The cascading failure of check dam systems during the 28 July 2022 Emamzadeh Davood flood in Iran

On July 18, 2022, an unexpected rainfall and flash flood struck the Emamzadeh Davood village in northwestern Tehran, the capital city of Iran, claiming the life of at least 23 people. In this brief communication, we report results from a recent investigation carried out by field surveys and remote sensing data, highlighting the role of anthropogenic factors and catastrophic failures in a series of check dams in intensifying the impacts of the 2022 Emamzadeh Davood event

Monitoring active open-pit mine stability in the Rhenish coalfields of Germany using a coherence-based SBAS method

With the recent progress in synthetic aperture radar (SAR) technology, especially the new generation of SAR satellites (Sentinel-1 and TerraSAR-X), our ability to assess slope stability in open-pit mines has significantly improved. The main objective of this work is to map ground displacement and slope instability over three open-pit mines, namely, Hambach, Garzweiler and Inden, in the Rhenish coalfields of Germany to provide long-term monitoring solutions for open-pit mining operations and their surroundings. Three SAR datasets, including Sentinel-1A data in ascending and descending orbits and TerraSAR-X data in a descending orbit, were processed by a modified small baseline subset (SBAS) algorithm, called coherence-based SBAS, to retrieve ground displacement related to the three open-pit mines and their surroundings. Despite the continuously changing topography over these active open-pit mines, the small perpendicular baselines of both Sentinel-1A and TerraSAR-X data were not affected by DEM errors and hence could yield accurate estimates of surface displacement. Significant land subsidence was observed over reclaimed areas, with rates exceeding 500 mm/yr, 380 mm/yr, and 310 mm/yr for the Hambach, Garzweiler and Inden mine, respectively. The compaction process of waste materials is the main contributor to land subsidence. Land uplift was found over the areas near the active working parts of the mines, which was probably due to excavation activities. Horizontal displacement retrieved from the combination of ascending and descending data was analysed, revealing an eastward movement with a maximum rate of ∼120 mm/yr on the western flank and a westward movement with a maximum rate of ∼ 60 mm/yr on the eastern flank of the pit. Former open-pit mines Fortuna-Garsdorf and Berghein in the eastern part of Rhenish coalfields, already reclaimed for agriculture, also show subsidence, at locations reaching 150 mm/yr. The interferometric results were compared, whenever possible, with groundwater information to analyse the possible reasons for ground deformation over the mines and their surroundings

Multi-temporal landslide activity investigation by spaceborne SAR interferometry: The case study of the Polish Carpathians

The main goal of this research is to verify the activity state of landslides provided by an existing landslide inventory map using Persistent Scatterers (PS) Interferometry (PSInSAR). The study was conducted in the Małopolskie municipality, a rural setting with sparse urbanization in the Polish Flysch Carpathians. PSInSAR has been applied using Synthetic Aperture Radar (SAR) data from ALOS PALSAR and Sentinel 1A/B with different acquisition geometries (ascending and descending orbit) to increase PS coverage and mitigate the geometric effects due to layover and shadowing. The Line-Of-Sight PSInSAR measurements were projected to the steepest slope, which allowed to homogenize the results from diverse acquisition modes and to compare the displacement velocities with different slope orientations. Additionally, landslide intensity (motion rate) and expected damage maps were generated and verified during field investigations. A high correlation between PSInSAR results and in-situ damage observations was confirmed. The activity state and landslide-related expected damage maps have been confirmed for 43 out of a total of 50 landslides investigated in the field. The short temporal baseline provided by both Sentinel satellites (1A/B data) increases the PS density significantly. The study substantiates the usefulness of SAR based landslide activity monitoring for land use and land development, even in rural areas

Multi-sensor remote sensing analysis of coal fire induced land subsidence in Jharia Coalfields, Jharkhand, India

The subsidence in coal mines induced by surface and subsurface fires leading to roof collapse, infrastructure loss, and loss of lives is a prominent concern. In the study, satellite imagery from thermal and microwave remote sensing data is used to deduce the effect of coal fires on subsidence in the Jharia Coalfields, India. The Thermal Infrared data acquired from the Landsat-8 (band 10) is used to derive the temperature anomaly maps. Persistent Scatterer Interferometry analysis was performed on sixty Sentinel-1, C-band images, the results are corrected for atmospheric error using Generic Atmospheric Correction Online Service for InSAR (GACOS) atmospheric modelling data and decomposed into vertical displacement values to quantify subsidence. A zone-wise analysis of the hazard patterns in the coalfields was carried out. Coal fire maps, subsidence velocity maps, and land cover maps were integrated to investigate the impact of the hazards on the mines and their surroundings. Maximum subsidence of approximately 20 cm/yr. and temperature anomaly of up to 25 °C has been observed. The findings exhibit a strong positive correlation between the subsidence velocity and temperature anomaly in the study area. Kusunda, Keshalpur, and Bararee collieries are identified as the most critically affected zones. The subsidence phenomenon in some collieries is extending towards the settlements and transportation networks and needs urgent intervention. © 2021 The Author

The June 2020 Aniangzhai landslide in Sichuan Province, Southwest China: slope instability analysis from radar and optical satellite remote sensing data

A large, deep-seated ancient landslide was partially reactivated on 17 June 2020 close to the Aniangzhai village of Danba County in Sichuan Province of Southwest China. It was initiated by undercutting of the toe of this landslide resulting from increased discharge of the Xiaojinchuan River caused by the failure of a landslide dam, which had been created by the debris flow originating from the Meilong valley. As a result, 12 townships in the downstream area were endangered leading to the evacuation of more than 20000 people. This study investigated the Aniangzhai landslide area by optical and radar satellite remote sensing techniques. A horizontal displacement map produced using cross-correlation of high-resolution optical images from Planet shows a maximum horizontal motion of approximately 15 meters for the slope failure between the two acquisitions. The undercutting effects on the toe of the landslide are clearly revealed by exploiting optical data and field surveys, indicating the direct influence of the overflow from the landslide dam and water release from a nearby hydropower station on the toe erosion. Pre-disaster instability analysis using a stack of SAR data from Sentinel-1 between 2014 and 2020 suggests that the Aniangzhai landslide has long been active before the failure, with the largest annual LOS deformation rate more than 50 mm/yr. The 3-year wet period that followed a relative drought year in 2016 resulted in a 14% higher average velocity in 2018–2020, in comparison to the rate in 2014–2017. A detailed analysis of slope surface kinematics in different parts of the landslide indicates that temporal changes in precipitation are mainly correlated with kinematics of motion at the head part of the failure body, where an accelerated creep is observed since spring 2020 before the large failure. Overall, this study provides an example of how full exploitation of optical and radar satellite remote sensing data can be used for a comprehensive analysis of destabilization and reactivation of an ancient landslide in response to a complex cascading event chain in the transition zone between the Qinghai-Tibetan Plateau and the Sichuan Basin. © 2021, The Author(s)

Automatic Flood Detection from Sentinel-1 Data Using a Nested UNet Model and a NASA Benchmark Dataset

During flood events near real-time, synthetic aperture radar (SAR) satellite imagery has proven to be an efficient management tool for disaster management authorities. However, one of the challenges is accurate classification and segmentation of flooded water. A common method of SAR-based flood mapping is binary segmentation by thresholding, but this method is limited due to the effects of backscatter, geographical area, and surface characterstics. Recent advancements in deep learning algorithms for image segmentation have demonstrated excellent potential for improving flood detection. In this paper, we present a deep learning approach with a nested UNet architecture based on a backbone of EfficientNet-B7 by leveraging a publicly available Sentinel‑1 dataset provided jointly by NASA and the IEEE GRSS Committee. The performance of the nested UNet model was compared with several other UNet-based convolutional neural network architectures. The models were trained on flood events from Nebraska and North Alabama in the USA, Bangladesh, and Florence, Italy. Finally, the generalization capacity of the trained nested UNet model was compared to the other architectures by testing on Sentinel‑1 data from flood events of varied geographical regions such as Spain, India, and Vietnam. The impact of using different polarization band combinations of input data on the segmentation capabilities of the nested UNet and other models is also evaluated using Shapley scores. The results of these experiments show that the UNet model architectures perform comparably to the UNet++ with EfficientNet-B7 backbone for both the NASA dataset as well as the other test cases. Therefore, it can be inferred that these models can be trained on certain flood events provided in the dataset and used for flood detection in other geographical areas, thus proving the transferability of these models. However, the effect of polarization still varies across different test cases from around the world in terms of performance; the model trained with the combinations of individual bands, VV and VH, and polarization ratios gives the best results

Tracking hidden crisis in India's capital from space: implications of unsustainable groundwater use.

Funder: Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum - GFZNational Capital Region (NCR, Delhi) in India is one of the fastest-growing metropolitan cities which is facing a severe water crisis due to increasing water demand. The over-extraction of groundwater, particularly from its unconsolidated alluvial deposits makes the region prone to subsidence. In this study, we investigated the effects of plummeting groundwater levels on land surface elevations in Delhi NCR using Sentinel-1 datasets acquired during the years 2014-2020. Our analysis reveals two distinct subsidence features in the study area with rates exceeding 11 cm/year in Kapashera-an urban village near IGI airport Delhi, and 3 cm/year in Faridabad throughout the study period. The subsidence in these two areas are accelerating and follows the depleting groundwater trend. The third region, Dwarka shows a shift from subsidence to uplift during the years which can be attributed to the strict government policies to regulate groundwater use and incentivizing rainwater harvesting. Further analysis using a classified risk map based on hazard risk and vulnerability approach highlights an approximate area of 100 square kilometers to be subjected to the highest risk level of ground movement, demanding urgent attention. The findings of this study are highly relevant for government agencies to formulate new policies against the over-exploitation of groundwater and to facilitate a sustainable and resilient groundwater management system in Delhi NCR

Estimation of atmospheric temperature and humidity profiles from MODIS data and radiosond data using artificial neural network

The aim of this study is to test the quality of the neural network for retrieving the temperature and humidity by comparison with the radiosond values and a linear regression method. Remote sensed images give useful information about the atmosphere. In this article, MODIS data is used to retrieve temperature and humidity profiles of the atmosphere. Two methods of linear regression and artificial neural network are used to retrieve the temperature and humidity profiles. A multilayer feed-forward neural network is tested to estimate the desired geophysical profiles. Retrievals are validated by comparison with coincident radiosond profiles

Финансовое состояние предприятия: оценка и направления улучшения (на примере ОАО «Речицкий комбинат хлебопродуктов»)

We greatly appreciate the thoughtful comments by Andrew Sowter and Francesca Cigna [1] on our paper [2]. Unfortunately, we overlooked the ISBAS acronym during the revision process of the article. Therefore, we would suggest to use the acronym of ESBAS (Enhanced Small BAseline Subset) for our method presented in Vajedian et al. [2

Spatial Variability of Relative Sea-Level Rise in Tianjin, China: Insight from InSAR, GPS, and Tide-Gauge Observations

The Tianjin coastal region in Bohai Bay, Northern China, is increasingly affected by storm-surge flooding which is exacerbated by anthropogenic land subsidence and global sea-level rise (SLR). We use a combination of synthetic aperture radar interferometry (InSAR), continuous GPS (CGPS), and tide-gauge observations to evaluate the spatial variability of relative SLR (RSLR) along the coastline of Tianjin. Land motion obtained by integration of 2 tracks of Sentinel-1 SAR images and 19 CGPS stations shows that the recent land subsidence in Tianjin downtown is less than 8 mm/yr, which has significantly decreased with respect to the last 50 years (up to 110 mm/yr in the 1980s). This might benefit from the South-to-North Water Transfer Project which has provided more than 1.8 billion cubic meters of water for Tianjin city since 2014 and reduced groundwater consumption. However, subsidence centers have shifted to suburbs, especially along the coastline dominated by reclaimed harbors and aquaculture industry, with localized subsidence up to 170 mm/yr. Combining InSAR observations with sea level records from tide-gauge stations reveals spatial variability of RSLR along the coastline. We find that, in the aquaculture zones along the coastline, the rates of land subsidence are as high as 82 mm/yr due to groundwater extraction for fisheries, which subsequently cause local sea levels to rise nearly 30 times faster than the global average. New insights into land subsidence and local SLR could help the country's regulators to make decisions on ensuring the sustainable development of the coastal aquaculture industry