Assessing the impact of aquaculture farms using remote sensing: an empirical neural network algorithm for Ildırı Bay, Turkey

Abstract

The potential impact of aquaculture on Ildırı Bay, Turkey, was assessed using remote sensing data collected over 37 d between September 2009 and February 2011. The dataset was improved by applying a local empirical neural network (NN) algorithm. Impact was evaluated in terms of total suspended matter (TSM) and Secchi disk depth (SDD) as effective variables showing changes in underwater light fields in each defined subarea. Subareas were farm sites with their peripheries (impact zones) and the whole study area for 2 different regions within the bay. Real-time datasets of TSM and SDD were obtained for 7 different days within the same period. To create an NN algorithm, the full swath of geo-located products (with 300 m resolution) from the MERIS sensor aboard ENVISAT was used along with in situ data. The NN algorithm showed good performance, with an accuracy of 97.46% for TSM and 99.58% for SDD. No significant (Fs > 0.05) impact on the environment was observed; however, the time series analyses of similarities and anomalies showed that the impact zones have different temporal characteristics compared to the whole bay and vice versa. The highest particle concentrations and lowest light penetration occurred in the spring and summer. Water circulation patterns were identified as the major force determining the distribution and hence the source of particles and were also applied to reflect the particle loads introduced by feeding activity performed in aquaculture facilities. The influence of dissolved organic carbon to TSM and SDD indicates that the contribution of colored dissolved organic matter is another important variable for effective monitoring of aquaculture activity in the bay.