, was used to locate the outcrops. Second, the fractal digital number-frequency algorithm was developed to preliminarily extract the lithology anomaly patches; and the obtained geological anomalies were generalized into three types: anomalies related to outcrops, anomalies caused by different forms of the same lithology, and random noise. Third, digital elevation model derived slope masks, in combination with the spatial intersection operation, was used to eliminate the pseudo-outcrop anomalies and noise. Finally, the accuracy assessment was conducted by referencing the local rock-outcrop database, and the misclassification rates for quartz sandstone, carbonate rock, gneiss, and andesite are 8.9%, 12.5%, 23%, and 48.3%, respectively. This study has contributed a useful case study for remote-sensing lithology mapping in shallowly covered areas, and the proposed method should have a great potential to be applied to many similar cases.

Scaling Effect of Fused ASTER-MODIS Land Surface Temperature in an Urban Environment.

There is limited research in land surface temperatures (LST) simulation using image fusion techniques, especially studies addressing the downscaling effect of LST image fusion. LST simulation and associated downscaling effect can potentially benefit the thermal studies requiring both high spatial and temporal resolutions. This study simulated LSTs based on observed Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LST imagery with Spatial and Temporal Adaptive Reflectance Fusion Model, and investigated the downscaling effect of LST image fusion at 15, 30, 60, 90, 120, 250, 500, and 1000 m spatial resolutions. The study area partially covered the City of Los Angeles, California, USA, and surrounding areas. The reference images (observed ASTER and MODIS LST imagery) were acquired on 04/03/2007 and 07/01/2007, with simulated LSTs produced for 4/28/2007. Three image resampling methods (Cubic Convolution, Bilinear Interpolation, and Nearest Neighbor) were used during the downscaling and upscaling processes, and the resulting LST simulations were compared. Results indicated that the observed ASTER LST and simulated ASTER LST images (date 04/28/2007, spatial resolution 90 m) had high agreement in terms of spatial variations and basic statistics based on a comparison between the observed and simulated ASTER LST maps. Urban developed lands possessed higher LSTs with lighter tones and mountainous areas showed dark tones with lower LSTs. The Cubic Convolution and Bilinear Interpolation resampling methods yielded better results over Nearest Neighbor resampling method across the scales from 15 to 1000 m. The simulated LSTs with image fusion can be used as valuable inputs in heat related studies that require frequent LST measurements with fine spatial resolutions, e.g., seasonal movements of urban heat islands, monthly energy budget assessment, and temperature-driven epidemiology. The observation of scale-independency of the proposed image fusion method can facilitate with image selections of LST studies at various locations.

Impacts of Climate and Supraglacial Lakes on the Surface Velocity of Baltoro Glacier from 1992 to 2017

The Baltoro Glacier is one of the largest glaciers in the Karakoram mountain range. Long-term monitoring of glacier dynamics provides key information on glacier evolution in a changing climate, which is essential for regional water resource and natural hazard management. On large glaciers, detailed field based mass balance is not feasible. Ice dynamic variations quantify changes in mass transport and possibly the influence of environmental parameters on the evolution of the glacier. Although velocity variations of Baltoro Glacier during winter and summer are linked to seasonally enhanced basal sliding, little is known about differences in timing and magnitude of (intra-)seasonal velocity variations and their determining mechanisms. We present time series of annual, seasonal, and intra-seasonal glacier surface velocities by means of intensity offset tracking applied on multi-mission Synthetic Aperture Radar (SAR) data for a period of 25 years from 1992 to 2017. Supraglacial lakes forming on the downstream glacier surface in summer were mapped from 1991 to 2017 based on the Normalized Difference Water Index (NDWI), calculated from multi-spectral Landsat and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) imagery. Additionally, precipitation data of the Tropical Rainfall Measurement Mission (TRMM) and temperature data of ERA-Interim were used to derive the Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) from 1998 to 2017. Linking surface velocities to the SPI confirmed a strong correlation between heavy precipitation events in winter and the magnitude and the timing of glacier acceleration in summer. Downstream extensions of summer acceleration that have been found since 2015 may be explained by additional water draining from an increased number of supraglacial lakes through crevasses that have been formed in consequence of higher initial velocities, evoked by strong winter precipitation. The warmer melt seasons observed in the years 2015 to 2017 additionally affects the formation of a supraglacial lake, so stronger summer acceleration events in recent years may be indirectly related to global warming.

Lithological Analysis of Nain Ophiolitic Zone Using ASTER Data

The Mesozoic ophiolitic Melange, north of Nain in the Central-East Iran Microplate (CEIM) comprises serpentinized ultramafic rocks, harzburgites, dunite, gabbro, peridotite, pelagic limestone and other carbonate rocks. The excellent and vast exposure of this desert region is well suited for geologic mapping of this rock suite using remote sensing, especially using data from the satellite-borne advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) imaging system which was designed for mapping mineral information. In this study, data processing methods like Method Minimum noise fraction (MNF), Feature Oriented Principal Components Selection (FPCS), Band Ratios (BR) and Optimum Index Factor (OIF) were used to process ASTER data to optimize the mapping of ophiolite rock types. For example, a simple color composites of OIF (Red: B3, Green: B4, and Blue: B8) and Band ratios (e.g. Red: (B2 + B4)/B3, Green: (B5 + B7)/B6, Blue: (B7 + B9)/B8) were useful for discriminating serpentinite, meta-basalt and granite rock types. It is concluded here that proposed ASTER data has the potential for mapping similar ophiolites elsewhere using the global archive of ASTER imagery.

산사태 취약성 분석: ASTER 위성영상을 이용한 점토광물인자 추출 및 공간데이터베이스의 SVM 통계기법 적용

ASTER 위성영상을 이용하여 팽창성 점토광물인 일라이트 인자 추출 및 SVM 통계분석을 통해 산사태 취약성을 평가하였다. 연구지역의 산사태 발생지역은 항공사진 판독 및 현장 조사를 통해 분석하였다. GIS 기반 공간데이터베이스로는 지형도, 토양도, 임상도, ASTER 위성사진을 이용하였다. 수치지형도에서는 경사 및 경사방향, 곡률도, 계곡과의 거리, 도로와의 거리, 토양도에서는 유효토심, 토질, 토양지형, 토양 배수정도 및 토양 모재, 임상도에서는 경급, 영급 및 밀도를 위성사진에서는 일라이트 인자를 추출하였다. 산사태 발생요인 데이터베이스와 SVM 통계분석 및 가중치 계산을 통해 각 요소간의 상관관계 취약성도를 구하였다. AUC 검증 결과 일라이트 인자 적용결과는 76.46%의 예측 정확도를 보였으며 일라이트 인자 미적용 모델은 74.09%의 예측 정확도를 나타내었다. 이는 일라이트 인자가 산사태 취약성도 작성에 있어 중요한 자료로 사용될 수 있음을 나타낸다. This study evaluates landslide susceptibility using statistical analysis by SVM (support vector machine) and the illite index of clay minerals extracted from ASTER(advanced spaceborne thermal emission and reflection radiometer) imagery which can be use to create mineralogical mapping. Landslide locations in the study area were identified from aerial photographs and field surveys. A GIS spatial database was compiled containing topographic maps (slope, aspect, curvature, distance to stream, and distance to road), maps of soil properties (thickness, material, topography, and drainage), maps of timber properties (diameter, age, and density), and an ASTER satellite imagery (illite index). The landslide susceptibility map was constructed through factor correlation using SVM to analyze the spatial database. Comparison of area under the curve values showed that using the illite index model provided landslide susceptibility maps that were 76.46% accurate, which compared favorably with 74.09% accuracy achieved without them.

Digital Mapping of Soil Properties Using Multivariate Statistical Analysis and ASTER Data in an Arid Region

Modeling and mapping of soil properties has been identified as key for effective land degradation management and mitigation. The ability to model and map soil properties at sufficient accuracy for a large agriculture area is demonstrated using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. Soil samples were collected in the El-Tina Plain, Sinai, Egypt, concurrently with the acquisition of ASTER imagery, and measured for soil electrical conductivity (ECe), clay content and soil organic matter (OM). An ASTER image covering the study area was preprocessed, and two predictive models, multivariate adaptive regression splines (MARS) and the partial least squares regression (PLSR), were constructed based on the ASTER spectra. For all three soil properties, the results of MARS models were better than those of the respective PLSR models, with cross-validation estimated R2 of 0.85 and 0.80 for ECe, 0.94 and 0.90 for clay content and 0.79 and 0.73 for OM. Independent validation of ECe, clay content and OM maps with 32 soil samples showed the better performance of the MARS models, with R2 = 0.81, 0.89 and 0.73, respectively, compared to R2 = 0.78, 0.87 and 0.71 for the PLSR models. The results indicated that MARS is a more suitable and superior modeling technique than PLSR for the estimation and mapping of soil salinity (ECe), clay content and OM. The method developed in this paper was found to be reliable and accurate for digital soil mapping in arid and semi-arid environments.

A software tool for retrieving land surface temperature from ASTER imagery

Cevresel verinin toplanmasi, analizi ve modellemesinde uzaktan algilama cok onemli ve iyi bilinen bir arac olmasina ragmen, termal kizilotesi (TIR) uzaktan algilamasina gereken onem verilmemistir. Kuresel cevre degisimi, iklim modelleri ve insan-cevre etkilesimleri gibi bircok cevresel calismalar icin yer yuzey sicakligi (LST) onemli bir parametredir. Bu calismada, Visual Basic NET programlama dili kullanilarak ASTER uydu goruntusunden yer yuzey sicakligini hesaplayan bir yazilim araci gelistirilmistir. Bu arac bes modulden olusmaktadir: 1- Dijital Sayilari (DN) Radyansa donusturme, 2- Yuzey yansima degerinin hesaplanmasi, 3- NDVI’in hesaplanmasi, 4- Yayinirligin hesaplanmasi ve 5- Sicakligin hesaplanmasi. Bu yazilim aracinda Jimenez Munoz ve Sobrino’nun ASTER uydu goruntusunden yer yuzey sicakliginin hesaplanmasi icin gelistirdigi Tek Kanal (Single Channel) Algoritmasi kullanilmistir. Bu programin termal cevre uzerine calisan bilim insanlari veya konuyla ilgilenen kisiler icin faydali olabilecegi umit edilmektedir.

ASTER, ALI and Hyperion sensors data for lithological mapping and ore minerals exploration.

This paper provides a review of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Imager (ALI), and Hyperion data and applications of the data as a tool for ore minerals exploration, lithological and structural mapping. Spectral information extraction from ASTER, ALI, and Hyperion data has great ability to assist geologists in all disciplines to map the distribution and detect the rock units exposed at the earth’s surface. The near coincidence of Earth Observing System (EOS)/Terra and Earth Observing One (EO-1) platforms allows acquiring ASTER, ALI, and Hyperion imagery of the same ground areas, resulting accurate information for geological mapping applications especially in the reconnaissance stages of hydrothermal copper and gold exploration, chromite, magnetite, massive sulfide and uranium ore deposits, mineral components of soils and structural interpretation at both regional and district scales. Shortwave length infrared and thermal infrared bands of ASTER have sufficient spectral resolution to map fundamental absorptions of hydroxyl mineral groups and silica and carbonate minerals for regional mapping purposes. Ferric-iron bearing minerals can be discriminated using six unique wavelength bands of ALI spanning the visible and near infrared. Hyperion visible and near infrared bands (0.4 to 1.0 μm) and shortwave infrared bands (0.9 to 2.5 μm) allowed to produce image maps of iron oxide minerals, hydroxyl-bearing minerals, sulfates and carbonates in association with hydrothermal alteration assemblages, respectively. The techniques and achievements reviewed in the present paper can further introduce the efficacy of ASTER, ALI, and Hyperion data for future mineral and lithological mapping and exploration of the porphyry copper, epithermal gold, chromite, magnetite, massive sulfide and uranium ore deposits especially in arid and semi-arid territory.

Mineral Exploration and Alteration Zone Mapping Using Mixture Tuned Matched Filtering Approach on ASTER Data at the Central Part of Dehaj-Sarduiyeh Copper Belt, SE Iran

This paper focuses on mapping jarosite and different types of alteration minerals for mineral exploration, particularly porphyry copper deposits and discriminating alteration zones with high-potential mineralization from those showing low potentials. The study area is situated at the Central Iranian Volcano-Sedimentary Complex, where the large copper deposits like Sarcheshmeh as well as numerous occurrences of copper exist. The visible near infrared and shortwave infrared (VNIR-SWIR) bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were used for mineral mapping. The spectra of diagnostic alteration mineral groups, including sericite-illite, pyrophyllite-alunite, kaolinite-dickite, chlorite-calcite-epidote, and jarosite were extracted from VNIR-SWIR bands of the ASTER imagery. These spectra were used for mineral identification through mixture tuned matched filtering (MTMF) algorithm. Results showed that alteration minerals, and the areas containing jarosite were discriminated from the surrounding districts, which illustrate the ASTER ability to provide information on the occurrence of these minerals. Identification of these areas is valuable for mineral exploration, discrimination of strong pyritization, gossans, and the mine tailings. Results also support the role of MTMF as an effective image processing technique for mineral mapping and exploration.

Field and ASTER imagery data for the setting of gold mineralization in Western Allaqi–Heiani belt, Egypt: A case study from the Haimur deposit

Although associated with carbonatized/listvenitized ophiolites and thrust structures, the morphology and internal structures of the auriferous quartz veins in the Haimur deposit suggest mineralization concurrent with NE–SW dextral brittle–ductile shear zones. The latter are attributed to intense transpression regime and are associated with (N)NE-trending tight to isoclinals folds that deform the early accretionary structures.Image processing techniques applicable to the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data are used for mapping structures and hydrothermal alteration associated with the Haimur deposit. The automated lineament extraction by LINE module on high resolution ASTER imagery provides efficient data for potential dilation loci. Emphasis is placedon reliability of mineral indicesextracted fromthe ASTER band ratios for identification of possibly mineralized alteration zones associated with NE-trending shear zones.Field and remote sensing data, together with the structural fabrics along the lode-associated shear zones clearly constrain on the genetic relationship between the Haimur gold deposit and post-accretionary transpression/shearing. We conclude that hydrothermal alteration zones that are confined to tightly enfolded ophiolites and transpressive shear zones along the Western Allaqi–Heiani belt are most potential targets for new exploration plans.

GIS Integration of Aster Stereo Imagery for the Support of Watershed Management

In this study, ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) stereo
 imagery was analysed in combination with Global Positioning System (GPS) data and field observations,
 using Geographical Information Systems (GIS) techniques to examine the potential of satellite
 remote sensing to support watershed management. Terrain elevation data were derived for the region
 of Heraklion, Crete. The accuracy of the ASTER derived DEM (Digital Elevation Model), was better
 than 20 meters (planimetric) and better than 15 meters (elevation). Supervised classification techniques
 were applied for land cover production using ASTER multi-spectral imagery and filed observations
 at predefined application sites. The determination of training areas required in the supervised
 classification scheme was based on these observations. Finally, GIS methods were applied to estimate
 watershed characterization parameters for the study area offering the advantages of spatial data handling
 capabilities and automatic extraction of thematic information. The drainage pattern, which was
 derived, provided a generally representative depiction of the watershed. The output pixel spacing of 15
 m of the produced DEM as well as the high spatial resolution of ASTER imagery found to be quite satisfactory
 for the watershed characterization of the study area, indicating the high potential of ASTER
 imagery to support watershed management.

Prediction of landslides using ASTER imagery and data mining models

The aim of this study was to identify landslide-related factors using only remotely sensed data and to present landslide susceptibility maps using a geographic information system, data-mining models, an artificial neural network (ANN), and an adaptive neuro-fuzzy interface system (ANFIS). Landslide-related factors were identified in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. The slope, aspect, and curvature of topographic features were calculated from a digital elevation model that was made using the ASTER imagery. Lineaments, land-cover, and normalized difference vegetative index layers were also extracted from the imagery. Landslide-susceptible areas were analyzed and mapped based on occurrence factors using the ANN and ANFIS. The generalized bell-shaped built-in membership function of the ANFIS was applied to landslide susceptibility mapping. Analytical results were validated using landslide test location data. In the validation results, the ANN model showed 80.42% prediction accuracy and the ANFIS model showed 86.55% prediction accuracy. These results suggest that the ANFIS model has a better performance than does the ANN in predicting landslide susceptibility.