Evaluation of the elevation model influence on the orthorectification of Sentinel-2 satellite images over Austria

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.

Download Full-text

Monitoring Acid Mine Drainage’s Effects on Surface Water in the Kizel Coal Basin with Sentinel-2 Satellite Images

Mine Water and the Environment ◽

10.1007/s10230-021-00761-7 ◽

2021 ◽

Author(s):

Sergey V. Pyankov ◽

Nikolay G. Maximovich ◽

Elena A. Khayrulina ◽

Olga A. Berezina ◽

Andrey N. Shikhov ◽

...

Keyword(s):

Surface Water ◽

Satellite Images ◽

Coal Basin ◽

Acid Mine ◽

Sentinel 2

Download Full-text

Application of Satellite Sentinel-2 Images to Study Alternate Sandbars Movement at Lower Vistula River (Poland)

Remote Sensing ◽

10.3390/rs13081505 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1505

Author(s):

Klaudia Kryniecka ◽

Artur Magnuszewski

Keyword(s):

Satellite Images ◽

Average Distance ◽

Low Flow ◽

Visual Interpretation ◽

Time Intervals ◽

Manual Delineation ◽

Vistula River ◽

Interclass Correlation ◽

Optical Satellite Images ◽

Sentinel 2

The lower Vistula River was regulated in the years 1856–1878, at a distance of 718–939 km. The regulation plan did not take into consideration the large transport of the bed load. The channel was shaped using simplified geometry—too wide for the low flow and overly straight for the stabilization of the sandbar movement. The hydraulic parameters of the lower Vistula River show high velocities of flow and high shear stress. The movement of the alternate sandbars can be traced on the optical satellite images of Sentinel-2. In this study, a method of sandbar detection through the remote sensing indices, Sentinel Water Mask (SWM) and Automated Water Extraction Index no shadow (AWEInsh), and the manual delineation with visual interpretation (MD) was used on satellite images of the lower Vistula River, recorded at the time of low flows (20 August 2015, 4 September 2016, 30 July 2017, 20 September 2018, and 29 August 2019). The comparison of 32 alternate sandbar areas obtained by SWM, AWEInsh, and MD manual delineation methods on the Sentinel-2 images, recorded on 20 August 2015, was performed by the statistical analysis of the interclass correlation coefficient (ICC). The distance of the shift in the analyzed time intervals between the image registration dates depends on the value of the mean discharge (MQ). The period from 30 July 2017 to 20 September 2018 was wet (MQ = 1140 m3 × s−1) and created conditions for the largest average distance of the alternate sandbar shift, from 509 to 548 m. The velocity of movement, calculated as an average shift for one day, was between 1.2 and 1.3 m × day−1. The smallest shift of alternate sandbars was characteristic of the low flow period from 20 August 2015 to 4 September 2016 (MQ = 306 m3 × s−1), from 279 to 310 m, with an average velocity from 0.7 to 0.8 m × day−1.

Download Full-text

Assessment of August 11, 2021 Bozkurt Flood Disaster With Sentinel-2 Satellite Images, Turkey

10.3997/2214-4609.202149bgs88 ◽

2021 ◽

Author(s):

A. Caglayan ◽

R. Saber ◽

V. Isik

Keyword(s):

Satellite Images ◽

Flood Disaster ◽

Sentinel 2

Download Full-text

Detecting the Tigris River Flood and its Impact on Residential and Touristic Areas in Mosul City Using Sentinel-2 Satellite Images

Iraqi National Journal of Earth Sciences ◽

10.33899/earth.2020.170348 ◽

2020 ◽

Vol 20 (1) ◽

pp. 92-106

Author(s):

Abdalrahman Qubaa ◽

Ayman Alsayiegh

Keyword(s):

Satellite Images ◽

Tigris River ◽

River Flood ◽

Sentinel 2

Download Full-text

STUDY OF THE RELIEF AND COASTLINE DYNAMICS OF LARGE COASTAL ACCUMULATIVE FORMS ACCORDING TO REMOTE SENSING DATA

Vestnik SSUGT (Siberian State University of Geosystems and Technologies) ◽

10.33764/2411-1759-2021-26-3-58-70 ◽

2021 ◽

Vol 26 (3) ◽

pp. 58-70

Author(s):

Viacheslav V. Krylenko ◽

◽

Marina V. Krylenko ◽

Alexander A. Aleynikov ◽

◽

...

Keyword(s):

Aerial Photography ◽

Satellite Images ◽

Remote Sensing Data ◽

New Methods ◽

New Information ◽

Relief Structure ◽

Shoreline Dynamics ◽

New Research ◽

Modern Technologies ◽

Sentinel 2

The study of the relief of large coastal accumulative forms, based on modern technologies, is rele-vant for solving many applied problems. Coastal and underwater bars, shoals, banks are characteristic elements of large coastal accumulative forms’ geosystems. Previously existing methods of relief re-searches, especially underwater, were labor-intensive and expensive. Accordingly, the development and implementation of new methods of geographical research are necessary. The Dolgaya Spit, includ-ing its underwater shoal and the Elenin Bank, is one of the largest accumulative forms of the Sea of Azov. The purpose of our work was to obtain new information on the relief structure and the shoreline dynamics of the Dolgaya Spit based on the use of new research methods. Digital models of surface and underwater relief were built on the basis of processing Sentinel-2 satellite images and data from unmanned aerial photography. The subsequent analysis allowed identify regularities that reflect the current and previous hydro-lithodynamic conditions that determined the transformation of the Dolgaya Spit during its evolution. The fulfilled studies confirmed the possibility of successful use of modern remote methods for studying the relief of coastal accumulative forms.

Download Full-text

A GENERATIVE ADVERSARIAL NETWORK APPROACH FOR SUPER-RESOLUTION OF SENTINEL-2 SATELLITE IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b1-2020-9-2020 ◽

2020 ◽

Vol XLIII-B1-2020 ◽

pp. 9-14

Author(s):

F. Pineda ◽

V. Ayma ◽

C. Beltran

Keyword(s):

High Resolution ◽

Satellite Images ◽

Resolution Enhancement ◽

Super Resolution ◽

Structural Similarity ◽

Generative Adversarial Network ◽

Acceptable Result ◽

Adversarial Network ◽

High Resolution Satellite Images ◽

Sentinel 2

Abstract. High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.

Download Full-text

Environmental classification of administrative units of the Ukrainian Carpathians

Visnyk of the Lviv University. Series Geography ◽

10.30970/vgg.2014.48.1347 ◽

2014 ◽

pp. 245-253

Author(s):

Ivan Kruhlov

Keyword(s):

Cluster Analysis ◽

Standard Deviation ◽

Satellite Images ◽

Environmental Classification ◽

Digital Elevation ◽

Elevation Data ◽

Digital Elevation Data ◽

Administrative Units ◽

Elevation Model

Boundaries of 43 administrative units (raions and oblast towns) were digitized and manually rectified using official schemes and satellite images. SRTM digital elevation data were used to calculate mean relative elevation and its standard deviation for each unit, as well as to delineate altitudinal bioclimatic belts and their portions within the units. These parameters were used to classify the units via agglomerative cluster analysis into nine environmental classes. Key words: cluster analysis, digital elevation model, geoecosystem, geo-spatial analysis.

Download Full-text