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The present study makes an attempt to describe spectral characteristics of the surface features in some parts of the Khuzestan region, using reflectance spectroscopy and ASTER data related to the hydrocarbon micro-seepage in Maroun and Mansouri oil fields. The study involves surface sediment sampling with appropriate dispersion of the samples, and laboratory analysis in terms of sediment and mineralogy through spectroscopy analysis and geological map. Soil samples were collected from 45 and 20 locations points for Maroun and Mansouri oilfields, respectively. ASD-Fieldspec3 spectroradiometer and The Spectral Geologist (TSG) software were used to determine the hydrocarbon absorptions and their mineral constituents. Four classes were identified as hydrocarbon absorption ranges, i.e.: 1220–1320 and 1350–1550 nm indicating presence of light hydrocarbons; 1700–1750 and 2290–2360 nm pertaining to heavy hydrocarbons. Results suggest that Maroun oilfield samples have stronger absorptions in heavy hydrocarbons absorption but weaker for the Mansouri oilfield samples. The Mansouri samples have an acceptable absorption in light hydrocarbons absorption (1175 nm). Furthermore, these spectra were used as ground truth for creating mineral zonation maps using Spectral Angle Mapper (SAM) classification applied to ASTER data. The results also suggest that surface indicators of hydrocarbon are associated with alteration minerals such as kaolinite, siderite and goethite for the Maroun oilfield whereas Mansouri oilfield exhibits only kaolinite group of minerals. In addition, a comparison by reflectance between the contaminated and uncontaminated soil samples was also carried out.

Digital elevation model (DEM) provides elevation values of the terrain. At present several open source DEMs are available and its vertical accuracy varies from place to place. The present study assessed the vertical accuracy of 1 arc-sec data of Advanced Land Observing Satellite digital surface model (AW3D30) Version-3, Shuttle Radar Topographic Mission (SRTM) DEM (V3), Carto DEM (V3R1) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (V2) using Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) and Real Time Kinematic Global Positioning System (RTK GPS) data in Southeast coast of India. Root mean square error (RMSE) was calculated for DEMs, showed the elevation accuracy as 2.48 m (AW3D30), 3.02 m (SRTM DEM), 3.88 m (Carto DEM) and 8.02 m (ASTER GDEM) in the Tamil Nadu state while it was 2.29 m (AW3D30), 3.10 m (SRTM DEM), 3.93 m (Carto DEM) and 8.98 m (ASTER GDEM) in the 10 km buffer zone of coastal region and the results are satisfactory than the official product’s specifications. Landform analysis was carried out at selected RTK GPS surveyed sites where the elevation of different coastal landforms is validated with DEMs and it was found the AW3D30 showed least RMSE than others. This paper suggests that AW3D30 is best for representing terrain elevation values followed by SRTM and Carto DEMs which are apt choices for application studies.

Wadi Khuda-Wadi Shut area in the South Eastern Desert (SED) of Egypt is mainly made up of gneisses, ophiolitic serpentinites and intermediate schistose metavolcanics. These metamorphic rock units are intruded by syn-late-tectonic diorite, granodiorite, ultramafic-mafic, and monzogranite intrusions. The gneisses comprise biotite hornblende gneiss, hornblende gneiss and migmatites. They are of granodiorite and diorite composition. The ophiolitic serpentinite with talc carbonate rocks were thrusted over the metavolcanics with NW-SE thrust contact. The late tectonic ultramafic-mafic rock units are represented in the study area by Dahanib pluton, which is consisted of layered peridotite-dunite associated with olivine gabrro, normal gabbro and gabbro-norite as well as rare hornblende gabbro. These varieties are related to the layered intrusions rather than Alaskan-types, and formed by magmatic differentiation. The granodiorite and monzogranite represent the youngest basement rock units in the study area. The principal components and band ratios of ASTER and Landsat-8 data were successfully used for the first time in lithological discrimination and geological mapping of the study area. This study concluded that, the interpreted data of ASTER and Landsat-8 combined with field study and petrographic investigation clearly discriminated the exposed rock units. Accordingly, a detailed geological map emphasizing the lithological units in Khuda-Shut area has been presented.

The integration of remotely sensed data allowed the successful characterization of the hydrothermal alteration zones of the Abu–Gaharish area in the Central Eastern Desert of Egypt using image transformation techniques. The integration of several evidential maps highlighted the plausible areas with high concentrations of argillic and phyllic, key hydrothermal minerals that reflect the intensity of hydrothermal effects and the probable sites of ore bodies. The excellent prospective zone identified in the map occupies around 1.39% of the study area. Kaolinite–smectite minerals with correlation scores of around 0.97 were consistent with areas of hydrothermal alteration and mining activities; however, propylitic minerals were confined to basic metavolcanics and serpentinites. The resulting predictive map was assessed using existing mining data with significant consistency; therefore, field verification is essential. The overall results showed that remotely sensed data represent a valuable mapping tool for geological and mineral resources reconnaissance in the arid regions

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2019-08-27, 資料番号: PA2010028000

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDA20100300). Many glaciers and ice caps on the Tibetan Plateau have retreated and lost mass in recent years in response to temperature increases, providing clear evidence of the impact of climate change on the region. There is increasing evidence that many of the glaciers on the Tibetan Plateau have also shown periodically dynamic behaviour in the form of glacier surging and some even catastrophic collapse events. In this study, we examine the prevalence of glacier surging at the Geladandong ice caps, North East Tibetan Plateau, to better understand the role of surge events in the evolution of glacier mass loss budgets. Using glacier surface elevation change data over the period 1969–2018 and glacier surface velocity data from the ITS_LIVE dataset, we find that 19 outlet glaciers of the ice caps are of surge-type. Our multi-temporal measurements of glacier mass balance show that surge-type glacier mass budgets vary depending on the portion of the surge-cycle captured by geodetic data. At the regional level, pre- and post-surge glacier mass loss variability does not bias regional mass budget estimates, but enhanced, or suppressed, mass loss estimates are likely when small groups of glaciers are examined. Our results emphasise the importance of accurate surge-type glacier inventories and the need to maximise geodetic data coverage over glacierised regions known to contain surge-type glaciers. Publisher PDF

An accuracy assessment of a classified thematic map is critical for the success of a remote sensing project. The identification and quantification of accuracy sources for classified mineral maps derived from satellite images enable confident decisions to be made for further exploration operations. Nineteen rock samples were collected from the different lithological units of the hydrothermally altered Kuh Panj Cu porphyry occurrence within the south-eastern part of Iran. SPECIM hyperspectral imaging was applied to the rock samples, followed by X-ray diffraction (XRD) analysis to compare the SPECIM mineral maps. The SPECIM results were then interpreted for mineralogy and compositional mapping extracted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. The spectral angle mapper (SAM) and real value−area (RV−A) fractal methods were applied on SPECIM and ASTER images to interpret the mineralogy and derived classified map products. Two methods, including confusion matrix and one minus standard deviation over mean, were used to assess the accuracy of the classified SPECIM and ASTER derived mineral maps. Performing sensitivity studies were investigated, including the effect of spatially displacing ASTER mineral mapping and changing the SAM-derived threshold values on the accuracy of the ASTER mineral map. The sensitivity analyses demonstrated that selecting an optimum SAM-derived threshold value is more important than spatial displacement. Finally, accuracy sources were summarized in an accuracy budget table. The results demonstrated 89 and 88% accuracy for SPECIM and 88 and 92% accuracy for ASTER mineral maps via the confusion matrix and one minus standard deviation over mean methods, respectively. The accuracy budget helped to evaluate and compare all sources of accuracy with their accuracy values.

Local climate zone (LCZ) maps have been used widely to study urban structures and urban heat islands. Because remote sensing data enable automated LCZ mapping on a large scale, there is a need to evaluate how well remote sensing resources can produce fine LCZ maps to assess urban thermal environments. In this study, we combined Sentinel-2 multispectral imagery and dual-polarized (HH + HV) PALSAR-2 data to generate LCZ maps of Nanchang, China using a random forest classifier and a grid-cell-based method. We then used the classifier to evaluate the importance scores of different input features (Sentinel-2 bands, PALSAR-2 channels, and textural features) for the classification model and their contribution to each LCZ class. Finally, we investigated the relationship between LCZs and land surface temperatures (LSTs) derived from summer nighttime ASTER thermal imagery by spatial statistical analysis. The highest classification accuracy was 89.96% when all features were used, which highlighted the potential of Sentinel-2 and dual-polarized PALSAR-2 data. The most important input feature was the short-wave infrared-2 band of Sentinel-2. The spectral reflectance was more important than polarimetric and textural features in LCZ classification. PALSAR-2 data were beneficial for several land cover LCZ types when Sentinel-2 and PALSAR-2 were combined. Summer nighttime LSTs in most LCZs differed significantly from each other. Results also demonstrated that grid-cell processing provided more homogeneous LCZ maps than the usual resampling methods. This study provided a promising reference to further improve LCZ classification and quantitative analysis of local climate.

The RST (Robust Satellite Techniques) approach is a multi-temporal scheme of satellite data analysis widely used to investigate and monitor thermal volcanic activity from space through high temporal resolution data from sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In this work, we present the results of the preliminary RST algorithm implementation to thermal infrared (TIR) data, at 90 m spatial resolution, from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Results achieved under the Google Earth Engine (GEE) environment, by analyzing 20 years of satellite observations over three active volcanoes (i.e., Etna, Shishaldin and Shinmoedake) located in different geographic areas, show that the RST-based system, hereafter named RASTer, detected a higher (around 25% more) number of thermal anomalies than the well-established ASTER Volcano Archive (AVA). Despite the availability of a less populated dataset than other sensors, the RST implementation on ASTER data guarantees an efficient identification and mapping of volcanic thermal features even of a low intensity level. To improve the temporal continuity of the active volcanoes monitoring, the possibility of exploiting RASTer is here addressed, in the perspective of an operational multi-satellite observing system. The latter could include mid-high spatial resolution satellite data (e.g., Sentinel-2/MSI, Landsat-8/OLI), as well as those at higher-temporal (lower-spatial) resolution (e.g., EOS/MODIS, Suomi-NPP/VIIRS, Sentinel-3/SLSTR), for which RASTer could provide useful algorithm’s validation and training dataset.

Forest fires threaten the population’s health, biomass, and biodiversity, intensifying the desertification processes and causing temporary damage to conservation areas. Remote sensing has been used to detect, map, and monitor areas that are affected by forest fires due to the fact that the different areas burned by a fire have similar spectral characteristics. This study analyzes the performance of the k-Nearest Neighbor (kNN) and Random Forest (RF) classifiers for the classification of an area that is affected by fires in central Portugal. For that, image data from Landsat-8, Sentinel-2, and Terra satellites and the peculiarities of each of these platforms with the support of Jeffries–Matusita (JM) separability statistics were analyzed. The event under study was a 93.40 km2 fire that occurred on 20 July 2019 and was located in the districts of Santarém and Castelo Branco. The results showed that the problems of spectral mixing, registration date, and those associated with the spatial resolution of the sensors were the main factors that led to commission errors with variation between 1% and 15.7% and omission errors between 8.8% and 20%. The classifiers, which performed well, were assessed using the receiver operating characteristic (ROC) curve method, generating maps that were compared based on the areas under the curves (AUC). All of the AUC were greater than 0.88 and the Overall Accuracy (OA) ranged from 89 to 93%. The classification methods that were based on the kNN and RF algorithms showed satisfactory results., Research was supported by PAIUJA-2019/2020 and CEACTEMA from University of Jaen (Spain), and RNM-282 research group from the Junta de Andalucia (Spain). Special thanks to the four anonymous reviewers for their insightful comments.

The Normalized Hotspot Indices (NHI) tool is a Google Earth Engine (GEE)-App developed to investigate and map worldwide volcanic thermal anomalies in daylight conditions, using shortwave infrared (SWIR) and near infrared (NIR) data from the Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel 2 and Landsat 8 satellites. The NHI tool offers the possibility of ingesting data from other sensors. In this direction, we tested the NHI algorithm for the first time on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. In this study, we show the results of this preliminary implementation, achieved investigating the Kilauea (Hawaii, USA), Klyuchevskoy (Kamchatka, Russia), Shishaldin (Alaska, USA), and Telica (Nicaragua) thermal activities of March 2000–2008. We assessed the NHI detections through comparison with the ASTER Volcano Archive (AVA), the manual inspection of satellite imagery, and the information from volcanological reports. Results show that NHI integrated the AVA observations, with a percentage of unique thermal anomaly detections ranging between 8.8% (at Kilauea) and 100% (at Shishaldin). These results demonstrate the successful NHI exportability to ASTER data acquired before the failure of SWIR subsystem. The full ingestion of the ASTER data collection, available in GEE, within the NHI tool allows us to develop a suite of multi-platform satellite observations, including thermal anomaly products from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), which could support the investigation of active volcanoes from space, complementing information from other systems.

In Antarctica, spectral mapping of altered minerals is very challenging due to the remoteness and inaccessibility of poorly exposed outcrops. This investigation evaluates the capability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite remote sensing imagery for mapping and discrimination of phyllosilicate mineral groups in the Antarctic environment of northern Victoria Land. The Mixture-Tuned Matched-Filtering (MTMF) and Constrained Energy Minimization (CEM) algorithms were used to detect the sub-pixel abundance of Al-rich, Fe3+-rich, Fe2+-rich and Mg-rich phyllosilicates using the visible and near-infrared (VNIR), short-wave infrared (SWIR) and thermal-infrared (TIR) bands of ASTER. Results indicate that Al-rich phyllosilicates are strongly detected in the exposed outcrops of the Granite Harbour granitoids, Wilson Metamorphic Complex and the Beacon Supergroup. The presence of the smectite mineral group derived from the Jurassic basaltic rocks (Ferrar Dolerite and Kirkpatrick Basalts) by weathering and decomposition processes implicates Fe3+-rich and Fe2+-rich phyllosilicates. Biotite (Fe2+-rich phyllosilicate) is detected associated with the Granite Harbour granitoids, Wilson Metamorphic Complex and Melbourne Volcanics. Mg-rich phyllosilicates are mostly mapped in the scree, glacial drift, moraine and crevasse fields derived from weathering and decomposition of the Kirkpatrick Basalt and Ferrar Dolerite. Chlorite (Mg-rich phyllosilicate) was generally mapped in the exposures of Granite Harbour granodiorite and granite and partially identified in the Ferrar Dolerite, the Kirkpatrick Basalt, the Priestley Formation and Priestley Schist and the scree, glacial drift and moraine. Statistical results indicate that Al-rich phyllosilicates class pixels are strongly discriminated, while the pixels attributed to Fe3+-rich class, Fe2+-rich and Mg-rich phyllosilicates classes contain some spectral mixing due to their subtle spectral differences in the VNIR+SWIR bands of ASTER. Results derived from TIR bands of ASTER show that a high level of confusion is associated with mafic phyllosilicates pixels (Fe3+-rich, Fe2+-rich and Mg-rich classes), whereas felsic phyllosilicates (Al-rich class) pixels are well mapped. Ground truth with detailed geological data, petrographic study and X-ray diffraction (XRD) analysis verified the remote sensing results. Consequently, ASTER image-map of phyllosilicate minerals is generated for the Mesa Range, Campbell and Priestley Glaciers, northern Victoria Land of Antarctica.

International audience; The soil mineralogical constitution directly influences its chemical, physical and hydraulic characteristics. Although very important, it is still rarely used for decision-making in agriculture, mainly due to the complexity and cost of standard analyzes. In this sense, the middle infrared spectroscopy (mid-IR, 4000 to 400 cm(-1)) has great potential to obtain soil mineralogical information quickly and accurately. Nevertheless, some soil constituents can severely influence the spectra and produce misinterpretations. In this research, we aim to detect changes in the mid-IR spectra caused by water, iron forms and organic matter (OM), and to relate soil attributes to laboratory spectra and remote sensing simulated spectral bands. The research area is located in Sao Paulo State, Brazil, where seventeen soil samples were collected. The reflectance intensities, shapes and absorption features of the mid-IR spectra before and after the removal of OM and iron forms and the addition of water were described. Soil attributes, such as kaolinite, gibbsite, 2:1 minerals among others were correlated with the mid-IR spectra and simulated ASTER spectral bands by Pearson's analysis, to verify its potential on mineralogical evaluation. The description of mid-IR revealed that the removal of the OM from the soil samples decreased the reflectance intensities between 4000 and 2000 cm(-1). Iron forms mainly influence the 3250 - 1200 cm(-1) spectral range and mask the spectral features of other minerals as well. The addition of water masked several absorption features and decreased the reflectance intensities from 3700 to 2700 cm(-1). High correlation coefficients were obtained between soil attributes and ASTER simulated spectral bands, which allowed the selection of potential spectral regions for future satellite sensors: 2760 - 2500 cm(-1) (3600 - 4000 nm), 2150 - 1875 cm(-1) (4600 - 5300 nm), and 840 - 740 cm(-1) (11900 - 3500 nm).

The increase in urbanization of cities coupled with some effects of climate change is leading to the emergence of urban microclimates. The rising temperatures in cities create the phenomenon known as Urban Heat Island (UHI). This is a difference between the temperature of urban and rural areas that intensifies more during the night and varies according to the season of the year. This paper focuses on the estimation of the UHI and its variations in and around the city of Strasbourg. To this end, thermal remote sensing data from different satellite has been used to isolate and analyse this phenomenon, specifically, Land Surface Temperatures (LSTs) provided by ASTER and MODIS. The LST provided by these sensors has been compared to air temperatures of the last 15 years from meteorological stations distributed throughout the city that have served as ground truth. Forty-seven meteorological stations spread throughout the area of interest provided measurements of air humidity and temperature. This valuable data base has allowed the assessment of the correlations between LST from satellite and ground truth air temperature. Based on satellite data resampled at different spatial units, this work led to the creation of the first UHI map of Strasbourg.

On 3 June 2018, Fuego volcano experienced a VEI = 3 eruption, which produced a pyroclastic density current (PDC) that devastated the La Réunion resort and the community of Los Lotes, resulting in over 100 deaths. To evaluate the potential hazard to the population centers surrounding Fuego associated with future PDC emplacement, we used an integrated remote sensing and flow modeling-based approach. The predominate PDC travel direction over the past 15 years was investigated using thermal infrared (TIR) data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument validated with ground reports from the National Institute of Seismology, Volcanology, Meteorology, and Hydrology (INSIVUMEH), the government agency responsible for monitoring. Two different ASTER-derived digital elevation model (DEM) products with varying levels of noise were also used to assess the uncertainty in the VolcFlow model results. Our findings indicate that the recent historical PDC travel direction is dominantly toward the south and southwest. Population centers in this region of Fuego that are within ~2 km of one of the volcano’s radial barrancas are at the highest risk during future large eruptions that produce PDCs. The ASTER global DEM (GDEM) product has the least random noise and where used with the VolcFlow model, had a significant improvement on its accuracy. Results produced longer flow runout distances and therefore better conveys a more accurate perception of risk. Different PDC volumes were then modeled using the GDEM and VolcFlow to determine potential inundation areas in relation to local communities.

Land surface temperature (LST) can fully reflect the water&ndash, heat exchange cycle of the earth surface that is important for the study of environmental change. There is little research on LST in the semi-arid region of Abha-Khamis-Mushyet, which has a complex topography. The study used LST data, retrieved from ASTER data in semi-arid mountain areas and discussed its relationship with land use/land cover (LULC), topography and the normalized difference vegetation index (NDVI). The results showed that the LST was significantly influenced by altitude and corresponding LULC type. In the study area, during the summer season, extreme high-temperature zones were observed, possibly due to dense concrete surfaces. LST among different types of land use differed significantly, being the highest in exposed rocky areas and built-up land, and the lowest in dense vegetation. NDVI and LST spatial distributions showed opposite trends. The LST&ndash, NDVI feature space showed a unique ABC obtuse-angled triangle shape and showed an overall negative linear correlation. In brief, the LST could be retrieved well by the emissivity derived NDVI TES method, which relied on upwelling, downwelling, and transmittance. In addition, the LST of the semi-arid mountain areas was influenced by elevation, slope zenith angle, aspect and LULC, among which vegetation and elevation played a key role in the overall LST. This research provides a roadmap for land-use planning and environmental conservation in mountainous urban areas.

Yellowstone National Park contains many dynamic thermal areas that are the surface expression of the deeper magmatic system. Recently, we discovered the emergence of a new thermal area, located near the northeast margin of the Sour Creek dome, between Tern Lake and a previously mapped thermal area. It was about 33,300 m2 in area and located in the backcountry. It was discovered in 2018 as a spatially anomalous area of warm pixels in a Landsat 8 nighttime thermal infrared image. A high-spatial-resolution airborne visible image from August 2017 revealed a large area of recently fallen trees, mostly devoid of vegetation, and with bright soil, similar to other nearby thermal areas. Field observations in August 2019 confirmed that this was a steam-heated acid-sulfate thermal area, with an arc-shaped zone of hydrothermally altered soil and heated ground, with surface temperatures of 60-80 °C, several steaming fumaroles, and boiling temperatures (93 °C) just beneath the surface. Fallen trees in contact with warm ground were being carbonized, yet there were some cooler areas with new trees growing. Archived satellite and airborne remote sensing data going back to 1994 revealed that this thermal area started emerging around 2000. It slowly increased in size and in heat output until around 2005. 2005 to 2012 was a period of more rapid growth. From 2012 to present the growth rate slowed and stabilized, showing relatively little change. The formation of this new thermal area was not clearly linked to distinct seismic or geodetic events, though the period of rapid growth did partly coincide with a period of rapid inflation, possibly suggesting a causative relationship. The identification of this emerging thermal area illustrates the importance of satellite thermal infrared imaging combined with high-spatial-resolution remote sensing data and field observations for mapping, measuring, and monitoring Yellowstone's thermal areas. It’s also an example of the dynamics we expect to observe within Yellowstone’s hydrothermal system. Changes in the size and distribution of such thermal areas constitute normal background hydrothermal activity at large caldera systems. This new thermal area does not appear to reflect any significant changes in the broader magmatic system.

Topografik yükseklikler birçok mühendislik uygulamasında ve yerbilimlerine ilişkin araştırmalarda kullanılmaktadır. Yüksek çözünürlüklü Sayısal Yükseklik Modelleri (SYM), günümüzde yükseklik verilerini elde etmenin en pratik ve ekonomik yoludur. SYM’lerinin üretiminde farklı yöntemler uygulanır. Bu modeller çeşitli hata kaynaklarından etkilenirler. Bu nedenle, SYM verilerini kullanmadan önce çalışma alanlarındaki performanslarını test etmek çalışmada gereksinim duyulan yükseklik doğruluğunun sağlanması için önemlidir. Genel bir yaklaşım olarak, Sayısal Yükseklik Modellerinin doğruluk analizinde topografyaya uygun dağılmış kontrol noktalarında Global Navigation Satellite System (GNSS) ve/veya nivelman yükseklikleri ile modelden elde edilen yükseklik farklarının karesel ortalama hata (k.o.h.) değerleri dikkate alınır. Bu çalışmada, yüksek çözünürlüklü global SYM’leri: ASTER GDEM (Advanced Spaceborne Thermal Emission and Reflection Radiometer-Gelişmiş Uzay Kaynaklı Termal Emisyon ve Yansıma Radyometresi), SRTM (Shuttle Radar Topography Mission-Mekik Radar Topografya Misyonu) ile bölgesel HGM DTED2 modellerinin doğrulukları GNSS/nivelman verileri kullanılarak analiz edilmiştir. Bu amaçla farklı topografik özellikteki alanları temsil eden üç ayrı GNSS/nivelman veri seti kullanılarak kontrol noktalarının dağılımının yanı sıra test alanı topografyasının SYM doğruluğuna etkisi incelenmiştir. Sonuçlar Türkiye'nin kuzeybatısındaki test edilen global ve bölgesel SYM’lerinin doğruluğunu topografyanın değişen özelliklerine bağlı olarak karşılaştırmakta ve değerlendirmektedir. Testler sonucu global SYM’lerinin doğruluğu 8.0 m iken bölgesel DTED2 SYM doğruluğu 6.0 m olarak bulunmuştur., The topographical heights are required in practice for a number of engineering applications as well as their specific use in many Earth science disciplines. Using a high-resolution Digital Elevation Model (DEM) is the most practical and economical way for obtaining the height data nowadays. These models include errors. So, it is important to clarify the quality as well as the accuracy of the DEM in the study areas before using its data. In general, validating DEMs using independent point-wise data such as GNSS and leveling heights provide an overall accuracy measure in terms of root means square error (r.m.s.e.) of the DEM derived heights. In this study three high-resolution digital elevation models ASTER, SRTM and Turkey Digital Topographic Data (DTED2) in 1 and 3 resolutions are assessed using GNSS/leveling data. Using three different sets of GNSS/leveling data invalidations it is aimed to clarify the role of the distribution of the ground-control points as well as the region’s characteristics, such as roughness of topography, land-cover, etc., in the validation results. The conclusions report the accuracy of the validated DEMs in northwest Turkey and hence include a categorization of DEM performances, generated from remotely sensed data and terrestrial techniques, depending on the topographical characteristics. In the test results the accuracies for global DTMs is 8.0 m, and for regional DTED2 DEM is 6.0 m.

Aster spathulifolius, a common ornamental and medicinal plant, is widely distributed in Korea and Japan, and is genetically classified into mainland and island types. Here, we sequenced the whole chloroplast genome of mainland A. spathulifolius and compared it with those of the island type and other Aster species. The chloroplast genome of mainland A. spathulifolius is 152,732 bp with a conserved quadripartite structure, has 37.28% guanine-cytosine (GC) content, and contains 114 non-redundant genes. Comparison of the chloroplast genomes between the two A. spathulifolius lines and the other Aster species revealed that their sequences, GC contents, gene contents and orders, and exon-intron structure were well conserved, however, differences were observed in their lengths, repeat sequences, and the contraction and expansion of the inverted repeats. The variations were mostly in the single-copy regions and non-coding regions, which, together with the detected simple sequence repeats, could be used for the development of molecular markers to distinguish between these plants. All Aster species clustered into a monophyletic group, but the chloroplast genome of mainland A. spathulifolius was more similar to the other Aster species than to that of the island A. spathulifolius. The accD and ndhF genes were detected to be under positive selection within the Aster lineage compared to other related taxa. The complete chloroplast genome of mainland A. spathulifolius presented in this study will be helpful for species identification and the analysis of the genetic diversity, evolution, and phylogenetic relationships in the Aster genus and the Asteraceae.

There are a significant number of image processing methods that have been developed during the past decades for detecting anomalous areas, such as hydrothermal alteration zones, using satellite images. Among these methods, dimensionality reduction or transformation techniques are known to be a robust type of methods, which are helpful, as they reduce the extent of a study area at the initial stage of mineral exploration. Principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) are the dimensionality reduction techniques known as multivariate statistical methods that convert a set of observed and correlated input variables into uncorrelated or independent components. In this study, these techniques were comprehensively compared and integrated, to show how they could be jointly applied in remote sensing data analysis for mapping hydrothermal alteration zones associated with epithermal Cu–Au deposits in the Toroud-Chahshirin range, Central Iran. These techniques were applied on specific subsets of the advanced spaceborne thermal emission and reflection radiometer (ASTER) spectral bands for mapping gossans and hydrothermal alteration zones, such as argillic, propylitic, and phyllic zones. The fuzzy logic model was used for integrating the most rational thematic layers derived from the transformation techniques, which led to an efficient remote sensing evidential layer for mineral prospectivity mapping. The results showed that ICA was a more robust technique for generating hydrothermal alteration thematic layers, compared to the other dimensionality reduction techniques. The capabilities of this technique in separating source signals from noise led to improved enhancement of geological features, such as specific alteration zones. In this investigation, several previously unmapped prospective zones were detected using the integrated hydrothermal alteration map and most of the known hydrothermal mineral occurrences showed a high prospectivity value. Fieldwork and laboratory analysis were conducted to validate the results and to verify new prospective zones in the study area, which indicated a good consistency with the remote sensing output. This study demonstrated that the integration of remote sensing-based alteration thematic layers derived from the transformation techniques is a reliable and low-cost approach for mineral prospectivity mapping in metallogenic provinces, at the reconnaissance stage of mineral exploration.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is a 14-channel imaging instrument operating on NASA’s Terra satellite since 1999. ASTER’s visible–near infrared (VNIR) instrument, with three bands and a 15 m Instantaneous field of view (IFOV), is accompanied by an additional band using a second, backward-looking telescope. Collecting along-track stereo pairs, the geometry produces a base-to-height ratio of 0.6. In August 2019, the ASTER Science Team released Version 3 of the global DEM (GDEM) based on stereo correlation of 1.8 million ASTER scenes. The DEM has 1 arc-second latitude and longitude postings (~30 m) and employed cloud masking to avoid cloud-contaminated pixels. Custom software was developed to reduce or eliminate artifacts found in earlier GDEM versions, and to fill holes due to the masking. Each 1×1 degree GDEM tile was manually inspected to verify the completeness of the anomaly removal, which was generally excellent except across some large ice sheets. The GDEM covers all of the Earth’s land surface from 83 degrees north to 83 degrees south latitude. This is a unique, global high spatial resolution digital elevation dataset available to all users at no cost. In addition, a second unique dataset was produced and released. The raster-based ASTER Global Water Body Dataset (ASTWBD) identifies the presence of permanent water bodies, and marks them as ocean, lake, or river. An accompanying DEM file indicates the elevation for each water pixel. To date, over 100 million 1×1 degree GDEM tiles have been distributed.

Land surface temperature (LST) is a vital physical parameter of earth surface system. Estimating high-resolution LST precisely is essential to understand heat change processes in urban environments. Existing LST products with coarse spatial resolution retrieved from satellite-based thermal infrared imagery have limited use in the detailed study of surface energy balance, evapotranspiration, and climatic change at the urban spatial scale. Downscaling LST is a practicable approach to obtain high accuracy and high-resolution LST. In this study, a machine learning-based geostatistical downscaling method (RFATPK) is proposed for downscaling LST which integrates the advantages of random forests and area-to-point Kriging methods. The RFATPK was performed to downscale the 90 m Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LST 10 m over two representative areas in Guangzhou, China. The 10 m multi-type independent variables derived from the Sentinel-2A imagery on 1 November 2017, were incorporated into the RFATPK, which considered the nonlinear relationship between LST and independent variables and the scale effect of the regression residual LST. The downscaled results were further compared with the results obtained from the normalized difference vegetation index (NDVI) based thermal sharpening method (TsHARP). The experimental results showed that the RFATPK produced 10 m LST with higher accuracy than the TsHARP; the TsHARP showed poor performance when downscaling LST in the built-up and water regions because NDVI is a poor indicator for impervious surfaces and water bodies; the RFATPK captured LST difference over different land coverage patterns and produced the spatial details of downscaled LST on heterogeneous regions. More accurate LST data has wide applications in meteorological, hydrological, and ecological research and urban heat island monitoring.

Validation of emissivity (&epsilon, ) retrievals from spaceborne thermal infrared (TIR) sensors typically requires spatial extrapolations over several orders of magnitude for a comparison between centimeter-scale laboratory &epsilon, measurements and the common decameter and lower resolution of spaceborne TIR data. In the case of NASA&rsquo, s Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) temperature and &epsilon, separation algorithm (TES), this extrapolation becomes especially challenging because TES was originally designed for the geologic surface of Earth, which is typically heterogeneous even at centimeter and decameter scales. Here, we used the airborne TIR hyperspectral Mako sensor with its 2.2 m/pixel resolution, to bridge this scaling issue and robustly link between ASTER TES 90 m/pixel emissivity retrievals and laboratory &epsilon, measurements from the Algodones dune field in southern California, USA. The experimental setup included: (i) Laboratory XRD, grain size, and TIR spectral measurements, (ii) radiosonde launches at the time of the two Mako overpasses for atmospheric corrections, (iii) ground-based thermal measurements for calibration, and (iv) analyses of ASTER day and night &epsilon, retrievals from 21 different acquisitions. We show that while cavity radiation leads to a 2% to 4% decrease in the effective emissivity contrast of fully resolved scene elements (e.g., slipface slopes and interdune flats), spectral variability of the site when imaged at 90 m/pixel is below 1%, because at this scale the dune field becomes an effectively homogeneous mixture of the different dune elements. We also found that adsorption of atmospheric moisture to grain surfaces during the predawn hours increased the effective &epsilon, of the dune surface by up to 0.04. The accuracy of ASTER&rsquo, s daytime emissivity retrievals using each of the three available atmospheric correction protocols was better than 0.01 and within the target performance of ASTER&rsquo, s standard emissivity product. Nighttime emissivity retrievals had lower precision (&lt, 0.03) likely due to residual atmospheric effects. The water vapor scaling (WVS) atmospheric correction protocol was required to obtain accurate (&lt, 0.01) nighttime ASTER emissivity retrievals.

Permafrost is degrading under current warming conditions, disrupting infrastructure, releasing carbon from soils, and altering seasonal water availability. Therefore, it is important to quantitatively map the change in the extent and depth of permafrost. We used satellite images of land-surface temperature to recognize and map the zero curtain, i.e., the isothermal period of ground temperature during seasonal freeze and thaw, as a precursor for delineating permafrost boundaries from remotely sensed thermal-infrared data. The phase transition of moisture in the ground allows the zero curtain to occur when near-surface soil moisture thaws or freezes, and also when ice-rich permafrost thaws or freezes. We propose that mapping the zero curtain is a precursor to mapping permafrost at shallow depths. We used ASTER and a MODIS-Aqua daily afternoon land-surface temperature (LST) timeseries to recognize the zero curtain at the 1-km scale as a "proof of concept. " Our regional mapping of the zero curtain over an area around the 7000 m high volcano Ojos del Salado in Chile suggests that the zero curtain can be mapped over arid regions of the world. It also indicates that surface heterogeneity, snow cover, and cloud cover can hinder the effectiveness of our approach. To be of practical use in many areas, it may be helpful to reduce the topographic and compositional heterogeneity in order to increase the LST accuracy. The necessary finer spatial resolution to reduce these problems is provided by ASTER (90 m). Fil: Batbaatar, Jigjidsurengiin. University of Washington; Estados Unidos Fil: Gillespie , Alan R.. University of Washington; Estados Unidos Fil: Sletten, Ronald S.. University of Washington; Estados Unidos Fil: Mushkin , Amit. University of Washington; Estados Unidos Fil: Amit, Rivka. Geological Survey Of Israel; Israel Fil: Trombotto, Dario Tomas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Provincia de Mendoza. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales. Universidad Nacional de Cuyo. Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales; Argentina Fil: Liu , Lu. University of Washington; Estados Unidos Fil: Petrie, Gregg. University of Washington; Estados Unidos

The East Tianshan Mountain is one of the most important gold ore forming zones in northwestern China and central Asia. The Chinese GaoFen-1 (GF-1), the first Chinese high resolution satellite, is characterized by its 2-m resolution PAN data. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the well-known earth observation satellite, is advanced by its finer spectral resolution owing 9 bands in the visible and near infrared (VNIR) to the short-wave infrared (SWIR) region. In this study, we fused the GF-1 PAN and the ASTER multispectral data using the well-known Gram-Schmidt Pan Sharpening (G-S) method to produce a new data with both high spatial and spectral resolution. Then different lithological units were mapped respectively using the fusion data, the ASTER data and the WorldView-3 data by support vector machine (SVM) method. In order to assess this fusion data, a comparison work was executed among the three mapping results. The comparison work indicated that lithological classification using the new fusion data is an efficient, robust and low cost method, and it could replace the WV-3 data in some large sale geological work.

Satellite remote sensing imagery is especially useful for geological investigations in Antarctica because of its remoteness and extreme environmental conditions that constrain direct geological survey. The highest percentage of exposed rocks and soils in Antarctica occurs in Northern Victoria Land (NVL). Exposed Rocks in NVL were part of the paleo-Pacific margin of East Gondwana during the Paleozoic time. This investigation provides a satellite-based remote sensing approach for regional geological mapping in the NVL, Antarctica. Landsat-8 and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) datasets were used to extract lithological-structural and mineralogical information. Several spectral-band ratio indices were developed using Landsat-8 and ASTER bands and proposed for Antarctic environments to map spectral signatures of snow/ice, iron oxide/hydroxide minerals, Al-OH-bearing and Fe, Mg-OH and CO3 mineral zones, and quartz-rich felsic and mafic-to-ultramafic lithological units. The spectral-band ratio indices were tested and implemented to Level 1 terrain-corrected (L1T) products of Landsat-8 and ASTER datasets covering the NVL. The surface distribution of the mineral assemblages was mapped using the spectral-band ratio indices and verified by geological expeditions and laboratory analysis. Resultant image maps derived from spectral-band ratio indices that developed in this study are fairly accurate and correspond well with existing geological maps of the NVL. The spectral-band ratio indices developed in this study are especially useful for geological investigations in inaccessible locations and poorly exposed lithological units in Antarctica environments.

Son yıllarda, jeolojik özelliklerin uzaktan algılama ile belirlenmesi ve yararlılığı konusunda çok sayıda çalışma gerçekleştirilmiştir. Jeolojik birimler içindeki kaya türlerini ayırtlamak için uydu görüntüleri ve yersel spectral ölçme verileri kullanılmaktadır. Uzaktan algılama görüntülerinin kullanılması harita çalışmalarının maliyetini düşürmekte ve zaman tasarrufu sağlamaktadır. Bu çalışmada, Sivas ili Zara ve Suşehri ilçeleri arasında yer alan Kösedağ yöresinde kayaç türlerini ayırtlamak ve litolojik birimler arasındaki dokunakları belirlemek için ASTER SWIR görüntülerinin kullanılabilirliği araştırılmaktadır. Bu sahada, ofiyolitik, andezitik, bazaltik ve siyenitik kayaçlar yüzeylemektedir. Bu kayaç türlerinden alınmış temsili örneklerin spektral özellikleri/belirteçleri ASD spectroradyometre ile ölçülerek belirlenmiştir. Belirlenmiş özellikler, referans spektra ASTER SWIR bant dalga boyu aralıklarına yeniden örneklenmiştir. Spektral sınıflandırma için eşleşen filtreleme (Matched filtering) yöntemi uygulanmıştır. Sonuçlar, belirtilen kayak tiplerinin ayıtlanmasının mümkün olduğunu ve aralarındaki sınırların inceleme alanının 1/100.000 ölçekli jeolojik haritası üzerinde çizilmiş dokunaklar ile çakıştığını göstermiştir, In recent years, a large number of studies have been carried out on determining geological characteristics using remote sensing studies and their usefulness. The integrating satellite images and terrestrial spectral data are used distinguishing lithologies in geological units. The use of remote sensing images helps to save time and to reduce the cost of mapping works. In this study, the usability of ASTER SWIR images in distinguishing rock types and in determination of contacts between lithological units in Kösedağ area between Zara and Suşehri towns of Sivas Province. Syenitic, andesitic, basaltic and ophiolitics rocks are cropt out in this area. Spectral signatures of the representative samples from these rock types were measured via ASD spectroradiometer. The signatures were resampled to ASTER SWIR bandwidth as end member. Matched filtering method was performed on the images for spectral classification. The results showed that distinguishing of the mentioned rock types is possible and the boundaries between rock types on the spectral images are mostly coincided with the boundaries on the 1:100.000 scale geological map of the study area

Quantifying and modeling of stream network using DEMs is the primary objective to understand earth surface processes. In the present study, DEMs of different quality, i.e. resolution, are evaluated for stream network quantification and modeling. The results are very encouraging in terms of the shape and geometry of the stream network. They emphasize the strong control of the DEM resolution and thresholding of flow accumulation/drainage area. Further, comparisons of the various morphometric parameters are also quite promising. The study highlights different relationships between various morphometric parameters obtained from the two DEMs used, thereby paving the way for the use of DEMs of different resolutions, interchangeably.

Digital Elevation Model (DEM) is one of the important parameters for soil erosion assessment. Notable uncertainties are observed in this study while using three high resolution open source DEMs. The Revised Universal Soil Loss Equation (RUSLE) model has been applied to analysis the assessment of soil erosion uncertainty using open source DEMs (SRTM, ASTER and CARTOSAT) and their increasing grid space (pixel size) from the actual. The study area is a part of the Narmada river basin in Madhya Pradesh state, which is located in the central part of India and the area covered 20,558 km2. The actual resolution of DEMs is 30 m and their increasing grid spaces are taken as 90, 150, 210, 270 and 330 m for this study. Vertical accuracy of DEMs has been assessed using actual heights of the sample points that have been taken considering planimetric survey based map (toposheet). Elevations of DEMs are converted to the same vertical datum from WGS 84 to MSL (Mean Sea Level), before the accuracy assessment and modelling. Results indicate that the accuracy of the SRTM DEM with the RMSE of 13.31, 14.51, and 18.19 m in 30, 150 and 330 m resolution respectively, is better than the ASTER and the CARTOSAT DEMs. When the grid space of the DEMs increases, the accuracy of the elevation and calculated soil erosion decreases. This study presents a potential uncertainty introduced by open source high resolution DEMs in the accuracy of the soil erosion assessment models. The research provides an analysis of errors in selecting DEMs using the original and increased grid space for soil erosion modelling.

High-mountain Asia (HMA) constitutes the largest glacierized region outside of the Earth's polar regions. Although available observations are limited, long-term records indicate sustained HMA glacier mass loss since ~1850, with accelerated loss in recent decades. Recent satellite data capture the spatial variability of this mass loss, but spatial resolution is coarse and some estimates for regional and HMA-wide mass loss disagree. To address these issues, we generated 5,797 high-resolution digital elevation models (DEMs) from available sub-meter commercial stereo imagery (DigitalGlobe WorldView-1/2/3 and GeoEye-1) acquired over HMA glaciers from 2007 to 2018 (primarily 2013–2017). We also reprocessed 28,278 ASTER DEMs over HMA from 2000 to 2018. We combined these observations to generate robust elevation change trend maps and geodetic mass balance estimates for 99% of HMA glaciers between 2000 and 2018. We estimate total HMA glacier mass change of −19.0 ± 2.5 Gt yr−1 (−0.19 ± 0.03 m w.e. yr−1). We document the spatial pattern of HMA glacier mass change with unprecedented detail, and present aggregated estimates for HMA glacierized sub-regions and hydrologic basins. Our results offer improved estimates for the HMA contribution to global sea level rise in recent decades with total cumulative sea-level rise contribution of ~0.7 mm from exorheic basins between 2000 and 2018. We estimate that the range of excess glacier meltwater runoff due to negative glacier mass balance in each basin constitutes ~12–53% of the total basin-specific glacier meltwater runoff. These results can be used for calibration and validation of glacier mass balance models, satellite gravimetry observations, and hydrologic models needed for present and future water resource management.

The earthen border wall (Great Wall) built by the Ming is largely made of wind-blown loess. However, does the composition of this loess change along the length of the wall in response to variations in regional sediment transport pathways and impacting on the wall’s erosional durability? To date, defining these sediment transport pathways has been a challenge because of the paucity of spatially-comprehensive, compositional information. Here, we show that satellite ASTER mineral maps, combined with field sample measurements along a 1200 km section of the Ming’s earthen wall, reveal both the compositional heterogeneity of loess as well as the complexity of the sediment transport pathways of individual loess components, including: (i) quartz sand from Cretaceous sandstones in the Gobi Desert; (ii) gypsum from evaporative lakes in the Tengger Desert; (iii) kaolinite from Devonian Molasse in the Qilian Shan; and (iv) chlorite and muscovite from meta-volcanic rocks exposed across the Alashan Block. Sediment transport pathways involve a combination of colluvial, aeolian and fluvial (ephemeral and permanent) processes shaped by the topography. ASTER enabled mapping of compositional gradients related to two pathways, namely: (i) quartz sand driven by aeolian saltation in concert with the Yellow River; and (ii) clay and fine silt travelling large distances (>500 km) by long-term wind suspension. The most intact section of wall is found along the Hexi Corridor, which is poor in quartz sand and rich in (kaolinitic) clay and fine-silt, driven by wind-shielding by the Alashan Block. We also found evidence that the Ming: (i) mined loess from close by the wall (

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra platform, which was launched in 1999, has three separate subsystems: a visible and near-infrared (VNIR) radiometer, a shortwave-infrared radiometer, and a thermal-infrared radiometer. The ASTER VNIR bands have been radiometrically corrected for approximately 14 years by the sensor degradation curves estimated from the onboard calibrator according to the original calibration plan. However, this calibration by the onboard calibrator encountered a problem; specifically, it is inconsistent with the results of vicarious calibration and cross calibration. Therefore, the ASTER VNIR processing was applied by the radiometric degradation curves calculated from the results of three calibration approaches, i.e., the onboard calibrator, the vicarious calibration, and the cross calibration since February 2014. Even though the current degradation curves were revised, the inter-band and lunar calibrations show some inconsistencies owing to the different traceability in the bands by different calibration approaches. In this study, the current degradation curves and their problems are explained, and the new curves that are derived from the vicarious calibration with lunar calibration are discussed. The new degradation curves that have the same traceability in the bands will be used for future ASTER VNIR processing.

Mapping hydrothermal alteration minerals using multispectral remote sensing satellite imagery provides vital information for the exploration of porphyry and epithermal ore mineralizations. The Ahar-Arasbaran region, NW Iran, contains a variety of porphyry, skarn and epithermal ore deposits. Gold mineralization occurs in the form of epithermal veins and veinlets, which is associated with hydrothermal alteration zones. Thus, the identification of hydrothermal alteration zones is one of the key indicators for targeting new prospective zones of epithermal gold mineralization in the Ahar-Arasbaran region. In this study, Landsat Enhanced Thematic Mapper+ (Landsat-7 ETM+), Landsat-8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing datasets were processed to detect hydrothermal alteration zones associated with epithermal gold mineralization in the Ahar-Arasbaran region. Band ratio techniques and principal component analysis (PCA) were applied on Landsat-7 ETM+ and Landsat-8 data to map hydrothermal alteration zones. Advanced argillic, argillic-phyllic, propylitic and hydrous silica alteration zones were detected and discriminated by implementing band ratio, relative absorption band depth (RBD) and selective PCA to ASTER data. Subsequently, the Bayesian network classifier was used to synthesize the thematic layers of hydrothermal alteration zones. A mineral potential map was generated by the Bayesian network classifier, which shows several new prospective zones of epithermal gold mineralization in the Ahar-Arasbaran region. Besides, comprehensive field surveying and laboratory analysis were conducted to verify the remote sensing results and mineral potential map produced by the Bayesian network classifier. A good rate of agreement with field and laboratory data is achieved for remote sensing results and consequential mineral potential map. It is recommended that the Bayesian network classifier can be broadly used as a valuable model for fusing multi-sensor remote sensing results to generate mineral potential map for reconnaissance stages of epithermal gold exploration in the Ahar-Arasbaran region and other analogous metallogenic provinces around the world.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which is a multiband pushbroom sensor suite onboard Terra, has successfully provided valuable multiband images for approximately 20 years since Terra’s launch in 1999. Since the launch, sensitivity degradations in ASTER’s visible and near infrared (VNIR) and thermal infrared (TIR) bands have been monitored and corrected with various calibration methods. However, a unignorable discrepancy between different calibration methods has been confirmed for the VNIR bands that should be assessed with another reliable calibration method. In April 2003 and August 2017, ASTER observed the Moon (and deepspace) for conducting a radiometric calibration (called as lunar calibration), which can measure the temporal variation in the sensor sensitivity of the VNIR bands enough accurately (better than 1%). From the lunar calibration, 3–6% sensitivity degradations were confirmed in the VNIR bands from 2003 to 2017. Since the measured degradations from the other methods showed different trends from the lunar calibration, the lunar calibration suggests a further improvement is needed for the VNIR calibration. Sensitivity degradations in the TIR bands were also confirmed by monitoring the variation in the number of saturated pixels, which were qualitatively consistent with the onboard and vicarious calibrations.

Several regions in the High Arctic still lingered poorly explored for a variety of mineralization types because of harsh climate environments and remoteness. Inglefield Land is an ice-free region in northwest Greenland that contains copper-gold mineralization associated with hydrothermal alteration mineral assemblages. In this study, Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and WorldView-3 multispectral remote sensing data were used for hydrothermal alteration mapping and mineral prospecting in the Inglefield Land at regional, local, and district scales. Directed principal components analysis (DPCA) technique was applied to map iron oxide/hydroxide, Al/Fe-OH, Mg-Fe-OH minerals, silicification (Si-OH), and SiO2 mineral groups using specialized band ratios of the multispectral datasets. For extracting reference spectra directly from the Landsat-8, ASTER, and WorldView-3 (WV-3) images to generate fraction images of end-member minerals, the automated spectral hourglass (ASH) approach was implemented. Linear spectral unmixing (LSU) algorithm was thereafter used to produce a mineral map of fractional images. Furthermore, adaptive coherence estimator (ACE) algorithm was applied to visible and near-infrared and shortwave infrared (VINR + SWIR) bands of ASTER using laboratory reflectance spectra extracted from the USGS spectral library for verifying the presence of mineral spectral signatures. Results indicate that the boundaries between the Franklinian sedimentary successions and the Etah metamorphic and meta-igneous complex, the orthogneiss in the northeastern part of the Cu-Au mineralization belt adjacent to Dallas Bugt, and the southern part of the Cu-Au mineralization belt nearby Marshall Bugt show high content of iron oxides/hydroxides and Si-OH/SiO2 mineral groups, which warrant high potential for Cu-Au prospecting. A high spatial distribution of hematite/jarosite, chalcedony/opal, and chlorite/epidote/biotite were identified with the documented Cu-Au occurrences in central and southwestern sectors of the Cu-Au mineralization belt. The calculation of confusion matrix and Kappa Coefficient proved appropriate overall accuracy and good rate of agreement for alteration mineral mapping. This investigation accomplished the application of multispectral/multi-sensor satellite imagery as a valuable and economical tool for reconnaissance stages of systematic mineral exploration projects in remote and inaccessible metallogenic provinces around the world, particularly in the High Arctic regions.

The ASTER Volcanic Ash Library (AVAL) is presented, developed using quantitative laboratory thermal infrared (TIR) emission spectroscopic methods, spanning the 2000−400 cm−1 (5−25 μm wavelength) range, including the Earth’s TIR atmospheric window (8−12 μm). Each spectral suite is unique owing to the chemical composition and proportion of glass to crystals per sample and is divided into six size fractions. AVAL, used with an appropriate spectral mixture model applied to orbital multispectral TIR data, provides a unique ability to study active volcanic ash plumes. We present the first example of this application to an ash plume produced by the Sakurajima Volcano in Japan. The emissivity variations measured in ash plumes using an ever-expanding ash spectral library will provide future quantitative inputs for both atmospheric models, where the ash composition is unknown or estimated, as well as compositional probes into ongoing eruptions.

Climate change amplified by rapidly increasing urbanization is resulting in rising temperatures within urban environments. In recent years, to mitigate this the design and construction of new buildings has emphasized &ldquo, smart&rdquo, methods and materials for individual buildings rather than landscape-level planning and evaluation of new developments. Remote Sensing potentially offers a cost-effective means to monitor effectiveness of landscape-level urban design and guide developers to improve thermal regimes. This paper focuses on satellite monitoring of thermal variation in the area of London most affected by construction in 2010&ndash, 2015. Split-window Land Surface Temperature (LST) models were applied to ASTER and Landsat 8 satellite imagery, requiring separate investigation of thermal trends due to temporal inconsistency. Getis-Ord-Gi* cluster analysis of the ASTER image identified three main thermal hot spots: Eastern, Stratford railway/underground station, and Stratford High Street. It is assumed that increased thermal stress within these areas is mainly from anthropogenic heat. However, local thermal variations for Eastern Olympic Village are attributed to changing meteorological conditions, facade materials, canyon morphology and orientation, or insufficient shading and ventilation. Future development of a new cultural hub at this location will significantly affect distribution of green spaces and influence their cooling ability.

Identifying hydrothermal zoning pattern associated with porphyry copper deposit is important for indicating its economic potential. Traditional approaches like systematic sampling and conventional geological mapping are time-consuming and labor extensive, and with limitations for providing small scale information. Recent developments suggest that remote sensing is a powerful tool for mapping and interpreting the spatial pattern of porphyry Cu deposit. In this study, we integrated in situ spectral measurement taken at the Yudai copper deposit in the Kalatag district, northwestern China, information obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), as well as the spectra of samples (hand-specimen) measured using an Analytical Spectral Device (ASD) FieldSpec4 high-resolution spectrometer in laboratory, to map the hydrothermal zoning pattern of the copper deposit. Results proved that the common statistical approaches, such as relative band depth and Principle Component Analysis (PCA), were unable to identify the pattern accurately. To address the difficulty, we introduced a curve-fitting technique for ASTER shortwave infrared data to simulate Al(OH)-bearing, Fe/Mg(OH)-bearing, and carbonate minerals absorption features, respectively. The results indicate that the absorption feature parameters can effectively locate the ore body inside the research region, suggesting the absorption feature parameters have great potentials to delineate hydrothermal zoning pattern of porphyry Cu deposit. We foresee the method being widely used in the future.

We used remote sensing data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite to identify the mineral properties and geothermal anomalies related to hot springs in the Tuzla area, including the fault system with NW-SE trend, which is located southwest of Çanakkale, NW Turkey. In the study area, the lithological units of the Tuzla geothermal field and the surrounding area consist of Miocene volcanic (trachyandesite, trachyte, and ignimbrites) and Pliocene sedimentary (conglomerate, sandstone, and mudstone) rocks with siliceous, argillaceous, and ferrous alteration linked to the geothermal fluid. ASTER visible/near-infrared (VNIR), short-wave infrared (SWIR), and TIR bands were analyzed by different approaches in order to highlight hot springs in the study area. From these approaches, band ratios were constructed from ASTER VNIR, SWIR, and TIR bands for obtaining geological properties of the region. The geothermal areas were defined by the minimum noise fraction (MNF) and principal component analysis (PCA) methods that was extracted from 5 thermal infrared (TIR) bands as well. Land surface temperatures (LST) support the results from MNF and PCA that were estimated for 5 TIR bands using the inversion of Planck function method. Four days of data including daytime and nighttime satellite images from ASTER were used for the analysis. The used procedure displayed a good match with the ground reality based on field observations in the Tuzla Region. © 2019, Saudi Society for Geosciences.

The Advanced Spaceborne Thermal Emission and Reflection Radiometer is one of five instruments operating on the National Aeronautics and Space Administration (NASA) Terra platform. Launched in 1999, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been acquiring optical data for 20 years. ASTER is a joint project between Japan’s Ministry of Economy, Trade and Industry; and U.S. National Aeronautics and Space Administration. Numerous reports of geologic mapping and mineral exploration applications of ASTER data attest to the unique capabilities of the instrument. Until 2000, Landsat was the instrument of choice to provide surface composition information. Its scanners had two broadband short wave infrared (SWIR) bands and a single thermal infrared band. A single SWIR band amalgamated all diagnostic absorption features in the 2−2.5 micron wavelength region into a single band, providing no information on mineral composition. Clays, carbonates, and sulfates could only be detected as a single group. The single thermal infrared (TIR) band provided no information on silicate composition (felsic vs. mafic igneous rocks; quartz content of sedimentary rocks). Since 2000, all of these mineralogical distinctions, and more, could be accomplished due to ASTER’s unique, high spatial resolution multispectral bands: six in the SWIR and five in the TIR. The data have sufficient information to provide good results using the simplest techniques, like band ratios, or more sophisticated analyses, like machine learning. A robust archive of images facilitated use of the data for global exploration and mapping.

Listvenites normally form during hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represent a key indicator for the occurrence of ore mineralizations in orogenic systems. Hydrothermal/metasomatic alteration mineral assemblages are one of the significant indicators for ore mineralizations in the damage zones of major tectonic boundaries, which can be detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data were used to detect listvenite occurrences and alteration mineral assemblages in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL), Antarctica. Spectral information for detecting alteration mineral assemblages and listvenites were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineralogical assemblages containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were detected in the damage zones of the study area by implementing PCA/ICA fusion to visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate lithological groups were mapped and discriminated using PCA/ICA fusion to thermal infrared (TIR) bands of ASTER. Fraction images of prospective alteration minerals, including goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and possible zones encompassing listvenite occurrences were produced using LSU and CEM algorithms to ASTER VNIR+SWIR spectral bands. Several potential zones for listvenite occurrences were identified, typically in association with mafic metavolcanic rocks (Glasgow Volcanics) in the Bowers Mountains. Comparison of the remote sensing results with geological investigations in the study area demonstrate invaluable implications of the remote sensing approach for mapping poorly exposed lithological units, detecting possible zones of listvenite occurrences and discriminating subpixel abundance of alteration mineral assemblages in the damage zones of the Wilson-Bowers and Bowers-Robertson Bay terrane boundaries and in intra-Bowers and Wilson terranes fault zones with high fluid flow. The satellite remote sensing approach developed in this research is explicitly pertinent to detecting key alteration mineral indicators for prospecting hydrothermal/metasomatic ore minerals in remote and inaccessible zones situated in other orogenic systems around the world.

This work presents development of an algorithm to reduce the spatial uncertainty of active fire locations within the 1 km MODerate resolution Imaging Spectroradiometer (MODIS Aqua and Terra) daytime detection footprint. The algorithm is developed using the finer 500 m reflective bands by leveraging on the increase in 2.13 &mu, m shortwave infrared reflectance due to the burning components as compared to the non-burning neighborhood components. Active fire presence probability class for each of the 500 m pixels within the 1 km footprint is assigned by locally adaptive contextual tests against its surrounding neighborhood pixels. Accuracy is assessed using gas flares and wildfires in conjunction with available high-resolution imagery. Proof of concept results using MODIS observations over two sites show that under clear sky conditions, over 84% of the 500 m locations that had active fires were correctly assigned to high to medium probabilities, and correspondingly low to poor probabilities were assigned to locations with no visible flaming fronts. Factors limiting the algorithm performance include fire size/temperature distributions, cloud and smoke obscuration, sensor point spread functions, and geolocation errors. Despite these limitations, the resulting finer spatial scale of active fire detections will not only help first responders and managers to locate actively burning fire fronts more precisely but will also be useful for the fire science community.

© 2019 by the authors. Polymetallic vein-type ores are important sources of precious metal and a principal type of orebody for various base-metals. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping hydrothermal alteration zones associated with epithermal polymetallic vein-type mineralization in the Toroud-Chahshirin Magmatic Belt (TCMB), North of Iran. The TCMB is the largest known goldfield and base metals province in the central-north of Iran. Propylitic, phyllic, argillic, and advanced argillic alteration and silicification zones are typically associated with Au-Cu, Ag, and/or Pb-Zn mineralization in the TCMB. Specialized image processing techniques, namely Selective Principal Component Analysis (SPCA), Band Ratio Matrix Transformation (BRMT), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) were implemented and compared to map hydrothermal alteration minerals at the pixel and sub-pixel levels. Subtle differences between altered and non-altered rocks and hydrothermal alteration mineral assemblages were detected and mapped in the study area. The SPCA and BRMT spectral transformation algorithms discriminated the propylitic, phyllic, argillic and advanced argillic alteration and silicification zones as well as lithological units. The SAM and MTMF spectral mapping algorithms detected spectrally dominated mineral groups such as muscovite/montmorillonite/illite, hematite/jarosite, and chlorite/epidote/calcite mineral assemblages, systematically. Comprehensive fieldwork and laboratory analysis, including X-ray diffraction (XRD), petrographic study, and spectroscopy were conducted in the study area for verifying the remote sensing outputs. Results indicate several high potential zones of epithermal polymetallic vein-type mineralization in the northeastern and southwestern parts of the study area, which can be considered for future systematic exploration programs. The approach used in this research has great implications for the exploration of epithermal polymetallic vein-type mineralization in other base metals provinces in Iran and semi-arid regions around the world.

The Pulang porphyry copper deposit (PCD), one of the main potential areas for copper resource exploration in China, exhibits typical porphyry alteration zoning. However, further investigation of the indicative significance of alteration minerals, additional insight into metallogenic characteristics, and prospecting guidelines continue to be challenging. In this study, ASTER and WorldView-3 data were used to map hydrothermal alteration minerals by employing band ratios, principal component analysis, and spectrum-area techniques; and subsequently, the indication significance of alteration minerals was studied in-depth. The following new insights into the metallogenic structure and spatial distribution of alteration zoning in Pulang PCD were obtained and verified. (1) A new NE trending normal fault, passing through the northeast of Pulang PCD, was discovered. (2) Two mineralization alteration centers, exhibiting alteration zoning characteristics of potassic-silicified, phyllic, and propylitic zones from the inside to the outside, were observed on both sides of the fault. (3) At the junction of the redivided potassic-silicification and phyllic zones, favorable prospecting potential areas were delineated. This study shows that the spectral/multi-sensor satellite data are valuable and cost-effective tools for the preliminary stages of porphyry copper exploration in inaccessible and remote areas around the world.

[EN] Climate change undoubtedly will affect snow events as temperature and precipitation are expected to change in the future. Spanish mountains are especially affected by that situation, since snow storage is there focussed on very specific periods of the hydrological year and plays a very important role in the management of water resources. In this study, an analysis of the behaviour of the complex snow-related phenomena in the four main mountain regions of Spain in the next 50 years is conducted. The ASTER hydrological model is applied using temperature and precipitation data as basic input, estimated under a climate change scenario. Results show different changes in the maximum and average expected flows, depending on the very different magnitude and sign of changes in precipitation. An increase of flooding episodes may occur as a result of a complex relation between changes in precipitation and an increase in maximum snowmelt intensities that range from 2.1% in the Pyrenees to 7.4% in the Cantabrian Mountains. However, common patterns are shown in a shorter duration of the snow bulk reserves, expected to occur 45 days earlier for the Cantabrian Mountains, and about 30 days for the rest of the studied mountain regions. Changes observed also lead to a concerning decrease in the regulatory effect of the snow-related phenomena in the Spanish rivers, with a decrease in the average snow accumulation that ranges from about 28% for the Pyrenees and Sierra Nevada to 42% for the Central System and the Cantabrian Mountains. A decrease in average flow is expected, fluctuating from 2.4% in the Pyrenees to 7.3% in Cantabrian Mountains, only increasing in the Central System by 4.0%, making all necessary to develop new adaptation measures to climate change., The authors acknowledge F. J. Sanchez, M. Aparicio and F. Pastor (Spanish Ministry for Ecological Transition and the Demographic Challenge), Tragsatec and ASTER model developer J. A. Collado (SPESA Ingenieria). The authors fully acknowledge the financial support provided by the Department of Geological and Geotechnical Engineering of the UPV.

Remote sensing streams continuous data feed from the satellite to ground station for data analysis. Often the data analytics involves analyzing data in real-time, such as emergency control, surveillance of military operations or scenarios that change rapidly. Traditional data mining requires all the data to be available prior to inducing a model by supervised learning, for automatic image recognition or classification. Any new update on the data prompts the model to be built again by loading in all the previous and new data. Therefore, the training time will increase indefinitely making it unsuitable for real-time application in remote sensing. As a contribution to solving this problem, a new approach of data analytics for remote sensing for data stream mining is formulated and reported in this paper. Fresh data feed collected from afar is used to approximate an image recognition model without reloading the history, which helps eliminate the latency in building the model again and again. In the past, data stream mining has a drawback in approximating a classification model with a sufficiently high level of accuracy. This is due to the one-pass incremental learning mechanism inherently exists in the design of the data stream mining algorithm. In order to solve this problem, a novel streamlined sensor data processing method is proposed called evolutionary expand-and-contract instance-based learning algorithm (EEAC-IBL). The multivariate data stream is first expanded into many subspaces, and then the subspaces, which are corresponding to the characteristics of the features are selected and condensed into a significant feature subset. The selection operates stochastically instead of deterministically by evolutionary optimization, which approximates the best subgroup. Followed by data stream mining, the model learning for image recognition is done on the fly. This stochastic approximation method is fast and accurate, offering an alternative to the traditional machine learning method for image recognition application in remote sensing. Our experimental results show computing advantages over other classical approaches, with a mean accuracy improvement at 16.62%.

This work seeks to implement surface indicators of porphyry copper deposits (PCDs) at known source regions and to apply these indicators to recognize high- and low-potential mineralization through remote sensing in other areas. Thirty copper deposits in central Iranian volcano-sedimentary complex, Kerman province, Southeast of Iran, which are different in grade and size, were selected as test sites. The abundances of alteration minerals at these deposits were discriminated using a partial sub-pixel unmixing algorithm, mixture tuned matched filtering (MTMF), on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to find​ an indicator whether the abundances of alteration minerals correspond to the grade and size of each deposit. In general, comparison of sub-pixel abundances with known mineral occurrences showed a reasonable correspondence such that areas with high abundances of alterations corresponded well with important mineralized districts. It is concluded that suggested sub-pixel analysis of ASTER data leads to identifying alteration zones with high-potential mineralization in PCDs.

In Open sources DEMs such as SRTM, ASTER and Cartosat-1, various factors affecting the accuracy of satellite based DEM such as errors during data collection, systematic errors and unknown errors that are geographically dependent on terrain conditions cannot be avoided. For these reasons it is very necessary to check and compare the performances and validation of the above mentioned different satellite based DEMs. Accuracy assessment of these DEM has been done using DGPS points. For these points proper interpolation of the surface was developed using different interpolation techniques. For the generation of the surface the first step was converting the satellite based DEMs height into linear interpolation contour maps of 1 m interval. Then came selecting random sample points on the contour line and generating the interpolated surface using different interpolation techniques such as IDW, GPI, RBF, OK and UK, LPI, TR and BI, which are commonly used in geomorphology research. This interpolated surface helps in proper representation of the terrain and was checked under different terrain surfaces. For validation of DGPS points the height was taken for ground control points and standard statistical tests such as ME and RMSE were applied. From above investigation, it is reveals that above mention DEMs which are used for study. Cartosat-1 (30 m) data product is better than SRTM (90 m) and ASTER (30 m) because it had produced low RMSE of 3.49 m without applying the interpolation method. Investigation also reveals after applying the interpolation techniques on this data error can be reduced. In the case of Cartosat-1 and SRTM, low RMSE and ME were produced by the BI method, where Cartosat-1 DEM had an RMSE of 3.36 m with ME of −2.74 m, respectively. But in this case, RMSE and ME of SRTM is 2.73 m and −0.36 m, respectively. BI is designed for image processing and can be used for imagery were a maximum height variation in satellite DEM and terrain height is minimum. But in the case of ASTER DEM, the GPI method with a high polynomial order of 9 had produced a low RMSE of 4.99 m. The GPI method can be applied where maximum height variation in satellite DEM and in terrain is more.

WOS: 000400660300007, The objective of the study was to carry out an automatic classification of the lithological units of interest using the integration of remote sensing image, in which various objects are spread on, and terrestrial spectral measurement data. Only endmembers of interest are classified using spectral classification methods such as Spectral Angle Mapper. Following the identification of the types of rock and minerals, integration of remote sensing images and spectral measurement data enable spectral classification. In this study, Short Wave Infrared detector images of Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite and spectroradiometer measurements were used. The study area, Golova with its geological diversity is located in the Kelkit Valley section of the North Anatolian Fault Zone in Northeast of Turkey. Seventeen rock samples were collected and their coordinates were recorded. The samples were categorized via spectral measurements on their thin sections through petrographic analyses. Marble and Meta lava with different lithological were selected as endmembers. SAM was used as the classification method that enables the analysis of the endmember with the threshold value of 0.009 radian for marble and 0.010 radian for metalava. SAM analysis was compared by visual analysis to principle component analysis, decorrelation stretch, band ratio (R: 4/7, G: 4/1, B (2/3) x (4/3)) and band combination analysis (R: 9, G: 4 and B: 5). This study demonstrates that the SAM method can be successfully used in both the integration of remote sensing image and terrestrial spectral measurement data in lithological classification. Both the endmembers of metalava and marbles were detected in the SAM results at the GPS coordinates noted down whilst collecting the rock samples for accuracy assessment., CUBAP [M-371, M-523], We extend our gratitude to Assoc. Prof. Dr. Ziyadin Cakir, Assoc. Prof. Dr. Gursel SUNAL and Assoc. Prof. Dr. Gultekin TOPUZ for their efforts in thin sectioning and petrographic analysis of the rock samples used in this study. We also extend our gratitude CUBAP that provided the M-371 and M-523 numbered projects and data.