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This paper describes the potentialities of data integration of high spatial resolution multispectral (MS) and single-polarization X-band radar for object-based image analysis (OBIA) using already available algorithms and techniques. GeoEye-1 (GE1) MS images (0.5/2.0 m) and COSMO-SkyMed (CSK®) 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 (OA) and about 25%–35% for k coefficient] of MS data, and unlike the outcomes of some new researches, also confirmed that, with appropriate preprocessing, traditional OBIA could also be applied to X-band radar data without the need of developing ad hoc algorithms.

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.

This paper describes the use of a new multi-image co-registration method tuned for SAR multi-temporal data collected with different orbits, viewing angles and polarization. Tests were performed using COSMO-SkyMed (X-band) and Sentinel-1 data (C-band) time series imaged in stripmap and spotlight modes. Results shows an overview sub-pixel accuracy also for the challenging co-registration of dual orbit image stacks (ascending vs. descending), where other methods fail.

In 2006 the Italian and the Canadian Space Agencies started a collaboration to evaluate the feasibility of the Joint Hyperspectral Mission (JHM), a new mission for Earth Observation devoted to environmental applications. JHM was designed to operate with a �0 m resolution hyperspectral sensor able to collect 210 narrow spectral bands in the range of 400-2500 nm. This paper presents a study carried on for the Italian Space Agency during Phase A, aimed to suggest an optimal spectral setup for the land-cover key application. Just referring to the mapping of forest species, results on simulated JHM data suggested that an optimal configuration can be obtained using a 50 nm bandwidth.

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.

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.

Satellite remote sensing is the process of collecting information about the Earth surface from the space through the measure of electromagnetic radiation. Nowadays, remote sensing is a mature technology used to extract, analyze, and detect changes of geographic and thematic information over large areas, inaccessible sites, or where only limited knowledge is available. In this chapter, we describe how satellite’s data collected over Multan (Pakistan) have been used for mapping and monitoring the dynamics of the urban area. A multi-scale approach allowed to evaluate the urban growth of the municipality area occurred in the last 2 decades with medium-resolution Landsat-5/TM time series. Urban green plots and infrastructures (buildings and roads) have been mapped at the local scale of the historic Walled City with the state-of-the-art GeoEye-1 and WorldView-2 very high-resolution multispectral imagery.

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 Moderate-Resolution Imaging Spectroradiometer data for monitoring large oil slicks with the fluorescence/emissivity index and object-based image analysis. Two case studies are presented: the Deepwater Horizon (2010) and the Campos Basin (2011) oil spill accidents. Results show that it 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.

This 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.

This paper describes the use of airborne hyperspectral remote sensing for mapping asbestos roofs in an orographic complex area in Northern Italy, the Aosta Valley. Using training samples collected during field surveys, thematic classification was able to detect the majority of asbestos surfaces. Considering the total amount of asbestos areas validation showed a correct detection of about 80%, while considering the number of asbestos roofs correctly detected this value decreased to 43%. This difference pointed out a clear relationship between data spatial resolution and asbestos roofs area. The study served as a first approach to an extensive use of the remote sensing technology for asbestos mapping over large areas and the encouraging results will support Public Administrations for decision making strategies and policies for their removal.