scholarly journals Automatic Open Water Flood Detection from Sentinel-1 Multi-Temporal Imagery

Water ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 3392
Author(s):  
Ivana Hlaváčová ◽  
Michal Kačmařík ◽  
Milan Lazecký ◽  
Juraj Struhár ◽  
Petr Rapant
Keyword(s):  
Minimum Cost ◽  
Open Water ◽  
Heavy Rain ◽  
Radar Data ◽  
Mean Value ◽  
False Alarms ◽  
Water Detection ◽  
Multi Temporal ◽  
Flood Detection ◽  
Data Flood

Many technical infrastructure operators manage facilities distributed over large areas. They face the problem of finding out if a flood hit a specific facility located in the open countryside. Physical inspection after every heavy rain is time and personnel consuming, and equipping all facilities with flood detection is expensive. Therefore, methods are being sought to ensure that these facilities are monitored at a minimum cost. One of the possibilities is using remote sensing, especially radar data regularly scanned by satellites. A significant challenge in this area was the launch of Sentinel-1 providing free-of-charge data with adequate spatial resolution and relatively high revisit time. This paper presents a developed automatic processing chain for flood detection in the open landscape from Sentinel-1 data. Flood detection can be started on-demand; however, it mainly focuses on autonomous near real-time monitoring. It is based on a combination of algorithms for multi-temporal change detection and histogram thresholding open-water detection. The solution was validated on five flood events in four European countries by comparing its results with flood delineation derived from reference datasets. Long-term tests were also performed to evaluate the potential for a false positive occurrence. In the statistical classification assessments, the mean value of user accuracy (producer accuracy) for open-water class reached 83% (65%). The developed solution typically provided flooded polygons in the same areas as the reference dataset, but of a smaller size. This fact is mainly attributed to the use of universal sensitivity parameters, independent of the specific location, which ensure almost complete successful suppression of false alarms.

Landsat thermal infrared to detect sub-canopy riparian flooding

2021 ◽  
Author(s):  
Emanuel Storey ◽  
Witold Krajewski ◽  
Efthymios Nikolopoulos
Keyword(s):  
Open Water ◽  
Thermal Infrared ◽  
Short Wave ◽  
Mapping Technique ◽  
Landsat 8 ◽  
Infrared Observations ◽  
Plant Canopies ◽  
Multi Temporal ◽  
Flood Detection

<p>Satellite based flood detection can enhance understanding of risk to humans and infrastructures, geomorphic processes, and ecological effects.  Such application of optical satellite imagery has been mostly limited to the detection of water exposed to sky, as plant canopies tend to obstruct water visibility in short electromagnetic wavelengths.  This case study evaluates the utility in multi-temporal thermal infrared observations from Landsat 8 as a basis for detecting sub-canopy fluvial inundation resulting in ambient temperature change.</p><p>We selected three flood events of 2016 and 2019 along sections of the Mississippi, Cedar, and Wapsipinicon Rivers located in Iowa, Minnesota, and Wisconsin, United States.  Classification of sub-canopy water involved logical, threshold-exceedance criteria to capture thermal decline within channel-adjacent vegetated zones.  Open water extent in the floods was mapped based on short-wave infrared thresholds determined parametrically from baseline (non-flooded) observations.  Map accuracy was evaluated using higher-resolution (0.5–5.0 m) synchronic optical imagery.</p><p>Results demonstrate improved ability to detect sub-canopy inundation when thermal infrared change is incorporated: sub-canopy flood class accuracy was comparable to that of open water in previous studies.  The multi-temporal open-water mapping technique yielded high accuracy as compared to similar studies.  This research highlights the utility of Landsat thermal infrared data for monitoring riparian inundation and for validating other remotely sensed and simulated flood maps.</p>

scholarly journals Comparison of Target Detectors to Identify Icebergs in Quad-Polarimetric L-Band Synthetic Aperture Radar Data

2021 ◽  
Vol 13 (9) ◽  
pp. 1753
Author(s):  
Johnson Bailey ◽  
Armando Marino ◽  
Vahid Akbari
Keyword(s):  
Sea Ice ◽  
Synthetic Aperture Radar ◽  
Roc Curves ◽  
Radar Data ◽  
Open Ocean ◽  
Synthetic Aperture ◽  
False Alarms ◽  
Synthetic Aperture Radar Data ◽  
Whitening Filter ◽  
Aperture Radar

Icebergs represent hazards to ships and maritime activities and therefore their detection is essential. Synthetic Aperture Radar (SAR) satellites are very useful for this, due to their capability to acquire data under cloud cover and during day and night passes. In this work, we compared six state-of-the-art polarimetric target detectors to test their performance and ability to detect small-sized icebergs <120 m in four locations in Greenland. We used four single-look complex (SLC) ALOS-2 quad-polarimetric images from JAXA for quad-polarimetric detection and we compared with dual-polarimetric detectors using only the channels HH and HV. We also compared these detectors with single-polarimetric intensity channels and we tested using two scenarios: open ocean and sea ice. Our results show that the multi-look polarimetric whitening filter (MPWF) and the optimal polarimetric detector (OPD) provide the most optimal performance in quad- and dual-polarimetric mode detection. The analysis shows that, overall, quad-polarimetric detectors provide the best detection performance. When the false alarm rate (PF) is fixed to 10-5, the probabilities of detection (PD) are 0.99 in open ocean and 0.90 in sea ice. Dual-polarimetric or single-polarimetric detectors show an overall reduction in performance (the ROC curves show a decrease), but this degradation is not very large (<0.1) when the value of false alarms is relatively high (i.e., we are interested in bigger icebergs with a brighter backscattering >120 m, as they are easier to detect). However, the differences between quad- and dual- or single-polarimetric detectors became much more evident when the PF value was fixed to low detection probabilities 10-6 (i.e., smaller icebergs). In the single-polarimetric mode, the HV channel showed PD values of 0.62 for open ocean and 0.26 for sea ice, compared to values of 0.81 (open ocean) and 0.77 (sea ice) obtained with quad-polarimetric detectors.

scholarly journals Comparison of Radar-Based Hail Detection Using Single- and Dual-Polarization

2019 ◽  
Vol 11 (12) ◽  
pp. 1436 ◽  
Author(s):  
Skripniková ◽  
Řezáčová
Keyword(s):  
Comparative Analysis ◽  
Heavy Rain ◽  
Radar Data ◽  
Severe Weather ◽  
Detection Methods ◽  
Polarimetric Radar ◽  
Dual Polarization ◽  
Rain Events ◽  
Single Polarization ◽  
Strong Agreement

The comparative analysis of radar-based hail detection methods presented here, uses C-band polarimetric radar data from Czech territory for 5 stormy days in May and June 2016. The 27 hail events were selected from hail reports of the European Severe Weather Database (ESWD) along with 21 heavy rain events. The hail detection results compared in this study were obtained using a criterion, which is based on single-polarization radar data and a technique, which uses dual-polarization radar data. Both techniques successfully detected large hail events in a similar way and showed a strong agreement. The hail detection, as applied to heavy rain events, indicated a weak enhancement of the number of false detected hail pixels via the dual-polarization hydrometeor classification. We also examined the performance of hail size detection from radar data using both single- and dual-polarization methods. Both the methods recognized events with large hail but could not select the reported events with maximum hail size (diameter above 4 cm).

scholarly journals Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications

2021 ◽  
Vol 13 (4) ◽  
pp. 604
Author(s):  
Donato Amitrano ◽  
Gerardo Di Martino ◽  
Raffaella Guida ◽  
Pasquale Iervolino ◽  
Antonio Iodice ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Microwave Remote Sensing ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Special Focus ◽  
Arid Environments ◽  
Synthetic Aperture Radar Data ◽  
Multi Temporal ◽  
Aperture Radar

Microwave remote sensing has widely demonstrated its potential in the continuous monitoring of our rapidly changing planet. This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special focus on topics like forestry, water resources management in semi-arid environments and floods. The analyzed literature is categorized on the base of the approach adopted and the data exploited and discussed in light of the downstream remote sensing market. The purpose is to highlight the main issues and limitations preventing the diffusion of synthetic aperture radar data in both industrial and multidisciplinary research contexts and the possible solutions for boosting their usage among end-users.

scholarly journals Ice-edge detection from Japanese C-band radar and high-frequency radar coastal stations

2013 ◽  
Vol 54 (62) ◽  
pp. 59-64 ◽  
Author(s):  
K. Shirasawa ◽  
N. Ebuchi ◽  
M. Leppäranta ◽  
T. Takatsuka
Keyword(s):  
Sea Ice ◽  
Edge Detection ◽  
High Frequency ◽  
Open Water ◽  
Radar Data ◽  
Hf Radar ◽  
Sea Surface ◽  
Geometrical Structures ◽  
Overlapping Data ◽  
Ice Edge

AbstractA C-band sea-ice radar (SIR) network system was operated to monitor the sea-ice conditions off the Okhotsk Sea coast of northern Hokkaido, Japan, from 1969 to 2004. The system was based on three radar stations, which were capable of continuously monitoring the sea surface as far as 60 km offshore along a 250 km long coastal section. In 2004 the SIR system was closed down and a sea surface monitoring programme was commenced using high-frequency (HF) radar; this system provides information on surface currents in open-water conditions, while areas with ‘no signal’ can be identified as sea ice. The present study compares HF radar data with SIR data to evaluate their feasibility for sea-ice remote sensing. The period of overlapping data was 1.5 months. The results show that HF radar information can be utilized for ice-edge mapping although it cannot fully compensate for the loss of the SIR system. In particular, HF radar does not provide ice concentration, ice roughness and geometrical structures or ice kinematics. The probability of ice-edge detection by HF radar was 0.9 and the correlation of the ice-edge distance between the radars was 0.7.

An Analysis of 2016-2018 Tornadoes and National Weather Service Tornado Warnings across the contiguous United States

2021 ◽  
Author(s):  
Evan S. Bentley ◽  
Richard L. Thompson ◽  
Barry R. Bowers ◽  
Justin G. Gibbs ◽  
Steven E. Nelson
Keyword(s):  
United States ◽  
Rotational Velocity ◽  
Radar Data ◽  
Probability Of Detection ◽  
False Alarms ◽  
National Weather Service ◽  
Low Level ◽  
Tornado Warnings ◽  
Tornado Warning ◽  
Weather Service

AbstractPrevious work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–2018) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance.Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1nm) circulations in a poor (STP=0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.

scholarly journals Analysis of Sentinel 1 Radar Data to Identify Frozing/Thawing Soils in the Area of Anadyr (Chukotka) and Belaya Gora (Yakutia)

2019 ◽  
pp. 29-37
Author(s):  
N. V. Rodionova
Keyword(s):  
Soil Layer ◽  
Texture Features ◽  
Threshold Value ◽  
Radar Data ◽  
Backscatter Coefficient ◽  
Frozen Soils ◽  
Multi Temporal ◽  
Db Difference ◽  
Archive Data

The paper deals with the distinction between thawed and frozen soils in the upper 5 cm layer for two stations in Russia: Belaya Gora (Yakutia) 68.5° N and Anadyr (Chukotka) 64.78° N — by using Sentinel 1 C-band radar data for the period of 2014–2016 years. Determination of the frozen/thawed soil state is carried out in three ways: 1) by multi-temporal radar data on the basis of a significant in 3–5 dB difference in the backscatter coefficient σ0 in the transition of freezing/thawing soil state, 2) by finding the threshold value of σ0 at which the temperature in the upper soil layer falls below 00С, 3) by texture features for one- channel images. The graphs of the AFI (air freezing index) for the period of 2012-2018 with trends are constructed based on the archive data of air temperature for the study areas.

scholarly journals Multi-Weather Evaluation of Nowcasting Methods Including a New Empirical Blending Scheme

Atmosphere ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1166
Author(s):  
Hsin-Hung Lin ◽  
Chih-Chien Tsai ◽  
Jia-Chyi Liou ◽  
Yu-Chun Chen ◽  
Chung-Yi Lin ◽  
...  
Keyword(s):  
Numerical Model ◽  
Model Prediction ◽  
Radar Data ◽  
False Alarms ◽  
Rainfall Events ◽  
System A ◽  
Radar Echo ◽  
Grid Points ◽  
Mean Square Errors ◽  
Heavy Rainfall Events

This study utilized a radar echo extrapolation system, a high-resolution numerical model with radar data assimilation, and three blending schemes including a new empirical one, called the extrapolation adjusted by model prediction (ExAMP), to carry out 150 min reflectivity nowcasting experiments for various heavy rainfall events in Taiwan in 2019. ExAMP features full trust in the pattern of the extrapolated reflectivity with intensity adjustable by numerical model prediction. The spatial performance for two contrasting events shows that the ExAMP scheme outperforms the others for the more accurate prediction of both strengthening and weakening processes. The statistical skill for all the sampled events shows that the nowcasts by ExAMP and the extrapolation system obtain the lowest and second lowest root mean square errors at all the lead time, respectively. In terms of threat scores and bias scores above certain reflectivity thresholds, the ExAMP nowcast may have more grid points of misses for high reflectivity in comparison to extrapolation, but serious overestimation among the points of hits and false alarms is the least likely to happen with the new scheme. Moreover, the event type does not change the performance ranking of the five methods, all of which have the highest predictability for a typhoon event and the lowest for local thunderstorm events.

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