In this work a classification of the main irrigated crops of the Piave river basin and an estimation of crop water requirements during the growing season are presented. The work is divided into two parts.
The first includes recognition, mapping and quantification of the main irrigated crops for thematic map production and a database creation. MIVIS hyperspectral airborne data, Landsat-TM/ETM+ multispectral satellite data and ground truth data were used for crop classification. A specific method of knowledge-based image classification was designed and used. The proposed method was compared with other per point conventional classification methods.
In the second part the crop water need estimation is discussed. Ground-climatological data of the study area ground-climatological stations were used. The water balance equation parameters were estimated on a ten-days basis. A spatial interpolation method was used to propagate these parameters at pixel spatial resolution to study area. Soil water deficit map for irrigation was produced and a flow rate estimation was performed.
In this paper, a method for the extraction of in-water structures (e.g., internal waves) and underwater structures (e.g., vertical transfer of sediments along the water column), observed in Multispectral Infrared and Visible Spectrometer (MIVIS) airborne images is discussed. Images of sea surface structures are captured by a variety of airborne or spaceborne remote sensing instruments, such as photographic cameras, optical scanners, synthetic aperture radars. Investigations on the generation process of these structures and on the local effects are of interest to biological oceanography, to fisheries biology as well as to hydrodynamics. Detection method is based on an interference created among low and mid- high frequencies of water-body images, acquired in spectral bands with different water penetration band characteristics. Features of sea-structures, semi-automatically identified, are mapped in image format.