In the Second World War Dutch New Guinea was a strategic battle front for both the Japanese and the Allied forces in the Pacific War. A lot of airstrips were constructed and bombed during this time, of which at least three (Mongosah, Otowari and Sagan) have never been visited after the war. This provided a great opportunity to find potential war heritage and airstrip equipment. Later this year an additional research team will go on an in-situ exploration to potentially find those objects. To do so, they needed a classification map giving information on the type and location of the vegetation. This map helps to know where to land with a helicopter, to setup base camp, to find travel ways, etc. Thus, the main objective of this thesis is to check whether it is possible to create a proper classification image with the available data. I used data obtained from the Sentinel 2 Mission (Optical data), the ALOS PALSAR Mission (L-Band Radar data) and the SRTM Mission (Digital elevation data). I pre-processed the data and used the supervised classification method, “Maximum Likelihood Classification” (MLC). I masked clouds via three different cloud masking methods, MLC Method, Threshold Method and Sen2cor (scene classification) Method. I compared the three different methods with each other and there is no significant difference between them. The classifications have been cross-validated with a reference validation dataset and the classified pixels are on average about 90% correctly classified.

Blue ice areas, are areas in Antarctica where, either due to local heat sources (areas with lower albedo and thus more absorption of shortwave radiation - i.e. Nunataks) or high windspeed, all the snow is melted or eroded away and the underlying (blue) ice is visible. This occurs often around the grounding line between the ice sheet and ice shelf. At this grounding line area, a micro climate exists above the blue ice, which increase surface melt, due to a combination of decreased albedo and warming due to the mixing of cold and warm air. Detection of surface melt on this blue ice is important because this warmer surface melt water results in the increase of hydrofracturing and as a result, the decrease of ice shelf stability. Radar imagery above snow areas is a effective method to detect surface melt, which also ensures a continuous data record. Above blue ice, this is continuous data record of surface melt is also desired, but not done yet and therefore the focus of this thesis is surface melt detection on blue ice with radar imagery. By using the method of Hui et al., 2014 to classify blue ice areas, it is shown that the blue ice area extent (non-stable blue ice) is increasing over the years in the peak of the melt season. However, the extent is slightly decreasing during the non-melt season (stable blue ice). The data of Sentinel-1B is used during the austral summer of 2017/2018, to detect surface melt on blue ice. This is done via interferometry (and the corresponding coherence) and with the backscatter coefficient. Coherence turns out the be an unreliable method to detect surface melt, since the influence of wind and precipitation on the decrease of coherence is dominant. Thus, surface melt detection via this method is difficult. Backscatter showed some potential to detect surface melt on blue ice, but due to the larger standard deviation than the actual decrease of backscatter (assumed due to surface melt), a clear distinction between blue ice and surface melt can not be made. Melt features, such as rivers, lakes and ponds are detectable with the backscatter, due to their distinctive shape. Since these melt features are linked to surface melt, backscatter can indirectly be used to detect surface melt on blue ice.