Bangladesh is one of the most flood-affected countries in the world. In the last few decades, flood frequency, intensity, duration, and devastation have increased in Bangladesh. Identifying flood-damaged areas is highly essential for an effective flood response. This study aimed at developing an operational methodology for rapid flood inundation and potential flood damaged area mapping to support a quick and effective event response. Sentinel-1 images from March, April, June, and August 2017 were used to generate inundation extents of the corresponding months. The 2017 pre-flood land cover maps were prepared using Landsat-8 images to identify major land cover on the ground before flooding. The overall accuracy of flood inundation mapping was 96.44% and the accuracy of the land cover map was 87.51%. The total flood inundated area corresponded to 2.01%, 4.53%, and 7.01% for the months April, June, and August 2017, respectively. Based on the Landsat-8 derived land cover information, the study determined that cropland damaged by floods was 1.51% in April, 3.46% in June, 5.30% in August, located mostly in the Sylhet and Rangpur divisions. Finally, flood inundation maps were distributed to the broader user community to aid in hazard response. The data and methodology of the study can be replicated for every year to map flooding in Bangladesh.

Thermokarst is the process of ground subsidence caused by either the thawing of ice-rich permafrost or the melting of massive ground ice. The consequences of permafrost degradation associated with thermokarst for surface ecology, landscape evolution, and hydrological processes have been of great scientific interest and social concern. Part of a tundra patch affected by wildfire in northern Alaska (27.5 km²) was investigated here, using remote sensing and in situ surveys to quantify and understand permafrost thaw dynamics after surface disturbances. A two-pass differential InSAR technique using L-band ALOS-PALSAR has been shown capable of capturing thermokarst subsidence triggered by a tundra fire at a spatial resolution of tens of meters, with supporting evidence from field data and optical satellite images. We have introduced a calibration procedure, comparing burned and unburned areas for InSAR subsidence signals, to remove the noise due to seasonal surface movement. In the first year after the fire, an average subsidence rate of 6.2 cm/year (vertical) was measured. Subsidence in the burned area continued over the following two years, with decreased rates. The mean rate of subsidence observed in our interferograms (from 24 July 2008 to 14 September 2010) was 3.3 cm/year, a value comparable to that estimated from field surveys at two plots on average (2.2 cm/year) for the six years after the fire. These results suggest that this InSAR-measured ground subsidence is caused by the development of thermokarst, a thawing process supported by surface change observations from high-resolution optical images and in situ ground level surveys.

Arctic landfast sea ice has undergone substantial changes in recent decades, affecting ice stability and including potential impacts on ice travel by coastal populations and on industry ice roads. We present a novel approach for evaluating landfast sea ice stability on a pan-Arctic scale using Synthetic Aperture Radar Interferometry (InSAR). Using Sentinel-1 images from spring 2017, we discriminate between bottomfast, stabilized, and nonstabilized landfast ice over the main marginal seas of the Arctic Ocean (Beaufort, Chukchi, East Siberian, Laptev, and Kara seas). This approach draws on the evaluation of relative changes in interferometric fringe patterns. This first comprehensive assessment of Arctic bottomfast sea ice extent has revealed that most of the bottomfast sea ice is situated around river mouths and coastal shallows. The Laptev and East Siberian seas dominate the aerial extent, covering roughly 4100 and 5100 km², respectively. These seas also contain the largest extent of stabilized and nonstabilized landfast ice, but are subject to the largest uncertainties surrounding the mapping scheme. Even so, we demonstrate the potential for using InSAR for assessing the stability of landfast ice in several key regions around the Arctic, providing a new understanding of how stability may vary between regions. InSAR-derived stability may serve for strategic planning and tactical decision support for different uses of coastal ice. In a case study of the Nares Strait, we demonstrate that interferograms may reveal early-warning signals for the breakup of stationary sea ice.

Ground ice is foundational to the integrity of Arctic ecosystems and infrastructure. However, we lack fine-scale ground ice maps across almost the entire Arctic, chiefly because there is no established method for mapping ice-rich permafrost from space. Here, we assess whether remotely sensed late-season subsidence can be used to identify ice-rich permafrost. The idea is that, towards the end of an exceptionally warm summer, the thaw front can penetrate materials that were previously perennially frozen, triggering increased subsidence if they are ice rich. Focusing on northwestern Alaska, we test the idea by comparing the Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) late-season subsidence observations to permafrost cores and an independently derived ground ice classification. We find that the late-season subsidence in an exceptionally warm summer was 4–8 cm (5th–95th percentiles) in the ice-rich areas, while it was low in ice-poor areas (−1 to 2 cm; 5th–95th percentiles). The distributions of the late-season subsidence overlapped by 2 %, demonstrating high sensitivity and specificity for identifying top-of-permafrost excess ground ice. The strengths of late-season subsidence include the ease of automation and its applicability to areas that lack conspicuous manifestations of ground ice, as often occurs on hillslopes. One limitation is that it is not sensitive to excess ground ice below the thaw front and thus the total ice content. Late-season subsidence can enhance the automated mapping of permafrost ground ice, complementing existing (predominantly non-automated) approaches based on largely indirect associations with vegetation and periglacial landforms. Thanks to its suitability for mapping ice-rich permafrost, satellite-observed late-season subsidence can make a vital contribution to anticipating terrain instability in the Arctic and sustainably stewarding its ecosystems.

Despite the significant progress that was achieved throughout the recent years, to this day, automatic change detection and classification from synthetic aperture radar (SAR) images remains a difficult task. This is, in large part, due to (a) the high level of speckle noise that is inherent to SAR data; (b) the complex scattering response of SAR even for rather homogeneous targets; (c) the low temporal sampling that is often achieved with SAR systems, since sequential images do not always have the same radar geometry (incident angle, orbit path, etc.); and (d) the typically limited performance of SAR in delineating the exact boundary of changed regions. With this paper we present a promising change detection method that utilizes SAR images and provides solutions for these previously mentioned difficulties. We will show that the presented approach enables automatic and high-performance change detection across a wide range of spatial scales (resolution levels). The developed method follows a three-step approach of (i) initial pre-processing; (ii) data enhancement/filtering; and (iii) wavelet-based, multi-scale change detection. The stand-alone property of our approach is the high flexibility in applying the change detection approach to a wide range of change detection problems. The performance of the developed approach is demonstrated using synthetic data as well as a real-data application to wildfire progression near Fairbanks, Alaska.