scholarly journals Land subsidence detection in Jakarta Province using Sentinel-1A Satellite Imagery

2021 ◽  
Vol 944 (1) ◽  
pp. 012036
Author(s):  
D Situmorang ◽  
R E Arhatin ◽  
J Lumban-Gaol
Keyword(s):  
Satellite Imagery ◽  
Land Subsidence ◽  
Land Surface ◽  
Satellite Images ◽  
Coastal Areas ◽  
Natural Processes ◽  
Subsidence Rate ◽  
A Value ◽  
Alluvial Deposition ◽  
Land Subsidence Rate

Abstract The land surface in Jakarta Province is thought to have experienced relatively continuous subsidence because of natural processes and artificial activities. This research was carried out to evaluate the rate of land subsidence in Jakarta Province. Based on this research, it can be shown from the Sentinel-1A satellite images that there has been landed subsidence. The data used are two pairs of Sentinel-1A Single Looking Complex (SLC) images acquired in 2019 and 2020. The data was processed using the DInSAR method to examine the rate of land subsidence. The results show that the land subsidence rate in Jakarta Province during the 2019-2020 period varies from 1.8 cm to -10.7 cm/year. The literature data results in 2016 experienced a decrease in land subsidence with a significant value of -12.6 cm/year. Land subsidence in 2017 averaged -1.8 cm/year. The land subsidence results from 2019 to 2020 have a value that tends to be lower than in 2016 of - 3.62 cm/year. Land subsidence occurs mostly in coastal areas and near estuaries caused by the nature of alluvial deposition materials. It has caused damages to road infrastructure in several regions of Jakarta Province, such as Mutiara Beach, West Cengkareng, and Pademangan.

Land Subsidence Detection in Jakarta Province Using Sentinel-1A Satellite Imagery

2021 ◽  
Author(s):  
David Situmorang ◽  
Risti Endrani Arhatin ◽  
Jonson Lumban-Gaol ◽  
Devyan Meisnnehr
Keyword(s):  
Satellite Imagery ◽  
Land Subsidence ◽  
Land Surface ◽  
Coastal Areas ◽  
Natural Processes ◽  
Subsidence Rate ◽  
Alluvial Deposition ◽  
Level 1 ◽  
The City ◽  
Land Subsidence Rate

<p>The land surface in DKI Jakarta Province is thought to have experienced relatively continuous subsidence because of natural processes and man-made activities. This research was carried out to evaluate the rate of land subsidence in Jakarta Province. The data used in this study are two pairs of Sentinel-1A level 1 Single Looking Complex (SLC) images which were acquired in 2019 and 2020. The data was processed using the DInSAR method to examine the rate of land subsidence.  The results show that the land subsidence rate in Jakarta Province during the 2019-2020 period varies from 1.8 cm to -10.7 cm/year. From 2019 to 2020, the average land subsidence in the City of North Jakarta is around –4.9 cm/year, East Jakarta is around –2.5 cm/year, West Jakarta is around –4.8 cm/year, Central Jakarta is around –3.1 cm/year, and South Jakarta about –2.8 cm/year. Land subsidence occurs mostly in coastal areas and near estuaries caused by the nature of alluvial deposition materials. It has caused damages to road infrastructure in several regions of Jakarta Province, such as Mutiara Beach, West Cengkareng, and Pademangan.</p><p> </p><p>Keywords: coastal areas, DInSAR, land subsidence, satellite imagery, Sentinel-1A</p>

scholarly journals Is There Deep-Seated Subsidence in the Houston-Galveston Area?

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jiangbo Yu ◽  
Guoquan Wang ◽  
Timothy J. Kearns ◽  
Linqiang Yang
Keyword(s):  
Land Subsidence ◽  
Groundwater Level ◽  
Land Surface ◽  
The Past ◽  
Subsidence Rate ◽  
Different Depths ◽  
Subsidence Monitoring ◽  
Near Future ◽  
Land Subsidence Rate

Long-term continuous groundwater level and land subsidence monitoring in the Houston-Galveston area indicates that, during the past two decades (1993–2012), the groundwater head has been increasing and the overall land subsidence rate has been decreasing. Assuming that the hydraulic head in the aquifer will reach or exceed the preconsolidation level in the near future, will subsidence in the Houston-Galveston area eventually cease? The key to answer this question is to identify if there is deep-seated subsidence in this area. This study investigated the recent subsidence observed at different depths in the Houston-Galveston area. The subsidence was recorded by using 13 borehole extensometers and 76 GPS antennas. Four of the GPS antennas are mounted on the deep-anchored inner pipes of borehole extensometers. We conclude that recent subsidence (1993–2012) in the Houston-Galveston area was dominated by the compaction of sediments within 600 m below the land surface. Depending on the location of specific sites, the compaction occurred within the Chicot aquifer and part or all of the Evangeline aquifer. No measurable compaction was observed within the Jasper aquifer or within deeper strata. Deep-seated subsidence is not likely occurring in the Houston-Galveston area.

scholarly journals LAND SUBSIDENCE MONITORING ON THE UNDERGROUND MRT CONSTRUCTION AREA IN JAKARTA, INDONESIA

2020 ◽  
Vol 14 (2) ◽  
pp. 190
Author(s):  
Chara Bernike ◽  
Masahiko Nagai ◽  
Takahiro Osawa
Keyword(s):  
Land Subsidence ◽  
Land Surface ◽  
Vertical Movement ◽  
Tectonic Activity ◽  
Geological Condition ◽  
Large Area ◽  
Persistent Scatterer Interferometry ◽  
Alos Palsar ◽  
Subsidence Rate ◽  
Land Subsidence Rate

Space utilization for measurement of the changes of Earth’s surface is an alternative monitoring technique. Covering a large area with better spatial resolution, comparable accuracy and costly low compared with conventional measurement have made SAR technology more popular in monitoring the subsidence. Land subsidence is the vertical movement of the land surface mainly triggered by fluid extraction, load of construction, geological condition, natural compaction or tectonic activity. Previous researchers have found that Jakarta has experienced land subsidence for decades. On the other hand, rapid urban growth and severe congestion problem in Jakarta are also crucial issues. By providing the first MRT subway, it is expected to be solved the congestion problem. Persistent Scatterer Interferometry Synthetic Aperture Radar (PSInSAR) is one of method to identify the deformation with millimeter accuracy. It was perfectly useful for studied on the land subsidence in consequence of the first MRT subway project in Jakarta. The land displacement monitoring along MRT subway trajectory has been acquired by processing Sentinel 1 and ALOS PALSAR-2. This study was able to investigate the land subsidence by using SARPROZ. Finally, the results of this study indicated that land subsidence occurred in the construction area with the lowest land subsidence rate of -0.6 cm/year and the highest land subsidence rate was -7.3 cm/year. Keywords: PSInSAR; subway; land subsidence; MRT.

scholarly journals Land subsidence rate analysis of Jakarta Metropolitan Region based on D-InSAR processing of Sentinel data C-Band frequency

2019 ◽  
Vol 1185 ◽  
pp. 012004 ◽  
Author(s):  
Joko Widodo ◽  
Arie Herlambang ◽  
Albertus Sulaiman ◽  
Pakhrur Razi ◽  
Yohandri ◽  
...  
Keyword(s):  
Land Subsidence ◽  
Metropolitan Region ◽  
Band Frequency ◽  
Subsidence Rate ◽  
Rate Analysis ◽  
Land Subsidence Rate

Land Subsidence Rate of Jakarta Metropolitan Area Observed By Stacking Sentinel-1 Data

2021 ◽  
Author(s):  
Agustan Agustan ◽  
Estu Kriswati ◽  
Takeo Ito ◽  
Firman Maliki Abdullah ◽  
Yudi Anantasena ◽  
...  
Keyword(s):  
Metropolitan Area ◽  
Land Subsidence ◽  
Subsidence Rate ◽  
Land Subsidence Rate

scholarly journals Automated Coastline Extraction Using the Very High Resolution WorldView (WV) Satellite Imagery and Developed Coastline Extraction Tool (CET)

2021 ◽  
Vol 11 (20) ◽  
pp. 9482
Author(s):  
Fran Domazetović ◽  
Ante Šiljeg ◽  
Ivan Marić ◽  
Josip Faričić ◽  
Emmanuel Vassilakis ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Land Surface ◽  
Coastal Areas ◽  
Ease Of Use ◽  
Future Research ◽  
Mapping Technique ◽  
Surface Model ◽  
Stereo Pair ◽  
Very High

The accurate extraction of a coastline is necessary for various studies of coastal processes, as well as for the management and protection of coastal areas. Very high-resolution satellite imagery has great potential for coastline extraction; however, noises in spectral data can cause significant errors. Here, we present a newly developed Coastal Extraction Tool (CET) that overcomes such errors and allows accurate and time-efficient automated coastline extraction based on a combination of WorldView-2 (WV-2) multispectral imagery and stereo-pair-derived digital surface model (DSM). Coastline extraction is performed and tested on the Iž-Rava island group, situated within the Northern Dalmatian archipelago (Croatia). Extracted coastlines were compared to (a) coastlines extracted from state topographic map (1:25,000), and (b) coastline extracted by another available tool. The accuracy of the extracted coastline was validated with centimeter accuracy reference data acquired using a UAV system (Matrice 600 Pro + MicaSense RedEdge-MX). Within the study area, two small islets were detected that have not been mapped during the earlier coastline mapping efforts. CET proved to be a highly accurate coastline mapping technique that successfully overcomes spectral-induced errors. In future research, we are planning to integrate data obtained by UAVs infrared thermography (IRT) and in situ sensors, measuring sea and land surface temperatures (SST and LST), into the CET, given that this has shown promising results. Considering its accuracy and ease of use, we suggest that CET can be applied for automated coastline extraction in other large and indented coastal areas. Additionally, we suggest that CET could be applied in longitudinal geomorphological coastal erosion studies for the automated detection of spatio-temporal coastline displacement.

scholarly journals Land subsidence characteristics and disaster prevention in the Tongzhou area, Beijing

2020 ◽  
Vol 382 ◽  
pp. 715-719
Author(s):  
Luo Yong ◽  
Zhao Long ◽  
Zhu Lin ◽  
Tian Fang ◽  
Lei Kunchao ◽  
...  
Keyword(s):  
Land Subsidence ◽  
Early Warning ◽  
Decision Makers ◽  
Disaster Prevention ◽  
Groundwater Extraction ◽  
Groundwater Exploitation ◽  
Scientific Support ◽  
Subsidence Rate ◽  
The Impact ◽  
Land Subsidence Rate

Abstract. Accurately assessing the impact of groundwater exploitation on land subsidence can provide scientific support for decision-makers. This article establishes a groundwater–subsidence model in a typical area using the land subsidence characteristics of Tongzhou, China, and subsequently classifies a land subsidence early warning zone based on the land subsidence rate. Results show that land subsidence occurred throughout the whole Tongzhou District. The land subsidence that developed to the west of Liuzhuang, Tongzhou city and Taihu was the most serious The maximum annual subsidence rate reached 120 mm a−1 under the current groundwater extraction conditions, and the early warning level for land subsidence reached its highest level. If the pumping of groundwater was reduced by 50 % in the second and the fourth aquifers and by 60 % in the third aquifer, the land subsidence early warning level would be largely reduced and would meet the requirements for land subsidence control.

scholarly journals Hydrologic control on natural land subsidence in the shallow coastal aquifer of the Ravenna coast, Italy

2020 ◽  
Vol 382 ◽  
pp. 263-268
Author(s):  
Marco Antonellini ◽  
Beatrice Maria Sole Giambastiani ◽  
Nicolas Greggio ◽  
Luciana Bonzi ◽  
Lorenzo Calabrese ◽  
...  
Keyword(s):  
Time Series ◽  
Water Table ◽  
Land Subsidence ◽  
Coastal Aquifer ◽  
Elastic Component ◽  
Data Set ◽  
Subsidence Rate ◽  
Natural Land ◽  
Primary Consolidation ◽  
Land Subsidence Rate

Abstract. Multiple processes contributing to natural land subsidence in a shallow coastal aquifer near Ravenna (Italy) were identified by analysing the relationships among different data set time series (water table level, rainfall, land reclamation drainage, sea level, etc.) and establishing the correlations with vertical ground motion observed at a high-resolution settlement gauge. Our study highlights the presence of three deformation components related to different processes controlling land subsidence: elastic, delayed-elastic, and irreversible (plastic) components. The elastic and delayed-elastic components are closely related to water table fluctuations that change the effective stress in two portions of the coastal aquifer at a daily (in the sandy unconfined portion) and seasonal time scales (in the layered clay-rich semi-confined prodelta portion), respectively. The irreversible component represents the trend in the land subsidence time series and is due to primary consolidation (pore pressure dissipation) of the fine-grained prodelta levels above where the settlement gauge is located. The amplitudes of the elastic component can be up to 0.2–0.3 mm whereas the amplitude of the delayed-elastic component reaches 0.89 mm. The primary consolidation rate of deformation is 0.9 mm yr−1 and constrains the likely age of prodelta sediments deposition to 1300–2800 years before present. The delayed-elastic subsidence rate has similar magnitude to that due to primary consolidation and is connected to poroelastic effects in the prodelta sequence following seasonal variations in water table. Our findings are important for planning land subsidence management and monitoring strategies especially where the surface aquifer structure is heterogeneous due to different depositional settings. The natural land subsidence rate in the Holocene sediments of the shallow coastal aquifer of Ravenna (North eastern Italy) that we measured in this study accounts for 10 %–20 % of the total current land subsidence rate observed in this portion of Ravenna coastal area (10–20 mm yr−1).

scholarly journals Subsidence monitoring with TerraSAR-X data in Beijing Central Business District and subway tunnelings, China

2020 ◽  
Vol 382 ◽  
pp. 125-130 ◽  
Author(s):  
Fengkai Li ◽  
Huili Gong ◽  
Beibei Chen ◽  
Mingliang Gao ◽  
Chaofan Zhou
Keyword(s):  
Land Subsidence ◽  
Large Scale ◽  
Central Business District ◽  
Economic Losses ◽  
Additional Stress ◽  
Engineering Construction ◽  
Subsidence Rate ◽  
Business District ◽  
Land Subsidence Rate ◽  
Building Loads

Abstract. Land subsidence caused by large-scale engineering construction may damage the surrounding infrastructures and cause huge economic losses in inner-city environments. In this study, we used PS-InSAR technology on 68 TerraSAR-X images to acquire deformation in the Beijing Plain between February 2010 and December 2018. Then, we calculated the additional stress derived from building loads using the method proposed by Boussinesq in the Central Business District (CBD). We found that the depth of influence of additional stresses induced by building loads was 80 m and that spatial distribution pattern of the land subsidence rate agreed well with the additional stress. We found that the influence range of ground subsidence caused by metro construction is 200 m at Ciqikou station by analyzing the subsidence rate profile perpendicular to subway line No. 7 and that the maximum land subsidence rate is 23.2 mm yr−1. Time series analysis of PS around Ciqikou station shows that land subsidence caused by excavation activities mainly occurs in the period of metro construction. Ground deformation rate decreases gradually after 372 d of subway operation. The results of both cases show that large-scale engineering construction will lead to significant land subsidence which should be considered in future urbanization.

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.

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