Monitoring algae growth by digital analysis of landsat data: the Orbetello Lagoon case study

Abstract

In the framework of the "Ninth UN/FAO International Training Course in Cooperation with the Government of Italy, Applications of Remote Sensing to Aquaculture and Inland Fisheries" held at the FAORSC, Rome, in the period 10 - 28 September 1983, studies were carried out using the Remote Sensing Centre's digital facilities to provide adequate satellite data for the field activities of the trainees in the Orbetello Lagoon area, approximately 150 km. NW of Rome. Since algae (identification, occurrence, area measurement) is of particular importance for the fisheries resources of the area, it was decided to investigate in detail this aspect of the environment, in view of applying the resulting methodology to other places. Two Computer Compatible Tapes (CCTs) of Landsat MSS data (date of acquisition 15.9.81 and 18.3.83 respectively) were analysed and classified. Preprocessing included accurate geometric and radiometric corrections. Pseudo-colour density slicing, contrast stretching, rationing and principal component analysis were used to enhance the algae occurence in the lagoon. Supervised Classification was then undertaken using the maximum likehood method. Post-classification processing (thematic results filtering, area masks, thematic, results of classification) was also used to refine classification, results, measure the algae occurence in separate portions of the lagoon, etc. Comparision with field data available at the Itthiologic Research Station near Orbetello showed that algae mapping accuracy varied between 80 and 87 percent according to the season and place. Further, it was recognised that not all the algae species occuring in the lagoon were included in the digital classification. It was, thus, decided to carry out spectral measurements in the field in order to discriminate the type of algae of the Orberello Lagoon which can be effectively monitored through satellite remote sensing and, consequently, increase the classification accuracy.