Commercial satellites for Earth observation can integrate conventional positioning and tracking systems for monitoring legal and illegal activities by sea, in order to effectively detect and prevent events threatening human life and environment. This study describes an object-oriented approach to detect vessels combining high- and medium-resolution optical and radar images. Once detected, the algorithm estimates their position, length, and heading and assigns a speed range. Tests are done using WorldView-2, QuickBird, GeoEye-1, Sentinel-2A, COSMO-SkyMed, and Sentinel-1 data imaged in several test sites including China, Australia, Italy, Hong Kong, and the western Mediterranean Sea. Validation of results with data from the automatic identification system shows that the estimates for length and heading have R2 = 0.85 and R2 = 0.92, respectively. Tests for evaluating speed from Sentinel-2 time-lag image displacement show encouraging results, with 70% of estimates’ residuals within ±2 m / s. Finally, our method is compared to the state-of-the-art search for unidentified maritime object (SUMO), provided by the European Commission’s Joint Research Centre. Finally, our method is compared to the state-of-the-art SUMO. Tests with Sentinel-1 data show similar results in terms of correct detections. Nevertheless, our method returns a smaller number of false alarms compared to SUMO.
Soil water erosion is one of the challenges that the European Union should deal with in the next years, due to its significant impacts on agriculture and natural hazards. In this work, a RUSLE (Revised Universal Soil Loss Equation)-like model has been applied to estimate soil water erosion in a Northern Italian Alpine basin (Val Camonica) by combining meteorological forcing with topography, soil properties and land cover. In the traditional formulation, land cover classes are assigned categorized cover management factor (Cfactor) value retrieved from existing literature (C-Land Cover formulation). However, Earth observation data have been proven effective in tuning the protective effect of vegetation on soil erosion dynamics. Thus, this method has been compared with two approaches (C-Satellite and C-Land Cover+Satellite) based on satellite-derived NDVI values to discretize C-factor values at a pixel scale. The C-Satellite formulation is based on an exponential law for correlating observed NDVI and C-factor values, irrespective of land cover classes. The C-Land Cover+Satellite method is based on the integration of land cover classification with NDVI maps. NDVI values have been retrieved from Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI time series imaged from 2000 to 2017. Results of the application of the RUSLE-like proposed approach to estimate soil water erosion in an Italian alpine basin, have shown that integrating satellite-derived spectral information within the land-cover based C-factor estimate can generate a more reliable soil loss estimate related to seasonal and long-term land cover 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<R2<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.
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.
Marine routes represent a huge portion of commercial and human trades, therefore surveillance, security and environmental protection themes are gaining increasing importance. Being able to overcome the limits imposed by terrestrial means of monitoring, ship detection from satellite has recently prompted a renewed interest for a continuous monitoring of illegal activities. This paper describes an automatic Object Based Image Analysis (OBIA) approach to detect vessels made of different materials in various sea environments. The combined use of multispectral and SAR images allows for a regular observation unrestricted by lighting and atmospheric conditions and complementarity in terms of geographic coverage and geometric detail. The method developed adopts a region growing algorithm to segment the image in homogeneous objects, which are then classified through a decision tree algorithm based on spectral and geometrical properties. Then, a spatial analysis retrieves the vessels’ position, length and heading parameters and a speed range is associated. Optimization of the image processing chain is performed by selecting image tiles through a statistical index. Vessel candidates are detected over amplitude SAR images using an adaptive threshold Constant False Alarm Rate (CFAR) algorithm prior the object based analysis. Validation is carried out by comparing the retrieved parameters with the information provided by the Automatic Identification System (AIS), when available, or with manual measurement when AIS data are not available. The estimation of length shows R2=0.85 and estimation of heading R2=0.92, computed as the average of R2 values obtained for both optical and radar images.
Radiometric image normalization is one of the basic pre-processing methods used in satellite time series analysis. This paper presents a new multi-image approach able to estimate the parameters of relative radiometric normalization through a multiple and simultaneous regression with a dataset of a generic number of images. The method was developed to overcome the typical drawbacks of standard one-to-one techniques, where image pairs are independently processed. The proposed solution is based on multi-image pseudo-invariant features incorporated into a unique regression solved via Least Squares. Results for both simulated and real data are presented and discussed.
The commercial market offers several software packages for the registration of remotely sensed data through standard
one-to-one image matching. Although very rapid and simple, this strategy does not take into consideration all the
interconnections among the images of a multi-temporal data set. This paper presents a new scientific software, called
Satellite Automatic Multi-Image Registration (SAMIR), able to extend the traditional registration approach towards
multi-image global processing. Tests carried out with high-resolution optical (IKONOS) and high-resolution radar
(COSMO-SkyMed) data showed that SAMIR can improve the registration phase with a more rigorous and robust
workflow without initial approximations, user’s interaction or limitation in spatial/spectral data size. The validation
highlighted a sub-pixel accuracy in image co-registration for the considered imaging technologies, including optical and
Map production and revision in mountain areas by means of high resolution satellite images showed to be a difficult task. This paper investigates the influence of the elevation accuracy in the updating of large scale geo-databases (geo-DBs) in a mountain urban area of the Italian Western Alps using high resolution panchromatic IKONOS images. A simulation of 1:10,000 scale geo-DB update was performed for the city of Sauze d'Oulx (Susa Valley, Italy) and tested with respect to the Regional technical specifications recently adopted in northern Italy. Finally, results were compared to the official geo-DB available for the test site. Results show that, depending on the digital elevation model's accuracy used, it is possible to satisfy the requirements for the residual's tolerance but not those for residual's mean and standard deviation, thus high resolution DEMs may be needed for updating large scale geo-BDs in mountain areas.