Open Access
Issue
BIO Web Conf.
Volume 97, 2024
Fifth International Scientific Conference of Alkafeel University (ISCKU 2024)
Article Number 00063
Number of page(s) 10
DOI https://doi.org/10.1051/bioconf/20249700063
Published online 05 April 2024
  • Maarez, H. G., Jaber, H. S., & Shareef, M. A. (2022). Utilization of Geographic Information System for hydrological analyses: A case study of Karbala province, Iraq. Iraqi Journal of Science, 4118–4130. [CrossRef] [Google Scholar]
  • Yousef, O. A. R., & Jaber, H. S. (2023, July). Study of desertification in Bahr Al-Najaf region by remote sensing data and GIS. In AIP Conference Proceedings (Vol. 2775, No. 1). AIP Publishing. [Google Scholar]
  • Faraj, J. I., & Mahmood, F. H. (2018). Extraction of Vacant Lands for Baghdad City Using Two Classification Methods of Very High-Resolution Satellite Images. Iraqi Journal of Science, 2336–2342. [Google Scholar]
  • Ali, A. H., & Jaber, H. S. (2020). Monitoring degradation of wetland areas using satellite imagery and geographic information system techniques. Iraqi Journal of Agricultural Sciences, 51(5). [Google Scholar]
  • Forkuor, G., Dimobe, K., Serme, I., & Tondoh, J. E. (2018). Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso. GIScience & remote sensing, 55(3), 331–354. [CrossRef] [Google Scholar]
  • Sekertekin, A., Marangoz, A. M., & Akcin, H. (2017). Pixel-based classification analysis of land use land cover using Sentinel-2 and Landsat-8 data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 91–93. [CrossRef] [Google Scholar]
  • Jakovljević, G. (2018). Land use/land cover mapping from sentinel 2 data using machine learning algorithms. In International conference on Contemporary Theory and Practice in Construction (No. 13). [Google Scholar]
  • Baamonde, S., Cabana, M., Sillero, N., Penedo, M. G., Naveira, H., & Novo, J. (2019). Fully automatic multitemporal land cover classification using Sentinel-2 image data. Procedia Computer Science, 159, 650–657. [CrossRef] [Google Scholar]
  • Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V. R., Murayama, Y., & Ranagalage, M. (2020). Sentinel-2 data for land cover/use mapping: A review. Remote Sensing, 12 (14), 2291. [CrossRef] [Google Scholar]
  • Aziz, N. A., & Alwan, I. A. (2021). An accuracy analysis comparison of supervised classification methods for mapping land cover using sentinel 2 images in the Al Hawizeh marsh area, southern Iraq. Geomatics and Environmental Engineering, 15(1), 5–21. [CrossRef] [Google Scholar]
  • Al-Helaly, M. H., Alwan, I. A., & Al-Hameedawi, A. N. (2021, August). Land covers monitoring for Bahar-Al-Najaf (Iraq) based on sentinel-2 imagery. In Journal of Physics: Conference Series (Vol. 1973, No. 1, p. 012189). IOP Publishing. [CrossRef] [Google Scholar]
  • Al-Helaly, M. H., Alwan, I. A., & AL-Hameedawi, A. N. (2022). Environmental Investigation of Bahar Al-Najaf Region Using Sentinel-2 Images. Engineering and Technology Journal, 40, 732–742. [CrossRef] [Google Scholar]
  • Ghalib, H. B., Al-Hawash, A. B., Al-Qurnaw, W. S., Sultan, B. H., & Al-enzy, A. W. (2019, July). Marshes waters sources hydrochemistryof the Bahr Al-Najaf at Najaf Province, Iraq. In Journal of Physics: Conference Series (Vol. 1279, No. 1, p. 012059). IOP Publishing. [CrossRef] [Google Scholar]
  • Yousef, O. A. R., & Jaber, H. S. (2023, July). Study of desertification in Bahr Al-Najaf region by remote sensing data and GIS. In AIP Conference Proceedings (Vol. 2775, No. 1). AIP Publishing. [Google Scholar]
  • Forkuor, G., Dimobe, K., Serme, I., & Tondoh, J. E. (2018). Landsat-8 vs. Sentinel-2: examining the added value of sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso. GIScience & remote sensing, 55(3), 331–354. [CrossRef] [Google Scholar]
  • Dibs, H., Jaber, H. S., & Al-Ansari, N. (2023). Multi-Fusion algorithms for Detecting Land Surface Pattern Changes Using Multi-High Spatial Resolution Images and Remote Sensing Analysis. Emerging Science Journal, 7(4), 1215–1231. [CrossRef] [Google Scholar]
  • Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for multi-class classification: an overview. arXiv preprint arXiv:2008.05756 [Google Scholar]
  • Wadeea, K., Jaber, H. S., & Merzah, Z.F. (2023). Management of the flood Disaster and Assessment their damaged areas using Remote sensing and GIS Techniques: A Case Study of Tigris River-Maysan Governorate, Iraq. AIP Conference Proceedings. [Google Scholar]
  • Bukheet, Y.C., Al-Abudi, B.Q. and Mahdi, M.S., 2016. Land Cover Change Detection of Baghdad City Using Multi-Spectral Remote Sensing Imagery. Iraqi Journal of Science, pp.195–214. [Google Scholar]
  • Aggarwal, N., Srivastava, M. and Dutta, M., 2016. Comparative analysis of pixel-based and object-based classification of high resolution remote sensing images—A review. International Journal of Engineering Trends and Technology, 38(1), pp.5–11. [CrossRef] [Google Scholar]
  • Mather, P. and Tso, B., 2016. Classification methods for remotely sensed data. CRC press. [CrossRef] [Google Scholar]
  • Abbas, Z. and Jaber, H.S., 2020, March. Accuracy assessment of supervised classification methods for extraction land use maps using remote sensing and GIS techniques. In IOP Conference Series: Materials Science and Engineering (Vol. 745, No. 1, p. 012166). IOP Publishing. [CrossRef] [Google Scholar]
  • Merzah, Z.F. and Jaber, H.S., 2020, March. Assessment of Atmospheric Correction Methods for Hyperspectral Remote Sensing Imagery Using Geospatial Techniques. In IOP Conference Series: Materials Science and Engineering (Vol. 745, No. 1, p. 012123). IOP Publishing. [CrossRef] [Google Scholar]
  • Miranda, E., Mutiara, A.B. and Wibowo, W.C., 2018, September. Classification of land cover from Sentinel-2 imagery using supervised classification technique (preliminary study). In 2018 International Conference on Information Management and Technology (ICIMTech) (pp. 69–74). IEEE. [CrossRef] [Google Scholar]
  • Blaschke, T., 2010. Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing, 65(1), pp.2–16. [CrossRef] [Google Scholar]
  • Li, M., Zang, S., Zhang, B., Li, S. and Wu, C., 2014. A review of remote sensing image classification techniques: The role of spatio-contextual information. European Journal of Remote Sensing, 47(1), pp.389–411. [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.