Composition of pixels.

<p>Proportion of pixels with more than 50% of the main lulc class in the original data set.</p

Predicted land use and land cover classes.

<p>Scenarios S1 (a) through S4 (d) and the original data set (e). The maps (WGS84 / UTM 52N; EPSG:32652) are from repetitions with the largest <i>F</i>-<i>score</i>. Classes with less than 20 original pixels and cloud contaminated pixels are marked as ‘NA’.</p

Illustration of SMOTE.

<p>Synthetic points (crosses denoted <i>s</i>1 through <i>s</i>5) generated by smote along the connection lines between a point (black dot denoted <i>Q</i>_<i>i</i>) and its <i>k</i> nearest neighbours (black dots). Here, <i>k</i> = 5 and oversampling rate <i>N</i> = 5.</p

Land use and land cover and location of the Haean catchment.

<p>The map on the left shows the original polygon data set by [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0190476#pone.0190476.ref020" target="_blank">20</a>] (WGS84 / UTM 52N; EPSG:32652) for the year 2010 with the 14 lulc classes used in this study.</p

Classification results.

<p>Median values from 10 repetitions in scenarios S1 (a) through S4 (d). A point on the diagonal (grey line) indicates a random guess. The order of the classes in the legend reflects the decreasing number of original pixels.</p

Distribution of the 14 LULC classes in the raster data set.

<p>The first 6 classes were used for classification.</p

Mutual information <i>MI</i>* between class labels and MODIS spectral bands.

<p>Results from 10 repetitions on 6 training folds in scenarios S1 through S4: (a) red band B1, (b) near-infrared band B2, (c) blue channel B3 and (d) mid-infrared band B7. The plain lines show the median and the shaded areas the 5% to 95% quantile range.</p

MartinetalPeerJ2015_rawdata

Data collected in a field experiment on cabbage plants in South Korea. Column headings are described in file sheet "Metadata". See the referenced paper (Martin et al 2015 PeerJ) for details on data collection methods and the experiment

MOESM4 of Remote monitoring of vigilance behavior in large herbivores using acceleration data

Additional file 4: Fig. S4. Proportion of active behavior per burst (p active, blue bars) over time of the day for animal 53 on March 1, 2013. The red line shows a moving average with a window width of 7 min. For state detection, the crossing points between the moving average and the threshold value (dotted horizontal line) were calculated, adjusted to the actual starting/ending positions of the active states and, finally, checked for a minimum duration of 7 min. The bold black lines show the final active states. The points below show the point in time when the collar took a GPS-position. Blue points indicate GPS-positions that were taken during resting states; green points indicate GPS-positions that were taken during active states. Framed green points were selected as reference points for the calculation of vigilance levels

Appendix A. Additional information explaining the statistical models with figures and tables, in specific, the area dependencies of landscape indices, the home range size of red and roe deer across spatio-temporal scales, table of random effects for mixed models on all spatio-temporal scales for red and roe deer, and tables of the mixed models with different correlation structure for all spatio-temporal scales for red and roe deer.

Additional information explaining the statistical models with figures and tables, in specific, the area dependencies of landscape indices, the home range size of red and roe deer across spatio-temporal scales, table of random effects for mixed models on all spatio-temporal scales for red and roe deer, and tables of the mixed models with different correlation structure for all spatio-temporal scales for red and roe deer