Open Access
Issue
E3S Web Conf.
Volume 386, 2023
Annual International Scientific Conferences: GIS in Central Asia – GISCA 2022 and Geoinformatics – GI 2022 “Designing the Geospatial Ecosystem”
Article Number 04010
Number of page(s) 11
Section GIS in Geodesy and Cartography
DOI https://doi.org/10.1051/e3sconf/202338604010
Published online 12 May 2023
  1. J. Dou, K.T. Chang, S. Chen, A. Yunus, J.K. Liu, H. Xia, Z. Zhu, Automatic CaseBased Reasoning Approach for Landslide Detection: Integration of Object-Oriented Image Analysis and a Genetic Algorithm, J. Remote Sensing, 7, 4318-42 (2015) [CrossRef] [Google Scholar]
  2. F. Guzzetti, A. Carrara, M. Cardinali, P. Reichenbach, Landslide hazard evaluation: a review of current techniques and their application in a multi-scale study, Central Italy J. Geomorphology, 31, 181-216 (1999) [CrossRef] [Google Scholar]
  3. F. Guzzetti, A.C. Mondini, M. Cardinali, F. Fiorucci, M. Santangelo, K-T Chang, Landslide inventory maps: new tools for an old problem, J. Earth-Science Reviews, 112, 42-66 (2012) [CrossRef] [Google Scholar]
  4. A. Stumpf, N. Kerle, Object-oriented mapping of landslides using Random Forests, J. Remote Sensing of Environment, 115, 2564-77 (2011) [CrossRef] [Google Scholar]
  5. M. Juliev, M. Mergili, I. Mondal, B. Nurtaev, A. Pulatov, J. Hübl, Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan, J. Science of The Total Environment (2018) [Google Scholar]
  6. Z. Chen, D. Song, M. Juliev, H. R Pourghasemi, Landslide susceptibility mapping using statistical bivariate models and their hybrid with normalized spatial-correlated scale index and weighted calibrated landslide potential model, J. Environ Earth Sci., 80, 324 (2021) [CrossRef] [Google Scholar]
  7. I. Mondal, S. Thakur, M. Juliev, T. K. De, Comparative analysis of forest canopy mapping methods for the Sundarban biosphere reserve, West Bengal, India, J. Environ Dev Sustain, 23, 15157-82 (2021) [CrossRef] [Google Scholar]
  8. I. Mondal, S. Thakur, M. Juliev, J. Bandyopadhyay, T.K. De, Spatio-temporal modelling of shoreline migration in Sagar Island, West Bengal, India, J. Coast Conserv, 24, 50 (2020) [CrossRef] [Google Scholar]
  9. Global Facility for Disaster Reduction and and Recovery (GFDRR), Central Asia and Caucasus Disaster Risk Management Initiative (CAC DRMI) (2009) [Google Scholar]
  10. M. Juliev, A. Pulatov, J. Hubl, Natural hazards in mountain regions of Uzbekistan: A review of mass movement processes in Tashkent province, Int. J. Scientific & Engineering Research, 8, 1102-8 (2017) [CrossRef] [Google Scholar]
  11. M. Juliev, A. Pulatov, S. Fuchs, J. Hübl, Analysis of Land Use Land Cover Change Detection of Bostanlik District, Uzbekistan, Polish J. Environmental Studies, 28, 323542 (2019) [CrossRef] [Google Scholar]
  12. S. Khasanov, M. Juliev, U. Uzbekov, I. Aslanov, I. Agzamova, N. Normatova, S. Islamov, G. Goziev, S. Khodjaeva, N. Holov, Landslides in Central Asia: a review of papers published in 2000–2020 with a particular focus on the importance of GIS and remote sensing techniques, J. GeoScape, 15, 134-45 (2021) [CrossRef] [Google Scholar]
  13. L. Gafurova, M. Juliev, Soil Degradation Problems and Foreseen Solutions in Uzbekistan, J. Regenerative Agriculture, ed D Dent and B Boincean (Cham: Springer International Publishing) 59-67 (2021) [CrossRef] [Google Scholar]
  14. T.R. Martha, N. Kerle, V. Jetten, C.J.V. Westen, K.V. Kumar, Characterising spectral, spatial and morphometric properties of landslides for semi-automatic detection using object-oriented methods, J. Geomorphology, 116, 24-36 (2010) [CrossRef] [Google Scholar]
  15. D. Lu, P. Mausel, E. Brondízio, E. Moran, Change detection techniques, Int. J. Remote Sensing, 25, 2365-401 (2004) [CrossRef] [Google Scholar]
  16. B. Alikhanov, M. Juliev, S. Alikhanova, I. Mondal, Assessment of influencing factor method for delineation of groundwater potential zones with geospatial techniques. Case study of Bostanlik district, Uzbekistan, J. Groundwater for Sustainable Development, 12, 100548 (2021) [CrossRef] [Google Scholar]
  17. Moosavi, A. Talebi, B. Shirmohammadi, Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method, J. Geomorphology, 204, 646-56 (2014) [CrossRef] [Google Scholar]
  18. D. Hölbling, C. Eisank, F. Albrecht, F. Vecchiotti, B. Friedl, E. Weinke, A. Kociu, Comparing Manual and Semi-Automated Landslide Mapping Based on Optical Satellite Images from Different Sensors, J. Geosciences, 7, 37 (2017) [CrossRef] [Google Scholar]
  19. B. Feizizadeh, T. Blaschke, D. Tiede, M. H. R. Moghaddam, Evaluating fuzzy operators of an object-based image analysis for detecting landslides and their changes, J. Geomorphology, 293, 240-54 (2017) [CrossRef] [Google Scholar]
  20. I.V. Belolipov, D. Zaurov, S.W. Eisenman, The Geography, Climate and Vegetation of Uzbekistan, Medicinal Plants of Central Asia: Uzbekistan and Kyrgyzstan, ed S W Eisenman, D E Zaurov and L Struwe (New York, NY: Springer New York), 5-7 (2013) [CrossRef] [Google Scholar]
  21. J. Gerts, M. Juliev, A. Pulatov, Multi-temporal monitoring of cotton growth through the vegetation profile classification for Tashkent province, Uzbekistan, J. GeoScape, 14, 62-9 (2020) [CrossRef] [Google Scholar]
  22. R. Niyazov, B. Nurtaev, Modern Seismogenic Landslides Caused by the Pamir-Hindu Kush Earthquakes and Their Consequences in Central Asia, J. Landslide Science and Practice: Volume 5: Complex Environment, ed C Margottini, P Canuti and K Sassa (Berlin, Heidelberg: Springer Berlin Heidelberg), 343-8 (2013) [CrossRef] [Google Scholar]
  23. S. K. McFEETERS, The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, Int. J. Remote Sensing, 17, 1425-32 (1996) [CrossRef] [Google Scholar]
  24. M. Immitzer, C. Atzberger, Early Detection of Bark Beetle Infestation in Norway Spruce (Picea abies, L.) using WorldView-2, J. Photogrammetrie Fernerkundung Geoinformation, 51-67 (2014) [Google Scholar]
  25. P. Toscani, M. Immitzer, C. Atzberger, Wavelet-based texture measures for objectbased classification of aerial images, J. pfg, 105-21 (2013) [CrossRef] [Google Scholar]
  26. T. Blaschke, B. Feizizadeh, D. Holbling, Object-Based Image Analysis and Digital Terrain Analysis for Locating Landslides in the Urmia Lake Basin, Iran, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 7, 4806-17 (2014) [CrossRef] [Google Scholar]
  27. J. Michel, D. Youssefi, M. Grizonnet, Stable Mean-Shift Algorithm and Its Application to the Segmentation of Arbitrarily Large Remote Sensing Images, J. IEEE Trans. Geosci. Remote Sensing, 53, 952-64 (2015) [CrossRef] [Google Scholar]
  28. J. Inglada, E. Christophe, The Orfeo Toolbox remote sensing image processing software, J. IEEE International Geoscience and Remote Sensing Symposium, 733-6 (2009) [Google Scholar]
  29. W-T. Ng, M. Meroni, M. Immitzer, S. Böck, U. Leonardi, F. Rembold, H. Gadain, C. Atzberger, Mapping Prosopis spp. with Landsat 8 data in arid environments: Evaluating effectiveness of different methods and temporal imagery selection for Hargeisa, Somaliland, Int. J. Applied Earth Observation and Geoinformation, 53, 7689 (2016) [Google Scholar]
  30. G. M. Foody, Status of land cover classification accuracy assessment, J. Remote Sensing of Environment, 80, 185-201 (2002) [CrossRef] [Google Scholar]
  31. K.C. Devkota, A.D. Regmi, H.R. Pourghasemi, K. Yoshida, B. Pradhan, I.C. Ryu, M.R. Dhital, O.F. Althuwaynee, Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models in GIS and their comparison at Mugling–Narayanghat road section in Nepal Himalaya, J. Natural Hazards, 65, 135-65 (2013) [CrossRef] [Google Scholar]
  32. I. Aslanov, K. Sh, O. Sh, A. Jumanov, Z. Jabbarov, I. Jumaniyazov, N. Namozov, Evaluation of soil salinity level through using Landsat-8 OLI in Central Fergana valley, Uzbekistan, J. E3S Web of Conferences, 258, (2021) [Google Scholar]

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