EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 491 (2024) E3S Web Conf., 491 (2024) 01027 References
Open Access
Issue
E3S Web Conf.
Volume 491, 2024
International Conference on Environmental Development Using Computer Science (ICECS’24)
Article Number 01027
Number of page(s) 10
Section Energy Management for Sustainable Environment
DOI https://doi.org/10.1051/e3sconf/202449101027
Published online 21 February 2024
  1. G. N. Rao, P. Jagadeeswara Rao, R. Duvvuru, S. Bendalam, and R. Gemechu, (2016)“An enhanced real-time forest fire assessment algorithm based on video by using texture analysis,” Perspect Sci (Neth), vol. 8, pp. 618–620, Sep. 2016, doi: 10.1016/j.pisc.2016.06.037. [CrossRef] [Google Scholar]
  2. F. Rodriguez-Jimenez, H. Lorenzo, C. Acuña-Alonso, and X. Alvarez, (2023)“PLSPM analysis of forest fires using remote sensing tools. The case of Xurés in the Transboundary Biosphere Reserve,” Ecol Inform, vol. 75, Jul. 2023, doi: 10.1016/j.ecoinf.2023.102010. [CrossRef] [Google Scholar]
  3. A. Heredia-Telles, P. M. López-Serrano, M. Molinier, and C. Wehenkel,(2023) “Evaluation of forest cover loss in properties in the Sierra Madre Occidental, State of Durango, Mexico, certified by the Forest Stewardship Council,” Trees, Forests and People, vol. 14, Dec. 2023, doi: 10.1016/j.tfp.2023.100454. [CrossRef] [Google Scholar]
  4. W. Yuchi et al.,(2016) “Blending forest fire smoke forecasts with observed data can improve their utility for public health applications,” Atmos Environ, vol. 145, pp. 308–317, Nov. 2016, doi: 10.1016/j.atmosenv.2016.09.049. [CrossRef] [Google Scholar]
  5. R. C. Grecchi et al.,(2017) “An integrated remote sensing and GIS approach for monitoring areas affected by selective logging: A case study in northern Mato Grosso, Brazilian Amazon,” International Journal of Applied Earth Observation and Geoinformation, vol. 61, pp. 70–80, Sep. 2017, doi: 10.1016/j.jag.2017.05.001. [CrossRef] [PubMed] [Google Scholar]
  6. G. Amarnath, S. Babar, and M. S. R. Murthy,(2017) “Evaluating MODIS-vegetation continuous field products to assess tree cover change and forest fragmentation in India – A multi-scale satellite remote sensing approach,” Egyptian Journal of Remote Sensing and Space Science, vol. 20, no. 2, pp. 157–168, Dec. 2017, doi:10.1016/j.ejrs.2017.05.004. [CrossRef] [Google Scholar]
  7. A. Appiah Mensah, D. Akoto Sarfo, and S. T. Partey,(2019) “Assessment of vegetation dynamics using remote sensing and GIS: A case of Bosomtwe Range Forest Reserve, Ghana,” Egyptian Journal of Remote Sensing and Space Science, vol. 22, no. 2, pp. 145–154, Aug. 2019, doi: 10.1016/j.ejrs.2018.04.004. [CrossRef] [Google Scholar]
  8. R. Husseini, D. T. Aboah, and H. Issifu, (2020)“Fire control systems in forest reserves: An assessment of three forest districts in the Northern region, Ghana,” Sci Afr, vol. 7, Mar. 2020, doi: 10.1016/j.sciaf.2019.e00245. [Google Scholar]
  9. S. Sannigrahi et al.,(2020) “Examining the effects of forest fire on terrestrial carbon emission and ecosystem production in India using remote sensing approaches,” Science of the Total Environment, vol. 725, Jul. 2020, doi: 10.1016/j.scitotenv.2020.138331. [CrossRef] [Google Scholar]
  10. P. Chawala and H. A. S. Sandhu,(2020) “Stubble burn area estimation and its impact on ambient air quality of Patiala & Ludhiana district, Punjab, India,” Heliyon, vol. 6, no. 1, Jan. 2020, doi: 10.1016/j.heliyon.2019.e03095. [CrossRef] [PubMed] [Google Scholar]
  11. Y. Deng, M. Wang, R. Yousefpour, and M. Hanewinkel,(2021) “Abiotic disturbances affect forest short-term vegetation cover and phenology in Southwest China,” Ecol Indic, vol. 124, May 2021, doi: 10.1016/j.ecolind.2021.107393. [CrossRef] [Google Scholar]
  12. C. A. Knight, R. E. Tompkins, J. A. Wang, R. York, M. L. Goulden, and J. J. Battles,(2021) “Accurate tracking of forest activity key to multi-jurisdictional management goals: A case study in California,” J Environ Manage, vol. 302, Jan. 2022, doi: 10.1016/j.jenvman.2021.114083. [Google Scholar]
  13. M. Natole, Y. Ying, A. Buyantuev, M. Stessin, V. Buyantuev, and A. Lapenis,(2021) “Patterns of mega-forest fires in east Siberia will become less predictable with climate warming,” Environmental Advances, vol. 4, Jul. 2021, doi: 10.1016/j.envadv.2021.100041. [CrossRef] [Google Scholar]
  14. F. Augustin et al.,(2022) “Projected changes in fire activity and severity feedback in the spruce–Feather moss forest of western Quebec, Canada,” Trees, Forests and People, vol. 8, Jun. 2022, doi: 10.1016/j.tfp.2022.100229. [CrossRef] [Google Scholar]
  15. F. S. Pramudya, L. Bong, E. Rolling, A. A. Awirya, and A. A. S. Gunawan,(2022) “Forest loss analysis and calculation with geospatial artificial intelligence: A case study of papua province,” in Procedia Computer Science, Elsevier B.V., 2022, pp. 346–355. doi: 10.1016/j.procs.2022.12.145. [Google Scholar]
  16. C. Maffei, R. Lindenbergh, and M. Menenti,(2021) “Combining multi-spectral and thermal remote sensing to predict forest fire characteristics,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 181, pp. 400–412, Nov. 2021, doi: 10.1016/j.isprsjprs.2021.09.016. [CrossRef] [Google Scholar]
  17. X. Y. Li et al.,(2021) “Influences of forest fires on the permafrost environment: A review,” Advances in Climate Change Research, vol. 12, no. 1. National Climate Center, pp. 48–65, Feb. 01, 2021. doi: 10.1016/j.accre.2021.01.001. [CrossRef] [Google Scholar]
  18. M. Mohajane et al., “Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area,” Ecol Indic, vol. 129, Oct. 2021, doi: 10.1016/j.ecolind.2021.107869. [CrossRef] [Google Scholar]
  19. P. E. Barni et al.,(2021) “Logging Amazon forest increased the severity and spread of fires during the 2015–2016 El Niño,” For Ecol Manage, vol. 500, Nov. 2021, doi: 10.1016/j.foreco.2021.119652. [CrossRef] [Google Scholar]
  20. E. Peña-Molina et al.,(2024) “Postfire damage zoning with open low-density LiDAR data sources in semi-arid forests of the Iberian Peninsula,” Remote Sens Appl, vol. 33, p. 101114, Jan. 2024, doi: 10.1016/j.rsase.2023.101114. [Google Scholar]
  21. K. L. Shive, A. Wuenschel, L. J. Hardlund, S. Morris, M. D. Meyer, and S. M. Hood,(2022) “Ancient trees and modern wildfires: Declining resilience to wildfire in the highly fire-adapted giant sequoia,” For Ecol anage, vol. 511, May 2022, doi: 10.1016/j.foreco.2022.120110. [Google Scholar]
  22. E. González de Andrés, T. A. Shestakova, R. C. Scholten, C. J. F. Delcourt, N. V. Gorina, and J. J. Camarero,(2022) “Changes in tree growth synchrony and resilience in Siberian Pinus sylvestris forests are modulated by fire dynamics and ecohydrological conditions,” Agric For Meteorol, vol. 312, Jan. 2022, doi:10.1016/j.agrformet.2021.108712. [Google Scholar]
  23. M. M. Hassan, I. Hassan, J. Southworth, and T. Loboda, (2022)“Mapping fireimpacted refugee camps using the integration of field data and remote sensing approaches,” International Journal of Applied Earth Observation and Geoinformation, vol. 115, Dec. 2022, doi: 10.1016/j.jag.2022.103120.10.1016/j.agrformet.2021.108712. [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.