For the single-date maps we use a single Sentinel-2 image (bands: blue, green, red, NIR and SWIR) and add the raw LiDAR data as an additional input to the classifier. The year-maps are based on the same bands but include a stack computed from all the images in one year with a low cloud coverage. A classification on multi-temporal images of Sentinel-2 data is deemed more robust as this also captures changes over time .
Classified year maps are presently available for 2000-2019 in the viewer. For 2000-2011 Landsat-5 data are used, from 2013-2014 Landsat-8 data, and 2015-2019 Sentinel-2 data. We do not include classification for 2012, as the data from Landsat-7 is affected by sensor failure . The full year image stack was created by building quarterly median images for the Landsat data, and monthly median images for Sentinel-2 data. This decision is based on image frequency for the region. Details on the Landsat classification method can be found at Harezlak et al. (2020). For each year in the Sentinel mission, the monthly medians yield an image with 12 times 5 bands (medians of blue, green, red, NIR and SWIR).
For classification we use the Random Forest classifier which is a robust classifier that (1) handles noisy and dissimilar data well and (2) discriminates reliably with mixed classes . As the day-maps were meant to be classified on-the-fly after user selection of a certain single Sentinel image, we optimised the classification time by the number pixels in the training and testing set and the number of trees in the random forest classifier (data not shown). Finally, we set the number of randomly selected pixels for each class to 200 (70% training, 30% testing) and limited the number of trees to 6. We tested classifying the Photo-Interpretation classes first and then aggregate to the Legger-classes versus classifying the Legger-classes directly, there was no difference in accuracy (data not shown). We opted for classifying the Legger-classes directly in the final product.
The post processing steps consisted of creating a change map and ranking and color-coding the changes to their relative change in hydraulic roughness. We used a green-yellow-red colour ramp. The largest decrease in hydraulic roughness (bush to water) was coded green, no change yellow, and highest increase in hydraulic roughness (water to bush) was coloured red.
The results are presented in an easy to use web-based map interface based Netlify and MapBox (Netlify, 2020; Mapbox, 2020) with a connection to GEE for data and computations. In this tool data layers are presented, and metrics are computed on-the-fly for relative vegetation cover per land ownership polygon. Classification results can also be downloaded for further analysis in a dedicated GIS.