Oil pollution at sea is one of the most critical and destructive consequences due to human activity in marine areas, with an impact on the environment that requires decades to be reabsorbed. Satellite based remote sensing systems could be implemented for a surveillance and monitoring network. At present, the SAR system is the most widely used sensor for this purpose as it offers day and night high resolution images and it is not influenced by the presence of cloud cover, dust or smoke over the scene. The operational capabilities of these kinds of sensors are limited by factors such as the low revisiting frequency over the scene, the data's high cost, and finally the inability to make a certain assessment of the nature of the detected event without collecting data from complementary instruments. SAR sensor limitations could be complemented by optical sensor capabilities. In particular, multispectral sensors like MODIS offer a high number of spectral bands to detect, identify, classify and describe an oil spill event, and guarantee daily image frequency. However, optical sensors are highly dependent on meteorological conditions over the study area, they offer only low and medium resolution images and, finally, dedicated algorithms for image processing do not exist at present. For these reasons, the optical sensors play only a subordinate role with reference to SAR sensors. This work shows the results achievable through the development of dedicated algorithms for automatic image processing from MODIS data, and a method to classify and describe oil spill events

Agricultural activities conducted in the Great Rift Valley of Kenya show a significant decline of productivity levels. This phenomenon is mainly related to limited availability of water resources, lack of supporting irrigation, and harvesting techniques ineffectiveness. Production risks reduction is closely related with a better use of water resources and a better understanding of the effects resulting from the multiple interactions between climate, agricultural vegetation, soil type, and crops management techniques. In this paper, a remote and automatic agricultural monitoring system is presented as an effective alternative to the most traditional in situ measurements and observations. We investigated the use of phenological information extracted from satellite imagery combined with crop calendar and supported by agro-ecological zoning (AEZ) in accurate crop classification and monitoring. Vegetation indices extracted from Landsat 8 imagery are capable to track the vegetation development through the year, then phenological profiles can be extracted and implemented into a multitemporal automatic classification process to detect agricultural areas and to discriminate among different crop species. The phenological profiles extracted by satellite imagery are compared with crop calendar data compiled by FAO for the area of interest. The classification procedure is supported by AEZs based on crop modeling and environmental matching procedures in order to identify crop-specific environmental limitations under assumed levels of inputs and management conditions. The FAO crop water productivity model AquaCrop is calibrated for wheat and maize yield mapping in the central highland of Kenya, handling both environmental and phenological data. The combined use of phenological data and AEZs results in a robust methodology with a classification overall accuracy of 91.35%. A good model performance is obtained relative to yield predictions, with R of 0.69 and 0.72.

Geostationary satellites like meteosat second generation (MSG) allow the detection and monitoring of thermal anomalies (wild fires and volcanic eruptions) with a refresh frequency ranging from 5 to 15 min. Such a frequency meets the requirements of the institutions involved in monitoring and containing the fire events and could provide information on the temporal behavior of the fire (through fire radiative power) and the spatial distribution of the events with the related hazard for the population and infrastructure when more occurrences are simultaneously present. A limitation of the operational applicability of this tool is currently represented by the low spatial resolution of the MSG/SEVIRI sensor ranging from 3 km at the equator to 4.5 km at Mediterranean latitudes. The limitations related to the sensitivity of the geostationary sensor to fire sizes have been, at least in part, overcome by introducing specific algorithms. However, the reduced accuracy in the geographic localization of the fire, which can, in principle, occupy any position in an area of about 16 km 2 (at Mediterranean latitudes), makes this information not very interesting for the institutions involved in firefighting. This paper analyzes the feasibility of improving the localization of the thermal anomalies (hotspots) by combining images acquired simultaneously from different MSG satellites located at different longitudes. In particular, we combine the images acquired by MSG-9 located at long. 9.0°, MSG-10 located at 0.0°and MSG-8 located at long. 41.5°. The results confirm the possibility of improving the accuracy of the detection by exploiting the observation of the events from different positions in space.

Oil pollution is one of the most destructive consequences due to human activities in the marine environment. Oil wastes come from many sources and take decades to be disposed of. Satellite based remote sensing systems can be implemented into a surveillance and monitoring network. In this study, a multi-temporal approach to the oil spill detection problem is investigated. Change Detection (CD) analysis was applied to MODIS/Terra and Aqua and OLI/Landsat 8 images of several reported oil spill events, characterized by different geographic location, sea conditions, source and extension of the spill. Toward the development of an automatic detection algorithm, a Change Vector Analysis (CVA) technique was implemented to carry out the comparison between the current image of the area of interest and a dataset of reference image, statistically analyzed to reduce the sea spectral variability between different dates. The proposed approach highlights the optical sensors’ capabilities in detecting oil spills at sea. The effectiveness of different sensors’ resolution towards the detection of spills of different size, and the relevance of the sensors’ revisiting time to track and monitor the evolution of the event is also investigated.

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The SBAM (Satellite Based Agricultural Monitoring) project, funded by the Italian Space Agency aims at: developing a validated satellite imagery based method for estimating and updating the agricultural areas in the region of Central-Africa; implementing an automated process chain capable of providing periodical agricultural land cover maps of the area of interest and, possibly, an estimate of the crop yield. The project aims at filling the gap existing in the availability of high spatial resolution maps of the agricultural areas of Kenya. A high spatial resolution land cover map of Central-Eastern Africa including Kenya was compiled in the year 2000 in the framework of the Africover project using Landsat images acquired, mostly, in 1995. We investigated the use of phenological information in supporting the use of remotely sensed images for crop classification and monitoring based on Landsat 8 and, in the near future, Sentinel 2 imagery. Phenological information on crop condition was collected using time series of NDVI (Normalized Difference Vegetation Index) based on Landsat 8 images. Kenyan countryside is mainly characterized by a high number of fragmented small and medium size farmlands that dramatically increase the difficulty in classification; 30 m spatial resolution images are not enough for a proper classification of such areas. So, a pan-sharpening FIHS (Fast Intensity Hue Saturation) technique was implemented to increase image resolution from 30 m to 15 m. Ground test sites were selected, searching for agricultural vegetated areas from which phenological information was extracted. Therefore, the classification of agricultural areas is based on crop phenology, vegetation index behaviour retrieved from a time series of satellite images and on AEZ (Agro Ecological Zones) information made available by FAO (FAO, 1996) for the area of interest. This paper presents the results of the proposed classification procedure in comparison with land cover maps produced in the past years by other projects. The results refer to the Nakuru County and they were validated using field campaigns data. It showed a satisfactory overall accuracy of 92.66 % which is a significant improvement with respect to previous land cover maps.