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.

scholarly journals DO THE VISUAL COMPLEXITY ALGORITHMS MATCH THE GENERALIZATION PROCESS IN GEOGRAPHICAL DISPLAYS?

2016 ◽  
Vol XLI-B2 ◽  
pp. 375-378 ◽  
Author(s):  
A. Brychtová ◽  
A. Çöltekin ◽  
V. Pászto
Keyword(s):  
Design Process ◽  
Large Scale ◽  
Satellite Images ◽  
Visual Complexity ◽  
Small Scale ◽  
Complexity Measures ◽  
Cartographic Generalization ◽  
Visual Clutter ◽  
Shaded Relief ◽  
Different Levels

In this study, we first develop a hypothesis that existing quantitative visual complexity measures will overall reflect the level of cartographic generalization, and test this hypothesis. Specifically, to test our hypothesis, we first selected common geovisualization types (i.e., cartographic maps, hybrid maps, satellite images and shaded relief maps) and retrieved examples as provided by Google Maps, OpenStreetMap and SchweizMobil by swisstopo. Selected geovisualizations vary in cartographic design choices, scene contents and different levels of generalization. Following this, we applied one of Rosenholtz et al.’s (2007) visual clutter algorithms to obtain quantitative visual complexity scores for screenshots of the selected maps. We hypothesized that visual complexity should be constant across generalization levels, however, the algorithm suggested that the complexity of small-scale displays (less detailed) is higher than those of large-scale (high detail). We also observed vast differences in visual complexity among maps providers, which we attribute to their varying approaches towards the cartographic design and generalization process. Our efforts will contribute towards creating recommendations as to how the visual complexity algorithms could be optimized for cartographic products, and eventually be utilized as a part of the cartographic design process to assess the visual complexity.

scholarly journals Fusion Analysis of Optical Satellite Images and Digital Elevation Model for Quantifying Volume in Debris Flow Disaster

2019 ◽  
Vol 11 (9) ◽  
pp. 1096 ◽  
Author(s):  
Hiroyuki Miura
Keyword(s):  
Debris Flow ◽  
Digital Elevation Model ◽  
Large Scale ◽  
Satellite Images ◽  
Erosion Depth ◽  
Digital Elevation ◽  
Elevation Model ◽  
Fusion Analysis ◽  
Optical Satellite Images ◽  
Debris Flow Disaster

Rapid identification of affected areas and volumes in a large-scale debris flow disaster is important for early-stage recovery and debris management planning. This study introduces a methodology for fusion analysis of optical satellite images and digital elevation model (DEM) for simplified quantification of volumes in a debris flow event. The LiDAR data, the pre- and post-event Sentinel-2 images and the pre-event DEM in Hiroshima, Japan affected by the debris flow disaster on July 2018 are analyzed in this study. Erosion depth by the debris flows is empirically modeled from the pre- and post-event LiDAR-derived DEMs. Erosion areas are detected from the change detection of the satellite images and the DEM-based debris flow propagation analysis by providing predefined sources. The volumes and their pattern are estimated from the detected erosion areas by multiplying the empirical erosion depth. The result of the volume estimations show good agreement with the LiDAR-derived volumes.

Channel Quality Approximation Based on Shadow Fading Model in GEO Mobile Satellite System

2013 ◽  
Vol 791-793 ◽  
pp. 1941-1944
Author(s):  
Ya Dan Zheng ◽  
Ming Ke Dong ◽  
Jian Jun Wu
Keyword(s):  
Large Scale ◽  
Adaptive Modulation ◽  
Original Data ◽  
Satellite System ◽  
Adaptive Modulation And Coding ◽  
Small Scale ◽  
Shadow Fading ◽  
Channel Quality ◽  
The Difference ◽  
Mobile Satellite

CQI(Channel Quality Indicator) is an essential indicator for AMC(Adaptive Modulation and Coding) technique in LTE. Due to the long delay of GEO satellite channel, CQI prediction is necessary to ensure effective AMC. This paper proposes the approximation from real CQI data containing small scale fading to that containing only large scale fading to do prediction. The concrete correlation features and the difference between the approximation and the original data are all analyzed. Simulation is done for confirmation. It shows that the approximate large scale CQI data is feasible and rational for prediction and ensuring AMC efficiency.

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>

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.

scholarly journals Effect of temporal and spatial resolution on identification of fishing activities in small-scale fisheries using pots and traps

2019 ◽  
Vol 76 (6) ◽  
pp. 1601-1609 ◽  
Author(s):  
Tania Mendo ◽  
Sophie Smout ◽  
Tommaso Russo ◽  
Lorenzo D’Andrea ◽  
Mark James
Keyword(s):  
Spatial Distribution ◽  
Spatial Resolution ◽  
Large Scale ◽  
Low Cost ◽  
Grid Cell ◽  
Analysis Data ◽  
Satellite System ◽  
Small Scale ◽  
Fishing Activity ◽  
Small Scale Fisheries

Abstract Analysis of data from vessel monitoring systems and automated identification systems in large-scale fisheries is used to describe the spatial distribution of effort, impact on habitats, and location of fishing grounds. To identify when and where fishing activities occur, analysis needs to take account of different fishing practices in different fleets. Small-scale fisheries (SSFs) vessels have generally been exempted from positional reporting requirements, but recent developments of compact low-cost systems offer the potential to monitor them effectively. To characterize the spatial distribution of fishing activities in SSFs, positions should be collected with sufficient frequency to allow detection of different fishing behaviours, while minimizing demands for data transmission, storage, and analysis. This study sought to suggest optimal rates of data collection to characterize fishing activities at appropriate spatial resolution. In a SSF case study, on-board observers collected Global Navigation Satellite System (GNSS) position and fishing activity every second during each trip. In analysis, data were re-sampled to lower temporal resolutions to evaluate the effect on the identification of number of hauls and area fished. The effect of estimation at different spatial resolutions was also explored. Consistent results were found for polling intervals <60 s in small vessels and <120 in medium and large vessels. Grid cell size of 100 × 100 m resulted in best estimations of area fished. Remote collection and analysis of GNSS or equivalent data at low cost and sufficient resolution to infer small-scale fisheries activities. This has significant implications globally for sustainable management of these fisheries, many of which are currently unregulated.

scholarly journals CHARACTERISTICS OF TEXTURE INDEX OF DAMAGED BUILDINGS USING TIME-SERIES HIGH-RESOLUTION OPTICAL SATELLITE IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1709-1714
Author(s):  
M. Sonobe
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Texture Analysis ◽  
Large Scale ◽  
Satellite Images ◽  
Image Interpretation ◽  
High Resolution Satellite Images ◽  
Survey Results ◽  
Occurrence Matrix ◽  
Optical Satellite Images

Abstract. A large-scale disaster has occurred due to the earthquake. In particular, 20% of the world's earthquakes with a magnitude of 6 or more occur near Japan. Damage analysis of buildings by image analysis have been effectively carried out using optical high-resolution satellite images and aerial photograph with spatial resolution of about 2 m or less. In this study, the damaged buildings caused by large-scale and continuous earthquakes in Kumamoto, Japan that occurred in April 2016 was selected as a typical example of damaged buildings. For these earthquake event, the applicability of damage distribution of buildings and recovery/restoration status by texture analysis was examined. The applicability of the representative in the dissimilarity texture analysis methods Gray- Level Co-occurrence Matrix (GLCM) method by image interpretation in the case of a large number of collapsed and wrecked buildings in a wide area was assessed. These results suggest that dissimilarity was applicable to the extraction of damaged and removed buildings in the event of such an earthquake. In addition, the analysis results were appropriately evaluated by comparing the field survey results with the image interpretation results of the pan-sharpened image. From these results, we confirmed the effectiveness of texture analysis using time-series high-resolution satellite images in grasping the damaged buildings before and immediately after the disaster and in the restoration situation 1 year after the disaster.

scholarly journals DO THE VISUAL COMPLEXITY ALGORITHMS MATCH THE GENERALIZATION PROCESS IN GEOGRAPHICAL DISPLAYS?

2016 ◽  
Vol XLI-B2 ◽  
pp. 375-378
Author(s):  
A. Brychtová ◽  
A. Çöltekin ◽  
V. Pászto
Keyword(s):  
Design Process ◽  
Large Scale ◽  
Satellite Images ◽  
Visual Complexity ◽  
Small Scale ◽  
Complexity Measures ◽  
Cartographic Generalization ◽  
Visual Clutter ◽  
Shaded Relief ◽  
Different Levels

In this study, we first develop a hypothesis that existing quantitative visual complexity measures will overall reflect the level of cartographic generalization, and test this hypothesis. Specifically, to test our hypothesis, we first selected common geovisualization types (i.e., cartographic maps, hybrid maps, satellite images and shaded relief maps) and retrieved examples as provided by Google Maps, OpenStreetMap and SchweizMobil by swisstopo. Selected geovisualizations vary in cartographic design choices, scene contents and different levels of generalization. Following this, we applied one of Rosenholtz et al.’s (2007) visual clutter algorithms to obtain quantitative visual complexity scores for screenshots of the selected maps. We hypothesized that visual complexity should be constant across generalization levels, however, the algorithm suggested that the complexity of small-scale displays (less detailed) is higher than those of large-scale (high detail). We also observed vast differences in visual complexity among maps providers, which we attribute to their varying approaches towards the cartographic design and generalization process. Our efforts will contribute towards creating recommendations as to how the visual complexity algorithms could be optimized for cartographic products, and eventually be utilized as a part of the cartographic design process to assess the visual complexity.

scholarly journals A NOVEL SHIP DETECTION METHOD FOR LARGE-SCALE OPTICAL SATELLITE IMAGES BASED ON VISUAL LBP FEATURE AND VISUAL ATTENTION MODEL

2016 ◽  
Vol XLI-B3 ◽  
pp. 917-921
Author(s):  
Sui Haigang ◽  
Song Zhina
Keyword(s):  
Visual Attention ◽  
Large Scale ◽  
Satellite Images ◽  
Visual Features ◽  
Support Vector ◽  
Biologically Inspired ◽  
Visual Attention Model ◽  
Attention Model ◽  
Ship Detection ◽  
Optical Satellite Images

Reliably ship detection in optical satellite images has a wide application in both military and civil fields. However, this problem is very difficult in complex backgrounds, such as waves, clouds, and small islands. Aiming at these issues, this paper explores an automatic and robust model for ship detection in large-scale optical satellite images, which relies on detecting statistical signatures of ship targets, in terms of biologically-inspired visual features. This model first selects salient candidate regions across large-scale images by using a mechanism based on biologically-inspired visual features, combined with visual attention model with local binary pattern (CVLBP). Different from traditional studies, the proposed algorithm is high-speed and helpful to focus on the suspected ship areas avoiding the separation step of land and sea. Largearea images are cut into small image chips and analyzed in two complementary ways: Sparse saliency using visual attention model and detail signatures using LBP features, thus accordant with sparseness of ship distribution on images. Then these features are employed to classify each chip as containing ship targets or not, using a support vector machine (SVM). After getting the suspicious areas, there are still some false alarms such as microwaves and small ribbon clouds, thus simple shape and texture analysis are adopted to distinguish between ships and nonships in suspicious areas. Experimental results show the proposed method is insensitive to waves, clouds, illumination and ship size.

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