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A catastrophic landslide occurred on 21 July 2020, 30 km from Enshi city, in Mazhe County of Hubei province, China. In this paper, we aimed to investigate the kinematic evolution and volumetric change related to this landslide using multi-source remote sensing measurements from synthetic aperture radar (SAR) and optical data. C-band Sentinel-1 and X-band TerraSAR-X SAR data are analyzed using several multi-temporal interferometry (MTI) time-series techniques including Persistent Scatterer Interferometry (PSI), Small Baseline subset (SBAS), and Combined eigenvalue maximum likelihood Phase Linking (CPL). The spatial pattern of surface deformation resulting from the interferometric analysis is then statistically analyzed to retrieve the pre-failure and post-failure displacements. Co-failure motions are analyzed using an image correlation technique applied to both the Planetscope and Sentinel-2 images. Moreover, 4 pairs of bistatic TerraSAR-X/TanDEM-X (TDX) data are utilized to generate high-precision digital elevation models (DEMs) and estimate the volumetric change related to the main slope failure. The pre-failure ground deformation analysis results suggest that the landslide was already active before the July 2020 failure, with the seasonality and hydraulic diffusivity being characteristics of a slow-moving landslide. Among the three different MTI methods applied, the CPL method results in a greater measurement points (MPs) density than the PSI and SBAS method when estimating the pre-failure movement of the Shaziba landslide. The July 2020 Shaziba disaster is divided into three main parts: (1) slightly horizontal deformation of 0.5–1.5 m within the northern part with ground and house cracks, (2) less collapse in the eastern part with horizontal motions reaching 30 m and (3) a highly eroded western part where vegetation was wholly lost in the main event, resulting in an collapse volume of approximately 4.98 million m3, out of which approximately around 3.4 million m3 was deposited and approximately 1.58 million m3 was washed away into the Qing River. After the failure, the marginal scrap of the main failure body, above crown of landslide and eastern part showed instability with rates of 20–30 mm/yr, suggesting that the failure zone may continue to expand.

The combined effects of climate change and human land use activities are increasing the occurrence of extreme events, such as floods and landslides worldwide. Between mid-March and the beginning of April 2019, almost the whole Iran was affected by intense record rainfall leading to widespread flash flooding. Starting in the northern province of Golestan and moving to the South and the West of the country, heavy rains have poured on 28 out of the 31 provinces. Approximately, 1900 cities and villages across the country were damaged with half a million people displaced at the peak of the disaster. The intense rainfall not only caused more than 140 rivers to burst their banks, but also triggered thousands of landslides. The presented work makes use of high resolution - spatial and temporal - optical and radar satellite remote sensing with the goal of performing large-area and at the same time detailed spatiotemporal analysis of landslide occurrence related to the main hydro-meteorological triggering events. Besides detection of rainfall and flood triggered landslides, pre- and post-failure landslide evolution has been analyzed including the first observed onset of landslide activation in February 2019 resulting from already increased precipitation in late fall and winter of 2018/19. Large-area landslide detection has been performed in a semi-automated way using time series of optical Planet Scope and Sentinel-2A/B data. For assessing the kinematics of pre- and post-failure slope motions, radar remote sensing based image correlation and differential interferometric synthetic aperture radar (DInSAR) methods have been applied using Sentinel-1A/B data. The obtained satellite remote sensing based results have been evaluated by field surveys conducted in cooperation between the GFZ Potsdam and the Forest, Range and Watershed Management Organization of Iran (FRWM) being responsible for landslide hazard and risk assessment as well as the design and implementation of mitigation measures. In this context, emphasis has been put on the potential of multi-sensor satellite remote sensing for improving large-area process monitoring and understanding, especially in relation to extreme triggering events of so far unprecedented magnitude which are likely to occur more frequently in the future because of ongoing climate change.

On November 12, 2017, a large earthquake (Mw = 7.3) hit Kermanshah province of Iran near the border of Iran and Iraq. At least 600 people were killed, 9000 injured and thousands left homeless. In this study we integrated observations from radar and optical remote sensing, seismology and field mapping to investigate source parameters, coseismically triggered slope failures and secondary faulting related to the Mw 7.3, Sarpol-e Zahab earthquake in Iran. Coseismic surface deformation was constrained by interferometric synthetic aperture radar (InSAR) analysis from SAR images obtained by L-band ALOS-2 and C-band Sentinel-1 satellites. ALOS-2 wide-swath (WS) interferograms were derived from ascending and descending orbits covering 09.08.2016-14.11.2017 and 04.10.2017-15.11.2017 time periods, respectively. Sentinel-1 TOPS coseismic interferograms were derived from ascending and descending orbits covering 11.11.2017-17.11.2017 and 12.11.2017-18.11.2017, respectively. Source parameters and slip models of the earthquake were then obtained by the Bayesian inversion of interferometric results using elastic dislocation modeling, considering the seismic parameters as a priori information. Preliminary results of geodetic source modeling suggests that the 12 November 2017 Sarpol-e Zahab earthquake was generated by blind ENE oblique thrust faulting with an average slip of approximately 4 m at a depth between 17 and 22 km. The 2017 Sarpol-e Zahab event also triggered a lot of coseismic slope failures including landslides and rock falls, and secondary faulting. Their potential locations were initially assessed by identifying local phase changes and areas affected by loss of coherence in the interferograms. Moreover, large-scale analysis of horizontal surface displacements was performed by pixel-offset tracking using Sentinel-1 SAR images. The results were evaluated by expert interpretation based on optical Sentinel-2 data in combination with geological information and field investigations. Largest horizontal deformation was observed for a co-seismically triggered landslide which was further analyzed using optical PlanetScope data acquired one day apart right before and after the earthquake (12th and 13th of November). Pixel-based change detection allowed exact spatial delineation of the displaced block covering an area of about 4 km2. Cross-correlation analysis resulted in a spatially detailed derivation of horizontal movement vectors indicating displacements in the range from about 5 to 30 m. The results of this study have shown that a combination of various remote sensing methods with modelling and field investigations is required for gaining an improved understanding of surface processes caused by the Mw 7.3, Sarpol-e Zahab earthquake. Complementary analysis of radar and optical remote sensing has led to a more comprehensive assessment of various types of co-seismically induced surface changes occurring at different spatial scales. Such information can contribute to an improved spatial characterization of co-seismically activated geological structures. This is especially important in the context of seismic hazard assessment for scarcely investigated areas like the one affected by the Sarpol-e Zahab earthquake in the border region between Iran and Iraq.

System Erde; 7, Satellite remote sensing offers the possibility to continuously, globally collect information about Earth’s surface properties, and thus possesses an enormous scientific and economic potential. The spatio-temporal recording of information opens up a wide range of possibilities for large-scale monitoring of changes and influencing factors, e.g. in atmospheric research, agriculture and forestry, geology, urban areas and the analysis of natural hazards. The European Union‘s Copernicus programme marks the beginning of a new era in satellite-based Earth observation by designing and launching the Sentinel series, an operational fleet of high-continuity, multi-combinable observation systems. This enables completely new objects of investigation, but also requires a new level of automated data processing in order to make the huge amounts of data available for analysis. This article provides an insight into current remote sensing research at the GFZ. First, it presents the activities related to the creation of preprocessing standards and interfaces for data evaluation, geo-data management and visualization. In the following, examples for multi-sensor analysis of land use potentials and natural hazards are shown, in order to finally discuss the role of the GFZ in the development of hyperspectral satellite missions and related data analysis.

Large areas in southern Kyrgyzstan are subjected to high and ongoing landslide activity; however, an objective and systematic assessment of landslide susceptibility at a regional level has not yet been conducted. In this paper, we investigate the contribution that remote sensing can provide to facilitate a quantitative landslide hazard assessment at a regional scale under the condition of data scarcity. We performed a landslide susceptibility and hazard assessment based on a multi-temporal landslide inventory that was derived from a 30-year time series of satellite remote sensing data using an automated identification approach. To evaluate the effect of the resulting inventory on the landslide susceptibility assessment, we calculated an alternative susceptibility model using a historical inventory that was derived by an expert through combining visual interpretation of remote sensing data with already existing knowledge on landslide activity in this region. For both susceptibility models, the same predisposing factors were used: geology, stream power index, absolute height, aspect and slope. A comparison of the two models revealed that using the multi-temporal landslide inventory covering the 30-year period results in model coefficients and susceptibility values that more strongly reflect the properties of the most recent landslide activity. Overall, both susceptibility maps present the highest susceptibility values for similar regions and are characterized by acceptable to high predictive performances. We conclude that the results of the automated landslide detection provide a suitable landslide inventory for a reliable large-area landslide susceptibility assessment. We also used the temporal information of the automatically detected multi-temporal landslide inventory to assess the temporal component of landslide hazard in the form of exceedance probability. The results show the great potential of satellite remote sensing for deriving detailed and systematic spatio-temporal information on landslide occurrences, which can significantly improve landslide susceptibility and hazard assessment at a regional scale, particularly in data-scarce regions such as Kyrgyzstan.

The scope of the Science Plan is to describe the scientific background, applications, and activities related to the Environmental Mapping and Analysis Program (EnMAP) mission. Primarily, the document addresses scientists and funding institutions, but it may also be of interest for environmental stakeholders and governmental bodies. It is conceived to be a living document that will be updated throughout the entire mission. Chapter 1 provides a brief overview of the principles and current state of imaging spectroscopy. This is followed by an introduction to the EnMAP mission, including its objectives and potential impact on international programs as well as major environmental and societal challenges to their understanding and management EnMAP can contribute. Chapter 2 describes the EnMAP system together with data products and access, calibration/validation issues, and synergies with other missions. Chapter 3 gives an overview of the relevance, current lines of research, and potential contributions of EnMAP for major fields of application, such as vegetation, geology and soils, coastal and inland waters, cryosphere, urban areas, atmosphere and hazards to address the environmental and societal challenges presented in Chapter 1. Finally, Chapter 4 outlines the scientific exploitation strategy, which includes the strategy for community building and training, preparatory flight campaigns and software developments. A list of abbreviations is provided in the annex to this document, while an extended glossary of terms and abbreviations is available at the EnMAP website.