scholarly journals A Tool for Pre-Operational Daily Mapping of Floods and Per-Manent Water Using Sentinel-1 Data

2021 ◽  
Vol 13 (7) ◽  
pp. 1342
Author(s):  
Luca Pulvirenti ◽  
Giuseppe Squicciarino ◽  
Elisabetta Fiori ◽  
Luca Ferraris ◽  
Silvia Puca
Keyword(s):  
Region Growing ◽  
Open Water ◽  
Optical Data ◽  
Management Service ◽  
Data Generation ◽  
End User ◽  
Mapping Algorithm ◽  
Output Layer ◽  
Water Mapping ◽  
Flood Maps

An automated tool for pre-operational mapping of floods and inland waters using Sentinel-1 data is presented. The acronym AUTOWADE (AUTOmatic Water Areas DEtector) is used to denote it. The tool provides the end user (Italian Department of Civil Protection) with a continuous, near real-time (NRT) monitoring of the extent of inland water surfaces (floodwater and permanent water). It implements the following operations: downloading of Sentinel-1 products; preprocessing of the products and storage of the resulting geocoded and calibrated data; generation of the intermediate products, such as the exclusion mask; application of a floodwater/permanent water mapping algorithm; generation of the output layer, i.e., a map of floodwater/permanent water; delivery of the output layer to the end user. The open floodwater/permanent water mapping algorithm implemented in AUTOWADE is based on a new approach, denoted as buffer-from-edge (BFE), which combines different techniques, such as clustering, edge filtering, automatic thresholding and region growing. AUTOWADE copes also with the typical presence of gaps in the flood maps caused by undetected flooded vegetation. An attempt to partially fill these gaps by analyzing vegetated areas adjacent to open water is performed by another algorithm implemented in the tool, based on the fuzzy logic. The BFE approach has been validated offline using maps produced by the Copernicus Emergency Management Service. Validation has given good results with a F1-score larger than 0.87 and a kappa coefficient larger than 0.80. The algorithm to detect flooded vegetation has been visually compared with optical data and aerial photos; its capability to fill some of the gaps present in flood maps has been confirmed.

scholarly journals Evaluating Simulated RADARSAT Constellation Mission (RCM) Compact Polarimetry for Open-Water and Flooded-Vegetation Wetland Mapping

2020 ◽  
Vol 12 (9) ◽  
pp. 1476 ◽  
Author(s):  
Ian Olthof ◽  
Thomas Rainville
Keyword(s):  
Stokes Parameter ◽  
Incidence Angle ◽  
Region Growing ◽  
Open Water ◽  
Synthetic Aperture ◽  
Wetland Mapping ◽  
Vegetation Region ◽  
Beam Mode ◽  
Flood Maps ◽  
Severe Flooding

When severe flooding occurs in Canada, the Emergency Geomatics Service (EGS) is tasked with creating and disseminating maps that depict flood extents in near real time. EGS flood mapping methods were created with efficiency and robustness in mind, to allow maps to be published quickly, and therefore have the potential to generate high-repeat water products that can enhance frequent wetland monitoring. The predominant imagery currently used is synthetic aperture radar (SAR) from RADARSAT-2 (R2). With the commissioning phase of the RADARSAT Constellation Mission (RCM) complete, the EGS is adapting its methods for use with this new source of SAR data. The introduction of RCM’s circular-transmit linear-receive (CTLR) beam mode provides the option to exploit compact polarimetric (CP) information not previously available with R2. The aim of this study was to determine the most effective CP parameters for use in mapping open water and flooded vegetation, using current EGS methodologies, and compare these products to those created by using R2 data. Nineteen quad-polarization R2 scenes selected from three regions containing wetlands prone to springtime flooding were used to create reference flood maps, using existing EGS tools. These scenes were then used to simulate 22 RCM CP parameters at different noise floors and spatial resolutions representative of the three RCM beam modes. Using multiple criteria, CP parameters were ranked in order of importance and entered into a stepwise classification procedure, for evaluation against reference R2 products. The top four CP parameters —m-chi-volume or m-delta-volume, RR intensity, Shannon Entropy intensity (SEi), and RV intensity—achieved a maximum agreement with baseline R2 products of upward of 98% across all 19 scenes and three beam modes. Separability analyses between flooded vegetation and other land-cover classes identified four candidate CP parameters—RH intensity, RR intensity, SEi, and the first Stokes parameter (SV0)—suitable for flooded-vegetation-region growing. Flooded-vegetation-region-growing CP thresholds were found to be dependent on incidence angle for each of these four parameters. After region growing using each of the four candidate CP parameters, RH intensity was deemed best to map flooded vegetation, based on our evaluations. The results of the study suggest a set of suitable CP parameters to generate flood maps from RCM data, using current EGS methodologies that must be validated further as real RCM data become available.

scholarly journals VCSEL Based Optoelectronic Oscillator (VBO) for 1.25 Gbit/s RZ Pulse Optical Data Generation

2018 ◽  
Author(s):  
Christian Daniel Munoz ◽  
Angelique Rissons ◽  
Fabien Destic ◽  
Juan Coronel Rico ◽  
Margarita Varon
Keyword(s):  
Optical Data ◽  
Data Generation ◽  
Optoelectronic Oscillator

Automatic Flood Monitoring based on SAR Intensity and Interferometric Coherence using Machine Learning

2020 ◽  
Author(s):  
Binayak Ghosh ◽  
Mahdi Motagh ◽  
Mahmud Haghshenas Haghighi ◽  
Setareh Maghsudi
Keyword(s):  
Machine Learning ◽  
Open Water ◽  
Semantic Segmentation ◽  
Flood Mapping ◽  
Assistance Programs ◽  
Amplitude Data ◽  
Flood Monitoring ◽  
Sar Data ◽  
Flood Detection ◽  
Flood Maps

<p><span xml:lang="EN-US" data-contrast="auto"><span>Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. Emergency responders frequently request satellite-based crisis information for flood monitoring to target the often-limited resources and to prioritize response actions throughout a disaster situation. Flood mapping algorithms are usually based on automatic thresholding algorithms for the initialization of the classification process in SAR amplitude data. These thresholding processes like Otsu thresholding, histogram leveling etc., are followed by clustering techniques like K-means, ISODATA for segmentation of water and non-water areas. These methods are capable of extracting the flood extent if there is a significant contrast between water and non-water areas in the SAR data. However, the classification result may be related to overestimations if non-water areas have a similar low backscatter as open water surfaces and also, these backscatter values differentiate from VV and VH polarizations. Our method aims at improving existing satellite-based emergency mapping methods by incorporating systematically acquired Sentinel-1A/B SAR data at high spatial (20m) and temporal (3-5 days) resolution. Our method involves a supervised learning method for flood detection by leveraging SAR intensity and interferometric coherence as well as polarimetry information. </span></span><span xml:lang="EN-US" data-contrast="auto"><span>It uses multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. By incorporating multitemporal satellite imagery, our method allows for rapid and accurate post-disaster damage assessment and can be used for better coordination of medium- and long-term financial assistance programs for affected areas. In this paper, we present a strategy using machine learning for semantic segmentation of the flood map, which extracts the </span></span><span xml:lang="EN-US" data-contrast="auto"><span>spatio</span></span><span xml:lang="EN-US" data-contrast="auto"><span>-temporal information from the SAR images having both </span></span><span xml:lang="EN-US" data-contrast="auto"><span>intensity</span></span><span xml:lang="EN-US" data-contrast="auto"><span> as well coherence bands. The flood maps produced by the fusion of intensity and coherence are validated against state-of-the art methods for producing flood maps.</span></span><span> </span></p>

scholarly journals Applicability of the MultiTemporal Coherence Approach to Sentinel-1 for the Detection and Delineation of Burnt Areas in the Context of the Copernicus Emergency Management Service

2019 ◽  
Vol 11 (22) ◽  
pp. 2607 ◽  
Author(s):  
Uxue Donezar ◽  
Teresa De Blas ◽  
Arantzazu Larrañaga ◽  
Fermín Ros ◽  
Lourdes Albizua ◽  
...  
Keyword(s):  
Emergency Management ◽  
Incidence Angle ◽  
European Space Agency ◽  
Optical Data ◽  
Mountainous Area ◽  
Management Service ◽  
Burnt Area ◽  
Area Index ◽  
Optical Imagery ◽  
Burnt Areas

In the framework of the Copernicus Emergency Management Service (EMS) Mapping Validation, the applicability of the MultiTemporal Coherence (MTC) technique using Sentinel-1 data and the software made available by the European Space Agency (ESA), the Sentinel Application Platform (SNAP), for the detection and delineation of burnt areas was tested. The main purpose of the study was to test a methodology that would benefit from the advantages of delineating burnt areas based on radar data with respect to optical data due to its capacity to acquire data both night and day and to avoid the interference of clouds and/or smoke. Moreover, the study aimed to acheive the delineation of the burnt areas using Sentinel-1 and SNAP in the frame of an emergency mapping where processing time is constrained due to the necessity of giving a quick response to the emergency. Four Sentinel-1 images were acquired over a mountainous area mainly covered by Mediterranean vegetation that suffered from massive forest fires in the summer of 2016. The burnt area delineation was obtained by an object-based image analysis (OBIA) of the resulting MTC image followed by a visual inspection. The effects of the polarization, the acquisition mode, and the incidence angle of the synthetic aperture radar (SAR) imagery were studied in order to assess the contribution of these sensor varaibles on the results. Results of the Sentinel-1 based delineation were compared to those using optical imagery, which is traditionally used for this application. Therefore, the fire delineation that was derived was compared to that derived using three optical images: pre- and post-event Sentinel-2 images and a post-event SPOT 6 image. The first two were used to calculate the differences of the burnt area index (dBAI), used to derive the burnt area delineation by OBIA and photo interpretation with the help of the SPOT 6 image. Results of the comparison showed the feasibility of using the MTC technique for burnt area delineation, as high overall accuracy values were observed when compared to the burnt area delineation derived from optical imagery. The importance of the incidence angle of the Sentinel-1 images was assessed as well, with lower angles resulting in higher overall accuracies. In addition, the availability of double polarization of the Sentinel-1 images, allowed us to give recommendations regarding which polarization gave the best results. The potential for the use of SAR data, obtaining equivalent results to those obtained from optical imagery, is significant in an emergency context given that radar sensors acquire images continuosly and in all weather conditions.

1.28 Tbit/s single-polarisation serial OOK optical data generation and demultiplexing

2009 ◽  
Vol 45 (5) ◽  
pp. 280 ◽  
Author(s):  
H.C. Hansen Mulvad ◽  
L.K. Oxenløwe ◽  
M. Galili ◽  
A.T. Clausen ◽  
L. Grüner-Nielsen ◽  
...  
Keyword(s):  
Optical Data ◽  
Data Generation

scholarly journals Use of ENVISAT ASAR Global Monitoring Mode to complement optical data in the mapping of rapid broad-scale flooding in Pakistan

2011 ◽  
Vol 15 (11) ◽  
pp. 3475-3494 ◽  
Author(s):  
D. O'Grady ◽  
M. Leblanc ◽  
D. Gillieson
Keyword(s):  
Specular Reflection ◽  
Incidence Angle ◽  
Temporal Frequency ◽  
Region Growing ◽  
Rapid Response ◽  
Optical Data ◽  
River Channels ◽  
Response Effort ◽  
Envisat Asar ◽  
Target Values

Abstract. Envisat ASAR Global Monitoring Mode (GM) data are used to produce maps of the extent of the flooding in Pakistan which are made available to the rapid response effort within 24 h of acquisition. The high temporal frequency and independence of the data from cloud-free skies makes GM data a viable tool for mapping flood waters during those periods where optical satellite data are unavailable, which may be crucial to rapid response disaster planning, where thousands of lives are affected. Image differencing techniques are used, with pre-flood baseline image backscatter values being deducted from target values to eliminate regions with a permanent flood-like radar response due to volume scattering and attenuation, and to highlight the low response caused by specular reflection by open flood water. The effect of local incidence angle on the received signal is mitigated by ensuring that the deducted image is acquired from the same orbit track as the target image. Poor separability of the water class with land in areas beyond the river channels is tackled using a region-growing algorithm which seeks threshold-conformance from seed pixels at the center of the river channels. The resultant mapped extents are tested against MODIS SWIR data where available, with encouraging results.

scholarly journals Detection of Seasonal Inundations by Satellite Data at Shkoder Urban Area, North Albania for Sustainable Management

2019 ◽  
Vol 11 (16) ◽  
pp. 4454 ◽  
Author(s):  
Stefano Morelli ◽  
Matteo Del Soldato ◽  
Silvia Bianchini ◽  
Veronica Pazzi ◽  
Ervis Krymbi ◽  
...  
Keyword(s):  
European Space Agency ◽  
Temporal Analysis ◽  
Radar Data ◽  
Optical Data ◽  
Future Perspective ◽  
Management Service ◽  
Surface Mapping ◽  
Water Index ◽  
Space Agency ◽  
Sentinel 2

The European Space Agency satellites Sentinel-1 radar and Sentinel-2 optical data are widely used in water surface mapping and management. In this work, we exploit the potentials of both radar and optical images for satellite-based quick detection and extent mapping of inundations/water raising events over Shkodër area, which occurred in the two last years (2017–2018). For instance, in March 2018 the Shkodër district (North Albania) was affected twice by the overflow of the Drin and Buna (Bojana) Rivers and by the Shkodër lake plain inundation. Sentinel-1 radar data allowed a rapid mapping of seasonal fluctuations and provided flood extent maps by discriminating water surfaces (permanent water and flood areas) from land/non-flood areas over all the informal zones of Shkodër city. By means of Sentinel-2 data, two color composites maps were produced and the Normalized Difference Water Index was estimated, in order to further distinguish water/moisturized soil surfaces from built-up and vegetated areas. The obtained remote sensing-based maps were combined and discussed with the urban planning framework in order to support a sustainable urban and environmental management. The provided multi-temporal analysis could be easily exploited by the local authorities for flood prevention and management purposes in the inherited territorial context. The proposed approach outputs were validated by comparing them with official Copernicus EMS (Emergency Management Service) maps available for one of the chosen events. The comparison shows good accordance results. As for a further enhancement in the future perspective, it is worth to highlight that a more accurate result could be obtained by performing a post-processing edit to further refine the flooded areas, such as water mask application and supervised classification to filter out isolated flood elements, to remove possible water-lookalikes and weed out false positives.

A Sentiment Score and a rating based Numeric Analysis Recommendations System using Web and Data Mining Approach: A Review

2019 ◽  
Vol 13 ◽  
Author(s):  
A. Lakshmi Holla ◽  
K.S. Kavitha
Keyword(s):  
Consumer Satisfaction ◽  
Data Generation ◽  
End User ◽  
Current Recommendation ◽  
Data Mining Approach ◽  
Main Challenge ◽  
Number Of Factors ◽  
Sentiment Score ◽  
The Right ◽  
Work Done

There is an enormous amount of online purchaseshappening in the web world. Some of the big giants who have dominated the E-Commerce market worldwide are Amazon, FlipKart, Walmart and many more. Data generation has increased exponentially and analysis of this dynamic data poses a major challenge. Further, facilitating consumer satisfaction by recommending the right product is another main challenge .This involvesa significant number of factors like review ratings, normalization, early rating, sentiment computations of a sentence consisting of conjunctions, categorizing the sentiment score as positive, negative and neutral score for a given productreview. Finally, the product which has the highest positive and least negative score must be suggested for the end user. In this paper, we discuss the work done under rating based numerical analysis methods which considers the transactions done by the end user. In the second part of the paper we present an overview of sentiment score based recommendation system.The main objective of this review is to understand and analyze the different methods used to improve the efficiency of the current recommendation systems, thereby enhancing the credibility of product recommendations.

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