With the launch of Sentinel-1 in 2014, a new era of openly accessible spaceborne radar imagery was begun, and its potential has been demonstrated throughout all fields of applications. However, while interferometric approaches to detect surface deformations are continuously being published, only a few studies address the derivation of digital elevation models (DEMs) from Sentinel-1 data. This is mainly because of the narrow orbital tube, which was primarily designed for subsidence measurements using differential interferometry. Nonetheless, the technical conditions are provided for successful applications involving DEM generation. These are outlined in the first part of this article with a focus on potential error sources and the impact of the most important constraints, namely, temporal and perpendicular baselines. The second part evaluates 21 studies on this topic, their aims, and how they dealt with error sources and the necessity of validation. These studies are then discussed based on the main challenges and potentials including how these can be tackled in the future to lay a solid foundation for scientific discourse.

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 km² 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.

Vietnam is exposed to different types of floods that cause severe economic losses, damage to infrastructure, and loss of life. Reliable information on the drivers, patterns and dynamics of flood risk is crucial for the identification, prioritization and planning of risk reduction and adaptation measures. Here, we present a systematic review of existing flood risk assessments in Vietnam. We evaluate the current status, persisting gaps, and challenges regarding the understanding and assessment of flood risk in the country. The literature review revealed that: (i) 65 % of the reviewed papers did not provide a clear definition of flood risk, (ii) assessments had a tendency to prioritize physical and environmental drivers of risk over social, economic or governance‐related drivers, (iii) future‐oriented assessments tended to focus on hazard and exposure trends, while vulnerability scenarios were often lacking, (iv) large and middle‐sized cities were assessed more frequently than others, (v) only few studies engaged with relevant local stakeholders for the assessment of risk and the development of potential solutions, and (vi) ecosystem‐based adaptation and flood risk insurance solutions were rarely considered. Based on these findings, we point out several directions for future research on flood risk in Vietnam.

Humanitarian action has rapidly adopted Earth observation (EO) and geospatial technologies shaping them according to their needs. Protracted crises and large-scale population displacements require up-to-date information in many facets of humanitarian action support, from mission planning, resource deployment and monitoring, to nutrition and vaccination campaigns, camp plotting, damage assessment, etc. Even though nearly all assets of remote sensing apply in such demanding scenarios, it remains a challenge to fully implement and sustain a trustful and reliable information service. This paper discusses achievements and open issues in the use and uptake of EO technology, from a technical and organisational point of view, motivated by an information service for Médecins Sans Frontières (MSF) and its extension to other NGO’s information needs in the humanitarian sector. With a focus on EO-based population estimation based on (semi-)automated dwelling counting from very high-resolution optical satellite imagery as well as the exploitation of data integration (including radar sensors), the paper also covers potential service elements with respect to environmental and ground- or surface water monitoring. It investigates workflow elements in relation to information extraction and delivery by illustrating a broad range of application scenarios, and discusses first operational solutions of a customized service portfolio.

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.

Ongoing urbanization leads to steady growth of urban areas. In the case of highly dynamic change of municipalities, due to the rates of change, responsible administrations often are challenged or struggle with capturing present states of urban sites or accurately planning future urban development. An interest for urban planning lies on socio-economic conditions, as consumption and production of disposable goods are related to economic possibilities. Therefore, we developed an approach to generate relevant parameters for infrastructure planning by means of remote sensing and spatial analysis. In this study, the single building defines the spatial unit for the parameters. In the case city Belmopan (Belize), based on WorldView-1 data we manually define a city covering building dataset. Residential buildings are classified to eight building types which are locally adapted to Belmopan. A random forest (RF) classifier is trained with locally collected training data. Through household interviews focusing on household assets, income and educational level, a socio-economic point (SEP) scaling is defined, which correlates very well with the defined building typology. In order to assign socio-economic parameters to the single building, five socio-economic classes (SEC) are established based on SEP statistics for the building types. The RF building type classification resulted in high accuracies. Focusing on the three categories to describe residential socio-economic states allowed high correlations between the defined building and socio-economic points. Based on the SEP we projected a citywide residential socio-economic building classification to support supply and disposal infrastructure planning.