You have requested a machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Neither SPIE nor the owners and publishers of the content make, and they explicitly disclaim, any express or implied representations or warranties of any kind, including, without limitation, representations and warranties as to the functionality of the translation feature or the accuracy or completeness of the translations.
Translations are not retained in our system. Your use of this feature and the translations is subject to all use restrictions contained in the Terms and Conditions of Use of the SPIE website.
21 December 1994Subpixel estimation of the Venice lagoon wetlands using Thematic Mapper data
This paper deals with the application of a modified linear mixture model (MLM) to the satellite image classification for a precise evaluation of the landscape unit surfaces in the lagoon environments where transitional zones between continent and sea waters are marked by clusters of mixed pixels. The importance of a precise classification for these border-pixels is evident since satellite observations could become a very precious tool in the monitoring of erosion/sedimentation rate of wetlands. The study area is located in the lagoon of Venice (Italy) which has been subjected to a slow but continuous sinking since the beginning of this century causing a remarkable loss in the extension of wetlands. A data set of three Landsat Thematic Mapper passages was used, 3-year intervalled one another, and covering the period from 1984 to 1990. Validation of the adopted methodology was made by the support of aerial color photos, taken the same days of the 1987 satellite overflight.
The alert did not successfully save. Please try again later.
Eugenio Zilioli, Pietro Alessandro Brivio, Michele Arrigazzi, Giovanni M. Lechi, "Subpixel estimation of the Venice lagoon wetlands using Thematic Mapper data," Proc. SPIE 2318, Recent Advances in Remote Sensing and Hyperspectral Remote Sensing, (21 December 1994); https://doi.org/10.1117/12.197227