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Academic literature on the topic 'Landsat and ASTER Data'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 February 2022

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Journal articles on the topic "Landsat and ASTER Data"

1

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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<p><strong>Abstract.</strong> 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.</p>

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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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Dissertations / Theses on the topic "Landsat and ASTER Data"

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Stolz, Tara Alexandra. "Geological Mapping of Orhon, Tariat, and Egiin Dawaa, Central Mongolia, through the Interpretation of Remote Sensing Data." Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1221081955.

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Biro, Turk Khalid Guma. "Geovisualisation of Multi-Temporal Satellite Data for Landuse/Landcover Change Analysis and its Impacts on Soil Properties in Gadarif Region, Sudan." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-83390.

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Several decades of intensive dryland-farming in the Gadarif Region, located in the Eastern part of Sudan, has led to rapid landuse/landcover (LULC) changes mainly due to agricultural expansion, government policies and environmental calamities such as drought. The study area represents part of the African Sahel. The fundamental goal of the thesis was to assess land degradation and the impact of agriculture expansion on land cover, soil and crops production. To analyse and to monitor the LULC changes, multi-temporal Landsat data of the years 1979, 1989 and 1999 and ASTER data of the year 2009 covering an area of approximately 1200 km² were used. For this a post-classification comparison technique was applied to detect LULC changes from satellite images. Six LULC classes were identified during the classification scheme, namely cultivated land, fallow land, woodland, bare land, settlement and water. For the four dates of satellite images the overall classification accuracy ranged from 86 % to 92 %. During the three decades of the study period an extensive change of LULC patterns occurred. The cultivated areas increased significantly, covering 81 % of the previous woodland in the period 1979 – 2009. Fallow land only increased during the period 1989 – 1999. Over the three decades, urban expansion continuously increased covering an area of 23, 21 and 27 km² for the periods 1979 – 1989, 1989 – 1999 and 1999 – 2009 respectively. The detailed LULC map of the study area was obtained by using a dual polarisation (HH and HV) TerraSAR-X data of the year 2009. The different LULCs of the study area were analysed by employing an object-oriented classification approach. For that purpose, multi-resolution segmentation of the Definiens Software was used for creating the image objects. Using the feature-space optimisation tool the attributes of the TerraSAR-X images were optimised in order to obtain the best separability among classes for the LULC mapping. In addition to the classes that have been obtained by the optical data, the following LULCs resulted from SAR data: harvested land, rock, settlement 1 (local-roof buildings) and settlement 2 (concrete roof buildings). The backscattering coefficients for some classes were different along HH and HV polarisation. The best separation distance of the tested spectral, shape and textural features showed different variations among the discriminated LULC classes. An overall accuracy of 84 % with a kappa value of 0.82 was resulted from the classification scheme. Accuracy differences among the classes were kept minimal. For more than six decades in the Gadarif Region mechanised dryland farming is practised. As a result, due to continuous conventional tillage, extensive woodcutting and over-grazing, serious soil degradation occurred. To discuss the impact of LULC changes on the selected soil properties, three main LULC types were chosen to be investigated, namely: cultivated land, fallow land and woodland. In addition to the reference soil profiles, soil samples were also collected at two depths from ten sample plots for each of the LULC type. For these soil samples, various soil properties such as texture, bulk density (BD), organic matter (OM), soil pH, electrical conductivity (EC), sodium adsorption ratio (SoAR), phosphorous (P) and potassium (K) were analysed. Laboratory tests proved that soil properties were significantly affected by LULC changes. Within the different LULC types, clay content in the surface layers (0 – 5 and 5 – 15 cm) varied from 59 % to 65 %, whereas silt fractions ranged from 27 % to 37 %. Soil BD, OM and P were significantly different (p < 0·05) across the three LULC types. Soil pH was signiﬁcantly different between cultivated land and woodland on one side and between fallow land and woodland on the other side. EC and SoAR values of fallow land were found to be signiﬁcantly different (p < 0·05) from woodland. The dryland vertisol of the Gadarif Region in Sudan produced more than one-third of the national production of sorghum – the main food stuff in the country. Soil compaction has been recognised as one of the major problems in crop production worldwide. Soil strength and infiltration rate are important variables for understanding and predicting the soil processes. The effects of three different landuse systems (cultivated land, fallow land and woodland) on soil compaction and infiltration rate were investigated at two sites of the study area. Site 1 represents the older one of the two. The soil penetration resistance (SPR) was measured in three depths using a manually operated cone penetrometer. Infiltration rate was measured in the field using a double-ring infiltrometer. Following the cone-penetrometer sampling, soil samples were collected to determine the variables that affect SPR and infiltration rate vs. particle size, dry BD, volumetric moisture content (VMC) and organic carbon (OC) content. Field measurements and soil samples were collected for each landuse type. The measured infiltration rate data were inserted into the Kostiakov Model in order to predict the cumulative soil water infiltration. Soil compaction for the cultivated land was 65 % larger in comparison to woodland. Woodland areas showed an increase in the infiltration rate by 87 % and 74 % compared to cultivated and fallow land respectively. Both study sites showed an increase in the dry BD when SPR is increasing, while VMC decreases with increasing SPR. Also, low OC contents were observed to be associated with high SPR values. For Site 1 the average coefficient of determination (R²) for the infiltration data fit to the Kostiakov Model were 0.65, 0.73 and 0.84 for cultivated land, fallow land and woodland respectively. However, for Site 2 they were 0.63, 0.76 and 0.78. In the Gadarif Region agriculture is the main activity and practised in many forms with a variety of environmental effects and consequences. Continuous ploughing of the cultivated land coupled with inproper soil management has contributed to soil deterioration when the landuse changed from woodland to cultivated and fallow land. Therefore, the development of sustainable landuse practises in the dryland-farming of the study area need to be improved in order to reduce the amount of soil degradation in the future
Mehrere Jahrzehnte intensiven Trockenfeldbaus in der Region von Gadarif, welche sich im östlichen Teil des Sudans befindet, führten hauptsächlich aufgrund von landwirtschaftlicher Expansion, politischen Beschlüssen der Regierung und Naturkatastrophen wie Trockenheit zu einer raschen Veränderung der Landnutzung und Landbedeckung. Das wesentliche Ziel dieser Dissertation war es, die Degradation des Landes, sowie die Auswirkungen von landwirtschaftlicher Expansion auf die Landbedeckung, den Boden und den Pflanzenbau im Untersuchungsgebiet, welches Teile der afrikanischen Sahelzone beinhaltet, abzuschätzen. Zur Analyse und Beobachtung der Veränderungen der Landnutzung und Landbedeckung wurden multi-temporale Landsat-Daten der Jahre 1979, 1989 und 1999 sowie ASTER-Daten aus dem Jahr 2009 genutzt, welche eine Fläche von schätzungsweise 1200 km² abdecken. Um Veränderungen von Landnutzung und Landbedeckung aus Satellitenbilddaten zu bestimmen, wurde ein auf Post-Klassifikation basierendes Vergleichsverfahren angewandt. Sechs Landnutzungs- und Landbedeckungsklassen, welche die Namen bewirtschaftetes Land, brach liegendes Land, Waldgebiet, Ödland, besiedeltes Land und Wasserfläche tragen, wurden während des Klassifikationsprozesses bestimmt. Für die vier Aufnahmezeitpunkte der Satellitendaten lag die allgemeine Klassifikationsgenauigkeit zwischen 86 % und 92 %. Während des dreißigjährigen Untersuchungszeitraums fand eine beträchtliche Veränderung der Landnutzungs- und Landbedeckungsstruktur statt. Bewirtschaftete Flächen nahmen in ihrem Anteil signifikant zu und bedeckten innerhalb des Zeitraums von 1979 bis 2009 81 % der früheren Waldgebiete. Der Anteil von brach liegendem Land nahm lediglich während des Zeitraums von 1989 bis 1999 zu. Besiedelte Gebiete breiteten sich über die drei Jahrzehnte kontinuierlich aus und wuchsen innerhalb des Zeitraums von 1979 bis 1989 um eine Fläche von 23 km², sowie um 21 km² zwischen 1989 und 1999 und um 27 km² in dem Zeitabschnitt 1999 – 2009. Eine detaillierte Karte zur Landnutzung und Landbedeckung des Untersuchungsgebiets wurde mittels der Nutzung dual polarisierter (HH und HV) TerraSAR-X Daten aus dem Jahr 2009 erzeugt. Die verschiedenen Landnutzungen und Landbedeckungen im Beobachtungsgelände wurden durch die Anwendung eines objektorientierten Klassifikationsansatzes analysiert. Um Bildobjekte zu erzeugen, wurde für diesen Zweck die auf einer mehrfachen Auflösung basierende Segmentierung der Software Definiens genutzt. Das Werkzeug Feature Space Optimisation wurde für die Optimierung der Attribute der TerraSAR-X Bilder angewandt, damit eine ideale Unterscheidungsfähigkeit entlang der Klassen für die Kartierung der Landnutzungen und Landbedeckungen erreicht werden kann. Zusätzlich zu jenen Klassen, welche mittels optischer Daten abgeleitet wurden, ergaben sich aus SAR-Daten noch die nachfolgenden Landnutzungen und Landbedeckungen: Abgeerntetes Land, Fels, Besiedlung 1 (Gebäude mit landestypischer Bedachung) und Besiedlung 2 (Gebäude mit Betondach). Die Koeffizienten der Rückstreuung entlang der Polarisationen HH und HV waren für einige Klassen unterschiedlich. Der günstigste Trennungsabstand der getesteten spektralen, formgebenden und texturalen Features ergab verschiedene Abweichungen zwischen den bestimmten Klassen der Landnutzung und Landbedeckung. Die Klassifikationsmaßnahmen ergaben eine Gesamtgenauigkeit von 84 % mit einem Kappa-Wert von 0.82. Genauigkeitsunterschiede entlang der Klassen wurden minimal gehalten. Seit über sechs Jahrzehnten wird in der Region Gadarif maschinenbetriebener Trockenfeldbau ausgeübt. In Folge dessen fand eine beträchtliche Abholzung und Überweidung sowie eine schwerwiegende Bodendegradation aufgrund des stetigen konventionellen Feldbaus statt. Um die Auswirkungen der Veränderung von Landnutzung und Landbedeckung auf die ausgewählten Bodenbeschaffenheiten auszuwerten, wurden drei Haupttypen der Landnutzung und Landbedeckung für die weitere Untersuchung ausgewählt: Bewirtschaftetes Land, brach liegendes Land, und Waldgebiet. Zusätzlich zu den Referenzbodenprofilen wurden außerdem für jeden Landnutzungs- und Landbedeckungstyp auf je zehn Probeflächen Bodenproben in zwei Tiefen entnommen. Bei diesen Bodenproben wurden zahlreiche Bodeneigenschaften analysiert, wie etwa Textur, Bodendichte (BD), organischer Materialgehalt (OM), pH-Wert des Bodens, elektrische Leitfähigkeit (EC), Adsorptionsgeschwindigkeit von Natrium (SoAR), Phosphorgehalt (P) sowie Kaliumgehalt (K). Labortests ergaben, dass die Bodeneigenschaften signifikant durch die Veränderungen der Landnutzung und Landbedeckung beeinflusst werden. Innerhalb der verschiedenen Landnutzungs- und Landbedeckungstypen variierte der Tongehalt in den Deckschichten (0 – 5 cm und 5 – 15 cm) zwischen 59 % und 65 %, wohin gegen sich die Lehmanteile von 27 % bis 37 % bewegten. Bodendichte, organischer Materialgehalt und Phosphorgehalt zeigten signifikant unterschiedliche Werte bei den drei Typen der Landnutzung und Landbedeckung (p < 0.05). Der pH-Wert des Bodens war signifikant verschieden zwischen bewirtschaftetem Land und Waldgebiet zum einen, und zwischen brach liegendem Land und Waldgebiet zum anderen. Die Werte der elektrischen Leitfähigkeit und der Adsorptionsgeschwindigkeit von Natrium bei brach liegendem Land erwiesen sich als maßgeblich verschieden zu jenen von Waldgebieten (p < 0.05). Auf dem Trockenland-Vertisolboden der Region Gadarif im Sudan wurde mehr als ein Drittel der nationalen Hirseproduktion erwirtschaftet – dem Haupternährungserzeugnis des Landes. Bodenverdichtung erwies sich als eines der weltweiten Hauptprobleme für den Pflanzenbau. Bodenfestigkeit und Versickerungsrate sind wichtige Variabeln, um Bodenprozesse verstehen und vorhersagen zu können. Die Auswirkungen der drei verschiedenen Landnutzungssysteme (bewirtschaftetes Land, brach liegendes Land und Waldgebiet) auf die Bodenverdichtung und Versickerungsrate wurden an zwei Standorten im Beobachtungsgebiet untersucht. Standort 1 ist der ältere der beiden. Der Widerstand der Bodenpenetration (SPR) wurde in drei Tiefen durch eine manuell angewandte Rammsonde gemessen. Mittels der Nutzung eines Doppelring-Infiltrometers ist die Versickerungsrate im Feld gemessen worden. Im Anschluss an die Probenentnahme mittels Rammsonden wurden Bodenproben gesammelt, um jene Variabeln bestimmen zu können, welche den Widerstand der Bodenpenetration sowie der Versickerungsrate im Vergleich zur Partikelgröße, zur trockenen Bodendichte, zum volumetrischen Feuchtigkeitsgehalt (VMC) und zum organischen Karbongehalt (OC) beeinflussen. Für jeden Landnutzungstypen wurden Feldmessungen durchgeführt und Bodenproben entnommen. Die gemessenen Daten der Versickerungsrate wurden in das Kostiakov-Modell eingespeist, um die gesamte Bodenwasserversickerung vorhersagen zu können. Die Bodenverdichtung bei bewirtschaftetem Land war 65 % stärker als bei Waldgebiet. Für Waldgebietsflächen wurde eine Zunahme der Versickerungsrate um 87 % verglichen mit bewirtschaftetem Land und um 74 % im Vergleich zu brach liegendem Land aufgezeigt. Beide Untersuchungsstandorte zeigten eine Zunahme in der trockenen Bodendichte für den Fall, dass der Widerstand der Bodenpenetration zunimmt, während der volumetrische Feuchtigkeitsgehalt mit zunehmendem Bodenpenetrationswiderstand abnimmt. Ebenso wurde beobachtet, dass ein geringer organischer Karbongehalt in Verbindung zu hohen Widerstandswerten der Bodenpenetration steht. Bei Standort 1 passte der durchschnittliche Bestimmungskoeffizient (R²) der Versickerungsrate zum Kostiakov-Modell mit den Werten 0.65 für bewirtschaftetes Land, 0.73 für brach liegendes Land und 0.84 für Waldgebiet. Für Standort 2 indessen ergaben die Werte 0.63, 0.76 und 0.78. Landwirtschaft, die in vielen Formen ausgeübt wird, ist die Haupttätigkeit in der Region Gadarif, und geht mit verschiedenartigsten Umweltauswirkungen und Konsequenzen einher. Kontinuierliche Feldbestellung des bewirtschafteten Landes, verbunden mit ungeeigneter Bodenbewirtschaftung, hat sich seit jenem Zeitpunkt, als sich die Landnutzung von Waldgebiet zu bewirtschaftetem und brach liegendem Land änderte, zu Bodenschädigung geführt. Daher muss die Entwicklung nachhaltiger Landnutzungspraktiken beim Trockenfeldbau im Untersuchungsgebiet verbessert werden, damit in Zukunft der Umfang der Bodendegradation verringert werden kann

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Al-Fares, Wafi [Verfasser], Christiane [Akademischer Betreuer] Schmullius, and Sören [Akademischer Betreuer] Hese. "Historical land use, land cover classification and its change detection mapping using Different Remotely Sensed Data from LANDSAT (MSS, TM and ETM+) and Terra (ASTER) sensors : a case study of the Euphrates River Basin in Syria with focus on agricultural irrigation projects / Wafi Al-Fares. Gutachter: Christiane Schmullius ; Sören Hese." Jena : Thüringer Universitäts- und Landesbibliothek Jena, 2012. http://d-nb.info/1028857896/34.

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Richter, Dietmar. "Flächennutzungswandel in Tirana : Untersuchungen anhand von Landsat TM, Terra ASTER und GIS." Universität Potsdam, 2007. http://opus.kobv.de/ubp/volltexte/2007/1301/.

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Die Zuwanderung nach Tirana führte im Verlauf der 1990er Jahre zu einem enormen Flächenverbrauch auf Kosten landwirtschaftlicher Flächen im Umland der albanischen Hauptstadt. Im Rahmen der vorliegenden Arbeit wird die Entwicklung des rasanten Flächenverbrauchs mit computergestützten Methoden dokumentiert. Grundlage der Untersuchung bilden zwei zu unterschiedlichen Zeitpunkten (1988 und 2000) aufgenommene Satellitenszenen, mit Hilfe derer eine Änderungsanalyse durchgeführt wird. Ziel der Änderungsanalyse ist es, den Flächennutzungswandel zu analysieren, Daten zu generieren und die Ergebnisse in geeigneter Weise zu visualisieren. Zu den protagonistischen Verfahren der Änderungsanalyse zählen sowohl die Maximum-Likelihood Klassifikation sowie ein wissensbasierter Klassifizierungsansatz. Die Ergebnisse der Änderungsanalyse werden in Änderungskarten dargestellt und mittels einer GIS-Software statistisch ausgewertet.

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Mendoza, Nolorbe Juan Neil. "Exploración de aguas subterráneas en la región Lambayeque – Perú usando imágenes Landsat y Aster." Master's thesis, Universidad Nacional Mayor de San Marcos, 2012. https://hdl.handle.net/20.500.12672/15193.

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Publicación a texto completo no autorizada por el autor
Con el propósito de complementar las diferentes técnicas de prospección geofísica aplicada a la exploración del agua subterránea se propone el uso de imágenes satelitales Landsat y Aster. El área de estudio es la Región Lambayeque que está ubicada en la costa norte del Perú, entre las coordenadas geográficas 5º 28' 36'' y 7º 14' 37'' de latitud Sur y 79º 41' 30'' y 80º 37' 23'' de longitud Oeste. Los datos utilizados son las imágenes registradas por el sensor ETM+ del satélite Landsat-7, ortorectificadas y con un porcentaje de nubes inferior al 10% de fecha 31-10- 2000 y las imágenes del modelo de elevación digital ASTER GDEM. Se realiza un análisis visual y estadístico de los datos imágenes usando diferentes técnicas como el ajuste de histogramas, composición de colores, componentes principales, filtros y fusiones de imágenes de diferentes resoluciones espaciales, para identificar unidades hidrogeológicas y el patrón de drenaje natural del área de estudio. Con los datos imágenes ETM+ de las bandas 3 y 4 se calcula el índice de vegetación diferencial normalizado (NDVI) y con los datos imágenes ETM+ de la banda 6 se estima la temperatura de la superficie terrestre del área de estudio. Los resultados del análisis estadístico de las imágenes ETM+ y ASTER GDEM se usan para clasificar mediante un árbol de decisiones las zonas potencialmente con agua subterránea, el resultado es un mapa temático con zonas potencialmente con agua subterránea. El mapa temático es validado parcialmente con el inventario de pozos de agua subterránea realizado por INRENA en la parte media baja de la cuenca ChancayLambayeque (INRENA, 2001) observando que el 73% de los pozos están ubicados dentro de las zonas referidas como potencialmente con agua subterránea. El resultado obtenido no es determinante en la existencia de acuíferos libres, se requieren de técnicas in situ para la determinación precisa de éstos. Sin embargo, el método proporciona información a nivel regional de zonas potencialmente con agua subterránea de la Región Lambayeque.
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Weber, Nadine. "Meso- und mikroskalige Untersuchungen der Landoberflächentemperaturen von Berlin." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2009. http://dx.doi.org/10.18452/15972.

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Städtische Gebiete unterscheiden sich von Flächen mit ruraler Prägung, im Ergebnis sind sie stark modifiziert bezüglich ihrer Strahlungs- und Energiebilanz. In der vorliegenden Arbeit werden die Oberflächentemperaturen der Metropole Berlin im Mesomaßstab, unter Verwendung von Satellitenaufnahmen der ASTER- und Landsat-5- und 7-Sensoren untersucht sowie durch zusätzliche Messungen mit einer Thermalbildkamera über einen Zeitraum von 17 Monaten im Mikromaßstab erweitert. Diese Daten kombiniert mit GIS-basierten Informationen über die Landnutzungs- und Strukturtypen werden für die Analyse der räumlichen und zeitlichen Verteilung der Oberflächentemperaturen genutzt ebenso wie zur Ermittlung funktioneller Beziehungen zwischen dem thermischen Verhalten der Oberflächen und der zugehörigen Stadtstruktur. Bei der Auswertung geht es vorrangig um physikalische Prozesse und Eigenschaften, die einen Einfluss auf energetische Flüsse und ihre Interaktion mit städtischen Oberflächen haben. Eine thermische Charakteristik einzelner Bezirke über verschiedene Nutzungsklassen bis hin zu einzelnen Materialien wird erstellt. Dabei sind Temperaturdifferenzen von mehreren zehntel Kelvin zwischen den typischen städtischen Oberflächen Dächern und Rasenflächen zu beobachten. Die Resultate zeigen, dass die Verteilung der LST sehr verschieden ist und stark korreliert mit den Landbedeckungen. Es wird dargestellt, welche Stadtstrukturen besonders thermisch belastet sind, welche individuelle thermische Bedeutung einzelne Materialien haben. Besonderes Augenmerk wird auf Möglichkeiten der Beeinflussung durch Abschattung gelegt. Durch Verschattung können Differenzen der Oberflächentemperaturen von mehr als 10 Kelvin erreicht werden. Abschließend werden Modellierungen zur Verifizierung der Kameramessungen sowie zum Aufzeigen des Einflusses minimaler Änderungen in kleinräumigen Klimaten genutzt.
Urban areas differ from surfaces of rural character. They are very modified in their radiation- and energy balance. In this study land-surface temperatures of the city of Berlin are analyzed with the help of satellite pictures of the ASTER- and Landsat-5 and -7 sensors in mesoscale and then extended by extra measurements of an infrared camera in microscale over the course of 17 months. This data combined with GIS based information on different land use and -structures are used for the analysis of spatial and time distribution, as well as for the determination of functional relations between thermal behaviour of surfaces and the related urban structures. The evaluation mainly deals with physical processes and properties that have an influence on energetic flows and their interactions with urban surfaces. A thermal characteristic of individual districts, from different land use classes to specific materials is being created. In this there are differences in temperature of several tenths Kelvin between the typical urban surfaces of roofs and grass areas visible. The results show that the distribution of the LST varies immensely and correlates with the land coverage. It is shown, what urban structures are most thermic burdened, what individual significance specific materials have. Special attention is paid to the different possibilities of the influence through shadow. It is possible to reach a difference of surface temperatures of more than 10 Kelvin by shadow. At the end models with the 3-dimensional ENVImet are used to verify the camera measurements as well as to show the influence of minimal changes in microscale climate.

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Kirk, Judith. "Defining regenerating fire scars with Landsat TM data /." Title page, contents and abstract only, 1990. http://web4.library.adelaide.edu.au/theses/09AR/09ark59.pdf.

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Tetzlaff, Anke. "Coal fire quantification using ASTER, ETM and BIRD satellite instrument data." Diss., [S.l.] : [s.n.], 2004. http://edoc.ub.uni-muenchen.de/archive/00004398.

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Padgett-Vasquez, Steve. "Tracking landscape changes in the Upper Cahaba River watershed and its tributaries (1974-2007) using Landsat and ASTER multipsectral image." Birmingham, Ala. : University of Alabama at Birmingham, 2010. https://www.mhsl.uab.edu/dt/2010m/padgett-vasquez.pdf.

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El-Sawaf, Amro. "Monitoring changes in field geometry using LANDSAT digital data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/mq22593.pdf.

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Books on the topic "Landsat and ASTER Data"

1

Tilton, James C. Landsat-4 and Landsat-5 multispectral scanner coherent noise characterization and removal. Greenbelt, Md: Goddard Space Flight Center, 1988.

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Rand, Robert S. Multispectral image maps from Landsat thematic mapper data. Fort Belvoir, Va: U.S. Army Corps of Engineers, Engineer Topographic Laboratories, 1991.

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Zevenbergen, Adrian W. Runoff curve numbers for rangeland from Landsat data. Beltsville, Md: Hydrology Laboratory, Agricultural Research Service, U.S. Dept. of Agriculture, 1985.

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Griffiths, Geoffrey Hugh. Mapping rangeland vegetation in Northern Kenya from Landsat data. Birmingham: University of Aston. Department of Civil Engineering and Construction, 1985.

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Jacobson, John E. Shrubsteppe mapping of eastern Washington using landsat satellite thematic mapper data. [Olympia, Wash.]: Spatial Data Management Section, Science Division, Wildlife Program, Washington Dept. of Fish and Wildlife, 2000.

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Goddard Space Flight Center. Earth Observing System. Vol.IIa Data and information systems: Report of the EOS Data Panel. Greenbelt, Md: Goddard Space Flight Center, 1986.

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Privatizing government information: The effects of policy on access to Landsat satellite data. Metuchen, NJ: Scarecrow Press, 1995.

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Martin, Larry R. G. Monitoring urban development in the Toronto Region employing Landsat Earth Satellite data. [Ottawa, Ont: Canada Mortgage and Housing Corporation], 1985.

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Martinuzzi, Sebastian. Creating cloud-free landsat ETM+ data sets in tropical landscapes: Cloud and cloud-shadow removal. Portland, OR: U.S. Dept. of Agriculture, Pacific Northwest Research Station, 2007.

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Vinluan, Randy John N. Coastal habitat mapping in support of fisheries management using Landsat 7 ETM + data. Quezon City, Philippines: Fisheries Resource Management Project, 2004.

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Book chapters on the topic "Landsat and ASTER Data"

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Lasaponara, Rosa, and Nicola Masini. "Investigating Satellite Landsat TM and ASTER Multitemporal Data Set to Discover Ancient Canals and Acqueduct Systems." In Computational Science and Its Applications – ICCSA 2012, 497–511. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31137-6_38.

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Lanorte, Antonio, Fortunato De Santis, Angelo Aromando, and Rosa Lasaponara. "Low Cost Pre-operative Fire Monitoring from Fire Danger to Severity Estimation Based on Satellite MODIS, Landsat and ASTER Data: The Experience of FIRE-SAT Project in the Basilicata Region (Italy)." In Computational Science and Its Applications – ICCSA 2012, 481–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31137-6_37.

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Perry, Sandra, and Fred Kruse. "ASTER Data Use in Mining Applications." In Land Remote Sensing and Global Environmental Change, 301–23. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6749-7_14.

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Netzband, Maik, Elisabeth Schöpfer, and Matthias S. Möller. "Monitoring Urban Change with ASTER Data." In Land Remote Sensing and Global Environmental Change, 397–419. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6749-7_17.

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Toutin, Thierry. "ASTER Stereoscopic Data and Digital Elevation Models." In Land Remote Sensing and Global Environmental Change, 439–61. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6749-7_19.

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Rösel, Anja. "Melt Pond Determination from Landsat Satellite Data." In Detection of Melt Ponds on Arctic Sea Ice with Optical Satellite Data, 27–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-37033-5_4.

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Knepper, Daniel H. "Mapping hydrothermal alteration with Landsat Thematic Mapper data." In Remote Sensing in Exploration Geology: Golden, Colorado to Washington, D.C., June 30–July 8, 1989, 13–21. Washington, D. C.: American Geophysical Union, 1989. http://dx.doi.org/10.1029/ft182p0013.

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Arvidson, T., S. N. Goward, and D. L. Williams. "Characterizing the Landsat Global Long-Term Data Record." In Lecture Notes in Geoinformation and Cartography, 65–73. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-93962-7_6.

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Nobakht, M., M. Motagh, H. U. Wetzel, and M. A. Sharifi. "Spatial and Temporal Kinematics of the Inylchek Glacier in Kyrgyzstan Derived from Landsat and ASTER Imagery." In International Association of Geodesy Symposia, 145–49. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10828-5_21.

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Nichol, Janet E., Muhammad Bilal, Majid Nazeer, and Man Sing Wong. "Urban Pollution." In Urban Informatics, 243–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_16.

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AbstractThis chapter depicts the state of the art in remote sensing for urban pollution monitoring, including urban heat islands, urban air quality, and water quality around urban coastlines. Recent developments in spatial and temporal resolutions of modern sensors, and in retrieval methodologies and gap-filling routines, have increased the applicability of remote sensing for urban areas. However, capturing the spatial heterogeneity of urban areas is still challenging, given the spatial resolution limitations of aerosol retrieval algorithms for air-quality monitoring, and of modern thermal sensors for urban heat island analysis. For urban coastal applications, water-quality parameters can now be retrieved with adequate spatial and temporal detail even for localized phenomena such as algal blooms, pollution plumes, and point pollution sources. The chapter reviews the main sensors used, and developments in retrieval algorithms. For urban air quality the MODIS Dark Target (DT), Deep Blue (DB), and the merged DT/DB algorithms are evaluated. For urban heat island and urban climatic analysis using coarse- and medium- resolution thermal sensors, MODIS, Landsat, and ASTER are evaluated. For water-quality monitoring, medium spatial resolution sensors including Landsat, HJ1A/B, and Sentinel 2, are evaluated as potential replacements for expensive routine ship-borne monitoring.

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Conference papers on the topic "Landsat and ASTER Data"

1

Gao, Feng, Jeffrey G. Masek, Robert E. Wolfe, and Bin Tan. "Normalizing Landsat and ASTER data using MODIS data products for forest change detection." In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5650978.

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Spruce, J. P., J. Smoot, and W. Graham. "Developing new coastal forest restoration products based on Landsat, ASTER, and MODIS data." In OCEANS 2009. IEEE, 2009. http://dx.doi.org/10.23919/oceans.2009.5422124.

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Hostert, Patrick. "Sensitivity study for urban change analysis comparing Landsat-ETM+ and Terra-ASTER data." In Remote Sensing for Environmental Monitoring, GIS Applications, and Geology III. SPIE, 2004. http://dx.doi.org/10.1117/12.513302.

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Eslami, Alireza, Majid Ghaderi, Amin Beiranvand Pour, and Mazlan Hashim. "Integration of ASTER and Landsat TM satellite data for lithological mapping and chromite prospecting." In 2015 International Conference on Space Science and Communication (IconSpace). IEEE, 2015. http://dx.doi.org/10.1109/iconspace.2015.7283783.

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Meng, Xiangchen, Hua Li, Yongming Du, Qinhuo Liu, Jinshan Zhu, and Lin Sun. "Retrieving land surface temperature from Landsat 8 TIRS data using RTTOV and ASTER GED." In IGARSS 2016 - 2016 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2016. http://dx.doi.org/10.1109/igarss.2016.7730121.

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Zhang, Yuhuan, Bo Zhong, Qinhuo Liu, Hua Li, and Lin Sun. "BRDF of Badain Jaran Desert retrieval using Landsat TM/ETM+ and ASTER GDEM data." In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6049475.

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Nori, Wafa, and Elmar Csaplovics. "Integrated analysis of Aster and Landsat data to map land cover change using vegetation indices." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2013. http://dx.doi.org/10.1117/12.2027700.

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Qiu, Hong-lie, and Junping Zhong. "Remote sensing of landcover changes in Turfan Basin of northwest China using Landsat and ASTER data." In Multispectral Image Processing and Pattern Recognition, edited by Qingxi Tong, Yaoting Zhu, and Zhenfu Zhu. SPIE, 2001. http://dx.doi.org/10.1117/12.441400.

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Adiri, Zakaria, Abderrazak El Harti, Amine Jellouli, Lhou Maacha, and El Mostafa Bachaoui. "Lithological mapping using multispectral ASTER and Landsat 8 data in the Bas Drâa inlier, Moroccan Anti Atlas." In SPIE Remote Sensing, edited by Ulrich Michel, Karsten Schulz, Manfred Ehlers, Konstantinos G. Nikolakopoulos, and Daniel Civco. SPIE, 2015. http://dx.doi.org/10.1117/12.2193775.

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Teodoro, Ana Claudia Moreira, Ubaldo Gemusse, and Alexandre Lima. "Pegmatite spectral behavior considering ASTER and Landsat 8 OLI data in Naipa and Muiane mines (Alto Ligonha, Mozambique)." In Earth Resources and Environmental Remote Sensing/GIS Applications, edited by Ulrich Michel and Karsten Schulz. SPIE, 2018. http://dx.doi.org/10.1117/12.2325555.

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Reports on the topic "Landsat and ASTER Data"

1

Toutin, Th. DEM Generation from New VIR Sensors: IKONOS, ASTER and Landsat-7. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219770.

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Toutin, Th, and P. Cheng. DEM Generation with ASTER Stereo Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219789.

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Toutin, Th. 3D Topographic Mapping with ASTER Stereo Data in Rugged Topography. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2002. http://dx.doi.org/10.4095/219818.

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Cheng, P., Th Toutin, and T. Victor. Unlocking the Potential for Landsat 7 Data. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219639.

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Teillet, P. M., G. Fedosejevs, R. R. Irish, J. Barker, and B L Markham. Landsat-7 ETM+ and Landsat-5 TM Cross-Calibration Based on Tandem Data Sets. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219729.

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6

Rand, Robert S., John E. Anderson, and Donald A. Davis. Multispectral Image Maps from Landsat Thematic Mapper Data. Fort Belvoir, VA: Defense Technical Information Center, September 1991. http://dx.doi.org/10.21236/ada240453.

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Lewis, April A. Using Landsat 5 TM Data to Investigate Flood Phenomena. Fort Belvoir, VA: Defense Technical Information Center, June 1994. http://dx.doi.org/10.21236/ada303908.

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D'Entremont, Robert P. The Adjacency Effect of Clouds in LANDSAT MSS Data. Fort Belvoir, VA: Defense Technical Information Center, July 1993. http://dx.doi.org/10.21236/ada271444.

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Rencz, A. N., J. Aylsworth, and W. W. Shilts. Application of Landsat Tm Data To Mapping Surficial Geology. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1992. http://dx.doi.org/10.4095/133343.

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Teillet, P. M., J. Barker, B. L. Markham, R. R. Irish, G. Fedosejevs, and J. C. Storey. Radiometric cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM aensors based on tandem data sets. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/219720.

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