Applying Remotely Sensed Environmental Information to Model Mosquito Populations

Maria Kofidou, Michael de Courcy Williams, Andreas Nearchou, Stavroula Veletza, Alexandra Gemitzi and Ioannis Karakasiliotis
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Maria Kofidou: Department of Environmental Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Michael de Courcy Williams: Laboratory of Biology, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
Andreas Nearchou: Laboratory of Biology, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
Stavroula Veletza: Laboratory of Biology, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece
Alexandra Gemitzi: Department of Environmental Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Ioannis Karakasiliotis: Laboratory of Biology, Department of Medicine, Democritus University of Thrace, 68100 Alexandroupolis, Greece

Sustainability, 2021, vol. 13, issue 14, 1-17

Abstract: Vector borne diseases have been related to various environmental parameters and environmental changes like climate change, which impact their propagation in time and space. Remote sensing data have been used widely for monitoring environmental conditions and changes. We hypothesized that changes in various environmental parameters may be reflected in changes in mosquito population size, thus impacting the temporal and spatial patterns of vector diseases. The aim of this study is to analyze the effect of environmental variables on mosquito populations using the remotely sensed Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) obtained from Landsat 8, along with other factors, such as altitude and water covered areas surrounding the examined locations. Therefore, a Multilayer Perceptron (MLP) Artificial Neural Network (ANN) model was developed and tested for its ability to predict mosquito populations. The model was applied in NE Greece using mosquito population data from 17 locations where mosquito traps were placed from June to October 2019. All performance metrics indicated a high predictive ability of the model. LST was proved to be the factor with the highest relative importance in the prediction of mosquito populations, whereas the developed model can predict mosquito populations 13 days ahead to allow a substantial window for appropriate control measures.

Keywords: mosquito populations; water areas; NDVI; LST; remote sensing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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