Establishing the timings of rainfall-triggered landslides using Sentinel-1 satellite radar data

2021 ◽  
Author(s):  
Katy Burrows ◽  
Odin Marc ◽  
Dominique Remy
Keyword(s):  
Time Series ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Heavy Rainfall ◽  
Hazard Mitigation ◽  
Radar Data ◽  
Mitigation Strategies ◽  
Radar Images ◽  
Satellite Radar ◽  
The Impact

<p>Heavy rainfall can trigger thousands of landslides, which have a significant effect on the landscape and can pose a hazard to people and infrastructure. Inventories of rainfall triggered landslides are used to improve our understanding of the physical mechanisms that cause the event, in assessing the impact of the event and in the development of hazard mitigation strategies. Inventories of rainfall-triggered landslides are most commonly generated using optical or multispectral satellite imagery, but such imagery is often obscured by cloud-cover associated with the rainfall event. Cloud-free optical satellite images may not be available until several weeks following an event. In the case where rain falls over a long period of time, for example during the monsoon season or successive typhoon events, the timing of the triggered landslides is usually poorly constrained. This lack of information on landslide timing limits both hazard mitigation strategies and our ability to model the physical processes behind the triggered landsliding.</p><p>Satellite radar has emerged recently as an alternative source of information on landslides. The removal of vegetation and movement of material due to a landslide alters the scattering properties of the Earth’s surface, thus giving landslides a signal in satellite radar imagery. Satellite radar data can be acquired in all weather conditions, and the regular and frequent acquisitions of the Sentinel-1 constellation, could allow landslide timing to be constrained to within a few days.  Satellite radar data has been successfully used in detecting the spatial distribution of landslides whose timing is known a-priori (for example those triggered by earthquakes). Here we demonstrate that time series of Sentinel-1 satellite radar images can also be used to achieve the opposite: the identification of landslide timing for an event whose spatial extent is known.</p><p>We analyse radar coherence and amplitude times series to identify changes in the time series associated with landslide occurrence. We compare pixels within each landslide with nearby pixels outside each landslide that have been identified to be similar in pre-rainfall Sentinel-1 and Sentinel-2 imagery. We test our methods on rainfall-triggered landslides in Nepal and Japan, both of which are mountainous countries that experience regular heavy rainfall events that are often obscured by cloud cover in optical satellite imagery.</p>

Comparison of radar and raingauge measurements during heavy rainfall

2005 ◽  
Vol 51 (2) ◽  
pp. 195-201 ◽  
Author(s):  
T. Einfalt ◽  
M. Jessen ◽  
B. Mehlig
Keyword(s):  
Time Series ◽  
Heavy Rainfall ◽  
Radar Data ◽  
Spatial Extent ◽  
Small Scale ◽  
Rainfall Events ◽  
Radar Images ◽  
X Band ◽  
Raingauge Data ◽  
Quality Check

Five heavy small-scale rainfall events in North Rhine-Westphalia (Germany) were investigated with radar and raingauge data. Special attention was paid to quality check and adjustment of radar data. Attenuation effects could be observed on both, C-Band and on X-Band radar. Adjustment of radar data to raingauge values turned out to be very difficult in the vicinity of heavy local rain cells. For the five affected regions the precipitation was quantified in the form of areal time series and cumulated radar images. As further result of this project, the spatial extent of the precipitation fields was identified and compared with radar and raingauge data.

scholarly journals In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series

2019 ◽  
Vol 11 (2) ◽  
pp. 118 ◽  
Author(s):  
Valérie Demarez ◽  
Florian Helen ◽  
Claire Marais-Sicre ◽  
Frédéric Baup
Keyword(s):  
Time Series ◽  
Shuttle Radar Topography Mission ◽  
Irrigation Scheduling ◽  
Landsat 8 ◽  
Radar Images ◽  
Optical Images ◽  
Multi Temporal ◽  
Irrigated Crops ◽  
Elevation Model ◽  
The Impact

Numerous studies have reported the use of multi-spectral and multi-temporal remote sensing images to map irrigated crops. Such maps are useful for water management. The recent availability of optical and radar image time series such as the Sentinel data offers new opportunities to map land cover with high spatial and temporal resolutions. Early identification of irrigated crops is of major importance for irrigation scheduling, but the cloud coverage might significantly reduce the number of available optical images, making crop identification difficult. SAR image time series such as those provided by Sentinel-1 offer the possibility of improving early crop mapping. This paper studies the impact of the Sentinel-1 images when used jointly with optical imagery (Landsat8) and a digital elevation model of the Shuttle Radar Topography Mission (SRTM). The study site is located in a temperate zone (southwest France) with irrigated maize crops. The classifier used is the Random Forest. The combined use of the different data (radar, optical, and SRTM) improves the early classifications of the irrigated crops (k = 0.89) compared to classifications obtained using each type of data separately (k = 0.84). The use of the DEM is significant for the early stages but becomes useless once crops have reached their full development. In conclusion, compared to a “full optical” approach, the “combined” method is more robust over time as radar images permit cloudy conditions to be overcome.

scholarly journals A Preliminary Assessment of the Impacts of Multiple Temporal-scale Variations in Particulate Matter on its Source Apportionment

2018 ◽  
Author(s):  
Xing Peng ◽  
Jian Gao ◽  
Guoliang Shi ◽  
Xurong Shi ◽  
Yanqi Huangfu ◽  
...  
Keyword(s):  
Time Series ◽  
Particulate Matter ◽  
Source Apportionment ◽  
Chemical Species ◽  
Principal Component ◽  
Temporal Scale ◽  
Mitigation Strategies ◽  
Total Variance ◽  
Secondary Source ◽  
The Impact

Abstract. Time series of pollutant concentrations consist of variations at different time scales that are attributable to many processes/sources (data noise, source intensities, meteorological conditions, climate, etc.). Improving the knowledge of the impact of multiple temporal-scale components on pollutant variations and pollution levels can provide useful information for suitable mitigation strategies for pollutant control during a high pollution episode. To investigate the source factors driving these variations, the Kolmogorov-Zurbenko (KZ) filter was used to decompose the time series of PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) and chemical species into intra-day, diurnal, synoptic, and baseline temporal-scale components (TS components). The synoptic TS component has the largest amplitude and relative contributions (about 50 %) to the total variance of SO42−, NH4+, and OC concentrations. The diurnal TS component has the largest relative contributions to the total variance of PM2.5, NO3−, EC, Ca, and Fe concentrations, ranging from 32 % to 47 %. To investigate the source impacts on PM2.5 from different TS components, four datasets RI (intra-day removed), RD (diurnal removed), RS (synoptic removed), and RBL (baseline removed) were created by respectively removing the intra-day, diurnal, synoptic, and baseline TS component from the original datasets. Multilinear Engine 2 (ME-2) and/or principal component analysis was applied to these four datasets as well as the original datasets for source apportionment. ME-2 solutions using the original and RI dataset identify crustal dust contributions. For the solutions from original, RI, RD, and RS datasets, the total primary source impacts are close, ranging from 35.1 to 40.4 μg m−3 during the entire sampling period. For the secondary source impacts, solutions from the original, RI and RD dataset give similar source impacts (about 30 μg m−3), which were higher than the impacts derived from the RS datasets (21.2 μg m−3).

scholarly journals Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region

2019 ◽  
Vol 11 (3) ◽  
pp. 334 ◽  
Author(s):  
Cecília Lira Melo de Oliveira Santos ◽  
Rubens Augusto Camargo Lamparelli ◽  
Gleyce Kelly Dantas Araújo Figueiredo ◽  
Stéphane Dupuy ◽  
Julie Boury ◽  
...  
Keyword(s):  
Time Series ◽  
Cloud Cover ◽  
Radar Data ◽  
Agricultural Landscapes ◽  
Gap Filling ◽  
Use Of Time ◽  
High Quality Image ◽  
Low Cloud ◽  
Low Cloud Cover ◽  
Heterogeneous Region

Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km2 in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak.

Ground displacement over Petrinja area caused by earthquake M6.2: interdisciplinary analysis of geodesy and geology based on analysis from SAR Sentinel-1 data

2021 ◽  
Author(s):  
Olga Bjelotomić Oršulić ◽  
Tvrtko Korbar ◽  
Danko Markovinović ◽  
Matej Varga ◽  
Tomislav Bašić
Keyword(s):  
Horizontal Displacement ◽  
Ground Displacement ◽  
North West ◽  
Radar Images ◽  
New Findings ◽  
West Part ◽  
Vertical Ground ◽  
Spatio Temporal ◽  
Satellite Radar ◽  
The Impact

<p>At the very end of the year 2020, at 29th of December, hazard earthquake of M=6.2 hit near Petrinja, at NW of Croatia. Earthquake have been felt in a circumstance of a 400 kilometers, leaving in an epicenter vicinity inconceivable damage, devastated towns and obstructed lives. In order to obtain the first emergency crisis numbers over the impact of the earthquake on a ground motion, we have analyzed open satellite radar images of Copernicus Sentinel-1 along with the seismic faults. Multiple spatio-temporal Copernicus Sentinel-1 C-SAR images were used and processed for the differentiating the <em>before</em> and <em>after</em> earthquake state of the art. This presentation shows the results of the SAR conducted analysis, with the results of ground displacement in vertical up-down and horizontal east-west direction. The results show the vertical ground displacement to extent of -12 cm at southern area to +22cm at north-west part of a wide area covered by the earthquake impact regarding the epicenter. The horizontal displacement is detected in range between 30 cm towards west and 40 cm towards east is detected around the epicenter area, and +/-5cm horizontal displacement over a wider affected area indicate a spatial extent and hazardous impact the mainshock event made. The SAR results were verified by including the analysis over one station from the national positioning reference frame CROPOS. Accordingly, we obtained matching results of 5 cm easting shift and -3 cm subsidence on Sisak GNSS CROPOS station which coressponds to our SAR findings. Furthermore, geological interepretation of new findings is given based on results detecting Pokupsko and Petrinja fault.</p>

Twelve-Year Landslide Risk Assessment in Villa de Independencia, Bolivia, with Sentinel-1 and ALOS-1/2 InSAR Observations

2020 ◽  
Author(s):  
Chuang Song ◽  
Zhenhong Li ◽  
Stefano Utili ◽  
Chen Yu
Keyword(s):  
Time Series ◽  
Eigenvalue Decomposition ◽  
Landslide Risk ◽  
Time Interval ◽  
Deformation Pattern ◽  
Radar Images ◽  
Satellite Radar ◽  
North Edge ◽  
The Village ◽  
Landslide Movement

<p>Monitoring of slow landslide movement on a local scale with Interferometric Synthetic Aperture Radar (InSAR) observations can provide long-term deformation information and assist in identifying failure triggers. We combined three different tracks of satellite radar images spanning 12 years from ALOS-1 PALSAR-1, ALOS-2 PALSAR-2, and Sentinel-1 to assess the evolution of a landslide in Bolivia where the village of Independencia lies at the slope foot. For ALOS-1 PALSAR, SAR data was acquired on 15 dates during the period from 28 February 2007 to 11 March 2011 in ascending mode. For ALOS-2 PALSAR-2, eight acquisitions between 07 October 2015 and 29 November 2017 were available in ascending mode. The low temporal resolution of ALOS images makes the detection of deforming signal difficult though the L-band data captures more coherent pixels on vegetation areas than C-band. Sentinel-1 data with a minimum time interval of six days from 16 October 2014 to 08 September 2019 (144 images) is collected and processed to recover the dynamic behaviour of the landslide movement.</p><p>To explore the sensitivity of different InSAR time series analysis methods on revealing the deformation pattern of the landslide, we respectively used Persistent Scatterer Interferometry (PSI), Small Baseline Subset (SBAS) algorithm and Distributed Scatterer Interferometry (DSI) based on phase eigenvalue-decomposition to process the mentioned multiple satellite radar observations. Overlapping valid pixels from these three methods share similar temporal evolution while SBAS and DSI trace more measurement points than PSI in spatial distribution. Preliminary results show that the village central exhibits extremely slow movements (<= 10 mm/yr) with seasonal oscillation. The north edge of the village in the middle of the landslide body retains stable until 2018. Deformation time series after early 2018 perform an acceleration from about 5 mm/yr to 15 mm/yr. Such acceleration may result from artificial irrigation activities, precipitation or internal landslide reactivation, and we expect to collect more ground evidence to interpret the acceleration. To conclude, the failure risk of this landslide is relatively higher since 2018 and is more noteworthy than before.</p>

scholarly journals Shallow cumuli cover and its uncertainties from ground-based lidar–radar data and sky images

2020 ◽  
Vol 13 (4) ◽  
pp. 2099-2117
Author(s):  
Erin A. Riley ◽  
Jessica M. Kleiss ◽  
Laura D. Riihimaki ◽  
Charles N. Long ◽  
Larry K. Berg ◽  
...  
Keyword(s):  
Cloud Cover ◽  
Single Layer ◽  
Radar Data ◽  
Cloud Detection ◽  
Data Merging ◽  
Data Products ◽  
Two Factors ◽  
Ground Based Lidar ◽  
The Impact ◽  
Wide Fov

Abstract. Cloud cover estimates of single-layer shallow cumuli obtained from narrow field-of-view (FOV) lidar–radar and wide-FOV total sky imager (TSI) data are compared over an extended period (2000–2017 summers) at the established United States Atmospheric Radiation Measurement mid-continental Southern Great Plains site. We quantify the impacts of two factors on hourly and sub-hourly cloud cover estimates: (1) instrument-dependent cloud detection and data merging criteria and (2) FOV configuration. Enhanced observations at this site combine the advantages of the ceilometer, micropulse lidar (MPL) and cloud radar in merged data products. Data collected by these three instruments are used to calculate narrow-FOV cloud fraction (CF) as a temporal fraction of cloudy returns within a given period. Sky images provided by TSI are used to calculate the wide-FOV fractional sky cover (FSC) as a fraction of cloudy pixels within a given image. To assess the impact of the first factor on CF obtained from the merged data products, we consider two additional subperiods (2000–2010 and 2011–2017 summers) that mark significant instrumentation and algorithmic advances in the cloud detection and data merging. We demonstrate that CF obtained from ceilometer data alone and FSC obtained from sky images provide the most similar and consistent cloud cover estimates; hourly bias and root-mean-square difference (RMSD) are within 0.04 and 0.12, respectively. However, CF from merged MPL–ceilometer data provides the largest estimates of the multiyear mean cloud cover, about 0.12 (35 %) and 0.08 (24 %) greater than FSC for the first and second subperiods, respectively. CF from merged ceilometer–MPL–radar data has the strongest subperiod dependence with a bias of 0.08 (24 %) compared to FSC for the first subperiod and shows no bias for the second subperiod. The strong period dependence of CF obtained from the combined ceilometer–MPL–radar data is likely results from a change in what sensors are relied on to detect clouds below 3 km. After 2011, the MPL stopped being used for cloud top height detection below 3 km, leaving the radar as the only sensor used in cloud top height retrievals. To quantify the FOV impact, a narrow-FOV FSC is derived from the TSI images. We demonstrate that FOV configuration does not modify the bias but impacts the RMSD (0.1 hourly, 0.15 sub-hourly). In particular, the FOV impact is significant for sub-hourly observations, where 41 % of narrow- and wide-FOV FSC differ by more than 0.1. A new “quick-look” tool is introduced to visualize impacts of these two factors through integration of CF and FSC data with novel TSI-based images of the spatial variability in cloud cover. The influence of cloud field organization, such cloud streets parallel to the wind direction, on narrow- and wide-FOV cloud cover estimates can be visually assessed.

scholarly journals SATELLITE RADAR MONITORING OF OIL POLLUTION IN THE WATER AREAS BETWEEN ANAPA AND GELENDZHIK IN 2018–2020

2021 ◽  
Vol 49 (1) ◽  
pp. 163-185
Author(s):  
N. A. Knyazev ◽  
O. Yu. Lavrova ◽  
A. G. Kostianoy
Keyword(s):  
Black Sea ◽  
Oil Pollution ◽  
Radar Data ◽  
Oil Products ◽  
Satellite Monitoring ◽  
The Black Sea ◽  
Northeastern Part ◽  
Radar Images ◽  
Data Archives ◽  
Satellite Radar

The paper presents the results of satellite monitoring of oil pollution in the northeastern part of the Black Sea in the area between Anapa and Gelendzhik in 2018–2020. The monitoring was carried out using the archives of radar data obtained by SAR-C radars installed on the Sentinel-1A and -1B satellites. The work with the data archives was carried out using the tools of the “See the Sea” (STS) information system developed at the Space Research Institute of the Russian Academy of Sciences. The conducted satellite monitoring revealed the main sources of sea surface pollution with oil products in the study area. The overwhelming pollution (85%) is associated with discharges of water containing oil products from moving vessels. With the help of STS tools, a map of oil pollution detected on radar images was compiled, on the basis of which the main areas of oil pollution were identified. These include the main shipping routes to the Novorossiysk Sea Port, the anchorage of ships and the water areas of the Tsemes (Novorossiysk) Bay and Gelendzhik Bay. Seasonal and interannual variability of oil pollution was determined on the basis of satellite information for the area between Anapa and Gelendzhik. The results of the 2018–2020 monitoring were compared with those obtained during similar monitoring carried out in 2006–2010. It was concluded that there has been no reduction in the amount of detected pollution, which negatively affects the ecological state of the northeastern part of the Black Sea.

scholarly journals PEMANFAATAN DATA SATELIT GMS MULTI KANAL UNTUK KEGIATAN TEKNOLOGI MODIFIKASI CUACA

2016 ◽  
Vol 17 (2) ◽  
pp. 47
Author(s):  
Muhamad Djazim Syaifullah ◽  
Satyo Nuryanto
Keyword(s):  
Grain Size ◽  
Data Management ◽  
Satellite Imagery ◽  
Satellite Data ◽  
Cloud Cover ◽  
Target Selection ◽  
Radar Data ◽  
Field Validation ◽  
Cloud Seeding ◽  
Weather Modification

IntisariTulisan ini menyajikan pemanfaatan data satelit GMS (Geostationary Meteorological Satellites) multi kanal untuk informasi perawanan dalam rangka mendukung kegiatan teknologi modifikasi cuaca. Pemanfaatan data satelit meliputi proses pengunduhan data, proses kalibrasi dan visualisasi citra satelit sehingga dapat diinterpretasi. Pemrosesan data satelit juga meliputi jenis dan tipe awan serta ukuran butir awan. Dengan diketahuinya tipe dan jenis awan maka pemilihan target awan dalam pelaksanaan Teknologi Modifikasi Cuaca (TMC) dapat lebih efektif. Data Satelit GMS yang berupa data PGM untuk berbagai kanal telah dimanfaatkan untuk analisis cuaca dan mendukung pelaksanaan kegiatan Teknologi Modifikasi Cuaca (TMC). Dari analisis beberapa kanal Infra Merah (IR) dapat diperoleh tipe/jenis awan dan ukuran butiran awan yang sangat bermanfaat untuk kepentingan Teknologi Modifikasi Cuaca. Diperlukan pengelolaan data yang lebih intensif baik manajemen data maupun kontinuitas pengunduhan data untuk menjamin kelancaran analisis. Selain itu juga diperlukan validasi lapangan misalnya dengan data radar analisis menjadi semakin akurat.  AbstractThis paper presents the utilization of GMS (Geostationary Meteorological Satellites) multichannel satellite data for cloud cover information in order to support the activities of weather modification technology or cloud seeding. These utilizations covering the process of data downloading, process calibration and visualization of satellite imagery so that it can be interpreted. Processing of satellite data also includes the type of cloud as well as cloud grain size. By knowing the type of cloud, the cloud target selection in the execution of Weather Modification Technology can be more effective. From the analysis of several Infrared (IR) channels can be obtained type/kind of cloud and grain size of the clouds that are beneficial to the interests of cloud seeding. It is required a more intensive data management and continuity of data download. It is also necessary field validation for example with radar data. The purpose of data management was the data processing became more efficient. 

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