scholarly journals Technologies for environmental monitoring of the city

2021 ◽  
Author(s):  
O. M. Bahatska ◽  
◽  
N. A. Pasichnyk ◽  
O. O. Opryshko ◽  
◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Agricultural Practices ◽  
Optical Range ◽  
Plant Identification ◽  
Direct Sunlight ◽  
Foreign Objects ◽  
Optical Images ◽  
Optical Satellite Images ◽  
Color Components

IoT technologies in the Big Data concept can radically change approaches in agricultural practices, but it is necessary to work out methods of processing and interpreting information that can be effective in crop practice. Since the dimensions of plants are too small for satellite imagery, the development of technologies can be done on trees whose dimensions are sufficient for their identification in satellite imagery. The purpose of the work is to identify and assess the condition of plantations, in particular trees, with the determination of their positioning on satellite images of megacities. Digital photographs created by optical and infrared lenses of the Obolonskyi district of Kyiv were used for the research. It was found that in the optical range for objects under direct sunlight, plant identification is possible, while shaded areas are identified with significant errors. When using the index for IR shooting IRtree = C1 - C2 + 100 it was possible to identify individual ranges that belong to the crown of trees and grass in direct sunlight and to some extent in the shade, which could not be achieved with the index for optical range GBtree = G - B + 100. Monochrome infrared and optical images were not suitable for plant identification, because when objects were in the shadow of buildings, the ranges of intensity of the color components of plants were superimposed on the ranges of foreign objects. For infrared and optical satellite images, spectral indices have been proposed that take into account several color components to assess the condition of plantations. For tree crowns under direct sunlight, approximately the same results were obtained for the proposed indices. However, the indices proposed for infrared photography are more selective, as they were able to identify separately the crowns of trees and plants on lawns, both in direct sunlight and in the shade of buildings.

Images Time Series Analysis for Land Deformation and Mapping Shallow Landslides based on SENTINEL - 1 and Optical Satellite Images Data: Case Study on Batam Island, Indonesia

2020 ◽  
Author(s):  
Tiggi Choanji ◽  
Michel Jaboyedoff ◽  
Marc-Henri Derron ◽  
Li Fei ◽  
Chunwei Sun
Keyword(s):  
Time Series ◽  
Satellite Images ◽  
Shallow Landslide ◽  
Shallow Landslides ◽  
Optical Images ◽  
Interferometry Method ◽  
Land Deformation ◽  
Optical Satellite Images ◽  
Immense Potential

<p>As a growing city, Batam Islands has an immense potential to become one of the strategic positions in Southeast Asia. However, as the city developed, it also followed with the deformation and potential areas which has prone to shallow landslides. Using 32 Sentinel-1A Satellite Images Data and 17 years of Optical images data, analysis of time series is conducted using Persistent Scattered Interferometry method and mapped for landslide events in the Islands. As a result, several regions impacted 4 – 10 mm/year of velocity deformation in the center part of the island and several locations simulated to be prone to shallow landslide. So, by coupling method of SAR data and optical images, has giving prominent possibility for detecting and predicting hazard potential in this island.</p>

scholarly journals Exploiting High Geopositioning Accuracy of SAR Data to Obtain Accurate Geometric Orientation of Optical Satellite Images

2021 ◽  
Vol 13 (17) ◽  
pp. 3535
Author(s):  
Zhongli Fan ◽  
Li Zhang ◽  
Yuxuan Liu ◽  
Qingdong Wang ◽  
Sisi Zlatanova
Keyword(s):  
Satellite Imagery ◽  
Image Matching ◽  
Satellite Images ◽  
Three Dimensional ◽  
Phase Correlation ◽  
Feature Descriptor ◽  
Matching Method ◽  
Reference Images ◽  
Sar Data ◽  
Optical Satellite Images

Accurate geopositioning of optical satellite imagery is a fundamental step for many photogrammetric applications. Considering the imaging principle and data processing manner, SAR satellites can achieve high geopositioning accuracy. Therefore, SAR data can be a reliable source for providing control information in the orientation of optical satellite images. This paper proposes a practical solution for an accurate orientation of optical satellite images using SAR reference images to take advantage of the merits of SAR data. Firstly, we propose an accurate and robust multimodal image matching method to match the SAR and optical satellite images. This approach includes the development of a new structural-based multimodal applicable feature descriptor that employs angle-weighted oriented gradients (AWOGs) and the utilization of a three-dimensional phase correlation similarity measure. Secondly, we put forward a general optical satellite imagery orientation framework based on multiple SAR reference images, which uses the matches of the SAR and optical satellite images as virtual control points. A large number of experiments not only demonstrate the superiority of the proposed matching method compared to the state-of-the-art methods but also prove the effectiveness of the proposed orientation framework. In particular, the matching performance is improved by about 17% compared with the latest multimodal image matching method, namely, CFOG, and the geopositioning accuracy of optical satellite images is improved, from more than 200 to around 8 m.

scholarly journals Monitoring of the landslide area state on Bureya river in 2018-2019 according to radar and optical satellite images

2019 ◽  
pp. 26-35
Author(s):  
V. G. Bondur ◽  
L. N. Zakharova ◽  
A. I. Zakharov
Keyword(s):  
Optical Sensors ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite System ◽  
Small Scale ◽  
Landslide Area ◽  
Interferometric Technique ◽  
Optical Images ◽  
Optical Satellite Images ◽  
Synthetic Aperture Radars

The monitoring results of the current state of landslide area on the Bureya River in 20182019 are given using images from synthetic aperture radars and optical sensors of Sentinel multi-satellite system. Differential radar interferometry technique allowed to reveal the stability of the landslide surface in the first four months after the landslide and since the end of July 2019. Small-scale dynamics of the surface within the landslide circus was detected. It is shown that the interferometric technique is inapplicable for the observation of the large-scale modifications of the shoreline unlike the optical images where the effects of the collapse of the shoreline fragments and shoreline flooding were clearly observed compared also with radar amplitude images. The ongoing landslide activity within the landslide circus and the coastline collapse area was detected using satellite images. It requires the establishment of continuous monitoring of this and other dangerous landslide zones on Bureya River.

AUTOMATIC REGISTRATION OF VECTOR DATA WITH OPTICAL IMAGES

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 191-196
Author(s):  
Y. Tanguy ◽  
J. Michel ◽  
G. Salgues
Keyword(s):  
High Resolution ◽  
Image Registration ◽  
Prior Knowledge ◽  
Open Source Software ◽  
Satellite Images ◽  
Automatic Registration ◽  
Vector Data ◽  
Optical Images ◽  
Optical Satellite Images ◽  
Very High

Abstract. This paper presents a method to perform automatic vector-to-image registration. The proposed method performs well on different kinds of optical satellite images from Very High Resolution (VHR, sub-meter resolution) to images in the 10/20 m resolution range. It allows to automatically register vector dataset such as urban maps (by using building layers). In contrast with existing methods, our method needs few prior-knowledge on the features to match and can therefore adapt to different landscapes.This paper demonstrates the method robustness in several use-cases and presents the implementation which will soon be available as open-source software.

scholarly journals Satellite Pre-Failure Detection and In Situ Monitoring of the Landslide of the Tunnel du Chambon, French Alps

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 313 ◽  
Author(s):  
Mathilde Desrues ◽  
Pascal Lacroix ◽  
Ombeline Brenguier
Keyword(s):  
Satellite Images ◽  
Failure Detection ◽  
Satellite Observation ◽  
In Situ Monitoring ◽  
Tertiary Creep ◽  
Strong Activity ◽  
French Alps ◽  
Optical Images ◽  
Optical Satellite Images

Recent studies using satellite data have shown a growing interest in detecting and anticipating landslide failures. However, their value for an actual landslide prediction has shown variable results. Therefore, the use of satellite images for that purpose still requires additional attention. Here, we study the landslide of the Tunnel du Chambon in the French Alps that ruptured in July 2015, generating major impacts on economic activity and infrastructures. To evaluate the contribution of very high-resolution optical satellite images to characterize and potentially anticipate the landslide failure, we conduct here a retro analysis of its evolution. Two time periods are analyzed: September 2012 to September 2014, and May to July 2015. We combine Pléiades optical images analysis and geodetic measurements from in situ topographic monitoring. Satellite images were correlated to detect pre-failure motions, showing 1.4-m of displacement between September 2012 and September 2014. In situ geodetic measures were used to analyze motions during the main activity of the landslide in June and July 2015. Topographic measurements highlight different areas of deformations and two periods of strong activity, related to the last stage of the tertiary creep and to anthropic massive purges of unstable masses. The law of acceleration toward the rupture observed in June and July 2015 over the topographic targets also fits well the satellite observation between 2012 and 2014, showing that the landslide probably already entered into tertiary creep 2.5 years before its failure.

A Novel Change Detection Method for Unregistered Optical Satellite Images

2012 ◽  
Vol E95.B (5) ◽  
pp. 1890-1893
Author(s):  
Wang LUO ◽  
Hongliang LI ◽  
Guanghui LIU ◽  
Guan GUI
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Detection Method ◽  
Optical Satellite Images

scholarly journals CNN Algorithm for Roof Detection and Material Classification in Satellite Images

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1592
Author(s):  
Jonguk Kim ◽  
Hyansu Bae ◽  
Hyunwoo Kang ◽  
Suk Gyu Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Satellite Imagery ◽  
Satellite Images ◽  
External Shocks ◽  
Satellite Imaging ◽  
Material Classification ◽  
Model Algorithm ◽  
Great Damage

This paper suggests an algorithm for extracting the location of a building from satellite imagery and using that information to modify the roof content. The materials are determined by measuring the conditions where the building is located and detecting the position of a building in broad satellite images. Depending on the incomplete roof or material, there is a greater possibility of great damage caused by disaster situations or external shocks. To address these problems, we propose an algorithm to detect roofs and classify materials in satellite images. Satellite imaging locates areas where buildings are likely to exist based on roads. Using images of the detected buildings, we classify the material of the roof using a proposed convolutional neural network (CNN) model algorithm consisting of 43 layers. In this paper, we propose a CNN structure to detect areas with buildings in large images and classify roof materials in the detected areas.

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

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.

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