Abstract. Due to their remoteness, altitude and harsh climatic conditions, little is known about the glaciological parameters of ice caps on the Tibetan Plateau. This study presents a geodetic mass balance estimate of the Purogangri Ice Cap, Tibet's largest ice field between 2000 and 2012. We utilized data from the actual TerraSAR-X mission and its add-on for digital elevation measurements and compared it with elevation data from the Shuttle Radar Topography Mission. The employed data sets are ideal for this approach as both data sets were acquired at X-band at nearly the same time of the year and are available at a fine grid spacing. In order to derive surface elevation changes we employed two different methods. The first method is based on differential synthetic radar interferometry while the second method uses common DEM differencing. Both approaches revealed a slightly negative mass budget of −44 ± 15 and −38 ± 23 mm w.eq. a −1 (millimeter water equivalent) respectively. A slightly negative trend of −0.15 ± 0.01 km 2 a −1 in glacier extent was found for the same time period employing a time series of Landsat data. Overall, our results show an almost balanced mass budget for the studied time period. Additionally, we detected one continuously advancing glacier tongue in the eastern part of the ice cap.
Approximately one million refugees of the Rohingya minority population in Myanmar crossed the border to Bangladesh on 25 August 2017, seeking shelter from systematic oppression and persecution. This led to a dramatic expansion of the Kutupalong refugee camp within a couple of months and a decrease of vegetation in the surrounding forests. As many humanitarian organizations demand frameworks for camp monitoring and environmental impact analysis, this study suggests a workflow based on spaceborne radar imagery to measure the expansion of settlements and the decrease of forests. Eleven image pairs of Sentinel-1 and ALOS-2, as well as a digital elevation model, were used for a supervised land cover classification. These were trained on automatically-derived reference areas retrieved from multispectral images to reduce required user input and increase transferability. Results show an overall decrease of vegetation of 1500 hectares, of which 20% were used to expand the camp and 80% were deforested, which matches findings from other studies of this case. The time-series analysis reduced the impact of seasonal variations on the results, and accuracies between 88% and 95% were achieved. The most important input variables for the classification were vegetation indices based on synthetic aperture radar (SAR) backscatter intensity, but topographic parameters also played a role.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.