info:eu-repo/semantics/article
Remote sensing application to estimate fish kills by Saprolegniasis in a reservoir
Fecha
2019-06-15Registro en:
Bonansea, Matias; Mancini, Miguel Alberto; Ledesma, María Micaela; Ferrero, Susana; Rodríguez, Claudia María del Valle; et al.; Remote sensing application to estimate fish kills by Saprolegniasis in a reservoir; Elsevier; Science of the Total Environment; 669; 15-6-2019; 930-937
0048-9697
CONICET Digital
CONICET
Autor
Bonansea, Matias
Mancini, Miguel Alberto
Ledesma, María Micaela
Ferrero, Susana
Rodríguez, Claudia María del Valle
Pinotti, Lucio Pedro
Resumen
Saprolegniasis is one of the most economical and ecologically harmful diseases in different species of fish. Low water temperature is one of the most important factors which increases stress and creates favourable conditions for the proliferation of Saprolegniasis. Therefore, the monitoring of water surface temperature (WST) is fundamental for a better understanding of Saprolegniasis. The objective of this study was to develop a predictive algorithm to estimate the probability of fish kills caused by Saprolegniasis in Río Tercero reservoir (Argentina). WST was estimated by Landsat 7 and 8 imagery using the Single-Channel method. Logistic regression was used to relate WST estimated from 2007 to 2017 with different episodes of fish kills by Saprolegniasis registered in the reservoir during this period of time. Results showed that the algorithm created with the first quartile (25th percentile) of the WST values estimated by Landsat sensors was the most suitable model to estimate Saprolegniasis in the studied reservoir.