scholarly journals MONITORING OF WET SNOW AND ACCUMULATIONS AT HIGH ALPINE GLACIERS USING RADAR TECHNOLOGIES

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1063-1068 ◽  
Author(s):  
A. Wendleder ◽  
A. Heilig ◽  
A. Schmitt ◽  
C. Mayer
Keyword(s):  
Mass Balance ◽  
Single Point ◽  
Ground Truth ◽  
Radar Data ◽  
Sar Images ◽  
Ground Truth Data ◽  
Temporal Shift ◽  
Spatial Coverage ◽  
Wet Snow ◽  
Periodic Monitoring

Conventional studies to assess the annual mass balance for glaciers rely on single point observations in combination with model and interpolation approaches. Just recently, airborne and spaceborne data is used to support such mass balance determinations. Here, we present an approach to map temporal changes of the snow cover in glaciated regions of Tyrol, Austria, using SAR-based satellite data. Two dual-polarized SAR images are acquired on 22 and 24 September 2014. As X and C-band reveal different backscattering properties of snow, both TerraSAR-X and RADARSAT-2 images are analysed and compared to ground truth data. Through application of filter functions and processing steps containing a Kennaugh decomposition, ortho-rectification, radiometric enhancement and normalization, we were able to distinguish between dry and wet parts of the snow surface. The analyses reveal that the wet-snow can be unambiguously classified by applying a threshold of -11 dB. Bare ice at the surface or a dry snowpack does not appear in radar data with such low backscatter values. From the temporal shift of wet-snow, a discrimination of accumulation areas on glaciers is possible for specific observation dates. Such data can reveal a periodic monitoring of glaciers with high spatial coverage independent from weather or glacier conditions.

scholarly journals Towards Automatic Recognition of Wakes Generated by Dark Vessels in Sentinel-1 Images

2021 ◽  
Vol 13 (10) ◽  
pp. 1955
Author(s):  
Maria Daniela Graziano ◽  
Alfredo Renga
Keyword(s):  
Surface Film ◽  
A Priori ◽  
Ground Truth ◽  
Detection Performance ◽  
Sar Images ◽  
Ground Truth Data ◽  
Low Wind Speed ◽  
Maritime Surveillance ◽  
Strong Transition ◽  
Detection Capabilities

The recognition of wakes generated by dark vessels is a tremendous and interesting challenge in the field of maritime surveillance by Synthetic Aperture Radar (SAR) images. The paper aims at assessing the detection performance in different scenarios by processing Sentinel-1 SAR images along with ground truth data. Results confirm that the Radon-based approach is an effective technique for wake-based detection of dark vessels, and they lead to a deeper understanding of the effects of different sea and wind conditions. In general, the best applicative scenario is a marine image characterized by homogeneous sea clutter; the presence of natural surface film or strong transition from low wind speed areas to more windy zones worsen the detection performance. Nonetheless, the proposed approach features dark vessel detection capabilities by identifying their wakes, without any a priori knowledge of their positions.

scholarly journals Development of radar glacier zones on the King George Island ice cap, Antarctica, during austral summer 1996/97 as observed in ERS-2 SAR data

2000 ◽  
Vol 31 ◽  
pp. 357-363 ◽  
Author(s):  
Matthias Braun ◽  
Frank Rau ◽  
Helmut Saurer ◽  
Hermann Gobmann
Keyword(s):  
Meteorological Data ◽  
Austral Summer ◽  
Late Summer ◽  
Ground Truth ◽  
King George Island ◽  
Equilibrium Line Altitude ◽  
Sar Images ◽  
Ice Cap ◽  
Wet Snow ◽  
Frozen Percolation

AbstractBased on a time series of European remote-sensing satellite (ERS-2) synthetic-aperture radar (SAR) images from 1996/97, ablation on the King George Island (Antarctica) ice cap is documented. Snowmelt patterns were monitored by mapping the dynamic evolution of radar glacier zones and their boundaries. On the ice cap, all major radar glacier zones except the dry-snow radar zone were identified during the observed period While winter was characterized by a frozen-percolation radar zone, the ablation season was characterized by wet-snow and bare-ice radar zones. A striking bright backscatter signature indicated the presence of a highly reflective zone in the lower parts of the wet-snow zone. It was attributed to a phase 2 melt (P2) radar zone, which is characterized by a metamorphosed and roughened surface of a melting snow cover. Due to the absence of simultaneously acquired ground-truth information, concurrent meteorological data proved to be essential for interpreting the SAR images. Although the maximum elevation of the ice cap does not exceed 680 ma.s.L, ablation patterns obviously reflect altitudinal control. Melt onset up to 530 m a.s.l. was initiated by an advection event at the end of October 1996. A wet snowpack on the entire ice cap corresponds with a prolonged period of high temperatures in January 1997. However, the highest parts of the ice cap were affected by occasional melt-freeze cycles. The transient snowline at the end of February was determined as being at 250 m a i l. This late-summer snowline was regarded as an approximation of the equilibrium-line altitude for the 1996/97 ablation season.

scholarly journals An approach for detecting changes related to natural disasters using Synthetic Aperture Radar data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 819-826 ◽  
Author(s):  
N. Milisavljevic ◽  
D. Closson ◽  
F. Holecz ◽  
F. Collivignarelli ◽  
P. Pasquali
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Truth ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Optical Remote Sensing ◽  
Ground Truth Data ◽  
Synthetic Aperture Radar Data ◽  
Before And After ◽  
The Impact ◽  
Aperture Radar

Land-cover changes occur naturally in a progressive and gradual way, but they may happen rapidly and abruptly sometimes. Very high resolution remote sensed data acquired at different time intervals can help in analyzing the rate of changes and the causal factors. In this paper, we present an approach for detecting changes related to disasters such as an earthquake and for mapping of the impact zones. The approach is based on the pieces of information coming from SAR (Synthetic Aperture Radar) and on their combination. The case study is the 22 February 2011 Christchurch earthquake. <br><br> The identification of damaged or destroyed buildings using SAR data is a challenging task. The approach proposed here consists in finding amplitude changes as well as coherence changes before and after the earthquake and then combining these changes in order to obtain richer and more robust information on the origin of various types of changes possibly induced by an earthquake. This approach does not need any specific knowledge source about the terrain, but if such sources are present, they can be easily integrated in the method as more specific descriptions of the possible classes. <br><br> A special task in our approach is to develop a scheme that translates the obtained combinations of changes into ground information. Several algorithms are developed and validated using optical remote sensing images of the city two days after the earthquake, as well as our own ground-truth data. The obtained validation results show that the proposed approach is promising.

scholarly journals An example of SAR-derived image segmentation for landslides detection

2018 ◽  
Author(s):  
Giuseppe Esposito ◽  
Alessandro Cesare Mondini ◽  
Ivan Marchesini ◽  
Paola Reichenbach ◽  
Paola Salvati ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Rapid Assessment ◽  
Ground Truth ◽  
Radar Data ◽  
Mass Movements ◽  
Ground Truth Data ◽  
Before And After ◽  
Satellite Radar ◽  
Gis Software ◽  
Derived Data

A rapid assessment of the areal extent of landslide disasters is one of the main challenges facing by the scientific community. Satellite radar data represent a powerful tool for the rapid detection of landslides over large spatial scales, even in case of persistent cloud cover. To define landslide locations, radar data need to be firstly pre-processed and then elaborated for the extraction of the required information. Segmentation represents one of the most useful procedures for identifying land cover changes induced by landslides. In this study, we present an application of the i.segment module of GRASS GIS software for segmenting radar-derived data. As study area, we selected the Tagari River valley in Papua New Guinea, where massive landslides were triggered by a M7.5 earthquake on February 25, 2018. A comparison with ground truth data revealed a suitable performance of i.segment. Particular segmentation patterns, in fact, resulted in the areas affected by landslides with respect to the external ones, or to the same areas before the earthquake. These patterns highlighted a relevant contrast of radar backscattering values recorded before and after the landslides. With our procedure, we were able to define the extension of the mass movements that occurred in the study area, just three days after the M7.5 earthquake.

scholarly journals An example of SAR-derived image segmentation for landslides detection

2018 ◽  
Author(s):  
Giuseppe Esposito ◽  
Alessandro Cesare Mondini ◽  
Ivan Marchesini ◽  
Paola Reichenbach ◽  
Paola Salvati ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Rapid Assessment ◽  
Ground Truth ◽  
Radar Data ◽  
Mass Movements ◽  
Ground Truth Data ◽  
Before And After ◽  
Satellite Radar ◽  
Gis Software ◽  
Derived Data

A rapid assessment of the areal extent of landslide disasters is one of the main challenges facing by the scientific community. Satellite radar data represent a powerful tool for the rapid detection of landslides over large spatial scales, even in case of persistent cloud cover. To define landslide locations, radar data need to be firstly pre-processed and then elaborated for the extraction of the required information. Segmentation represents one of the most useful procedures for identifying land cover changes induced by landslides. In this study, we present an application of the i.segment module of GRASS GIS software for segmenting radar-derived data. As study area, we selected the Tagari River valley in Papua New Guinea, where massive landslides were triggered by a M7.5 earthquake on February 25, 2018. A comparison with ground truth data revealed a suitable performance of i.segment. Particular segmentation patterns, in fact, resulted in the areas affected by landslides with respect to the external ones, or to the same areas before the earthquake. These patterns highlighted a relevant contrast of radar backscattering values recorded before and after the landslides. With our procedure, we were able to define the extension of the mass movements that occurred in the study area, just three days after the M7.5 earthquake.

Landfill characterization by multi-method geophysical investigation: the case study of Leppe (Germany)

2020 ◽  
Author(s):  
Tom Debouny ◽  
David Caterina ◽  
Itzel Isunza Manrique ◽  
Pascal Beese-Vasbender ◽  
Frédéric Nguyen
Keyword(s):  
Seismic Velocity ◽  
Biogas Production ◽  
Geophysical Methods ◽  
Ground Truth ◽  
Spectral Ratio ◽  
Added Value ◽  
Geophysical Investigation ◽  
Major Drawback ◽  
Ground Truth Data ◽  
Spatial Coverage

&lt;p&gt;Whether environmental or economic interests are at stake, characterization of landfills is becoming a key operation. Characterization not only concerns old landfills, but also modern engineered landfills where the assessment and monitoring of internal processes such as leachate and biogas generation is of a primary importance. Nowadays, characterization is mostly carried out by conventional invasive methods based on drilling/trenching, sampling and laboratory analyses. Although they provide direct and analytical information, their spatial coverage, or representability, remains a major drawback. In addition, they can be expensive and increase the risk of damaging contamination barriers. Therefore, non- to minimally- invasive characterization geophysical techniques emerge as a complementary option. They allow to better capture the spatial heterogeneity across a site and are more cost-effective than punctual measurements alone. Furthermore, when compared with limited ground truth data, they may provide insights into waste composition, water content or temperature. The present study highlights the added value of a multiple geophysical approach to characterize a landfill located in Engelskirchen in Germany. Leppe landfill was used as a municipal solid waste (MSW) deposit site from 1982 until the end of 2004. Since then, only ash coming from the MSW incineration is discarded, mostly on top of the previous MSW deposit. The combination of geophysical methods used in this study included electrical resistivity tomography (ERT), &amp;#160;induced polarization (IP), multichannel analysis of surface waves (MASW) and horizontal to vertical noise spectral ratio (HVSNR). The 3D ERT and IP model allowed to identify dry zones within the waste (which may have a direct impact on biogas production) and to roughly discriminate the layer of ash from the MSW layer. Seismic velocity model provided by MASW permitted to significantly improve the delineation between the two layers. HVNSR results combined with the information provided by MASW were used to estimate the thickness of the top layer on a larger area using a bilayer hypothesis. These geophysical characterization results were validated with available ground truth data. Overall, in the present case seismic methods showed to be more suited than geoelectrical techniques for the distinction between the ash and MSW layers.&lt;/p&gt;

scholarly journals Validation of Automatic Cb Observations for METAR Messages without Ground Truth

2015 ◽  
Vol 54 (10) ◽  
pp. 2063-2075 ◽  
Author(s):  
Otto Hyvärinen ◽  
Elena Saltikoff ◽  
Harri Hohti
Keyword(s):  
Latent Class Analysis ◽  
Statistical Estimation ◽  
Latent Class ◽  
Detection System ◽  
Ground Truth ◽  
Radar Data ◽  
Convective Rainfall ◽  
Ground Truth Data ◽  
Finnish Meteorological Institute ◽  
Meteorological Institute

AbstractIn aviation meteorology, METAR messages are used to disseminate the existence of cumulonimbus (Cb) clouds. METAR messages are traditionally constructed manually from human observations, but there is a growing trend toward automation of this process. At the Finnish Meteorological Institute (FMI), METAR messages incorporate an operational automatic detection of Cb based solely on weather radar data, when manual observations are not available. However, the verification of this automatic Cb detection is challenging, as good ground truth data are not often available; even human observations are not perfect as Cb clouds can be obscured by other clouds, for example. Therefore, statistical estimation of the relevant verification measures from imperfect observations using latent class analysis (LCA) was explored. In addition to radar-based products and human observations, the convective rainfall rate from EUMETSAT’s Nowcasting Satellite Application Facility and lightning products from the Finnish lightning network were used for determining the existence of Cb clouds. Results suggest that LCA gives reasonable estimates of verification measures and, based on these estimates, the Cb detection system at FMI gives results comparable to human observations.

scholarly journals An Urban Flooding Index for Unsupervised Inundated Urban Area Detection Using Sentinel-1 Polarimetric SAR Images

2021 ◽  
Vol 13 (22) ◽  
pp. 4511
Author(s):  
Hui Zhang ◽  
Zhixin Qi ◽  
Xia Li ◽  
Yimin Chen ◽  
Xianwei Wang ◽  
...  
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
Ground Truth ◽  
Detection Accuracy ◽  
Urban Flooding ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Ground Truth Data ◽  
Optical Images ◽  
Coefficient Increase

Urban flooding causes a variation in radar return from urban areas. However, such variation has not been thoroughly examined for different polarizations because of the lack of polarimetric SAR (PolSAR) images and ground truth data simultaneously collected over flooded urban areas. This condition hinders not only the understanding of the effect mechanism of urban flooding under different polarizations but also the development of advanced methods that could improve the accuracy of inundated urban area detection. Using Sentinel-1 PolSAR and Jilin-1 high-resolution optical images acquired on the same day over flooded urban areas in Golestan, Iran, this study investigated the characteristics and mechanisms of the radar return changes induced by urban flooding under different polarizations and proposed a new method for unsupervised inundated urban area detection. This study found that urban flooding caused a backscattering coefficient increase (BCI) and interferometric coherence decrease (ICD) in VV and VH polarizations. Furthermore, VV polarization was more sensitive to the BCI and ICD than VH polarization. In light of these findings, the ratio between the BCI and ICD was defined as an urban flooding index (UFI), and the UFI in VV polarization was used for the unsupervised detection of flooded urban areas. The overall accuracy, detection accuracy, and false alarm rate attained by the UFI-based method were 96.93%, 91.09%, and 0.95%, respectively. Compared with the conventional unsupervised method based on the ICD and that based on the fusion of backscattering coefficients and interferometric coherences (FBI), the UFI-based method achieved higher overall accuracy. The performance of VV was evaluated and compared to that of VH in the flooded urban area detection using the UFI-, ICD-, and FBI-based methods, respectively. VV polarization produced higher overall accuracy than VH polarization in all the methods, especially in the UFI-based method. By using VV instead of VH polarization, the UFI-based method improved the detection accuracy by 38.16%. These results indicated that the UFI-based method improved flooded urban area detection by synergizing the BCI and ICD in VV polarization.

scholarly journals An example of SAR-derived image segmentation for landslides detection

2018 ◽  
Author(s):  
Giuseppe Esposito ◽  
Alessandro Cesare Mondini ◽  
Ivan Marchesini ◽  
Paola Reichenbach ◽  
Paola Salvati ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Rapid Assessment ◽  
Ground Truth ◽  
Radar Data ◽  
Mass Movements ◽  
Ground Truth Data ◽  
Before And After ◽  
Satellite Radar ◽  
Gis Software ◽  
Derived Data

A rapid assessment of the areal extent of landslide disasters is one of the main challenges facing by the scientific community. Satellite radar data represent a powerful tool for the rapid detection of landslides over large spatial scales, even in case of persistent cloud cover. To define landslide locations, radar data need to be firstly pre-processed and then elaborated for the extraction of the required information. Segmentation represents one of the most useful procedures for identifying land cover changes induced by landslides. In this study, we present an application of the i.segment module of GRASS GIS software for segmenting radar-derived data. As study area, we selected the Tagari River valley in Papua New Guinea, where massive landslides were triggered by a M7.5 earthquake on February 25, 2018. A comparison with ground truth data revealed a suitable performance of i.segment. Particular segmentation patterns, in fact, resulted in the areas affected by landslides with respect to the external ones, or to the same areas before the earthquake. These patterns highlighted a relevant contrast of radar backscattering values recorded before and after the landslides. With our procedure, we were able to define the extension of the mass movements that occurred in the study area, just three days after the M7.5 earthquake.

Corner Reflectors for Validation of Ice Flow Velocity Derived from SAR Images along the CHINARE-Route in Antarctica

2020 ◽  
Author(s):  
Gang Qiao ◽  
Rongxing Li ◽  
Tong Hao ◽  
Xiaohua Tong ◽  
Yanjun Li ◽  
...  
Keyword(s):  
Mass Balance ◽  
Flow Velocity ◽  
Incident Angle ◽  
Surface Shape ◽  
Sar Images ◽  
Ice Flow ◽  
Ground Truth Data ◽  
Radar Images ◽  
Ice Velocity

&lt;p&gt;Ice flow velocity is an important parameter for evaluating the stability of Antarctic ice shelves and analyzing the mass balance of the ice sheet. Large scale ice flow maps can be produced from satellite images with ground control and validation. Among various ground targets, corner reflectors show distinct intensity characteristics on SAR images due to its highly reflective surface shape and have been used for calibration and validation. This paper focuses on design and implementation of a set of corner reflectors to obtain the first-hand data of in-situ ice flow velocity for SAR image based ice velocity maps. The results should further help evaluate mass balance changes in East Antarctica using the input-output method.&lt;/p&gt;&lt;p&gt;Generally, the remote sensing method uses airborne or satellite optical and radar images from multiple periods to map ice flow velocity fields. The ground truth data are often sparse due to the harsh environment in the polar region. The annual Chinese Antarctic Research Expedition (CHINARE) makes it possible to obtain period field data of ice velocity within its campaign regions. The ~1200 km CHINARE-Route runs from Zhongshan Station to Kunlun Station along which the ice flow velocity varies from a few meters per year to 100s meters per year. 5 corner reflectors have been designed and installed along the 31st CHINARE-Route in 2015 and the 35th CHINARE-Route in 2019 (M1, M2 and M3 in the 31st CHINARE, A1and A2 in the 35th CHINARE). The ice flow velocities at the installation locations are of different orders of magnitude, about 44 m per year at the locations of M1 and A1, 93 m per year at M2 and M3 and 73 m per year at A2. The satellite orbit inclination, incident angle and the installation location were used to calculate the azimuth and elevation angles of the corner reflectors for installation. At all reflector locations GPS positions were collected at the time of installation. After that, the second time GPS coordinates of M3 in the 34th CHINARE in 2018, the third time GPS coordinates of M3, the second time GPS coordinates of A1 and A2 in the 36th CHINARE at the end of 2019 were measured respectively. TerraSAR-X was used to image the reflectors.&lt;/p&gt;&lt;p&gt;The results show that the mean in-situ ice flow velocity of M3 is 96.83 m per year between Feb. 2015 and Dec. 2019, with 97.51 m per year between Feb. 2015 and Jan. 2018 and 95.81m per year between Jan. 2018 and Dec. 2019. The in-situ ice flow velocity is 54.9 m per year at A1 between Jan. 2019 and Dec. 2019 and 86.92 m per year at A2 between Feb. 2019 and Dec. 2019. More TerraSAR-X and COSMO-SkyMed data will be used to extract the ice velocity corresponding to GPS measurements. The detailed information will be presented at the meeting.&lt;/p&gt;

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