ISODATA (Iterative Self-Organizing Data Technique of Analysis) and neural network classification methods were carried out to map shallow Posidonia oceanica meadows in coastal areas of the Mediterranean Sea, using Coastal Zone Colour Scanner (CZCS) airborne sensor data obtained at different altitudes and an aerophotogrammetric image. Reference test points of P. oceanica have been checked against aerial photographs. The neural-based classification method gives the best performance (92-95%) for all the images of the set, except for the highest altitude flight (1000 m, accuracy 74%). ISODATA classification of CZCS images was generally more accurate (81-85%) than applied to the aerophotogrammetric image (79%). The study also indicated that 4 m represents the 'critical' resolution useful for the extraction of reliable information within the study analysed area. Where P. oceanica forms dense and continuous meadows, a lower resolution (such as those obtainable from satellite sensors) could be successfully applied.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
Document Type: Research Article
Dipartimento di Scienze Botaniche, Università di Palermo, Via Archirafi, 38, 90123 Palermo, Italy;, Email: [email protected]
Dipartimento di Ingegneria Idraulica ed Applicazioni Ambientali, Università di Palermo, Viale delle Scienze, 90128 Palermo, Italy
Publication date: 2003-07-01
More about this publication?