Recovery of Water Quality and Detection of Algal Blooms in Lake Villarrica through Landsat Satellite Images and Monitoring Data

Phytoplankton is considered a strong predictor of the environmental quality of lakes, while Chlorophyll-a is an indicator of primary productivity. In this study, 25 LANDSAT images covering the 2014-2021 period were used to predict Chlorophyll-a in the Villarrica lacustrine system. A Chlorophyll-a recovery algorithm was calculated using two spectral indices (FAI and SABI). The indices that presented the best statistical indicators were the floating algal index (R-2 = 0.87) and surface algal bloom index (R-2 = 0.59). A multiparametric linear model for Chlorophyll-a estimation was constructed with the indices. Statistical indicators were used to validate the multiple linear regression model used to predict Chlorophyll-a by means of spectral indices, with the following results: a MBE of 0.136 mu g/L, RMSE of 0.055 mu g/L, and NRMSE of 0.019%. All results revealed the strength of the model. It is necessary to raise awareness among the population that carries out activities around the lake in order for them to take policy actions related to water resources in this Chilean lake. Furthermore, it is important to note that this study is the first to address the detection of algal blooms in this Chilean lake through remote sensing.

Automated summary

Phytoplankton and Chlorophyll-a were studied as predictors of lake environmental quality and primary productivity in the Villarrica lacustrine system. LANDSAT images from 2014-2021 were used to predict Chlorophyll-a using the floating algal index and surface algal bloom index. A multiparametric linear model was constructed with these indices and validated with statistical indicators, showing the strength of the model. This study is important as it addresses algal bloom detection through remote sensing for the first time in this Chilean lake.