Journal articles: 'Landsat and ASTER Data'

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Kumar, Sujit, and Tapasi Bhandary. "Comparative Study of Landsat and Aster Data by Morphometric Analysis." Civil Engineering Journal 1, no. 2 (December 1, 2015): 21–25. http://dx.doi.org/10.28991/cej-2015-00000007.

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In this paper, a case-study is presented to differentiate between Landsat and Aster data by morphometric analysis. For this the Aster and Landsat digital elevation model (DEM) data of the same study area was taken and then both the data was delineated for the same (common) outlet. The major differences found in between Landsat and Aster data after delineation are in the number of first order stream, axial length of streams, average width and size of watershed. The case study presented will be useful in demonstrating the fact that Landsat DEM has better accuracy than Aster DEM for land cover areas when the DEM data characteristic are kept similar.

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Ourhzif, Z., A. Algouti, A. Algouti, and F. Hadach. "LITHOLOGICAL MAPPING USING LANDSAT 8 OLI AND ASTER MULTISPECTRAL DATA IN IMINI-OUNILLA DISTRICT SOUTH HIGH ATLAS OF MARRAKECH." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1255–62. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1255-2019.

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Abstract. This study exploited the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat 8 Operational Land Imager (OLI) data in order to map lithological units and structural map in the south High Atlas of Marrakech. The method of analysis was used by principal component analysis (PCA), band ratios (BR), Minimum noise fraction (MNF) transformation. We performed a Support Vector Machine (SVM) classification method to allow the joint use of geomorphic features, textures and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. SVM based on ground truth in addition to the results of PCA and BR show an excellent correlation with the existing geological map of the study area. Consequently, the methodology proposed demonstrates a high potential of ASTER and Landsat 8 OLI data in lithological units discrimination. The application of the SVM methods on ASTER and Landsat satellite data show that these can be used as a powerful tool to explore and improve lithological mapping in mountainous semi-arid, the overall classification accuracy of Landsat8 OLI data is 97.28% and the Kappa Coefficient is 0.97. The overall classification accuracy of ASTER using nine bands (VNIR-SWIR) is 74.88% and the Kappa Coefficient is 0.71.

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Van Niekerk, Elna. "Visual interpretation of ASTER satellite data, Part II: Land use mapping in Mpumalanga,South Africa." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 26, no. 4 (September 22, 2007): 247–64. http://dx.doi.org/10.4102/satnt.v26i4.137.

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Since the initiation in 1960 of the era of satellite remote sensing to detect the different characteristics of the earth, a powerful tool was created to aid researchers. Many land-use studies were undertaken using Landsat MSS, Landsat TM and ETM, as well as SPOT satellite data. The application of these data to the mapping of land use and land cover at smaller scales was constrained by the limited spectral and/or spatial resolution of the data provided by these satellite sensors. In view of the relatively high cost of SPOT data, and uncertainty regarding the future continuation of the Landsat series, alternative data sources need to be investigated. In the absence of published previous research on this issue in South Africa, the purpose of this article is to investigate the value of visual interpretation of ASTER satellite images for the identification and mapping of land-use in an area in South Africa. The study area is situated in Mpumalanga, in the area of Witbank, around the Witbank and Doorndraai dams. This area is characterised by a variety of urban, rural and industrial land uses. Digital image processing of one Landsat 5 TM, one Landsat 7 ETM and one ASTER satellite image was undertaken, including atmospheric correction and georeferencing, natural colour composites, photo infrared colour composites (or false colour satellite images), band ratios, Normalised Difference Indices, as well as the Brightness, Greenness and Wetness Indices. The efficacy with which land use could be identified through the visual interpretation of the processed Landsat 5 TM, Landsat 7 TM and ASTER satellite images was compared. The published 1:50 000 topographical maps of the area were used for the purpose of initial verification. Findings of the visual interpretation process were verified by field visits to the study area. The study found that the ASTER satellite data produced clearer results and therefore have a higher mapping ability and capacity than the Landsat satellite data. Hence, it is anticipated that the use of the full range of the spectral resolution of the ASTER satellite data – which were not available for this study – in statistical pattern recognition and classification methods will enhance the value of the process. Statistical methods are often used to produce visual information which could be applied to prepare land-use change inventories. This should be addressed in future research projects. Should the Landsat programme be terminated, ASTER satellite data might provide the best alternative for a variety of research projects, but if the Landsat project is continued, the ASTER satellite data could be used very effectively in conjunction with the Landsat satellite data. Since it is foreseen that the ASTER satellite data will be available for at least the next 12 to 15 years, it will continue to provide exciting possibilities for the development of programmes to monitor land-use and land-use change. This could then be used by all three levels of government to reach their goals in terms of agricultural planning, town and regional planning and environmental management. These requirements are described in the Integrated Development Programmes (IDP) of the different local governments.

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Beiranvand Pour, A., M. Hashim, and M. Pournamdari. "CHROMITITE PROSPECTING USING LANDSAT TM AND ASTER REMOTE SENSING DATA." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2/W2 (October 19, 2015): 99–103. http://dx.doi.org/10.5194/isprsannals-ii-2-w2-99-2015.

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Studying the ophiolite complexes using multispectral remote sensing satellite data are interesting because of high diversity of minerals and the source of podiform chromitites. This research developed an approach to discriminate lithological units and detecting host rock of chromitite bodies within ophiolitic complexes using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat Thematic Mapper (TM) satellite data. Three main ophiolite complexes located in south of Iran have been selected for the study. Spectral transform techniques, including minimum noise fraction (MNF) and specialized band ratio were employed to detect different rock units and the identification of high-potential areas of chromite ore deposits within ophiolitic complexes. A specialized band ratio (4/1, 4/5, 4/7) of ASTER, MNF components and Spectral Angle Mapper (SAM) on ASTER and Landsat TM data were used to distinguish ophiolitic rock units. Results show that the specialized band ratio was able to identify different rock units and serpentinized dunite as host rock of chromitites within ophiolitic complexes, appropriately. MNF components of ASTER and Landsat TM data were suitable to distinguish ophiolitic rock complexes at a regional scale. The integration of SAM and Feature Level Fusion (FLF) used in this investigation discriminated the ophiolitic rock units and prepared detailed geological map for the study area. Accordingly, high potential areas (serpentinite dunite) were identified in the study area for chromite exploration targets.The approach used in this research offers the image processing techniques as a robust, reliable, fast and cost-effective method for detecting serpentinized dunite as host rock of chromitite bodies within vast ophiolite complexes using ASTER and Landsat TM satellite data.

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Anul Haq, M. "Comparative Analysis of Hyperspectral and Multispectral Data for Mapping Snow Cover and Snow Grain Size." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 499–504. http://dx.doi.org/10.5194/isprsarchives-xl-8-499-2014.

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The present study demonstrates the potential of imaging spectroscopy to produce the snow cover maps and estimation of snow grain size in the Himalayan region. Snow cover maps and snow grain size produce from imaging spectroscopy data were also compared with multispectral imagery (i.e. Landsat 8 and ASTER). Snow grain size was estimated using the snow grain index and compared with the asymptotic radiative transfer (ART) theory method. The overall matching area was 78.29 % among different snow grain size classes using grain index Method and ART method. An attempt has been made to derive the snow grain size using Landsat 8 and ASTER data for the same area. It was found that grain size derived from Landsat 8 and ASTER data show correlation of 81.67 % and 86.34 % respectively. The snow cover maps were produced using Normalized Difference Snow Index (NDSI). Snow cover maps were also produced using ASTER imagery for the same area and compared with Hyperion snow cover maps. The correlation between both snow cover maps were show 91 % correlation.

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Van Niekerk, Elna, and Luke Sandham. "Visual interpretation of ASTER satellite data, Part 1: Geologic mapping in the Cradle of Humankind World Heritage Site." Suid-Afrikaanse Tydskrif vir Natuurwetenskap en Tegnologie 26, no. 3 (September 21, 2007): 177–95. http://dx.doi.org/10.4102/satnt.v26i3.132.

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Since the first earth observing satellite was launched in 1972, remote sensing has become a powerful tool in the arsenal of geoscientists. This satellite became known as Landsat 1 and carried the Multispectral Scanner (MSS) delivering imagery at a spatial resolution of 80, and spectral resolution from blue to near infrared. Ongoing satellite and sensor development to the end of the century produced the Landsat Thematic Mapper (TM) with improved spatial and spectral resolution, as well as the SPOT series of satellites delivering the highest spatial but limited spectral resolution. These developments culminated in the SPOT 4 (1998) and Landsat Enhanced Thematic Mapper (1999) sensors. While Landsat ETM in particular provided much improved spatial and spectral resolutions, on the basis of which a large amount of geoscientific remote sensing was conducted world wide, the data did not provide adequate spectral and spatial sensitivity to be optimally effective for geological mapping at the local scale. On 18 December 1999 the Terra platform was launched, carrying five remote sensing instruments, including ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer). ASTER consists of three separate instrument subsystems, each operating in a different spectral region, and using separate optical systems. These are the Visible and Very Near Infrared (VNIR) subsystem with a 15m-spatial resolution, the Short Wave Infrared (SWIR) subsystem with a 30m-spatial resolution and the Thermal Infrared (TIR) subsystem with a 90m-spatial resolution. ASTER effectively offers an improvement on Landsat MSS, Landsat TM, Landsat ETM+ and SPOT spectral and spatial resolutions. Given the paucity of published research on geological remote sensing at the local scale in South Africa, and particularly on the use of ASTER for geological mapping in South Africa, it is imperative that the value of ASTER be investigated. This article reports on the improved detail and scale achieved in the mapping of litho-stratigraphy, geological structures and mining-related features by the visual interpretation of processed ASTER images. ASTER imagery obtained from the EOS website was subjected to a range of image enhancement and analysis techniques including colour composites, band ratios, normalised difference indices, regression and decorrelation, in order to obtain optimal visual interpretability. Eight images thus obtained could be used for visual analysis, and it became evident that litho-stratigraphy, faults, fracture zones and elements of the regional seam system, as well as remnants of mining activities, were readily identifiable. Some of these were in accordance with the most recent and accurate geological map of the area, but many of them had apparently not been mapped. These features were annotated and were verified by field checks. In all cases the accuracy of detection and location from satellite imagery was confirmed on the ground. The improved detail and accuracy obtained by visual interpretation of processed ASTER satellite data for mapping a section of the Cradle of Humankind World Heritage Site demonstrated the potential value of this data for a variety of other geoscience applications. It appears that the improved accuracy can be ascribed jointly to the higher spatial and spectral resolution provided by ASTER data.

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Safari, M., A. B. Pour, A. Maghsoudi, and M. Hashim. "TARGETING HYDROTHERMAL ALTERATIONS UTILIZING LANDSAT-8 ANDASTER DATA IN SHAHR-E-BABAK, IRAN." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W5 (October 5, 2017): 153–57. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w5-153-2017.

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Shahr-e-Babak tract of the Kerman metalogenic belt is one of the most potential segments of Urumieh–Dokhtar (Sahand-Bazman) magmatic arc. This area encompasses several porphyry copper deposits in exploration, development and exploitation hierarchy. The aim of this study is to map hydrothermal alterations caused by early Cenozoic magmatic intrusions in Shahr-e-Babak area. To this purpose, mineral mapping methods including band combinations, ratios and multiplications as well as PCA and MNF data space transforms in SWIR and VNIR for both ASTER and OLI sensors. Alteration zones according to spectral signatures of each type of alteration mineral assemblages such as argillic, phyllic and propylitic are successfully mapped. For enhancing the target areas false color composites and HSI-RGB color space transform are performed on developed band combinations. Previous studies have proven the robust application of ASTER in geology and mineral exploration; nonetheless, the results of this investigation prove applicability of OLI sensor from landsat-8 for alteration mapping. According to the results, evidently OLI sensor data can accurately map alteration zones. Additionally, the 12-bit quantization of OLI data is its privilege over 8-bit data of ASTER in VNIR and SWIR, thus OLI high quality results, which makes it easy to distinguish targets with enhanced color contrast between the altered and unaltered rocks.

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Mazzeo, Giuseppe, Micheal S. Ramsey, Francesco Marchese, Nicola Genzano, and Nicola Pergola. "Implementation of the NHI (Normalized Hot Spot Indices) Algorithm on Infrared ASTER Data: Results and Future Perspectives." Sensors 21, no. 4 (February 23, 2021): 1538. http://dx.doi.org/10.3390/s21041538.

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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.

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Pour, A. B., M. Hashim, and J. K. Hong. "APPLICATION OF MULTISPECTRAL SATELLITE DATA FOR GEOLOGICAL MAPPING IN ANTARCTIC ENVIRONMENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W1 (September 29, 2016): 77–81. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w1-77-2016.

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Remote sensing imagery is capable to provide a solution to overcome the difficulties associated with geological field mapping in the Antarctic. Advanced optical and radar satellite imagery is the most applicable tool for mapping and identification of inaccessible regions in Antarctic. Consequently, an improved scientific research using remote sensing technology would be essential to provide new and more complete lithological and structural data to fill the numerous knowledge gaps on Antarctica’s geology. In this investigation, Oscar coast area in Graham Land, Antarctic Peninsula (AP) was selected to conduct a remote sensing study using Landsat-7 Thematic Mapper (TM), Landsat-8 and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Contrast-enhanced Red-Green-Blue (RGB) composites, band ratios and Relative Band Depth (RBD) image processing techniques were applied to Landsat-8 and ASTER dataset for establishing the spectral separation of the main lithologic groups exposed in the study area. The outcomes of this investigation demonstrated the applications of SWIR and TIR bands of the multispectral remote sensing datasets to identify lithological units and producing geological maps with suitable accuracy of ice-free rock regions in the Antarctic Peninsula. The results could be extended to map coverage of non-investigated regions further east and validated previously inferred geological observations concerning other rocks and mineral deposits throughout the Antarctica.

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Hyun, Chang-Uk, and Hyeong-Dong Park. "Assessment of Topographic Normalization in Jeju Island with Landsat 7 ETM+ and ASTER GDEM Data." Korean Journal of Remote Sensing 28, no. 4 (August 30, 2012): 393–407. http://dx.doi.org/10.7780/kjrs.2012.28.4.4.

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Toomey, Michael, and Lee A. Vierling. "Multispectral remote sensing of landscape level foliar moisture: techniques and applications for forest ecosystem monitoring." Canadian Journal of Forest Research 35, no. 5 (May 1, 2005): 1087–97. http://dx.doi.org/10.1139/x05-043.

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Broad-scale monitoring of varying moisture levels of leaves has ramifications for understanding fire potential, biogeochemistry, and ecosystem dynamics. Five different shortwave infrared (SWIR)-derived spectral indices, principal components analysis (PCA), and the tasseled cap transformation (TCT), derived from Landsat Thematic Mapper (TM) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data, were used to quantify landscape-level foliar moisture in an ecosystem dominated by Pinus ponderosa P. & C. Lawson. Landsat TM data demonstrated stronger correlations with in situ calculations of foliar moisture than did ASTER data. The second principal component correlated strongly with ground data (r2 = 0.765). The Landsat-derived TCT wetness component was significantly correlated with ground data (r2 = 0.638) as well as a normalized difference NIR/SWIR ratio (r2 = 0.834). The spectral indices and TCT are more practical for ecosystem moisture monitoring than PCA because of the empirical nature of PCA. Based on these results, we recommend modifications to existing methods of satellite-based fire susceptibility monitoring to account for primary effects of vegetation curing and temporal variation in ground fuels.

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Kääb, Andreas, Bernard Lefauconnier, and Kjetil Melvold. "Flow field of Kronebreen, Svalbard, using repeated Landsat 7 and ASTER data." Annals of Glaciology 42 (2005): 7–13. http://dx.doi.org/10.3189/172756405781812916.

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AbstractKnowledge about the spatio-temporal distribution of fast-flowing Arctic glaciers is still limited. Kronebreen, Svalbard, in particular, includes the confluence − and the dynamic interplay − of the fast-flowing Kronebreen and the currently slow-flowing Kongsvegen. In this study, image-matching techniques on the basis of repeated Landsat 7 Enhanced Thematic Mapper Plus (ETM+) pan and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite data are applied in order to derive surface velocity fields of the lowermost 10 km of Kronebreen for the annual periods 1999/2000, 2000/01, 2001/02 and a 40 day period around July 2001. This work perfectly complements differential synthetic aperture radar interferometry (DInSAR) studies available for Kronebreen. A complete surface velocity field is now available from combining the DInSAR studies for the upper part of the glacier and the optical image-matching study presented here. The data obtained within this study are also compared to velocity data of 1964, 1986, 1990 and 1996. As also suggested by previous studies, a significant spatio-temporal variability of the spring/summer and annual ice speeds becomes evident.

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Nourian, Noureddin, Shaban Shataee-Joibary, and Jahangir Mohammadi. "Assessment of Different Remote Sensing Data for Forest Structural Attributes Estimation in the Hyrcanian forests." Forest Systems 25, no. 3 (December 2, 2016): e074. http://dx.doi.org/10.5424/fs/2016253-08682.

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Aim of study: The objective of the study was the comparative assessment of various spatial resolutions of optical satellite imagery including Landsat-TM, ASTER, and Quickbird data to estimate the forest structure attributes of Hyrcanian forests, Golestan province, northernIran.Material and methods: The 112 square plots with area of0.09 ha were measured using a random cluster sampling method and then stand volume, basal area, and tree stem density were computed using measured data. After geometric and atmospheric corrections of images, the spectral attributes from original and different synthetic bands were extracted for modelling. The statistical modelling was performed using CART algorithm. Performance assessment of models was examined using the unused validation plots by RMSE and bias measures.Main Results: The results showed that model of Quickbird data for stand volume, basal area, and tree stem density had a better performance compared to ASTER and TM data. However, estimations by ASTER and TM imagery had slightly similar results for all three parameters.Research highlights: This study exposed that the high-resolution satellite data are more useful for forest structure attributes estimation in the Hyrcanian broadleaves forests compared with medium resolution images without consideration of images costs. However, regarding to be free of the most medium resolution data such as ASTER and TM,ETM+ or OLI images, these data can be used with slightly similar results. Keywords: Forest structure attributes; quickbird; ASTER; TM; CART algorithm; Hyrcanian forests.

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Racoviteanu, A., Y. Arnaud, M. Williams, and W. F. Manley. "Spatial patterns in glacier area and elevation changes from 1962 to 2006 in the monsoon-influenced eastern Himalaya." Cryosphere Discussions 8, no. 4 (July 18, 2014): 3949–98. http://dx.doi.org/10.5194/tcd-8-3949-2014.

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Abstract. This study presents spatial patterns in glacier area and elevation changes in the monsoon-influenced part of the Himalaya (eastern Nepal and Sikkim) at multiple spatial scales. We combined Corona KH4 and topographic data with more recent remote-sensing data from Landsat 7 Enhanced Thematic Mapper Plus (ETM+), the Advanced Spaceborne Thermal Emission Radiometer (ASTER), QuickBird (QB) and WorldView-2 (WV2) sensors. We present: (1) spatial patterns of glacier parameters based on a new "reference" geospatial Landsat/ASTER glacier inventory from ~ 2000; (2) changes in glacier area (1962–2006) and their dependence on topographic variables (elevation, slope, aspect, percent debris cover) as well as climate variables (solar radiation and precipitation), extracted on a glacier-by-glacier basis and (3) changes in glacier elevations for debris-covered tongues and their relationship to surface temperature extracted from ASTER data. Glacier mapping from 2000 Landsat/ASTER yielded 1463 km2 ± 88 km2 total glacierized area in Nepal (Tamor basin) and Sikkim (Zemu basin), parts of Bhutan and China, of which we estimated 569 km2 ± 34 km2 to be located in Sikkim. Supraglacial debris covered 11% of the total glacierized area, and supraglacial lakes covered about 5.8% of the debris-covered area. Based on analysis of high-resolution imagery, we estimated an area loss of −0.24% ± 0.08% yr−1 from the 1960's to the 2010's, with a higher rate of retreat in the last decade (−0.43% yr−1 ± 0.9 % from 2000 to 2006) compared to the previous decades (−0.20% yr−1 ± 0.16% from 1962 to 2000). Retreat rates of clean glaciers were −0.7% yr−1, almost double than those of debris-covered glaciers (−0.3% yr−1). Debris-covered tongues experienced an average lowering of −30.8 m ± 39 m from 1960's to 2000's (−0.8 m ± 0.9 m yr−1), with enhanced thinning rates in the upper part of the debris covered area, and overall thickening at the glacier termini.

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Abrams, M. "ASTER GLOBAL DEM VERSION 3, AND NEW ASTER WATER BODY DATASET." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 13, 2016): 107–10. http://dx.doi.org/10.5194/isprsarchives-xli-b4-107-2016.

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In 2016, the US/Japan ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) project released Version 3 of the Global DEM (GDEM). This 30 m DEM covers the earth’s surface from 82N to 82S, and improves on two earlier versions by correcting some artefacts and filling in areas of missing DEMs by the acquisition of additional data. The GDEM was produced by stereocorrelation of 2 million ASTER scenes and operation on a pixel-by-pixel basis: cloud screening; stacking data from overlapping scenes; removing outlier values, and averaging elevation values. As previously, the GDEM is packaged in ~ 23,000 1 x 1 degree tiles. Each tile has a DEM file, and a NUM file reporting the number of scenes used for each pixel, and identifying the source for fill-in data (where persistent clouds prevented computation of an elevation value). An additional data set was concurrently produced and released: the ASTER Water Body Dataset (AWBD). This is a 30 m raster product, which encodes every pixel as either lake, river, or ocean; thus providing a global inland and shore-line water body mask. Water was identified through spectral analysis algorithms and manual editing. This product was evaluated against the Shuttle Water Body Dataset (SWBD), and the Landsat-based Global Inland Water (GIW) product. The SWBD only covers the earth between about 60 degrees north and south, so it is not a global product. The GIW only delineates inland water bodies, and does not deal with ocean coastlines. All products are at 30 m postings.

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Abrams, M. "ASTER GLOBAL DEM VERSION 3, AND NEW ASTER WATER BODY DATASET." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 13, 2016): 107–10. http://dx.doi.org/10.5194/isprs-archives-xli-b4-107-2016.

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In 2016, the US/Japan ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) project released Version 3 of the Global DEM (GDEM). This 30 m DEM covers the earth’s surface from 82N to 82S, and improves on two earlier versions by correcting some artefacts and filling in areas of missing DEMs by the acquisition of additional data. The GDEM was produced by stereocorrelation of 2 million ASTER scenes and operation on a pixel-by-pixel basis: cloud screening; stacking data from overlapping scenes; removing outlier values, and averaging elevation values. As previously, the GDEM is packaged in ~ 23,000 1 x 1 degree tiles. Each tile has a DEM file, and a NUM file reporting the number of scenes used for each pixel, and identifying the source for fill-in data (where persistent clouds prevented computation of an elevation value). An additional data set was concurrently produced and released: the ASTER Water Body Dataset (AWBD). This is a 30 m raster product, which encodes every pixel as either lake, river, or ocean; thus providing a global inland and shore-line water body mask. Water was identified through spectral analysis algorithms and manual editing. This product was evaluated against the Shuttle Water Body Dataset (SWBD), and the Landsat-based Global Inland Water (GIW) product. The SWBD only covers the earth between about 60 degrees north and south, so it is not a global product. The GIW only delineates inland water bodies, and does not deal with ocean coastlines. All products are at 30 m postings.

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Silvestri, Malvina, Vito Romaniello, Simon Hook, Massimo Musacchio, Sergio Teggi, and Maria Fabrizia Buongiorno. "First Comparisons of Surface Temperature Estimations between ECOSTRESS, ASTER and Landsat 8 over Italian Volcanic and Geothermal Areas." Remote Sensing 12, no. 1 (January 4, 2020): 184. http://dx.doi.org/10.3390/rs12010184.

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The ECO System Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a new space mission developed by NASA-JPL which launched on July 2018. It includes a multispectral thermal infrared radiometer that measures the radiances in five spectral channels between 8 and 12 μm. The primary goal of the mission is to study how plants use water by measuring their temperature from the vantage point of the International Space Station. However, as ECOSTRESS retrieves the surface temperature, the data can be used to measure other heat-related phenomena, such as heat waves, volcanic eruptions, and fires. We have cross-compared the temperatures obtained by ECOSTRESS, the Advanced Spaceborne Thermal Emission and Reflectance radiometer (ASTER) and the Landsat 8 Thermal InfraRed Sensor (TIRS) in areas where thermal anomalies are present. The use of ECOSTRESS for temperature analysis as well as ASTER and Landsat 8 offers the possibility of expanding the availability of satellite thermal data with very high spatial and temporal resolutions. The Temperature and Emissivity Separation (TES) algorithm was used to retrieve surface temperatures from the ECOSTRESS and ASTER data, while the single-channel algorithm was used to retrieve surface temperatures from the Landsat 8 data. Atmospheric effects in the data were removed using the moderate resolution atmospheric transmission (MODTRAN) radiative transfer model driven with vertical atmospheric profiles collected by the University of Wyoming. The test sites used in this study are the active Italian volcanoes and the Parco delle Biancane geothermal area (Italy). In order to test and quantify the difference between the temperatures retrieved by the three spaceborne sensors, a set of coincident imagery was acquired and used for cross comparison. Preliminary statistical analyses show a very good agreement in terms of correlation and mean values among sensors over the test areas.

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H., Lakshmi Ram Prasath, and Kusuma K.N. "Lithological Mapping using Landsat 8 OLI and ASTER TIR Multispectral Data- A Comparative Study." International Journal of Advanced Remote Sensing and GIS 7, no. 1 (July 24, 2018): 2728–45. http://dx.doi.org/10.23953/cloud.ijarsg.369.

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Xian, George, Mike Crane, and Cory McMahon. "Quantifying Multi-temporal Urban Development Characteristics in Las Vegas from Landsat and ASTER Data." Photogrammetric Engineering & Remote Sensing 74, no. 4 (April 1, 2008): 473–81. http://dx.doi.org/10.14358/pers.74.4.473.

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Xu, Yong, Chao Ren, Meng Cai, Ng Yan Yung Edward, and Tianjun Wu. "Classification of Local Climate Zones Using ASTER and Landsat Data for High-Density Cities." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 7 (July 2017): 3397–405. http://dx.doi.org/10.1109/jstars.2017.2683484.

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Akumu, Clement Elumpe, Sumith Pathirana, Serwan Baban, and Danny Bucher. "Monitoring coastal wetland communities in north-eastern NSW using ASTER and Landsat satellite data." Wetlands Ecology and Management 18, no. 3 (February 11, 2010): 357–65. http://dx.doi.org/10.1007/s11273-010-9176-0.

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Paul, Frank, and Andreas Kääb. "Perspectives on the production of a glacier inventory from multispectral satellite data in Arctic Canada: Cumberland Peninsula, Baffin Island." Annals of Glaciology 42 (2005): 59–66. http://dx.doi.org/10.3189/172756405781813087.

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AbstractThe consequences of global warming on land ice masses are difficult to assess in detail, as two-dimensional glacier inventory data are still missing for many remote regions of the world. As the largest future temperature increase is expected to occur at high latitudes, the glaciers and ice caps in the Arctic will be particularly susceptible to the expected warming. This study demonstrates the possibilities of space-borne glacier inventorying at a remote site on Cumberland Peninsula, a part of Baffin Island in Arctic Canada, thereby providing glacier inventory data for this region. Our approach combines Landsat ETM+ and Terra ASTER satellite data, an ASTER-derived digital elevation model (DEM) and Geographic Information System-based processing. We used thresholded ratio images from ETM+ bands 3 and 5 and ASTER bands 3 and 4 for glacier mapping. Manual delineation of Little Ice Age trimlines and moraines has been applied to calculate area changes for 225 glaciers, yielding an average area loss of 11%. A size distribution has been obtained for 770 glaciers that is very different from that for Alpine glaciers. Numerous three-dimensional glacier parameters were derived from the ASTER DEM for a subset of 340 glaciers. The amount of working time required for the processing has been tracked, and resulted in 5 min per glacier, or 7 years for all estimated 160 000 glaciers worldwide.

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Fujisada, Hiroyuki, Minoru Urai, and Akira Iwasaki. "Manual-Based Improvement Method for the ASTER Global Water Body Data Base." Remote Sensing 12, no. 20 (October 15, 2020): 3373. http://dx.doi.org/10.3390/rs12203373.

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A water body detection technique is an essential part of digital elevation model (DEM) generation to delineate land–water boundaries and to set flattened elevations. The initial tile-based water body data that are created during production of the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) GDEM, as a by-product, are incorporated into ASTER GDEM V3 to improve the quality. At the same time as ASTER GDEM V3, the Global Water Body Data Base (ASTWBD) Version 1 is also released to the public. The ASTWBD generation consists of two parts: separation from land area, and classification into three categories: sea, lake, and river. Sea water bodies have zero elevation. Lake water bodies have flattened elevations. River water bodies have a gradual step-down from upstream to downstream with a step of one meter. The separation process from land area is carried out automatically using an algorithm, except for sea-ice removal, to delineate the real seashore lines in the high latitude areas; almost all of the water bodies are created through this process. The classification process into three categories, i.e., sea, river, and lake, is carried out, and incorporated into ASTER GDEM V3. For inland water bodies, it is not possible to perfectly detect all water bodies using reflectance and spectral index, which are the only available parameters for optical sensors. The only way available to identify the undetected inland water bodies is to manually copy them with visual inspection from the earth’s surface images, like Landsat images. GeoCover2000 images are the main part of the object images. Color–Land ASTER MosaicS (CLAMS) images are used to cover the deficiency of the GeoCover2000 images. This kind of time-consuming, unsophisticated way is inevitable as it is a manual-based method to improve the quality of the ASTWBD. This paper describes the manual-based improvement method; specifically, how deficient water body images are efficiently copied as rasterized images from the earth’s surface images to obtain a more complete global water body data set.

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Liu, Tong, Tsuyoshi Kinouchi, and Fabiola Ledezma. "Characterization of recent glacier decline in the Cordillera Real by LANDSAT, ALOS, and ASTER data." Remote Sensing of Environment 137 (October 2013): 158–72. http://dx.doi.org/10.1016/j.rse.2013.06.010.

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Gao, Jay, and Yansui Liu. "Mapping of land degradation from space: a comparative study of Landsat ETM+ and ASTER data." International Journal of Remote Sensing 29, no. 14 (June 14, 2008): 4029–43. http://dx.doi.org/10.1080/01431160801891887.

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Holden, Zachary A., Penelope Morgan, Alistair M. S. Smith, and Lee Vierling. "Beyond Landsat: a comparison of four satellite sensors for detecting burn severity in ponderosa pine forests of the Gila Wilderness, NM, USA." International Journal of Wildland Fire 19, no. 4 (2010): 449. http://dx.doi.org/10.1071/wf07106.

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Methods of remotely measuring burn severity are needed to evaluate the ecological and environmental impacts of large, remote wildland fires. The challenges that were associated with the Landsat program highlight the need to evaluate alternative sensors for characterising post-fire effects. We compared statistical correlations between 55 Composite Burn Index field plots and spectral indices from four satellite sensors varying in spatial and spectral resolution on the 2003 Dry Lakes Fire in the Gila Wilderness, NM. Where spectrally feasible, burn severity was evaluated using the differenced Enhanced Vegetation Index (dEVI), differenced Normalised Difference Vegetation Index (dNDVI) and the differenced Normalised Burn Ratio (dNBR). Both the dEVI derived from Quickbird and the dNBR derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) showed similar or slightly improved correlations over the dNBR derived from Landsat Thematic Mapper data (R2 = 0.82, 0.84, and 0.78 respectively). The relatively coarse resolution MODIS-derived NDVI image was weakly correlated with ground data (R2 = 0.38). Our results suggest that moderately high-resolution satellite sensors like Quickbird and ASTER have potential for providing accurate information about burn severity. Future research should develop stronger links between higher-resolution satellite data and burn severity across a range of environments.

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Bolouki, Seyed Mohammad, Hamid Reza Ramazi, Abbas Maghsoudi, Amin Beiranvand Pour, and Ghahraman Sohrabi. "A Remote Sensing-Based Application of Bayesian Networks for Epithermal Gold Potential Mapping in Ahar-Arasbaran Area, NW Iran." Remote Sensing 12, no. 1 (December 27, 2019): 105. http://dx.doi.org/10.3390/rs12010105.

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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.

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Liu, Lin, and Yuanzhi Zhang. "Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong." Remote Sensing 3, no. 7 (July 13, 2011): 1535–52. http://dx.doi.org/10.3390/rs3071535.

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Bolch, Tobias, Manfred Buchroithner, Tino Pieczonka, and André Kunert. "Planimetric and volumetric glacier changes in the Khumbu Himal, Nepal, since 1962 using Corona, Landsat TM and ASTER data." Journal of Glaciology 54, no. 187 (2008): 592–600. http://dx.doi.org/10.3189/002214308786570782.

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AbstractMultitemporal space imagery from 1962 (Corona KH-4), 1992 (Landsat TM), 2001 and 2005 (Terra ASTER) was used to investigate the glacier changes in the Khumbu Himal, Nepal. The ice coverage in the investigation area decreased by about 5% between 1962 and 2005, with the highest retreat rates occurring between 1992 and 2001. The debris coverage increased concomitantly with the decrease in total glacier area. The clean-ice area decreased by >10%. Digital terrain model (DTM) generation from the early Corona KH-4 stereo data in this high-relief terrain is time-consuming, and the results still contain some elevation errors. However, these are minor in the snow-free areas with gentle slopes. Thus comparison of the surfaces of the debris-covered glacier tongues based on the Corona DTM and an ASTER DTM is feasible and shows the downwasting of the debris-covered glaciers. The highest downwasting rates, more than 20 m (>0.5 m a−1), can be found near the transition zone between the active and the stagnant glacier parts of the debris-covered glacier tongues. The downwasting is lower, but still evident, in the active ice areas and at the snout with thick debris cover. All investigated debris-covered glaciers in the study area show similar behaviour. The estimated volume loss for the investigated debris-covered glacier tongues is 0.19 km3.

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Pournamdari, M., and M. Hashim. "Feature level fusion for enhanced geological mapping of ophiolile complex using ASTER and Landsat TM data." IOP Conference Series: Earth and Environmental Science 18 (February 25, 2014): 012145. http://dx.doi.org/10.1088/1755-1315/18/1/012145.

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Pournamdari, Mohsen, Mazlan Hashim, and Amin Beiranvand Pour. "Application of ASTER and Landsat TM Data for Geological Mapping of Esfandagheh Ophiolite Complex, Southern Iran." Resource Geology 64, no. 3 (June 21, 2014): 233–46. http://dx.doi.org/10.1111/rge.12038.

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Svoboda, Felix, and Frank Paul. "A new glacier inventory on southern Baffin Island, Canada, from ASTER data: I. Applied methods, challenges and solutions." Annals of Glaciology 50, no. 53 (2009): 11–21. http://dx.doi.org/10.3189/172756410790595912.

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AbstractThe quantitative assessment of glacier changes as well as improved modeling of climate-change impacts on glaciers requires digital vector outlines of individual glacier entities. Unfortunately, such a glacier inventory is still lacking in many remote but extensively glacierized gions such as the Canadian Arctic. Multispectral satellite data in combination with digital elevation models (DEMs) a particularly useful for creating detailed glacier inventory data including topographic information for each entity. In this study, we extracted glacier outlines and a DEM using two adjacent Terra ASTER scenes acquired in August 2000 for a remote region on southern Baffin Island, Canada. Additionally, Little Ice Age (LIA) extents we digitized from trimlines and moraines visible on the ASTER scenes, and Landsat MSS and TM scenes from the years 1975 and 1990 we used to assess changes in glacier length and area. Because automated delineation of glaciers is based on a band in the shortwave infrared, we have developed a new semi-automated glacier-mapping approach for the MSS sensor. Wrongly classified debris-coved glaciers, water bodies and attached snowfields we corrected manually for both ASTER and MSS. Glacier drainage divides we manually digitized by combining visual interptation with DEM information. In this first paper, we describe the applied methods for glacier mapping and the glaciological challenges encounted (e.g. data voids, snow cover, ice caps, tributaries), while the second paper ports the data analyses and the derived changes.

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Henry, Mary C., John K. Maingi, and Jessica McCarty. "Fire on the Water Towers: Mapping Burn Scars on Mount Kenya Using Satellite Data to Reconstruct Recent Fire History." Remote Sensing 11, no. 2 (January 9, 2019): 104. http://dx.doi.org/10.3390/rs11020104.

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Mount Kenya is one of Kenya’s ‘water towers’, the headwaters for the country’s major rivers including the Tana River and Ewaso Nyiro River, which provide water and hydroelectric power to the semiarid region. Fires affect water downstream, but are difficult to monitor given limited resources of local land management agencies. Satellite-based remote sensing has the potential to provide long term coverage of large remote areas on Mount Kenya, especially using the free Landsat data archive and moderate resolution imaging spectroradiometer (MODIS) fire products. In this study, we mapped burn scars on Mount Kenya using 30 m Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) derived dNBR (change in normalized burn ratio) and MODIS active fire detection and burned area data for fires occurring from 2004 to 2015. We also analyzed topographic position (elevation, slope, aspect) of these fires using an ASTER global digital elevation model (GDEM v2) satellite-derived 30 m digital elevation model (DEM). Results indicate that dNBR images calculated from data acquired about one year apart were able to identify large fires on Mount Kenya that match locations (and timing) of MODIS active fire points and burned areas from the same time period, but we were unable to detect smaller and/or older fires.

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Beiranvand Pour, Amin, Tae-Yoon Park, Yongcheol Park, Jong Hong, Basem Zoheir, Biswajeet Pradhan, Iman Ayoobi, and Mazlan Hashim. "Application of Multi-Sensor Satellite Data for Exploration of Zn–Pb Sulfide Mineralization in the Franklinian Basin, North Greenland." Remote Sensing 10, no. 8 (July 27, 2018): 1186. http://dx.doi.org/10.3390/rs10081186.

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Geological mapping and mineral exploration programs in the High Arctic have been naturally hindered by its remoteness and hostile climate conditions. The Franklinian Basin in North Greenland has a unique potential for exploration of world-class zinc deposits. In this research, multi-sensor remote sensing satellite data (e.g., Landsat-8, Phased Array L-band Synthetic Aperture Radar (PALSAR) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) were used for exploring zinc in the trough sequences and shelf-platform carbonate of the Franklinian Basin. A series of robust image processing algorithms was implemented for detecting spatial distribution of pixels/sub-pixels related to key alteration mineral assemblages and structural features that may represent potential undiscovered Zn–Pb deposits. Fusion of Directed Principal Component Analysis (DPCA) and Independent Component Analysis (ICA) was applied to some selected Landsat-8 mineral indices for mapping gossan, clay-rich zones and dolomitization. Major lineaments, intersections, curvilinear structures and sedimentary formations were traced by the application of Feature-oriented Principal Components Selection (FPCS) to cross-polarized backscatter PALSAR ratio images. Mixture Tuned Matched Filtering (MTMF) algorithm was applied to ASTER VNIR/SWIR bands for sub-pixel detection and classification of hematite, goethite, jarosite, alunite, gypsum, chalcedony, kaolinite, muscovite, chlorite, epidote, calcite and dolomite in the prospective targets. Using the remote sensing data and approaches, several high potential zones characterized by distinct alteration mineral assemblages and structural fabrics were identified that could represent undiscovered Zn–Pb sulfide deposits in the study area. This research establishes a straightforward/cost-effective multi-sensor satellite-based remote sensing approach for reconnaissance stages of mineral exploration in hardly accessible parts of the High Arctic environments.

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Mwaniki, M. W., M. S. Moeller, and G. Schellmann. "A comparison of Landsat 8 (OLI) and Landsat 7 (ETM+) in mapping geology and visualising lineaments: A case study of central region Kenya." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 30, 2015): 897–903. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-897-2015.

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Availability of multispectral remote sensing data cheaply and its higher spectral resolution compared to remote sensing data with higher spatial resolution has proved valuable for geological mapping exploitation and mineral mapping. This has benefited applications such as landslide quantification, fault pattern mapping, rock and lineament mapping especially with advanced remote sensing techniques and the use of short wave infrared bands. While Landsat and Aster data have been used to map geology in arid areas and band ratios suiting the application established, mapping in geology in highland regions has been challenging due to vegetation land cover. The aim of this study was to map geology and investigate bands suited for geological applications in a study area containing semi arid and highland characteristics. Therefore, Landsat 7 (ETM+, 2000) and Landsat 8 (OLI, 2014) were compared in determining suitable bands suited for geological mapping in the study area. The methodology consist performing principal component and factor loading analysis, IHS transformation and decorrelation stretch of the FCC with the highest contrast, band rationing and examining FCC with highest contrast, and then performing knowledge base classification. PCA factor loading analysis with emphasis on geological information showed band combination (5, 7, 3) for Landsat 7 and (6, 7, 4) for Landsat 8 had the highest contrast and more contrast was enhanced by performing decorrelation stretch. Band ratio combination (3/2, 5/1, 7/3) for Landsat 7 and (4/3, 6/2, 7/4) for Landsat 8 had more contrast on geologic information and formed the input data in knowledge base classification. Lineament visualisazion was achieved by performing IHS transformation of FCC with highest contrast and its saturation band combined as follows: Landsat 7 (IC1, PC2, saturation band), Landsat 8 (IC1, PC4, saturation band). The results were compared against existing geology maps and were superior and could be used to update the existing maps.

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Genzano, Nicola, Francesco Marchese, Marco Neri, Nicola Pergola, and Valerio Tramutoli. "Implementation of Robust Satellite Techniques for Volcanoes on ASTER Data under the Google Earth Engine Platform." Applied Sciences 11, no. 9 (May 5, 2021): 4201. http://dx.doi.org/10.3390/app11094201.

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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.

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Fazliddinova, Madinabonu Zaxritdinovna, Akram Bayramovich Goipov, and Maftuna Asad qizi Saidova. "Depth Faults Along The Regmatic Network Of The Chatkalo-Kuramin Region And Their Relationship With Seismicity." American Journal of Applied sciences 02, no. 12 (December 27, 2020): 68–76. http://dx.doi.org/10.37547/tajas/volume02issue12-11.

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Lineaments were identified using LANDSAT-8 satellite images and digital elevation models obtained from the ASTER GDEM satellite over the Chatkal-Kuramin region. Taking into account the stock materials and a comprehensive analysis of the results of processing remote sensing data, a map of lineaments of a 1: 100,000 regmatic network was compiled. Based on the automated visual lineament analysis in the Geomatica PCI program, lineaments of the regmatic network were obtained, which are located in the focal zones of strong earthquakes.

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Nowicki, Scott A., Richard D. Inman, Todd C. Esque, Kenneth E. Nussear, and Christopher S. Edwards. "Spatially Consistent High-Resolution Land Surface Temperature Mosaics for Thermophysical Mapping of the Mojave Desert." Sensors 19, no. 12 (June 13, 2019): 2669. http://dx.doi.org/10.3390/s19122669.

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Daytime and nighttime thermal infrared observations acquired by the ASTER and MODIS instruments onboard the NASA Terra spacecraft have produced a dataset that can be used to map thermophysical properties across large regions, which have implications on surface processes, thermal environments and habitat suitability for desert species. ASTER scenes acquired between 2004 and 2012 are combined using new mosaicking and data-fusion techniques to produce a map of daytime and nighttime land surface temperature with coverage exclusive of the effects of clouds and weather. These data are combined with Landsat 7 visible imagery to generate a consistent map of apparent thermal inertia (ATI), which is related to the presence of exposed bedrock, rocks, fine-grained sediments and water on the surface. The resulting datasets are compared to known geomorphic units and surface types to generate an interpreted mechanical composition map of the entire Mojave Desert at 100 m per pixel that is most sensitive to large clast size distinctions in grain size distribution.

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Andrews Deller, M. E. "Facies discrimination in laterites using Landsat Thematic Mapper, ASTER and ALI data—examples from Eritrea and Arabia." International Journal of Remote Sensing 27, no. 12 (June 2006): 2389–409. http://dx.doi.org/10.1080/01431160600586050.

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Andrews Deller, M. E. "Facies discrimination in laterites using Landsat Thematic Mapper, ASTER and ALI data—examples from Eritrea and Arabia." International Journal of Remote Sensing 27, no. 22 (November 20, 2006): 5123. http://dx.doi.org/10.1080/01431160601069668.

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Pour, Amin Beiranvand, Yongcheol Park, Tae-Yoon S. Park, Jong Kuk Hong, Mazlan Hashim, Jusun Woo, and Iman Ayoobi. "Evaluation of ICA and CEM algorithms with Landsat-8/ASTER data for geological mapping in inaccessible regions." Geocarto International 34, no. 7 (February 9, 2018): 785–816. http://dx.doi.org/10.1080/10106049.2018.1434684.

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Toomey, Michael P., and Lee A. Vierling. "Estimating equivalent water thickness in a conifer forest using Landsat TM and ASTER data: a comparison study." Canadian Journal of Remote Sensing 32, no. 4 (January 2006): 288–99. http://dx.doi.org/10.5589/m06-024.

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Yang, Min, Guangli Ren, Ling Han, Huan Yi, and Ting Gao. "Detection of Pb–Zn mineralization zones in west Kunlun using Landsat 8 and ASTER remote sensing data." Journal of Applied Remote Sensing 12, no. 02 (May 15, 2018): 1. http://dx.doi.org/10.1117/1.jrs.12.026018.

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Aslan, N., and D. Koc-San. "ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 821–28. http://dx.doi.org/10.5194/isprsarchives-xli-b8-821-2016.

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The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88 % for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6 °C for 2001 and 6.8 °C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r2 = 0.7 and r2 = 0.9 for 2001 and 2014, respectively).

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Aslan, N., and D. Koc-San. "ANALYSIS OF RELATIONSHIP BETWEEN URBAN HEAT ISLAND EFFECT AND LAND USE/COVER TYPE USING LANDSAT 7 ETM+ AND LANDSAT 8 OLI IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 23, 2016): 821–28. http://dx.doi.org/10.5194/isprs-archives-xli-b8-821-2016.

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The main objectives of this study are (i) to calculate Land Surface Temperature (LST) from Landsat imageries, (ii) to determine the UHI effects from Landsat 7 ETM+ (June 5, 2001) and Landsat 8 OLI (June 17, 2014) imageries, (iii) to examine the relationship between LST and different Land Use/Land Cover (LU/LC) types for the years 2001 and 2014. The study is implemented in the central districts of Antalya. Initially, the brightness temperatures are retrieved and the LST values are calculated from Landsat thermal images. Then, the LU/LC maps are created from Landsat pan-sharpened images using Random Forest (RF) classifier. Normalized Difference Vegetation Index (NDVI) image, ASTER Global Digital Elevation Model (GDEM) and DMSP_OLS nighttime lights data are used as auxiliary data during the classification procedure. Finally, UHI effect is determined and the LST values are compared with LU/LC classes. The overall accuracies of RF classification results were computed higher than 88 % for both Landsat images. During 13-year time interval, it was observed that the urban and industrial areas were increased significantly. Maximum LST values were detected for dry agriculture, urban, and bareland classes, while minimum LST values were detected for vegetation and irrigated agriculture classes. The UHI effect was computed as 5.6 °C for 2001 and 6.8 °C for 2014. The validity of the study results were assessed using MODIS/Terra LST and Emissivity data and it was found that there are high correlation between Landsat LST and MODIS LST data (r2 = 0.7 and r2 = 0.9 for 2001 and 2014, respectively).

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Drachal, J., and A. K. Kawel. "DETERMINING THE SUITABILITY OF DIFFERENT DIGITAL ELEVATION MODELS AND SATELLITE IMAGES FOR FANCY MAPS. AN EXAMPLE OF CYPRUS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 14, 2016): 591–97. http://dx.doi.org/10.5194/isprsarchives-xli-b4-591-2016.

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The article describes the possibility of developing an overall map of the selected area on the basis of publicly available data. Such a map would take the form designed by the author with the colors that meets his expectations and a content, which he considers to be appropriate. Among the data available it was considered the use of satellite images of the terrain in real colors and, in the form of shaded relief, digital terrain models with different resolutions of the terrain mesh. Specifically the considered data were: MODIS, Landsat 8, GTOPO-30, SRTM-30, SRTM-1, SRTM-3, ASTER. For the test area the island of Cyprus was chosen because of the importance in tourism, a relatively small area and a clearly defined boundary. In the paper there are shown and discussed various options of the Cyprus terrain image obtained synthetically from variants of Modis, Landsat and digital elevation models of different resolutions.

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Pareta, Kuldeep, and Upasana Pareta. "Geomorphological Analysis and Hydrological Potential Zone of Baira River Watershed, Churah in Chamba District of Himachal Pradesh, India." Indonesian Journal of Science and Technology 2, no. 1 (April 1, 2017): 26. http://dx.doi.org/10.17509/ijost.v2i1.5987.

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In the present study, an attempt has been made to study the quantitative geomorphological analysis and hydrological characterization of 95 micro-watersheds (MWS) of Baira river watershed in Himachal Pradesh, India with an area of 425.25 Km2. First time in the world, total 173 morphometric parameters have been generated in a single watershed using satellite remote sensing data (i.e. IRS-P6 ResourceSAT-1 LISS-III, LandSAT-7 ETM+, and LandSAT-8 PAN & OLI merge data), digital elevation models (i.e. IRS-P5 CartoSAT-1 DEM, ASTER DEM data), and soI topographical maps of 1: 50,000 scale. The ninety-five micro-watersheds (MWS) of Baira river watershed have been prioritized through the morphometric analysis of different morphometric parameters (i.e. drainage network, basin geometry, drainage texture analysis, and relief characterizes ). The study has concurrently established the importance of geomorphometry as well as the utility of remote sensing and GIS technology for hydrological characterization of the watershed and there for better resource and environmental managements.

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Drachal, J., and A. K. Kawel. "DETERMINING THE SUITABILITY OF DIFFERENT DIGITAL ELEVATION MODELS AND SATELLITE IMAGES FOR FANCY MAPS. AN EXAMPLE OF CYPRUS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4 (June 14, 2016): 591–97. http://dx.doi.org/10.5194/isprs-archives-xli-b4-591-2016.

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The article describes the possibility of developing an overall map of the selected area on the basis of publicly available data. Such a map would take the form designed by the author with the colors that meets his expectations and a content, which he considers to be appropriate. Among the data available it was considered the use of satellite images of the terrain in real colors and, in the form of shaded relief, digital terrain models with different resolutions of the terrain mesh. Specifically the considered data were: MODIS, Landsat 8, GTOPO-30, SRTM-30, SRTM-1, SRTM-3, ASTER. For the test area the island of Cyprus was chosen because of the importance in tourism, a relatively small area and a clearly defined boundary. In the paper there are shown and discussed various options of the Cyprus terrain image obtained synthetically from variants of Modis, Landsat and digital elevation models of different resolutions.

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Miyazaki, H., M. Nagai, and R. Shibasaki. "DEVELOPMENT OF TIME-SERIES HUMAN SETTLEMENT MAPPING SYSTEM USING HISTORICAL LANDSAT ARCHIVE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1385–88. http://dx.doi.org/10.5194/isprsarchives-xli-b8-1385-2016.

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Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC), was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS) in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.

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50

Miyazaki, H., M. Nagai, and R. Shibasaki. "DEVELOPMENT OF TIME-SERIES HUMAN SETTLEMENT MAPPING SYSTEM USING HISTORICAL LANDSAT ARCHIVE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 1385–88. http://dx.doi.org/10.5194/isprs-archives-xli-b8-1385-2016.

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Abstract:

Methodology of automated human settlement mapping is highly needed for utilization of historical satellite data archives for urgent issues of urban growth in global scale, such as disaster risk management, public health, food security, and urban management. As development of global data with spatial resolution of 10-100 m was achieved by some initiatives using ASTER, Landsat, and TerraSAR-X, next goal has targeted to development of time-series data which can contribute to studies urban development with background context of socioeconomy, disaster risk management, public health, transport and other development issues. We developed an automated algorithm to detect human settlement by classification of built-up and non-built-up in time-series Landsat images. A machine learning algorithm, Local and Global Consistency (LLGC), was applied with improvements for remote sensing data. The algorithm enables to use MCD12Q1, a MODIS-based global land cover map with 500-m resolution, as training data so that any manual process is not required for preparation of training data. In addition, we designed the method to composite multiple results of LLGC into a single output to reduce uncertainty. The LLGC results has a confidence value ranging 0.0 to 1.0 representing probability of built-up and non-built-up. The median value of the confidence for a certain period around a target time was expected to be a robust output of confidence to identify built-up or non-built-up areas against uncertainties in satellite data quality, such as cloud and haze contamination. Four scenes of Landsat data for each target years, 1990, 2000, 2005, and 2010, were chosen among the Landsat archive data with cloud contamination less than 20%.We developed a system with the algorithms on the Data Integration and Analysis System (DIAS) in the University of Tokyo and processed 5200 scenes of Landsat data for cities with more than one million people worldwide.

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Journal articles on the topic 'Landsat and ASTER Data'

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