scholarly journals Supplemental Material: Airblasts caused by large slope collapses

2020 ◽  
Author(s):  
Ivanna M. Penna ◽  
et al.
Keyword(s):  
Kinematic Analysis ◽  
Satellite Images ◽  
Distal Part ◽  
Satellite Image ◽  
Rock Avalanche ◽  
Tree Trunk ◽  
Damage Area ◽  
Equal Angle ◽  
Lower Hemisphere ◽  
Detachment Zone

Figure S1. Sequence of satellite images showing the evolution of Yumthang rock avalanche; Figure S2. (A) View of the detachment zone of the Yumthang rock avalanche. Main discontinuities are indicated. (B) Hillshade with indication of the main sets of iscontinuities. The arrows indicate the downstream direction of the valleys. (C) Stereographic plots (Equal angle, lower hemisphere) with the kinematic analysis overlain. (D) General hillshade and satellite image showing the location of the detachment zone at the confluence of two valleys; Figure S3. Picture and schematic illustration of airblast interacting with the tree trunk on the distal part of the Yumthang airblast damage area; Table S1. Main discontinuities affecting the granite at the Yumthang headscarp.

scholarly journals Supplemental Material: Airblasts caused by large slope collapses

2020 ◽  
Author(s):  
Ivanna M. Penna ◽  
et al.
Keyword(s):  
Kinematic Analysis ◽  
Satellite Images ◽  
Distal Part ◽  
Satellite Image ◽  
Rock Avalanche ◽  
Tree Trunk ◽  
Damage Area ◽  
Equal Angle ◽  
Lower Hemisphere ◽  
Detachment Zone

Figure S1. Sequence of satellite images showing the evolution of Yumthang rock avalanche; Figure S2. (A) View of the detachment zone of the Yumthang rock avalanche. Main discontinuities are indicated. (B) Hillshade with indication of the main sets of iscontinuities. The arrows indicate the downstream direction of the valleys. (C) Stereographic plots (Equal angle, lower hemisphere) with the kinematic analysis overlain. (D) General hillshade and satellite image showing the location of the detachment zone at the confluence of two valleys; Figure S3. Picture and schematic illustration of airblast interacting with the tree trunk on the distal part of the Yumthang airblast damage area; Table S1. Main discontinuities affecting the granite at the Yumthang headscarp.

Assessment of mangrove forest change in Ca Mau province using satellite images in the period of 1988 - 2018

2021 ◽  
Vol 66 (1) ◽  
pp. 175-187
Author(s):  
Duong Phung Thai ◽  
Son Ton
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Image Interpretation ◽  
Image Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Forest Change ◽  
Shrimp Ponds ◽  
Area Classification

On the basis of using practical methods, satellite image processing methods, the vegetation coverage classification system of the study area, interpretation key for the study area, classification and post-classification pro cessing, this research introduces how to exploit and process multi-temporal satellite images in evaluating the changes of forest area. Landsat 4, 5 TM and Landsat 8 OLI remote sensing image data were used to evaluate the changes in the area of mangrove forests (RNM) in Ca Mau province in the periods of 1988 - 1998, 1998 - 2013, 2013 - 2018, and 1988 - 2018. The results of the image interpretation in 1988, 1998, 2013, 2018 and the overlapping of the above maps show: In the 30-year period from 1988 to 2018, the total area of mangroves in Ca Mau province was decreased by 28% compared to the beginning, from 71,093.3 ha in 1988 reduced to 51,363.5 ha in 2018, decreasing by 19,729.8 ha. The recovery speed of mangroves is 2 times lower than their disappearance speed. Specifically, from 1988 to 2018, mangroves disappeared on an area of 42,534.9 hectares and appeared on the new area of 22,805 hectares, only 12,154.5 hectares of mangroves remained unchanged. The fluctuation of mangrove area in Ca Mau province is related to the process of deforestation to dig shrimp ponds, coastal erosion, the formation of mangroves on new coastal alluvial lands and soil dunes in estuaries, as well as planting new mangroves in inefficient shrimp ponds.

scholarly journals Segmentation of Satellite Imagery of Amedi Site Using Chan–Vese Model with Saliency Estimation

2020 ◽  
Vol 1 (4) ◽  
pp. 125-134
Author(s):  
Pawan Rachee
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Object Segmentation ◽  
Satellite Image ◽  
Bilateral Filter ◽  
Histogram Equalization ◽  
Saliency Map ◽  
Enhancement Technique ◽  
Map Segmentation ◽  
Quantitative Manner

The images that have been taken from space satellites are described by satellite imagery. The presence of the earth's surface is detected by remote sensing. Normally the source of the satellite image is barely seen, because many points in the sky are obscured with cloud shadows. Therefore, one of the most important and ubiquitous tasks in image analysis is segmentation. Segmentation is the method of dividing a image into a collection of specific regions that vary in some essential qualitative or quantitative manner. In this paper we will focus on a method for segmenting images that was developed   Three different methods to detect the location of the satellite images have been studied, implemented, and tested; these are based on Chan-Vese and saliency map segmentation, and multi-resolution segmentation to obtain a proper object segmentation. In this study, the combination of the proposed segmentation automatic detection and image enhancement technique has been performed to reduce the noise of the original image. In addition, the Bilateral filter, and histogram equalization are used in these proposed techniques. Experimental results demonstrate that the suggested method can precisely extract the objective of Amedi site from the satellite images with difficult backgrounds and overlapping regions.

scholarly journals Revealing glacier flow and surge dynamics from animated satellite image sequences: examples from the Karakoram

2015 ◽  
Vol 9 (2) ◽  
pp. 2597-2623 ◽  
Author(s):  
F. Paul
Keyword(s):  
Satellite Images ◽  
Satellite Image ◽  
Time Lapse ◽  
Image Sequences ◽  
Flow Dynamics ◽  
Mountain Range ◽  
Time Period ◽  
Climatic Control ◽  
Long Time ◽  
Glacier Flow

Abstract. Although animated images are very popular on the Internet, they have so far found only limited use for glaciological applications. With long time-series of satellite images becoming increasingly available and glaciers being well recognized for their rapid changes and variable flow dynamics, animated sequences of multiple satellite images reveal glacier dynamics in a time-lapse mode, making the otherwise slow changes of glacier movement visible and understandable for a wide public. For this study animated image sequences were created from freely available image quick-looks of orthorectified Landsat scenes for four regions in the central Karakoram mountain range. The animations play automatically in a web-browser and might help to demonstrate glacier flow dynamics for educational purposes. The animations revealed highly complex patterns of glacier flow and surge dynamics over a 15-year time period (1998–2013). In contrast to other regions, surging glaciers in the Karakoram are often small (around 10 km2), steep, debris free, and advance for several years at comparably low annual rates (a few hundred m a−1). The advance periods of individual glaciers are generally out of phase, indicating a limited climatic control on their dynamics. On the other hand, nearly all other glaciers in the region are either stable or slightly advancing, indicating balanced or even positive mass budgets over the past few years to decades.

scholarly journals Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

2018 ◽  
Vol 10 (10) ◽  
pp. 1555 ◽  
Author(s):  
Caio Fongaro ◽  
José Demattê ◽  
Rodnei Rizzo ◽  
José Lucas Safanelli ◽  
Wanderson Mendes ◽  
...  
Keyword(s):  
Model Calibration ◽  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Soil Mapping ◽  
Spectral Signatures ◽  
Novel Approach ◽  
Multispectral Satellite Images ◽  
Reduced Costs ◽  
Multispectral Satellite Image

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km2 in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R2 = 0.83; RMSE = 65.0 g kg−1) and sand (R2 = 0.86; RMSE = 79.9 g kg−1). Multispectral satellite images were more stable for the identification of soil properties than relief parameters.

scholarly journals Towards Sub-Pixel Automatic Geometric Corrections of Very-High Resolution Panchromatic Satellite Data of Urban Areas

2019 ◽  
Vol 11 (9) ◽  
pp. 1097 ◽  
Author(s):  
Aleš Marsetič ◽  
Peter Pehani
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Urban Areas ◽  
Satellite Images ◽  
Reference Data ◽  
Geometric Model ◽  
Control Point ◽  
Satellite Image ◽  
Extraction Algorithm ◽  
Very High

This paper presents an automatic procedure for the geometric corrections of very-high resolution (VHR) optical panchromatic satellite images. The procedure is composed of three steps: an automatic ground control point (GCP) extraction algorithm that matches the linear features that were extracted from the satellite image and reference data; a geometric model that applies a rational function model; and, the orthorectification procedure. Accurate geometric corrections can only be achieved if GCPs are employed to precisely correct the geometric biases of images. Due to the high resolution and the varied acquisition geometry of images, we propose a fast, segmentation based method for feature extraction. The research focuses on densely populated urban areas, which are very challenging in terms of feature extraction and matching. The proposed algorithm is capable of achieving results with a root mean square error of approximately one pixel or better, on a test set of 14 panchromatic Pléiades images. The procedure is robust and it performs well in urban areas, even for images with high off-nadir angles.

Application of Polarization Remote Sensing to Identify Activity of Yitong-Shulan Fault, Jilin Province, China

2014 ◽  
Vol 1065-1069 ◽  
pp. 2246-2250
Author(s):  
Jian Sheng ◽  
Guang Yuan Yu ◽  
Yu Meng Wang ◽  
Han Lv
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Eastern China ◽  
Satellite Image ◽  
Jilin Province ◽  
Geophysical Method ◽  
Fault Activity ◽  
Shitoukoumen Reservoir ◽  
Observation Method ◽  
Buried Fault

Yitong-Shulan fault, one north section of the famed Tanlu grand fault zone in eastern China, is NNE-trending though the Jilin Province, China. In October 2010, Heilongjiang segment of this fault was discovered the evidence of its activity in Holonce, and further inferred it is associated with a paleoearthquake event. So the recognize of Yitong-Shulan fault Jilin section active in the early Quaternary capable of generating moderate quakes is doubted. Yitong-Shulan fault is almost covered by Quaternary strata in Jilin Province. Traditional method is difficult to explore buried fault, and geophysical method is partial and expensive. The polarization remote sensing is a kind of emerging earth observation method, which has high terrain-recognization resolution. The polarization remote sensing method can to indentify the scarps and displaced geomorphic objects along the fault though satellite images. It even can to discover the high of scarps, displacement of geomorphic objects, and so on. The fault activity can be indicated well by the interpretation of polarization remote sensing. In this paper, use the polarization remote sensing method to study the activity of Yitong-Shulan fault Jilin section. Satellite image near the Shulan City, Jilin Province interpreted by polarization remote sensing reveals that the obviously linear scarps which extend long the fault is 1-3m high. Along the fault various kinds of geomorphic objects are displaced. This interpretation result indicated the Shulan-Shitoukoumen Reservoir segment of the fault is active since Holocene. The fault activity also is proved by geophysical method.

scholarly journals Spatial and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011

2016 ◽  
Vol 11 (2) ◽  
Author(s):  
Timothy Shields ◽  
Jessie Pinchoff ◽  
Jailos Lubinda ◽  
Harry Hamapumbu ◽  
Kelly Searle ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Images ◽  
Population Distribution ◽  
Satellite Image ◽  
Public Health Research ◽  
Cluster Detection ◽  
Practical Significance ◽  
Programme Implementation ◽  
Detection Analysis ◽  
High Resolution Satellite Imagery

Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase). Comparison of the images indicated that 971 (25.4%) structures were added and 536 (14.0%) removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.

Quantization Encoding Algorithm Based Satellite Image Compression

2017 ◽  
Vol 8 (3) ◽  
pp. 740
Author(s):  
Anand M ◽  
V. Mathivananr
Keyword(s):  
Image Compression ◽  
Satellite Images ◽  
Satellite Image ◽  
Region Of Interest ◽  
Digital Data ◽  
Storage Space ◽  
Compression Process ◽  
Compression Technique ◽  
Transmission Bandwidth ◽  
Image Technique

In the field of digital data there is a demand in bandwidth for the transmission of the videos and images all over the worlds. So in order to reduce the storage space in the field of image applications there is need for the image compression process with lesser transmission bandwidth. So in this paper we are proposing a new image compression technique for the compression of the satellite images by using the Region of Interest (ROI) based on the lossy image technique called the Quantization encoding algorithm for the compression. The performance of our method can be evaluated and analyzing the PSNR values of the output images.

GIS Based Satellite Image Denoising Using Curvelet Transform

2017 ◽  
Vol 8 (3) ◽  
pp. 654
Author(s):  
S. Sinthuja ◽  
Santhosh Saravanan
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Human Visual System ◽  
Satellite Images ◽  
Satellite Image ◽  
Curvelet Transform ◽  
Intensity Difference ◽  
Significant Information ◽  
Pixel Intensity ◽  
The Earth

<p>Generally, satellite images contain very significant information about geographical features such as rivers, roads, building and bridges etc of the earth. Geographic Information System (GIS) requires these features for automatic detection and it has been corrupted by various types of noise. Curvelet Transform (CT) is used in the proposed system for denoising the images. Advantages of multi resolution image such as line, compatibility of human visual system and edge detection are provided. Then K-Means clustering is used in this system for segmentation purpose after the pre processing done.</p><p>First, K-Means algorithm is used for segmenting background and water then extraction of bridges is done based on pixel intensity difference.   </p>

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