Evaluating the addition of radar with optical data for vegetation mapping in a montane region in Sri Lanka

The use of freely-available multi-source imagery for mapping vegetation in montane terrain is important for many developing countries that do not have the funding for high-resolution data capture. Radar images are also now freely available and include Sentinel-1 in dual polarisation, and PALSAR-2. These images can penetrate cloud cover and provide the advantage of acquiring data in a cloudy tropical region. This research evaluated whether the addition of radar with optical and topographic data improves classification accuracy in a montane region in Sri Lanka. Six classification experiments were designed based on different combinations of image data to test whether radar data improved land cover classification accuracy compared with optical data alone. Random forest classifier in the Google Earth Engine has been utilised to classify the tropical montane vegetation. The results indicate that radar or optical data alone cannot obtain satisfactory results. However, when combining radar with optical data the overall accuracy increased by approximately 5%, and by an additional 2% when topography data were added. The highest accuracy (92%) was achieved with multiple imagery, and adding the vegetation indices improved the model slightly by 0.3%. In addition, feature importance analysis showed that radar data makes a significant contribution to the classification. These positive outcomes demonstrate that freely-accessible multi-source remotely-sensed data have impressive capability for vegetation mapping, and support the monitoring and managing of forest ecological resources in tropical montane regions.