scholarly journals Oil Spill Detection Analyzing “Sentinel 2” Satellite Images: A Persian Gulf Case Study

2018 ◽  
Author(s):  
M. Majidi Nezhad ◽  
D. Groppi ◽  
G. Laneve ◽  
P. Marzialetti ◽  
G. Piras
Keyword(s):  
Oil Spill ◽  
Persian Gulf ◽  
Satellite Images ◽  
Oil Spill Detection ◽  
Sentinel 2

scholarly journals Investigation of Proper Index of Oil Spill Detection Using Space-Borne Sentinel-2 (Case Study: The Persian Gulf, 15 Feb 2016)

2018 ◽  
Vol 9 (33) ◽  
pp. 31-40
Author(s):  
Nadia Talebpour ◽  
Taher Safarrad ◽  
Mohammad Akbarinasab ◽  
Masomeh Rasolian
Keyword(s):  
Oil Spill ◽  
Persian Gulf ◽  
Oil Spill Detection ◽  
The Persian Gulf ◽  
Sentinel 2

scholarly journals A Novel Approach to Estimate Diffuse Attenuation Coefficients for QuickBird Satellite Images: A Case Study at Kish Island, the Persian Gulf

2013 ◽  
Vol 41 (4) ◽  
pp. 797-806 ◽  
Author(s):  
Keivan Kabiri ◽  
Biswajeet Pradhan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Shattri Bin Mansor ◽  
Kaveh Samimi-Namin
Keyword(s):  
Persian Gulf ◽  
Satellite Images ◽  
Attenuation Coefficients ◽  
Novel Approach ◽  
The Persian Gulf

scholarly journals Synoptic Characteristics of Torrential Rains in Southwest and Southeast Iraq: A Case Study

2021 ◽  
Vol 32 (3) ◽  
pp. 1
Author(s):  
Aqeel Ghazi Mutar ◽  
Asraa Khtan ◽  
Loay E. George
Keyword(s):  
Satellite Images ◽  
Research Work ◽  
Heavy Rain ◽  
Moisture Flux ◽  
Relative Vorticity ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Modern Era ◽  
Sentinel 2

Torrential rains cause many losses in city infrastructure, crops, and deaths in several regions of the world including Iraq as in the case that we will discuss in this work, on January 28 and 29, 2019. Torrential rain caused the flow of torrents in several areas of Iraq and the neighboring areas. This research work aims to identify the synoptic characteristics of torrential rains and the causes of this case. This will be done by analyzing and interpreting the weather maps at different pressure levels with focusing on the troughs and fronts locations, relative vorticity, polar jet stream effect as well as the moisture flux. The Geographic Information System (GIS) was used to analyze the satellite images in order to calculate the Normalized Difference Water Index (NDWI) to confirm the heavy rain case. The weather maps were obtained from the Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2).  As for the satellite images we used the satellite imagery from Sentinel-2 and EMUTSAT.

scholarly journals A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Omid Ghorbanzadeh ◽  
Alessandro Crivellari ◽  
Pedram Ghamisi ◽  
Hejar Shahabi ◽  
Thomas Blaschke
Keyword(s):  
Satellite Images ◽  
Patch Size ◽  
Window Size ◽  
Convolutional Network ◽  
Digital Elevation ◽  
High Resolution Satellite Images ◽  
Elevation Model ◽  
Very High ◽  
Sentinel 2

AbstractEarthquakes and heavy rainfalls are the two leading causes of landslides around the world. Since they often occur across large areas, landslide detection requires rapid and reliable automatic detection approaches. Currently, deep learning (DL) approaches, especially different convolutional neural network and fully convolutional network (FCN) algorithms, are reliably achieving cutting-edge accuracies in automatic landslide detection. However, these successful applications of various DL approaches have thus far been based on very high resolution satellite images (e.g., GeoEye and WorldView), making it easier to achieve such high detection performances. In this study, we use freely available Sentinel-2 data and ALOS digital elevation model to investigate the application of two well-known FCN algorithms, namely the U-Net and residual U-Net (or so-called ResU-Net), for landslide detection. To our knowledge, this is the first application of FCN for landslide detection only from freely available data. We adapt the algorithms to the specific aim of landslide detection, then train and test with data from three different case study areas located in Western Taitung County (Taiwan), Shuzheng Valley (China), and Eastern Iburi (Japan). We characterize three different window size sample patches to train the algorithms. Our results also contain a comprehensive transferability assessment achieved through different training and testing scenarios in the three case studies. The highest f1-score value of 73.32% was obtained by ResU-Net, trained with a dataset from Japan, and tested on China’s holdout testing area using the sample patch size of 64 × 64 pixels.

scholarly journals Evaluating the potential of Sentinel-2 satellite images for water quality characterization of artificial reservoirs: The Bin El Ouidane Reservoir case study (Morocco)

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Karaoui Ismail ◽  
Abdelghani Boudhar ◽  
Arioua Abdelkrim ◽  
Hssaisoune Mohammed ◽  
Sabri Mouatassime ◽  
...  
Keyword(s):  
Water Quality ◽  
Satellite Images ◽  
Bin El Ouidane ◽  
Artificial Reservoirs ◽  
Sentinel 2

Comparison of a massive and diverse collection of ensembles and other classifiers for oil spill detection in SAR satellite images

2016 ◽  
Vol 28 (S1) ◽  
pp. 1101-1117 ◽  
Author(s):  
D. Mera ◽  
M. Fernández-Delgado ◽  
J. M. Cotos ◽  
J. R. R. Viqueira ◽  
S. Barro
Keyword(s):  
Oil Spill ◽  
Satellite Images ◽  
Oil Spill Detection
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