LIVIVO - Das Suchportal für Lebenswissenschaften

switch to English language
Zurück zur letzten Suche Suchhistorie
Erweiterte Suche

Ihre letzten Suchen

  1. AU="Hussain, Muhammad Afaq"
  2. AU="Werner Henkel"
  3. AU=Zellweger M J
  4. AU="Marasco, Michelangelo"
  5. AU="Landa-Moreno, Cinthia"
  6. AU="Kuntner, Matjaz"
  7. AU="Lemes, Robertha Mariana Rodrigues"
  8. AU="Riccioni, M E"
  9. AU="Traer, Colin J"
  10. AU="Cao, Xuejie"
  11. AU="Chen, Zishuo"
  12. AU="Kalachikov, Sergey"
  13. AU="Das, Tilak"
  14. AU="Bessat, Cécile"
  15. AU="Galina Velikova"
  16. AU="Greene, Sharrell"
  17. AU="Chen, Kallie J"
  18. AU="Schwab, Jörg O."
  19. AU="Ke Chen"
  20. AU="Hewei Liang"
  21. AU="Abreu, Cristina"
  22. AU="Mamani Ortiz, Yercin"
  23. AU="Castro, Lucíola de Fátima Albuquerque Almeida Peixoto"
  24. AU="Šimůnek, Tomáš"
  25. AU="Ong, Lizhen"
  26. AU="Chai, Chaoqing"
  27. AU="Maheswaran Kesavan"
  28. AU="Mehta, Mrunal"
  29. AU=Paredes Sergio D
  30. AU=Ghosh Nilanjan AU=Ghosh Nilanjan
  31. AU="Hofmann, Alexander"
  32. AU="Radici, Marco"
  33. AU="Noro, Fabrizia"
  34. AU="Wang, Jianzhao"
  35. AU="Divya Jeyam"
  36. AU="Wolf, Lisette"
  37. AU="Marjanovic, Nemanja Despot"
  38. AU="Jitxin, Lim"

Suchergebnis

Treffer 1 - 7 von insgesamt 7

Suchoptionen

  1. Artikel ; Online: Monitoring land subsidence in the Peshawar District, Pakistan, with a multi-track PS-InSAR technique.

    Hussain, Muhammad Afaq / Chen, Zhanlong / Khan, Junaid

    Environmental science and pollution research international

    2024  Band 31, Heft 8, Seite(n) 12271–12287

    Abstract: Peshawar is one of the most densely populated cities of Pakistan with high urbanization rate. The city overexploits groundwater resources for household and commercial usage which has caused land subsidence. Land subsidence has long been an issue in ... ...

    Abstract Peshawar is one of the most densely populated cities of Pakistan with high urbanization rate. The city overexploits groundwater resources for household and commercial usage which has caused land subsidence. Land subsidence has long been an issue in Peshawar due to insufficient groundwater removal. In this research, we employ the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technique with Sentinel-1 imaging data to observe the yearly land subsidence and generate accumulative time-series maps for the years (2018 to 2020) using the SAR PROcessing tool (SARPROZ). The PS-InSAR findings from two contiguous paths are combined by considering the variance over the overlapping area. The subsidence rates in the Peshawar are from -59 to 17 mm/yr. The results show that subsidence is -28.48 mm/yr in 2018, the subsidence reached -49.02 mm/yr in 2019, while in 2020, the subsidence reached -49.90 mm/yr. The findings indicate a notable rise in land subsidence between the years 2018 and 2020. Subsidence is predicted in the research region primarily due to excessive groundwater removal and soil consolidation induced by surficial loads. The correlation of land subsidence observations with groundwater levels and precipitation data revealed some relationships. Overall, the proposed method efficiently monitors, maps, and detects subsidence-prone areas. The utilization of land subsidence maps will enhance the efficiency of urban planning, construction of surface infrastructure, and the management of risks associated with subsidence.
    Mesh-Begriff(e) Radar ; Pakistan ; Environmental Monitoring/methods ; Groundwater ; Interferometry
    Sprache Englisch
    Erscheinungsdatum 2024-01-17
    Erscheinungsland Germany
    Dokumenttyp Journal Article
    ZDB-ID 1178791-0
    ISSN 1614-7499 ; 0944-1344
    ISSN (online) 1614-7499
    ISSN 0944-1344
    DOI 10.1007/s11356-024-31995-x
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Volltext online

    Zusatzmaterialien

    Kategorien

    Verfügbar in ZB MED Bonn

    Z 5915: Hefte anzeigen

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

    Fernleihe an ZB MED

    Sie können sich den gewünschten Titel als lokale Nutzerin oder lokaler Nutzer von ZB MED direkt an den Standort Köln schicken lassen.

  2. Artikel: Monitoring Land Subsidence Using PS-InSAR Technique in Rawalpindi and Islamabad, Pakistan

    Khan, Junaid / Ren, Xingwei / Hussain, Muhammad Afaq / Jan, M. Qasim

    Remote Sensing. 2022 Aug. 03, v. 14, no. 15

    2022  

    Abstract: Land subsidence is a major concern in vastly growing metropolitans worldwide. The most serious risks in this scenario are linked to groundwater extraction and urban development. Pakistan’s fourth-largest city, Rawalpindi, and its twin Islamabad, located ... ...

    Abstract Land subsidence is a major concern in vastly growing metropolitans worldwide. The most serious risks in this scenario are linked to groundwater extraction and urban development. Pakistan’s fourth-largest city, Rawalpindi, and its twin Islamabad, located at the northern edge of the Potwar Plateau, are witnessing extensive urban expansion. Groundwater (tube-wells) is residents’ primary daily water supply in these metropolitan areas. Unnecessarily pumping and the local inhabitant’s excessive demand for groundwater disturb the sub-surface’s viability. The Persistent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR) approach, along with Sentinel-1 Synthetic Aperture Radar (SAR) imagery, were used to track land subsidence in Rawalpindi-Islamabad. The SARPROZ application was used to study a set of Sentinel-1 imagery obtained from January 2019 to June 2021 along descending and ascending orbits to estimate ground subsidence in the Rawalpindi-Islamabad area. The results show a significant increase (−25 to −30 mm/yr) in subsidence from −69 mm/yr in 2019 to −98 mm/yr in 2020. The suggested approach effectively maps, detects, and monitors subsidence-prone terrains and will enable better planning, surface infrastructure building designs, and risk management related to subsidence.
    Schlagwörter groundwater ; groundwater extraction ; infrastructure ; interferometry ; risk management ; subsidence ; synthetic aperture radar ; urbanization ; viability ; water supply ; Pakistan
    Sprache Englisch
    Erscheinungsverlauf 2022-0803
    Erscheinungsort Multidisciplinary Digital Publishing Institute
    Dokumenttyp Artikel
    ZDB-ID 2513863-7
    ISSN 2072-4292
    ISSN 2072-4292
    DOI 10.3390/rs14153722
    Datenquelle NAL Katalog (AGRICOLA)

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  3. Artikel: Landslide Susceptibility Mapping Using Machine Learning Algorithm: A Case Study Along Karakoram Highway (KKH), Pakistan

    Hussain, Muhammad Afaq / Chen, Zhanlong / Kalsoom, Isma / Asghar, Aamir / Shoaib, Muhammad

    Journal of the Indian Society of Remote Sensing. 2022 May, v. 50, no. 5

    2022  

    Abstract: The China–Pakistan Karakoram Highway links China to South Asia and the Middle East through Pakistan. Rockfall and debris flows are dangerous geological risks on the main route, and they often disrupt traffic and result in fatalities. As a result, the ... ...

    Abstract The China–Pakistan Karakoram Highway links China to South Asia and the Middle East through Pakistan. Rockfall and debris flows are dangerous geological risks on the main route, and they often disrupt traffic and result in fatalities. As a result, the landslide susceptibility map (LSM) evolution along the highway could make it safer to drive. In this article, remote sensing data are combined with machine learning algorithms such as Random Forest (RF), Extreme Gradient Boosting (XGBoost), and k-Nearest Neighbors (KNN) to develop the LSM. Initially, 274 landslide locations we determined and mapped in ArcGIS software and randomly divided into a ratio of 8/2. Secondly, ten landslide susceptibility factors were developed using satellite imagery and different topographical and geological maps. Finally, the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC) value, was used to estimate the model's effectiveness. Our consequences showed that, for three models, the RF, XGBoost, and KNN models, as well as slope, elevation, and distance from the river parameters, had the maximum influence upon landslide sensitivity. Accordingly, the prediction rates are 83.5%, 82.7%, and 80.7% for RF, XGBoost, and KNN. Furthermore, the RF method has better efficiency as compared to other models on the base of AUC. Our findings show that all three machine learning algorithms positively impact, and the results may assist the highway's safe operations.
    Schlagwörter algorithms ; case studies ; computer software ; landslides ; models ; prediction ; remote sensing ; rivers ; rockfalls ; traffic ; Middle East ; Pakistan
    Sprache Englisch
    Erscheinungsverlauf 2022-05
    Umfang p. 849-866.
    Erscheinungsort Springer India
    Dokumenttyp Artikel
    ZDB-ID 2439566-3
    ISSN 0974-3006 ; 0255-660X
    ISSN (online) 0974-3006
    ISSN 0255-660X
    DOI 10.1007/s12524-021-01451-1
    Datenquelle NAL Katalog (AGRICOLA)

    Volltext online

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  4. Artikel ; Online: Sentinel-1A for monitoring land subsidence of coastal city of Pakistan using Persistent Scatterers In-SAR technique.

    Hussain, Muhammad Afaq / Chen, Zhanlong / Shoaib, Muhammad / Shah, Safeer Ullah / Khan, Junaid / Ying, Zheng

    Scientific reports

    2022  Band 12, Heft 1, Seite(n) 5294

    Abstract: Karachi is located in the southern part of Pakistan along the Arabian Sea coast. Relevant institutions are concerned about the possibility of ground subsidence in the city, contributing to the comparative sea-level rise. So yet, no direct measurement of ... ...

    Abstract Karachi is located in the southern part of Pakistan along the Arabian Sea coast. Relevant institutions are concerned about the possibility of ground subsidence in the city, contributing to the comparative sea-level rise. So yet, no direct measurement of the subsidence rate and its relation to city submergence danger has been made. SAR (Synthetic Aperture Radar) interferometry is a powerful method for obtaining millimeter-accurate surface displacement measurements. The Sentinel-1 satellite data provide extensive geographical coverage, regular acquisitions, and open access. This research used the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technology with Sentinel-1 SAR images to monitor ground subsidence in Karachi, Pakistan. The SARPROZ software was used to analyze a series of Sentinel-1A images taken from November 2019 to December 2020 along ascending and descending orbit paths to assess land subsidence in Karachi. The cumulative deformation in Line of Sight (LOS) ranged from - 68.91 to 76.06 mm/year, whereas the vertical deformation in LOS ranged from - 67.66 to 74.68 mm/year. The data reveal a considerable rise in subsidence from 2019 to 2020. The general pattern of subsidence indicated very high values in the city center, whereas locations outside the city center saw minimal subsidence. Overall, the proposed technique effectively maps, identifies, and monitors land areas susceptible to subsidence. This will allow for more efficient planning, construction of surface infrastructure, and control of subsidence-induced risks.
    Mesh-Begriff(e) Cities ; Environmental Monitoring/methods ; Interferometry/methods ; Pakistan ; Radar
    Sprache Englisch
    Erscheinungsdatum 2022-03-28
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-09359-7
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  5. Artikel: PS-InSAR-Based Validated Landslide Susceptibility Mapping along Karakorum Highway, Pakistan

    Hussain, Muhammad Afaq / Chen, Zhanlong / Wang, Run / Shoaib, Muhammad

    Remote Sensing. 2021 Oct. 15, v. 13, no. 20

    2021  

    Abstract: Landslide classification and identification along Karakorum Highway (KKH) is still challenging due to constraints of proposed approaches, harsh environment, detail analysis, complicated natural landslide process due to tectonic activities, and data ... ...

    Abstract Landslide classification and identification along Karakorum Highway (KKH) is still challenging due to constraints of proposed approaches, harsh environment, detail analysis, complicated natural landslide process due to tectonic activities, and data availability problems. A comprehensive landslide inventory and a landslide susceptibility mapping (LSM) along the Karakorum Highway were created in recent research. The extreme gradient boosting (XGBoost) and random forest (RF) models were used to compare and forecast the association between causative parameters and landslides. These advanced machine learning (ML) models can measure environmental issues and risks for any area on a regional scale. Initially, 74 landslide locations were determined along the KKH to prepare the landslide inventory map using different data. The landslides were randomly divided into two sets for training and validation at a proportion of 7/3. Fifteen landslide conditioning variables were produced for susceptibility mapping. The interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technique investigated the deformation movement of extracted models in the susceptible zones. It revealed a high line of sight (LOS) deformation velocity in both models’ sensitive zones. For accuracy comparison, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve approach was used, which showed 93.44% and 92.22% accuracy for XGBoost and RF, respectively. The XGBoost method produced superior results, combined with PS-InSAR results to create a new LSM for the area. This improved susceptibility model will aid in mitigating the landslide disaster, and the results may assist in the safe operation of the highway in the research area.
    Schlagwörter deformation ; interferometry ; inventories ; landslides ; models ; synthetic aperture radar ; tectonics ; Pakistan
    Sprache Englisch
    Erscheinungsverlauf 2021-1015
    Erscheinungsort Multidisciplinary Digital Publishing Institute
    Dokumenttyp Artikel
    ZDB-ID 2513863-7
    ISSN 2072-4292
    ISSN 2072-4292
    DOI 10.3390/rs13204129
    Datenquelle NAL Katalog (AGRICOLA)

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  6. Artikel: PS-InSAR Based Monitoring of Land Subsidence by Groundwater Extraction for Lahore Metropolitan City, Pakistan

    Hussain, Muhammad Afaq / Chen, Zhanlong / Zheng, Ying / Shoaib, Muhammad / Ma, Junwei / Ahmad, Ijaz / Asghar, Aamir / Khan, Junaid

    Remote Sensing. 2022 Aug. 14, v. 14, no. 16

    2022  

    Abstract: Groundwater dynamics caused by extraction and recharge are one of the primary causes of subsidence in the urban environment. Lahore is the second largest metropolitan city in Pakistan. The rapid expansion of this urban area due to high population density ...

    Abstract Groundwater dynamics caused by extraction and recharge are one of the primary causes of subsidence in the urban environment. Lahore is the second largest metropolitan city in Pakistan. The rapid expansion of this urban area due to high population density has increased the demand for groundwater to meet commercial and household needs. Land subsidence due to inadequate groundwater extraction has long been a concern in Lahore. This paper aims to present the persistent scatterer interferometry synthetic aperture radar (PS-InSAR) technique for monitoring the recent land subsidence in Lahore, based on the Sentinel-1 data obtained from January 2020 to December 2021. PS-InSAR techniques are very efficient and cost-effective, determining land subsidence and providing useful results. Areas of high groundwater discharge are prone to high subsidence of −110 mm, while the surroundings show an uplifting of +21 mm during the study period. The PS-InSAR study exposes the subsidence area in detail, particularly when the subsoil is characterized by alluvial and clay deposits and large building structures. This type of observation is quite satisfactory and similar to ground-based surface deformation pertinent to a high subsidence rate. Results will enable more effective urban planning, land infrastructure building, and risk assessment related to subsidence.
    Schlagwörter clay ; cost effectiveness ; deformation ; groundwater ; groundwater extraction ; infrastructure ; interferometry ; population density ; risk assessment ; subsidence ; subsoil ; synthetic aperture radar ; urban areas ; Pakistan
    Sprache Englisch
    Erscheinungsverlauf 2022-0814
    Erscheinungsort Multidisciplinary Digital Publishing Institute
    Dokumenttyp Artikel
    ZDB-ID 2513863-7
    ISSN 2072-4292
    ISSN 2072-4292
    DOI 10.3390/rs14163950
    Datenquelle NAL Katalog (AGRICOLA)

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

  7. Artikel ; Online: Landslide Susceptibility Mapping Using Machine Learning Algorithm Validated by Persistent Scatterer In-SAR Technique.

    Hussain, Muhammad Afaq / Chen, Zhanlong / Zheng, Ying / Shoaib, Muhammad / Shah, Safeer Ullah / Ali, Nafees / Afzal, Zeeshan

    Sensors (Basel, Switzerland)

    2022  Band 22, Heft 9

    Abstract: Landslides are the most catastrophic geological hazard in hilly areas. The present work intends to identify landslide susceptibility along Karakorum Highway (KKH) in Northern Pakistan, using landslide susceptibility mapping (LSM). To compare and predict ... ...

    Abstract Landslides are the most catastrophic geological hazard in hilly areas. The present work intends to identify landslide susceptibility along Karakorum Highway (KKH) in Northern Pakistan, using landslide susceptibility mapping (LSM). To compare and predict the connection between causative factors and landslides, the random forest (RF), extreme gradient boosting (XGBoost), k nearest neighbor (KNN) and naive Bayes (NB) models were used in this research. Interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technology was used to explore the displacement movement of retrieved models. Initially, 332 landslide areas alongside the Karakorum Highway were found to generate the landslide inventory map using various data. The landslides were categorized into two sections for validation and training, of 30% and 70%. For susceptibility mapping, thirteen landslide-condition factors were created. The area under curve (AUC) of the receiver operating characteristic (ROC) curve technique was utilized for accuracy comparison, yielding 83.08, 82.15, 80.31, and 72.92% accuracy for RF, XGBoost, KNN, and NB, respectively. The PS-InSAR technique demonstrated a high deformation velocity along the line of sight (LOS) in model-sensitive areas. The PS-InSAR technique was used to evaluate the slope deformation velocity, which can be used to improve the LSM for the research region. The RF technique yielded superior findings, integrating with the PS-InSAR outcomes to provide the region with a new landslide susceptibility map. The enhanced model will help mitigate landslide catastrophes, and the outcomes may help ensure the roadway's safe functioning in the study region.
    Mesh-Begriff(e) Algorithms ; Bayes Theorem ; Geographic Information Systems ; Landslides ; Machine Learning
    Sprache Englisch
    Erscheinungsdatum 2022-04-19
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2052857-7
    ISSN 1424-8220 ; 1424-8220
    ISSN (online) 1424-8220
    ISSN 1424-8220
    DOI 10.3390/s22093119
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

    Zusatzmaterialien

    Kategorien

    Über subito bestellen

    Dieser Service ist kostenpflichtig (siehe Lieferbedingungen von subito). Bestellungen, die einen Artikel nebst Supplementary Material umfassen, werden grundsätzlich wie mehrfache Bestellungen bearbeitet. Gebühren fallen in diesen Fällen für jede einzelne Bestellung an.

Zum Seitenanfang