Petroleum exploration in Africa from space

Hydrocarbons 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

Supporting hydrocarbon exploration in new venture areas with optical remote sensing

In 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

Analysis of changes in crop farming in the Dudh Koshi (Nepal) driven by climate changes

Nepal is one of the poorest nations of the world and the Koshi Basin includes some of the poorest regions of this country. It's farming system is subsistence agriculture, mainly rainfed, with crop productivity among the lowest in South Asia. Nepal is also severely impacted by climate changes, such as retreat of glaciers, rise in temperature, erratic rainfalls and increase in frequency of extreme weather. This paper describes the spatio-temporal evolution of cultivated land in Dudh Koshi during the last four decades (1970s-2010s), by mapping the farming of its four main cereals in the districts of Solukhumbu, Okhaldunga and Kothang from space. The analysis of satellite time series showed a 10% of increment in farmland from 1970s to 1990s, and about 60% in the following twenty years. With a shift of cropping to higher altitudes. Data belonging to of the second twenty years are strongly correlated with the population growth observed in the same period (0.97<0.99) and consistent with the average daily caloric intake. Finding confirms the result of recent studies that agriculture practices once distributed in lowland areas have now spread to higher altitudes and seems to suggest that demographic and socioeconomic pressures are driving the expansion, while climatic and topographic parameters are just channeling the expansion. Apart from any policies that could change the tack, Dudh Koshi should be able to meet the increasing demand of cereals in the near future and climate seems not being a limiting factor for further development as it will be the availability of an irrigation system

Hierarchical classification of complex landscape with VHR pan-sharpened satellite data and OBIA techniques

Land-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

Optical 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

Satellite remote sensing for hydrocarbon exploration in new venture areas

Multispectral remote sensing is an emerging technology for the oil & gas industry. Since its first application, Earth Observation has seen an enormous breakthrough in a brand-new field such as geosciences for hydrocarbon exploration: both the awareness of the microseepage phenomenon and data processing methods for its detection have greatly improved in the last years. This paper describes a case study of microseepage signals detection in the East Africa Rift System, onshore of Lake Albert, using multi-sensor satellite time series. Results clearly show that the spectral anomalies identified from satellite are closely related to the known oilfields and that the microseepage maps can provide new high-quality data to reduce exploration risk

Integration of COSMO-SkyMed and GeoEye-1 Data With Object-Based Image Analysis

This 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

Detection of moving vehicles with WorldView-2 satellite data

Traffic 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

Monitoring large oil slick dynamics with moderate resolution multispectral satellite data

Accidental release of crude oil into the sea due to human activity causes water pollution and heavy damages to natural ecosystems killing birds, fish, mammals and other organisms. A number of monitoring systems are used for tracking the spills and their effects on the marine environment, as well as for collecting data for feeding models. Among them, Earth observation technologies play a crucial role and moderate spatial resolution satellite systems are able to collect images with a very short revisit time or even daily. This paper describes the use of MODIS (Moderate Resolution Imaging Spectroradiometer) data for monitoring large oil slicks with the Fluorescence/Emissivity Index (FEI) and Object Based Image Analysis (OBIA). Two case studies are presented: the Deepwater Horizon (2010) and the Campos Basin (2011) oil spill accidents. Results show that is possible to track the dynamics of the slick both for massive and long-lasting accidents and for smaller and very quick accidents. The main advantages of the method proposed are a straightforward implementation, a fast and semi-automated data processing and the capability of integration of daytime and nighttime acquisitions, as well as its adaptability to different sensors

Integrating Landsat and Sentinel-2 time series for enhancing the estimation of soil erosion in the Alps

Soil erosion is a naturally dynamic phenomenon, but human activities, human-induced forces and climate change have further accelerated this process. As a consequence, the mean annual soil loss rate in Europe exceeds the average soil formation rate. That means soil can be actually considered as a non-renewable resource. Therefore, understanding spatial patterns and temporal trends of soil loss could support government land use policies and strategies for reducing this overlooked natural hazard. Among the factors driving soil erosion dynamics, meteorological forcings and land cover/land use vary in time and space, while other factors like soil properties (i.e. their pedology) and topography can be considered constant over time. Soil erosion can be reduced acting on land cover/land use, but, this factor is the most complex and expensive to be continuatively monitored by national or regional agencies through field campaign or ad hoc surveys. However, satellites for Earth Observation can help a lot in monitoring spatial and temporal land cover changes and the cover management factor can be effectively estimated through spectral indices (e.g. NDVI). This paper describes an ad hoc implementation of the Revised Universal Soil Loss Equation (RUSLE) model to provide dynamic maps of soil erosion. In this work, we evaluate the benefits of integrating Landsat and Sentinel-2 time series to study soil erosion in an alpine river basin of Italy. Thus, reducing the revisit time up to 5 days allows to consider the effects on soil erosion of land use/land cover modification caused by extreme meteorological events, that otherwise would be missed if estimating soil erosion through institutional product of land use/land cover. Besides, the high revisit time provided by the combination of the two sensors could provide snow cover maps of the study area, useful for quantifying the contribution to erosion of soil covered by snow. Results demonstrate that the ESA’ Sentinel-2 twin satellites could effectively enhance the estimate of the cover management factor, both spatially and temporally