1,720,968 research outputs found
Petroleum exploration in Africa from space
Full text linkHydrocarbons are nonrenewable resources but today they are the cheaper and easier energy we have access and will remain the main source of energy for this century. Nevertheless, their exploration is extremely high-risk, very expensive and time consuming. In this context, satellite technologies for Earth observation can play a fundamental role by making hydrocarbon exploration more efficient, economical and much more eco-friendly. Complementary to traditional geophysical methods such as gravity and magnetic (gravmag) surveys, satellite remote sensing can be used to detect onshore long-term biochemical and geochemical alterations on the environment produced by invisible small fluxes of light hydrocarbons migrating from the underground deposits to the surface, known as microseepage effect. This paper describes two case studies: one in South Sudan and another in Mozambique. Results show how remote sensing is a powerful technology for detecting active petroleum systems, thus supporting hydrocarbon exploration in remote or hardly accessible areas and without the need of any exploration license
Detection of moving vehicles with WorldView-2 satellite data
No full textTraffic monitoring in urban areas is a complex issue and recent Remote Sensing technologies can play an important role in planning and monitoring the urban environment. In this study a semi-automatic object-oriented workflow was designed to detect moving vehicles and their speed from single pass WorldView-2 multispectral data. The time lag in data recording between each spectral band causes a small image displacement of moving objects and this discrepancy is used to detect moving vehicles, their speed and direction of travel. The method proposed was applied to a very complex study area in the historical core of city of Multan, in the Pakistani southern province of Punjab, where very small and extremely dense built-up old style houses are mixed together with narrow roads and bazaar streets. First results show interesting applications of this new technology, with achieved accuracies of about 67% evaluated comparing automatic detection vs. manual interpretation
Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques
Full text linkLand-cover/land-use thematic maps are a major need in urban and country planning. This paper demonstrates the capabilities of Object Based Image Analysis in multi-scale thematic classification of a complex sub-urban landscape with simultaneous presence of agricultural, residential and industrial areas using pan-sharpened very high resolution satellite imagery. The classification process was carried out step by step through the creation of different hierarchical segmentation levels and exploiting spectral, geometric and relational features. The framework returned a detailed land-cover/land-use map with a Cohen’s kappa coefficient of 0.84 and an overall accuracy of 85%
Mapping large-scale microseepage signals for supporting oil and gas exploration in new ventures
No full textOptical remote sensing is emerging among non-conventional geophysical methods for oil & gas exploration and mineral prospecting. Complementary to all traditional technologies such as seismic, magnetic, gravity or electric methods, multispectral imaging is able to detect long-term biochemical and geochemical environmental alterations, known as microseepage effect, produced by invisible small fluxes of light hydrocarbons migrating from the underground deposits to the surface. This paper describes a case study where satellite multispectral data were used to detect large-scale microseepage signals nearby Lake Turkana (Republic of Kenya). The satellite analysis highlighted the presence of invisible surface signals on top of several oilfields discovered only many years after the image collection
Integration of COSMO-SkyMed and GeoEye-1 Data With Object-Based Image Analysis
Full text linkThis paper describes the potentialities of data integration of high spatial resolution multispectral and single-polarization X-band radar for Object Based Image Analysis (OBIA) using already available algorithms and techniques. GeoEye-1 multispectral images (0.5/2.0 m) and COSMO-SkyMed stripmap images (3.0 m) were collected over a complex test site in the Venetian Lagoon, made up of an intricate mixture of settlements, cultivations, channels, roads, and marshes. The validation confirmed that the integration of optical and radar data substantially increased the thematic accuracy (about 20-30% for overall accuracy and about 25-35% for k coefficient) of multispectral data and, unlike the outcomes of some new researches, also confirmed that with appropriate pre-processing traditional OBIA could be applied also to X-band radar data without the need of developing ad hoc algorithms
Minimum noise fraction transform for improving the classification of airborne hyperspectral data: two case studies
No full textThis paper investigates the use of Minimum Noise Fraction (MNF) components to improve the spectral separability of two specific thematic classes in airborne hyperspectral imagery using Spectral Angle Mapper (SAM). Particularly, we compared trends on data distribution before and after MNF transform. Two different data sets recorded with the Multispectral Infrared Visible Imaging Spectrometer (MIVIS) were analyzed. In the first case study, the classification of MNF-transformed data led to an overall enhancement in mapping asbestos roofs. In the second case study, the classification of MNF-transformed data succeeded to distinguish between two different artificial lakes, whereas classification of original hyperspectral data failed. Overall, this study showed how the use of MNF as pre-processing could improve the capability to extract information from two different airborne hyperspectral data sets
Supporting hydrocarbon exploration in new venture areas with optical remote sensing
Full text linkIn past time, exploration geologists mainly used Earth Observation systems for basin-wide analysis of gravimetry, magnetomerty, structural faults, lithology and land-cover. After two decades of research, nowadays multispectral and hyperspectral remote sensing represent a cutting-edge technology in the oil and gas industry. The application fields of optical remote sensing not only range from the monitoring of the oilfields to the evaluation of pollution, but also to hydrocarbon exploration. With reference to exploration activities, the observation of the territory from above into several different wavelengths is able to supply inestimable geophysical information related to the microseepage effect, different and complementary to tradition geophysical methods.
It is almost accepted that many of the oil and gas fields leak light hydrocarbon gases along nearly vertical pathways and, thus, their detection with multi/hyperspectral imaging can support the detection of active petroleum systems. Indeed, several independent oil companies are using satellite and airborne observations for reducing exploration risks in new venture areas and for optimizing their seismic surveys.
This study shows some examples of microseepage-related geochemical and geobotanical alterations detected in several different environments, from sandy desert to vegetated savannah, both using airborne hyperspectral data and multispectral satellite time series. All the examples analyze real onshore concession blocks in Africa and Asia and results clearly show a correlation between the spectral signals recorded form remote with in situ measures, well logs, the knowledge of the subsurface and the position of known oilfields
Fusione di dati COSMO-SkyMed e Geoeye-1 per la classificazione del land cover nella laguna di Venezia
Full text linkIl presente studio si inquadra nel Programma di ricerca “The demonstration of the COSMO-SkyMed capabilities and exploitation for science and civilian applications” dell’Agenzia Spaziale Italiana e ha lo scopo di dimostrare le potenzialità dell’utilizzo congiunto di dati ottici multispettrali e SAR in banda X ad alta risoluzione geometrica per la classificazione del land-cover/land-use. Un primo test di classificazione tematica con dati GeoEye-1 e COSMO-SkyMed è stato realizzato nella zona litoranea circostante la città di Venezia, scelta per la presenza di un land-cover ricco e unico nel suo genere. Il confronto tra le classificazioni pixel-based e object-based mostrano che come la seconda dia sempre risultati più accurati. Inoltre, l’aggiunta del dato radar a quello ottico è ininfluente nel caso della classificazione pixel-based (si passa da una Overall Accuracy di 68,4% a 69,4%) mentre produce un incremento di accuratezza di circa il 15% nel caso dell’OBIA (si passa da una Overall Accuracy di 73,0% a 88,8%)
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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