The aim of this paper is to remark possibilities to use WorldView-2 imagery for coastline extraction. Applications are conducted on a Phlegrean area in the Campania Region (Italy): the considered range of coastline is particularly interesting because it shows two typologies of shoreline including reefs interspersed with segments of sandy beach. Two indices are used: Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI).To enhance geometric resolution of the results pan-sharpening is applied so as to obtain maps with the same pixel dimensions of the panchromatic data. To solve the problem of thresholds determination that typically affects the classification, Maximum Likelihood method based on training sites is adopted to distinguish bare soil and sea water. Best results are given by NDWI and, comparing the resultant coastline with that obtained with visual interpretation of images, shifts of less than 1 m outcome from pan-sharpened data.

Greenhouse detection and mapping via remote sensing is a complex task, which has already been addressed in numerous studies. In this research, the innovative goal relies on the identification of greenhouse horticultural crops that were growing under plastic coverings on 30 September 2013. To this end, object-based image analysis (OBIA) and a decision tree classifier (DT) were applied to a set consisting of eight Landsat 8 OLI images collected from May to November 2013. Moreover, a single WorldView-2 satellite image acquired on 30 September 2013, was also used as a data source. In this approach, basic spectral information, textural features and several vegetation indices (VIs) derived from Landsat 8 and WorldView-2 multi-temporal satellite data were computed on previously segmented image objects in order to identify four of the most popular autumn crops cultivated under greenhouse in Almería, Spain (i.e., tomato, pepper, cucumber and aubergine). The best classification accuracy (81.3% overall accuracy) was achieved by using the full set of Landsat 8 time series. These results were considered good in the case of tomato and pepper crops, being significantly worse for cucumber and aubergine. These OPEN ACCESSRemote Sens. 2015, 7 7379 results were hardly improved by adding the information of the WorldView-2 image. The most important information for correct classification of different crops under greenhouses was related to the greenhouse management practices and not the spectral properties of the crops themselves.

This paper demonstrates that accurate data concerning bathymetry as well as environmental conditions in shallow waters can be acquired using sensors that are integrated into the same marine vehicle. An open prototype of an unmanned surface vessel (USV) named MicroVeGA is described. The focus is on the main instruments installed on-board: a differential Global Position System (GPS) system and single beam echo sounder; inertial platform for attitude control; ultrasound obstacle-detection system with temperature control system; emerged and submerged video acquisition system. The results of two cases study are presented, both concerning areas (Sorrento Marina Grande and Marechiaro Harbour, both in the Gulf of Naples) characterized by a coastal physiography that impedes the execution of a bathymetric survey with traditional boats. In addition, those areas are critical because of the presence of submerged archaeological remains that produce rapid changes in depth values. The experiments confirm that the integration of the sensors improves the instruments’ performance and survey accuracy.

In this study, several methods to compute land surface temperatures (LST) from Landsat TM5 data are compared. Two different approaches are considered. An image based approach that takes into account atmospherically corrected data by using a dark object subtraction model (DOS-1) and computes the emissivity as NDVI function. The emissivity of a surface is controlled by such factors as water content, chemical composition, structure and roughness; it can be determined as the contribution of the different components that belong to the pixels according to their proportions. NDVI method takes into account that vegetation and soils are the main surface cover for the terrestrial component. This emissivity is used to compute the LST by the inversion of Planck function. The other approach applies atmospheric correction to thermal infrared band and considers a constant emissivity of 0.95. Furthermore, the land surface temperature is computed by hybrid methods that result from the merger of the two initially considered approaches. These results are compared with the surface temperature measured by airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS). The LST measured by MIVIS sensor can be considered closer to the real surface temperature because the data are acquired at an altitude of 1500 m and are not affected by significant atmospheric effects such as for satellite data, acquired at 705 km from the Earth's surface. The best results are obtained by considering variable emissivity.

The use of remote-sensing images is becoming common practice in the fight against environmental crimes. However, the challenge of exploiting the complementary information provided by radar and optical data, and by more conventional sources encoded in geographic information systems, is still open. In this work, we propose a new workflow for the detection of potentially hazardous cattle-breeding facilities, exploiting both synthetic aperture radar and optical multitemporal data together with geospatial analyses in the geographic information system environment. The data fusion is performed at a feature-based level. Experiments on data available for the area of Caserta, in southern Italy, show that the proposed technique provides very high detection capability, up to 95%, with a very low false alarm rate. A fast and easy-to-use system has been realized based on this approach, which is a useful tool in the hand of agencies engaged in the protection of territory