PD2021_Info

Proiecte de cercetare postdoctorală (PD)

Pachet de informații (aprobat prin Ordinul MCID nr. 95/03.06.2021)

Cererea de finanțare o Secțiunea A: Informații generale (se completează doar pe platforma de depunere on-line, www.uefiscdi-direct.ro)

Secțiunile B și C* (se completează, se salvează în format .pdf textual și se încarcă pe platforma de depunere on-line www.uefiscdi-direct.ro)
La fiecare secțiune se va menține textul care marchează informaţiile şi secţiunile obligatorii ale cererii.
*S-a înlocuit cererea de finanţare pentru corelarea textului din secţiunile B3 şi C3.
 
Notă:

Secţiunile B1.2 şi B2 a aplicaţiei vor fi publice. Acestea vor fi încărcate în platforma de depunere, atât ca parte integrantă a cererii de finanţare, cât şi completate în platforma de depunere.

Standarde minimale de eligibilitate pentru directorul de proiect (se completează doar pe platforma de depunere on-line, www.uefiscdi-direct.ro) Standarde minimale de eligibilitate pentru mentor (se completează doar pe platforma de depunere on-line, www.uefiscdi-direct.ro) Declaraţii: (se completează, se semnează şi se încarcă în platforma de depunere, scanate în format .pdf) o ANEXA 7 – Declaraţie pe propria răspundere a directorului de proiect privind nefinanţarea din alte surse, certificarea legalităţii și corectitudinea informațiilor o ANEXA 8 – Declaraţie pe propria răspundere a instituţiei gazdă prin care se certifică acceptarea implementării proiectului în instituţie o ANEXA 9 – Declaraţia pe propria răspundere a instituţiei gazdă privind încadrarea în definiţia organizaţiei de cercetare Nu trebuie depusă de către universitățile acreditate, institutele Academiei Române și institutele naționale de cercetare – dezvoltare. o ANEXA 10 – Declaraţia pe propria răspundere privind eligibilitatea financiară a instituției gazdă o ANEXA 11 – Acordul mentorului de a mentora directorul de proiect (nu este un formular cu format impus) Domenii științifice Fișa de evaluare

 
 
Întrebări frecvente – actualizat în 28.06.2021

Informații utile: o Cererile de finanțare se depun prin intermediul platformei web – www.uefiscdi-direct.ro, nefiind necesară depunerea și în format tipărit; o Platforma de depunere este disponibilă începând cu data de 10.06.2021 o Termenul limită pentru depunerea cererilor de finanțare este 07.07.2021, ora 16:00; o Se vor lua în calcul pentru analiza eligibilităţii doar propunerile de proiecte care la termenul limită se găsesc pe platforma de depunere on-line în stare „finalizat”.  

 

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PN-III-P1-1.1-PD-2021-0145

 

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2022, Decembrie – Raport proiect PN-III-P1-1.1-PD-2021-0145

 

RAPORTARE ȘTIINȚIFICĂ Etapa 2022

 Cod proiect: PN-III-P1-1.1-PD-2021-0145

Contract nr. PD 85 din 19/04/2022

Denumire proiect: SISTEM UAV HIBRID LIDAR ȘI FOTOGRAMMETRIE PENTRU FUZIUNEA DATELOR ÎN CONSTRUCȚII ȘI MONITORIZAREA TERENURILOR [FLYSURVEY]

 

 

Nr. etapă: 1

Nume etapă: [WP1] Investigatii si demersuri preliminarii obtinerii de DFM (digital feature model)

 

Rezumatul etapei 1, 2022: Prima etapa a proiectului a constat intr-un amplu proces de investigare a literaturii de specialitate si a cercetarilor privind  folosirea tehnologiilor LiDAR si digital photography Structure-from-Motion (SfM). Pe langa cercetarile bibliografice, s-au efectuat demersuri pregatitoare procesului de achizitie de echipamente ce urmeaza sa inceapa in prima luna a etapei 2 (ianuarie 2023), si s-au desfasurat unele proceduri administrative prevazute in cadrul proiectului (ex. intocmirea paginii web a proiectului). In cadrul acestei etape s-au finalizat cercetarile unui studiu de caz in care s-au folosit principii si obiective din metodologia conceputa in proiectul Flysurvey. Diseminarea rezultatelor a fost efectuata prin publicarea unui articol intr-un jurnal indexat WoS cu factor de impact (IF), situat in Q1. In cadrul etapelor 2 si 3, complexitatea cercetarilor urmeaza sa se amplifice prin achizitia si implementarea de senzori si tehnologii moderne (sistemul UAV / drona profesionala, senzorul LiDAR si senzorul RGB DJI P1), in vederea elaborarii si testarii metodologiei inovative DFM, conform propunerii de proiect.

 

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rezultate

Conform ‘Contractului de Finanțare’ pentru PD 85, din 19/04/2022, s-au desfășurat toate activitățile programate in cadrul proiectului pentru perioada de raportare (01.04.2022-31.12.2022), acestea fiind indeplinite in procent de 100%.

 

Principalele rezultate: [Articole] Publicarea unui manuscris intr-un jurnal Q1: Remote Sens. 202214(22), 5822; https://doi.org/10.3390/rs14225822

(This article belongs to the Special Issue UAVs for Civil Engineering Applications)
 
 
 
 
Citare articol (stiluri de citare):
 
MDPI and ACS Style

Sestras, P.; Bilașco, Ș.; Roșca, S.; Veres, I.; Ilies, N.; Hysa, A.; Spalević, V.; Cîmpeanu, S.M. Multi-Instrumental Approach to Slope Failure Monitoring in a Landslide Susceptible Newly Built-Up Area: Topo-Geodetic Survey, UAV 3D Modelling and Ground-Penetrating Radar. Remote Sens. 202214, 5822. https://doi.org/10.3390/rs14225822

AMA Style

Sestras P, Bilașco Ș, Roșca S, Veres I, Ilies N, Hysa A, Spalević V, Cîmpeanu SM. Multi-Instrumental Approach to Slope Failure Monitoring in a Landslide Susceptible Newly Built-Up Area: Topo-Geodetic Survey, UAV 3D Modelling and Ground-Penetrating Radar. Remote Sensing. 2022; 14(22):5822. https://doi.org/10.3390/rs14225822

Chicago/Turabian Style

Sestras, Paul, Ștefan Bilașco, Sanda Roșca, Ioel Veres, Nicoleta Ilies, Artan Hysa, Velibor Spalević, and Sorin M. Cîmpeanu. 2022. „Multi-Instrumental Approach to Slope Failure Monitoring in a Landslide Susceptible Newly Built-Up Area: Topo-Geodetic Survey, UAV 3D Modelling and Ground-Penetrating Radar” Remote Sensing 14, no. 22: 5822. https://doi.org/10.3390/rs14225822

 

Multi-Instrumental Approach to Slope Failure Monitoring in a Landslide Susceptible Newly Built-Up Area: Topo-Geodetic Survey, UAV 3D Modelling and Ground-Penetrating Radar

 
1 Faculty of Civil Engineering, Technical University of Cluj-Napoca, 400020 Cluj-Napoca, Romania
2 Faculty of Geography, Babes-Bolyai University, 400006 Cluj-Napoca, Romania
Cluj-Napoca Subsidiary Geography Section, Romanian Academy, 400015 Cluj-Napoca, Romania
Faculty of Architecture and Engineering, Epoka University, 1000 Tirana, Albania
Geography Department, Faculty of Philosophy, University of Montenegro, 81400 Niksic, Montenegro
Faculty of Land Reclamation and Environmental Engineering, University of Agronomic Sciences and Veterinary Medicine Bucharest, 011464 Bucharest, Romania
 
 

Abstract

Slope failures and landslides cause economic damage and deaths worldwide. These losses can be minimized by integrating different methodologies, instruments, and data monitoring to predict future landslides. In the constantly growing metropolitan area of Cluj-Napoca, Romania, changes in land cover, land use, and build-up areas are an issue. The unprecedented urban sprawl pushed the city limits from the Somes River to hilly terrain prone to landslides and erosion. This study focuses on a landslide-prone area where a previous slope failure caused significant economic losses. It combines topo-geodetic measurements, UAV monitoring of surface displacement, GIS spatial analysis, ground-penetrating radar investigations, and geotechnical assessment. Two years of data show that the slope is undergoing surface erosion, with soil displacements of a few centimeters. Geodetic monitoring of the retaining wall’s control points indicates a small rotation. Coupled with georadar profile imaging showing changes in soil and rock layers with an uplift trend, it was deduced that the area suffers from a global instability. The findings provide valuable information about the dynamics of landslides and erosion for forecasting future movements and developing preventative strategies based on a new methodology that combines affordable and prevalent instrumentation and techniques.
 
Keywords: geodesytopographyGNSStotal stationsurface erosiondeep landslidedisplacements GIS spatial analysisgeomaticsgeotechnics
Remote Sens. 202214(22), 5822; https://doi.org/10.3390/rs14225822
Special Issue:  UAVs for Civil Engineering Applications
 
Imagini sugestive cu activitatile si rezultatele obtinute, prezentate in articol

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Figure 1. Landslide susceptibility map [4] with relevant hotspots

 

Figure 2. The geographic location of the study area

 

Figure 3. Aerial photos of the emerged residential complex and monitored study area

 

Figure 4. Collage of images depicting the old retaining wall right before failure, and the damages that ensued on the industrial production hall after the slope failure

 

Figure 5. Collage of images depicting the newly constructed retaining wall and land improvement measures.

 

Figure 6. Methodological flowchart

 

 

Figure 7. Map of the prior research study’s landslide susceptibility [4] (a) and of the study area (b); the twelve factors that have been examined as potential influencing factors for slope mass movement: altitude (c), slope (d), aspect (e), distance to settlements (f), roads (g), hydrography (h), wetness index (i), stream power index (j), land-use (k), geology (l), depth of fragmentation (m), and fragmentation density (n)

 

Figure 8. Established local geodetic network

 

Figure 9. Obtained orthophoto with GCPs and CPs positioning and the instrumentation used

 

Figure 10. Obtained orthophoto with GPR profile locations and the instrumentation used

 

Figure 11. Displacement analysis on each axis, as well as overall spatial values

 

Figure 12. Surface movement rate

 

Figure 13. GPR longitudinal profile

 

References

  1. Corpade, C.; Man, T.; Petrea, D.; Corpade, A.-M.; Moldovan, C. Changes in landscape structure induced by transportation projects in Cluj-Napoca periurban area using GIS. Carpathian J. Earth Environ. Sci. 20149, 177–184. [Google Scholar]
  2. Dolean, B.-E.; Bilașco, Ș.; Petrea, D.; Moldovan, C.; Vescan, I.; Roșca, S.; Fodorean, I. Evaluation of the Built-Up Area Dynamics in the First Ring of Cluj-Napoca Metropolitan Area, Romania by Semi-Automatic GIS Analysis of Landsat Satellite Images. Appl. Sci. 202010, 7722. [Google Scholar] [CrossRef]
  3. Cebotari, S.; Cristea, M.; Moldovan, C.; Zubașcu, F. Renewable Energy’s Impact on Rural Development in Northwestern Romania. Energy Sustain. Dev. 201737, 110–123. [Google Scholar] [CrossRef]
  4. Sestras, P.; Bilasco, S.; Roşca, S.; Naș, S.; Bondrea, M.; Gâlgău, R.; Vereş, I.; Salagean, T.; Spalevic, V.; Cimpeanu, S. Landslides Susceptibility Assessment Based on GIS Statistical Bivariate Analysis in the Hills Surrounding a Metropolitan Area. Sustainability 201911, 1362. [Google Scholar] [CrossRef]
  5. Sestras, P.; Bilașco, Ș.; Roșca, S.; Dudic, B.; Hysa, A.; Spalević, V. Geodetic and UAV Monitoring in the Sustainable Management of Shallow Landslides and Erosion of a Susceptible Urban Environment. Remote Sens. 202113, 385. [Google Scholar] [CrossRef]
  6. Bilaşco, Ş.; Roşca, S.; Fodorean, I.; Vescan, I.; Filip, S.; Petrea, D. Quantitative evaluation of the risk induced by dominant geomorphological processes on different land uses, based on GIS spatial analysis models. Front. Earth Sci. 201812, 311–324. [Google Scholar]
  7. Bălteanu, D.; Micu, M.; Jurchescu, M.; Malet, J.-P.; Sima, M.; Kucsicsa, G.; Dumitrică, C.; Petrea, D.; Mărgărint, M.C.; Bilaşco, S.T.; et al. National-scale landslide susceptibility map of Romania in a European methodological framework. Geomorphology 2020371, 107432. [Google Scholar] [CrossRef]
  8. Kerekes, A.H.; Poszet, S.L.; Andrea, G.Á.L. Landslide susceptibility assessment using the maximum entropy model in a sector of the Cluj–Napoca Municipality, Romania. Rev. Geomorfol. 201820, 130–146. [Google Scholar] [CrossRef]
  9. Kerekes, A.H.; Poszet, S.L.; Baciu, L.C. Investigating land surface deformation using InSAR and GIS techniques in Cluj–Napoca city’s most affected sector by urban sprawl (Romania). Rev. Geomorfol. 202022, 43–59. [Google Scholar] [CrossRef]
  10. Roşca, S.; Bilaşco, Ş.; Petrea, D.; Fodorean, I.; Vescan, I.; Filip, S. Application of landslide hazard scenarios at annual scale in the Niraj River basin (Transylvania Depression, Romania). Nat. Hazards 201577, 1573–1592. [Google Scholar]
  11. Galli, M.; Ardizzone, F.; Cardinali, M.; Guzzetti, F.; Reichenbach, P. Comparing landslide inventory maps. Geomorphology 200894, 268–289. [Google Scholar] [CrossRef]
  12. Cruden, D.M.; Varnes, D.J. Landslides: Investigation and mitigation. Chapter 3-Landslide types and processes. Transp. Res. Board Spec. Rep. 1996247, 36–75. [Google Scholar]
  13. Artese, S.; Perrelli, M. Monitoring a Landslide with High Accuracy by Total Station: A DTM-Based Model to Correct for the Atmospheric Effects. Geosciences 20188, 46. [Google Scholar] [CrossRef]
  14. Guzzetti, F.; Mondini, A.C.; Cardinali, M.; Fiorucci, F.; Santangelo, M.; Chang, K.T. Landslide inventory maps: New tools for an old problem. Earth Sci. Rev. 2012112, 42–66. [Google Scholar] [CrossRef]
  15. Corominas, J.; van Westen, C.; Frattini, P.; Cascini, L.; Malet, J.-P.; Fotopoulou, S.; Catani, F.; Van Den Eeckhaut, M.; Mavrouli, O.; Agliardi, F.; et al. Recommendations for the quantitative analysis of landslide risk. Bull. Eng. Geol. Environ. 201473, 209–263. [Google Scholar] [CrossRef]
  16. Stiros, S.C.; Vichas, C.; Skourtis, C. Landslide Monitoring Based on Geodetically Derived Distance Changes. J. Surv. Eng. 2004130, 156–162. [Google Scholar] [CrossRef]
  17. Tsaia, Z.; Youa, G.J.Y.; Leea, H.Y.; Chiub, Y.J. Use of a total station to monitor post-failure sediment yields in landslide sites of the Shihmen reservoir watershed. Geomorphology 2012139–140, 438–451. [Google Scholar] [CrossRef]
  18. Westoby, M.J.; Brasington, J.; Glasser, N.F.; Hambrey, M.J.; Reynolds, J.M. “Structure-from-motion” photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology 2012179, 300–314. [Google Scholar] [CrossRef]
  19. Turner, D.; Lucieer, A.; De Jong, S.M. Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV). Remote Sens. 20157, 1736–1757. [Google Scholar] [CrossRef]
  20. Al-Rawabdeh, A.; Moussa, A.; Foroutan, M.; El-Sheimy, N.; Habib, A. Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications. Sensors 201717, 2378. [Google Scholar] [CrossRef]
  21. Devoto, S.; Macovaz, V.; Mantovani, M.; Soldati, M.; Furlani, S. Advantages of Using UAV Digital Photogrammetry in the Study of Slow-Moving Coastal Landslides. Remote Sens. 202012, 3566. [Google Scholar] [CrossRef]
  22. Akca, D. Photogrammetric monitoring of an artificially generated shallow landslide. Photogramm. Rec. 201328, 178–195. [Google Scholar] [CrossRef]
  23. Jaboyedoff, M.; Oppikofer, T.; Abellán, A.; Derron, M.H.; Loye, A.; Metzger, R.; Pedrazzini, A. Use of LIDAR in landslide investigations: A review. Nat. Hazards 201261, 5–28. [Google Scholar] [CrossRef]
  24. Dewitte, O.; Jasselette, J.C.; Cornet, Y.; Van Den Eeckhaut, M.; Collignon, A.; Poesen, J.; Demoulin, A. Tracking landslide displacements by multi-temporal DTMs: A combined aerial stereophotogrammetric and LIDAR approach in western Belgium. Eng. Geol. 200899, 11–22. [Google Scholar] [CrossRef]
  25. Görüm, T. Landslide recognition and mapping in a mixed forest environment from airborne LiDAR data. Eng. Geol. 2019258, 105155. [Google Scholar] [CrossRef]
  26. Syzdykbayev, M.; Karimi, B.; Karimi, H.A. Persistent homology on LiDAR data to detect landslides. Remote Sens. Environ. 2020246, 111816. [Google Scholar] [CrossRef]
  27. Bernat Gazibara, S.; Krkač, M.; Mihalić Arbanas, S. Landslide inventory mapping using LiDAR data in the City of Zagreb (Croatia). J. Maps 201915, 773–779. [Google Scholar] [CrossRef]
  28. Peduto, D.; Oricchio, L.; Nicodemo, G.; Crosetto, M.; Ripoll, J.; Buxó, P.; Janeras, M. Investigating the kinematics of the unstable slope of Barbera de la Conca (Catalonia, Spain) and the effects on the exposed facilities by GBSAR and multi-source conventional monitoring. Landslides 202118, 457–469. [Google Scholar] [CrossRef]
  29. Althuwaynee, O.F.; Pradhan, B.; Lee, S. A novel integrated model for assessing landslide susceptibility mapping using CHAID and AHP pair-wise comparison. Int. J. Remote Sens. 201637, 1190–1209. [Google Scholar] [CrossRef]
  30. Martha, T.R.; Kerle, N.; Jetten, V.; van Westen, C.J.; Kumar, K.V. Landslide volumetric analysis using cartosat-1-derived dems. IEEE Geosci. Remote Sens. Lett. 20107, 582–586. [Google Scholar] [CrossRef]
  31. Cigna, F.; Bianchini, S.; Casagli, N. How to assess landslide activity and intensity with persistent scatterer interferometry (PSI): The PSI-based matrix approach. Landslides 201210, 267–283. [Google Scholar] [CrossRef]
  32. Lu, P.; Catani, F.; Tofani, V.; Casagli, N. Quantitative hazard and risk assessment for slow-moving landslides from persistent Scatterer interferometry. Landslides 201411, 685–696. [Google Scholar] [CrossRef]
  33. Ghorbanzadeh, O.; Didehban, K.; Rasouli, H.; Kamran, K.V.; Feizizadeh, B.; Blaschke, T. An Application of Sentinel-1, Sentinel-2, and GNSS Data for Landslide Susceptibility Mapping. ISPRS Int. J. Geo Inf. 20209, 561. [Google Scholar] [CrossRef]
  34. Simeoni, L.; Ferro, E.; Tombolato, S. Reliability of Field Measurements of Displacements in Two Cases of Viaduct-Extremely Slow Landslide Interactions. Eng. Geol. Soc. Territ. 20152, 125–128. [Google Scholar]
  35. Afeni, T.B.; Cawood, F.T. Slope Monitoring using Total Station: What are the Challenges and How Should These be Mitigated? S. Afr. J. Geomat. 20132, 41–53. [Google Scholar]
  36. Sestras, P. Methodological and On-Site Applied Construction Layout Plan with Batter Boards Stake-Out Methods Comparison: A Case Study of Romania. Appl. Sci. 202111, 4331. [Google Scholar] [CrossRef]
  37. Salagean, T.; Rusu, T.; Onose, D.; Farcas, R.; Duda, B.; Sestras, P. The use of laser scanning technology in land monitoring of mining areas. Carpathian J. Earth Environ. Sci. 201611, 565573. [Google Scholar]
  38. Song, Y.; Wu, P. Earth Observation for Sustainable Infrastructure: A Review. Remote Sens. 202113, 1528. [Google Scholar] [CrossRef]
  39. Sestras, P.; Roșca, S.; Bilașco, Ș.; Naș, S.; Buru, S.M.; Kovacs, L.; Spalević, V.; Sestras, A.F. Feasibility Assessments Using Unmanned Aerial Vehicle Technology in Heritage Buildings: Rehabilitation-Restoration, Spatial Analysis and Tourism Potential Analysis. Sensors 202020, 2054. [Google Scholar] [CrossRef]
  40. Solazzo, D.; Sankey, J.B.; Sankey, T.T.; Munson, S.M. Mapping and measuring aeolian sand dunes with photogrammetry and LiDAR from unmanned aerial vehicles (UAV) and multispectral satellite imagery on the Paria Plateau, AZ, USA. Geomorphology 2018319, 174–185. [Google Scholar] [CrossRef]
  41. Oniga, V.-E.; Breaban, A.-I.; Pfeifer, N.; Chirila, C. Determining the Suitable Number of Ground Control Points for UAS Images Georeferencing by Varying Number and Spatial Distribution. Remote Sens. 202012, 876. [Google Scholar] [CrossRef]
  42. Oniga, V.-E.; Breaban, A.-I.; Pfeifer, N.; Diac, M. 3D Modeling of Urban Area Based on Oblique UAS Images—An End-to-End Pipeline. Remote Sens. 202214, 422. [Google Scholar] [CrossRef]
  43. Glira, P.; Pfeifer, N.; Mandlburger, G. Hybrid Orientation of Airborne Lidar Point Clouds and Aerial Images. ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci. 20194, 567–574. [Google Scholar] [CrossRef]
  44. Bandini, F.; Sunding, T.P.; Linde, J.; Smith, O.; Jensen, I.K.; Köppl, C.J.; Bauer-Gottwein, P. Unmanned Aerial System (UAS) observations of water surface elevation in a small stream: Comparison of radar altimetry, LIDAR and photogrammetry techniques. Remote Sens. Environ. 2020237, 111487. [Google Scholar] [CrossRef]
  45. Cramer, M.; Haala, N.; Laupheimer, D.; Mandlburger, G.; Havel, P. Ultra-High Precision UAV-Based Lidar and Dense Image Matching. In Proceedings of the ISPRS TC I Mid-term Symposium “Innovative Sensing—From Sensors to Methods and Applications”, Karlsruhe, Germany, 10–12 October 2018. [Google Scholar]
  46. Pirasteh, S.; Li, J. Landslides investigations from geoinformatics perspective: Quality, challenges, and recommendations. Geomatics, Nat. Hazards Risk 20178, 448–465. [Google Scholar] [CrossRef]
  47. Lissak, C.; Maquaire, O.; Malet, J.P.; Lavigne, F.; Virmoux, C.; Gomez, C.; Davidson, R. Ground-penetrating radar observations for estimating the vertical displacement of rotational landslides. Nat. Hazards Earth Syst. Sci. 201515, 1399–1406. [Google Scholar] [CrossRef]
  48. Qi, L.; Tan, W.; Huang, P.; Xu, W.; Qi, Y.; Zhang, M. Landslide Prediction Method Based on a Ground-Based Micro-Deformation Monitoring Radar. Remote Sens. 202012, 1230. [Google Scholar] [CrossRef]
  49. Hussain, Y.; Cardenas-Soto, M.; Martino, S.; Moreira, C.; Borges, W.; Hamza, O.; Prado, R.; Uagoda, R.; Rodríguez-Rebolledo, J.; Silva, R.C.; et al. Multiple Geophysical Techniques for Investigation and Monitoring of Sobradinho Landslide, Brazil. Sustainability 201911, 6672. [Google Scholar] [CrossRef]
  50. Verbovšek, T.; Košir, A.; Teran, M.; Zajc, M.; Popit, T. Volume determination of the Selo landslide complex (SW Slovenia): Integrating field mapping, ground penetrating radar and GIS approaches. Landslides 201714, 1265–1274. [Google Scholar] [CrossRef]
  51. Barnhardt, W.A.; Kayen, R.E. Radar structure of earthquake-induced, coastal landslides in Anchorage, Alaska. Environ. Geosci. 20007, 38–45. [Google Scholar] [CrossRef]
  52. Bichler, A.; Bobrowsky, P.; Best, M.; Douma, M.; Hunter, J.; Calvert, T.; Burns, R. Three-dimensional mapping of a landslide using a multi-geophysical approach: The Quesnel Forks landslide. Landslides 20041, 29–40. [Google Scholar] [CrossRef]
  53. Sass, O.; Bell, R.; Glade, T. Comparison of GPR, 2D-resistivity and traditional techniques for the subsurface exploration of the Öschingen landslide, Swabian Alb (Germany). Geomorphology 200893, 89–103. [Google Scholar] [CrossRef]
  54. Mantovani, M.; Devoto, S.; Forte, E.; Mocnik, A.; Pasuto, A.; Piacentini, D.; Soldati, M. A multidisciplinary approach for rock spreading and block sliding investigation in the north-western coast of Malta. Landslides 201310, 611–622. [Google Scholar] [CrossRef]
  55. Kadioglu, S.; Ulugergerli, E.U. Imaging karstic cavities in transparent 3D volume of the GPR data set in Akkopru dam, Mugla, Turkey. Nondestruct. Test. Eval. 201227, 263–271. [Google Scholar] [CrossRef]
  56. Kannaujiya, S.; Chattoraj, S.L.; Jayalath, D.; Bajaj, K.; Podali, S.; Bisht, M.P.S. Integration of satellite remote sensing and geophysical techniques (electrical resistivity tomography and ground penetrating radar) for landslide characterization at Kunjethi (Kalimath), Garhwal Himalaya, India. Nat. Hazards 201997, 1191–1208. [Google Scholar] [CrossRef]
  57. Şerban, G.; Rus, I.; Vele, D.; Breţcan, P.; Alexe, M.; Petrea, D. Flood-prone area delimitation using UAV technology, in the areas hard-to-reach for classic aircrafts: Case study in the north-east of Apuseni Mountains, Transylvania. Nat. Hazards 201682, 1817–1832. [Google Scholar] [CrossRef]
  58. Hysa, A.; Spalevic, V.; Dudic, B.; Roșca, S.; Kuriqi, A.; Bilașco, Ș.; Sestras, P. Utilizing the Available Open-Source Remotely Sensed Data in Assessing the Wildfire Ignition and Spread Capacities of Vegetated Surfaces in Romania. Remote Sens. 202113, 2737. [Google Scholar] [CrossRef]
  59. Matei, I.; Pacurar, I.; Rosca, S.; Bilasco, S.; Sestras, P.; Rusu, T.; Jude, E.T.; Tăut, F.D. Land Use Favourability Assessment Based on Soil Characteristics and Anthropic Pollution. Case Study Somesul Mic Valley Corridor, Romania. Agronomy 202010, 1245. [Google Scholar] [CrossRef]
  60. Fîrțală-Cioncuț, A.; Bilașco, S.; Fodorean, I.; Roșca, S.; Vescan, I. Identification and evaluation of the risk induced by landslides based on G.I.S. models of spatial analysis. Case study: Bicazu Ardelean, Romania. Nova Geodesia 20223, 52. [Google Scholar] [CrossRef]
  61. Jaedicke, C.; Van Den Eeckhaut, M.; Nadim, F.; Hervás, J.; Kalsnes, B.; Vangelsten, B.V.; Smith, J.T.; Tofani, V.; Ciurean, R.; Winter, M.G. Identification of landslide hazard and risk ‘hotspots’ in Europe. Bull. Eng. Geol. Environ. 201473, 325–339. [Google Scholar] [CrossRef]
  62. Jebur, M.N.; Pradhan, B.; Shafri, H.Z.M.; Yusoff, Z.M.; Tehrany, M.S. An integrated user-friendly ArcMAP tool for bivariate statistical modelling in geoscience applications. Geosci. Model Dev. 20158, 881–891. [Google Scholar] [CrossRef]
  63. Chalkias, C.; Ferentinou, M.; Polykretis, C. GIS Supported Landslide Susceptibility Modeling at Regional Scale: An Expert-Based Fuzzy Weighting Method. ISPRS Int. J. Geo Inf. 20143, 523–539. [Google Scholar] [CrossRef]
  64. Vakhshoori, V.; Zare, M. Is the ROC curve a reliable tool to compare the validity of landslide susceptibility maps? Geomat. Nat. Hazards Risk 20189, 249–266. [Google Scholar] [CrossRef]
  65. Borrelli, L.; Ciurleo, M.; Gullà, G. Shallow Landslide Susceptibility Assessment in Granitic Rocks Using Gis-Based Statistical Methods: The Contribution of the Weathering Grade Map. Landslides 201815, 1127–1142. [Google Scholar] [CrossRef]
  66. Ciurleo, M.; Cascini, L.; Calvello, M. A comparison of statistical and deterministic methods for shallow landslide susceptibility zoning in clayey soils. Eng. Geol. 2017223, 71–81. [Google Scholar] [CrossRef]
  67. Pelzer, H. Zur Analyse Geodatischer Deformations-Messungen; Verlag der Bayer. Akad. d. Wiss.: Munchen, Germany, 1971; Volume 164. [Google Scholar]
  68. Baarda, W. A Testing Procedure for Use in Geodetic Networks; Rijkscommissie Voor Geodesie: Delft, The Netherlands, 1968; Volume 2. [Google Scholar]
  69. Chrzanowski, A. Optimization of the breakthrough accuracy in tunneling surveys. Can. Surv. 198135, 5–16. [Google Scholar] [CrossRef]
  70. Chrzanowski, A.; Chen, Y.; Romero, P.; Secord, J.M. Integration of geodetic and geotechnical deformation surveys in the geosciences. Tectonophysics 1986130, 369–383. [Google Scholar] [CrossRef]
  71. Kersten, T.; Kobe, M.; Gabriel, G.; Timmen, L.; Schön, S.; Vogel, D. Geodetic monitoring of sub erosion-induced subsidence processes in urban areas. J. Appl. Geod. 201711, 21–29. [Google Scholar]
  72. Hassan, K.M.Z. Comparative evaluation among various robust estimation methods in deformation analysis. Spat. Inf. Res. 201624, 485–492. [Google Scholar] [CrossRef]
  73. Bilașco, Ș.; Hognogi, G.-G.; Roșca, S.; Pop, A.-M.; Iuliu, V.; Fodorean, I.; Marian-Potra, A.-C.; Sestras, P. Flash Flood Risk Assessment and Mitigation in Digital-Era Governance Using Unmanned Aerial Vehicle and GIS Spatial Analyses Case Study: Small River Basins. Remote Sens. 202214, 2481. [Google Scholar] [CrossRef]
  74. Akturk, E.; Altunel, A.O. Accuracy assesment of a low-cost UAV derived digital elevation model (DEM) in a highly broken and vegetated terrain. Measurement 2019136, 382–386. [Google Scholar] [CrossRef]
  75. Gong, C.; Lei, S.; Bian, Z.; Liu, Y.; Zhang, Z.; Cheng, W. Analysis of the development of an erosion gully in an open-cast coal mine dump during a winter freeze-thaw cycle by using low-cost UAVs. Remote Sens. 201911, 1356. [Google Scholar] [CrossRef]
  76. Han, X.; Thomasson, J.A.; Xiang, Y.; Gharakhani, H.; Yadav, P.K.; Rooney, W.L. Multifunctional Ground Control Points with a Wireless Network for Communication with a UAV. Sensors 201919, 2852. [Google Scholar] [CrossRef] [PubMed]
  77. Lendzioch, T.; Langhammer, J.; Jenicek, M. Estimating Snow Depth and Leaf Area Index Based on UAV Digital Photogrammetry. Sensors 201919, 1027. [Google Scholar] [CrossRef] [PubMed]
  78. Okeson, T.J.; Barrett, B.J.; Arce, S.; Vernon, C.A.; Franke, K.W.; Hedengren, J.D. Achieving Tiered Model Quality in 3D Structure from Motion Models Using a Multi-Scale View-Planning Algorithm for Automated Targeted Inspection. Sensors 201919, 2703. [Google Scholar] [CrossRef] [PubMed]
  79. Cignetti, M.; Godone, D.; Wrzesniak, A.; Giordan, D. Structure from Motion Multisource Application for Landslide Characterization and Monitoring: The Champlas du Col Case Study, Sestriere, North-Western Italy. Sensors 201919, 2364. [Google Scholar] [CrossRef]
  80. Leary, R.J.; Hensleigh, J.W.; Wheaton, D.J.M.; Demeurichy, K.D. Recommended Geomorphic Change Detection Procedures for Repeat TLS Surveys from Hells Canyon, Idaho; Utah State University: Logan, UT, USA, 2012. [Google Scholar]
  81. Xie, P.; Wen, H.; Xiao, P. Evaluation of ground-penetrating radar (GPR) and geology survey for slope stability study in mantled karst region. Environ. Earth Sci. 201877, 122. [Google Scholar] [CrossRef]
  82. Hallal, N.; Yelles Chaouche, A.; Hamai, L.; Lamali, A.; Dubois, L.; Mohammedi, Y.; Hamidatou, M.; Djadia, L.; Abtout, A. Spatiotemporal evolution of the El Biar landslide (Algiers): New field observation data constrained by ground-penetrating radar investigations. Bull. Eng. Geol. Environ. 201978, 5653–5670. [Google Scholar] [CrossRef]
  83. Costea, A.; Bilasco, S.; Irimus, I.-A.; Rosca, S.; Vescan, I.; Fodorean, I.; Sestras, P. Evaluation of the Risk Induced by Soil Erosion on Land Use. Case Study: Guruslău Depression. Sustainability 202214, 652. [Google Scholar] [CrossRef]
  84. Bilașco, Ș.; Roșca, S.; Vescan, I.; Fodorean, I.; Dohotar, V.; Sestras, P. A GIS-Based Spatial Analysis Model Approach for Identification of Optimal Hydrotechnical Solutions for Gully Erosion Stabilization. Case Study. Appl. Sci. 202111, 4847. [Google Scholar] [CrossRef]
  85. Spalevic, V.; Barovic, G.; Vujacic, D.; Curovic, M.; Behzadfar, M.; Djurovic, N.; Dudic, B.; Billi, P. The Impact of Land Use Changes on Soil Erosion in the River Basin of Miocki Potok, Montenegro. Water 202012, 2973. [Google Scholar] [CrossRef]
  86. Chalise, D.; Kumar, L.; Spalevic, V.; Skataric, G. Estimation of Sediment Yield and Maximum Outflow Using the IntErO Model in the Sarada River Basin of Nepal. Water 201911, 952. [Google Scholar] [CrossRef]
  87. Nikolic, G.; Spalevic, V.; Curovic, M.; Khaledi Darvishan, A.; Skataric, G.; Pajic, M.; Kavian, A.; Tanaskovik, V. Variability of Soil Erosion Intensity Due to Vegetation Cover Changes: Case Study of Orahovacka Rijeka, Montenegro. Not. Bot. Horti Agrobot. Cluj Napoca 201847, 237–248. [Google Scholar] [CrossRef]
  88. Gocić, M.; Dragićević, S.; Radivojević, A.; Martić Bursać, N.; Stričević, L.; Đorđević, M. Changes in Soil Erosion Intensity Caused by Land Use and Demographic Changes in the Jablanica River Basin, Serbia. Agriculture 202010, 345. [Google Scholar] [CrossRef]

 

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Symposia

GEA International (Geo Eco-Eco Agro) Conference

 

About conference

International GEA (Geo Eco-Eco Agro) Conference is envisaged as an event during which researchers from the areas of Geosciences, Ecology-Economy and Agriculture, as well as from areas of Eco-Architecture and Rural Architecture, will present their work to each other. The Conference aims to be one day meeting point for international scientific discussion on various subjects of these sciences. The team of the International GEA (Geo Eco-Eco Agro) Conference is striving to bring together research and practices. The idea is to establish new bridges between researchers from the Region and wider; to meet each other and to stay connected.

 

 

Presentation Award

GEA (Geo Eco-Eco Agro) International Conference
28-31 May 2020, Podgorica, Montenegro
Presentation Award

Dear colleagues,

It is our great pleasure to announce that after your e-mail voting and based on the evaluation of your recommendations, the Evaluation committee for the Presentations Award, chaired by Professor Paolo BILLI from the International Platform for Dryland Research and Education, University of Tottori, Japan took the following decision:

GEA (Geo Eco-Eco Agro) International Conference
28-31 May 2020, Podgorica, Montenegro
Presentation Award

THE BEST ORAL PRESENTATIONS

The best Oral presentations awards, shared between three researchers, were given to:

BIN WANG
RARE EARTH ELEMENTS TRACING INTERRILL EROSION PROCESSES AS AFFECTED BY NEAR-SURFACE HYDRAULIC GRADIENTS
Bin WANG, Chenfeng WANG
[Code 050]

ZEINAB HAZBAVI
WHAT IS THE SMART WATERSHED MANAGEMENT?
Mahin KALEHHOUEI and Zeinab HAZBAVI
[Code 077]

PAUL SESTRAS
THE USE OF UAV SYSTEMS IN CADASTRAL SURVEYING AND
TECHNICAL DOCUMENTATIONS
Paul SESTRAS
[Code 051]