Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

< Back to Article

Fig 1.

Map of the geographical location of the study area.

Distribution of the sampling sites (green triangles) and arbitrary areas (polygons) used for time series analysis. Map generated in programming language R using Landsat 8 image from 2016-09-09.

More »

Fig 1 Expand

Table 1.

Descriptive statistics of in situ-measured Chlorophyll-a concentrations (μg*l-1).

More »

Table 1 Expand

Fig 2.

Observed chlorophyll-a values during the survey period.

Solid lines represent medians; boxes the interquartile ranges; whiskers minimum and maximum or 1.5 times the interquartile range (when outliers were present); points represent the outliers. A1-A6 arbitrary areas (see Fig 1 for details).

More »

Fig 2 Expand

Table 2.

Goodness of fit of the three best fitted SLR, MLR, and GAM, respectively, for log-transformed Chl-a estimation.

More »

Table 2 Expand

Table 3.

Predictive performance of the three best fitted SLR, MLR, and GAM, respectively, applied for log-transformed Chl-a estimation.

More »

Table 3 Expand

Fig 3.

Residual analysis of the best fitted SLR (top), MLR (center), and GAM (bottom), respectively.

More »

Fig 3 Expand

Table 4.

Descriptive statistics of coefficients of the best-fitted model.

More »

Table 4 Expand

Fig 4.

Predictions of Chl-a (μg*l-1) obtained from the best-fitted model and the Landsat imagery set for the period 2013–2017.

Points represent the means; whiskers represent the interquartile ranges. A1-A6 arbitrary areas (see Fig 1 for details).

More »

Fig 4 Expand

Fig 5.

Predicted Chlorophyll-a (μg*l-1) in the study area, corresponding to June 2013 to 2017.

Maps generated in programming language R.

More »

Fig 5 Expand