Open Access
Issue
E3S Web Conf.
Volume 211, 2020
The 1st JESSD Symposium: International Symposium of Earth, Energy, Environmental Science and Sustainable Development 2020
Article Number 05002
Number of page(s) 9
Section Forest Management
DOI https://doi.org/10.1051/e3sconf/202021105002
Published online 25 November 2020
  1. H. Purnomo, B. Okarda, B. Shantiko, R. Achdiawan, A. Dermawan, H. Kartodihardjo, A. A. Dewayani, Forest and land fires, toxic haze and local politics in Indonesia, Int. For. Rev. 21, 486 (2019) https://doi.org/10.1505/146554819827906799 [Google Scholar]
  2. C. Yuan, Z. Liu, Y. Zhang, Fire detection using infrared images for UAV-based forest fire surveillance, in 2017 Int. Conf. Unmanned Aircr. Syst. ICUAS 2017, IEEE, Miami, FL, USA, pp. 567–572 (2017) https://doi.org/10.1109/ICUAS.2017.7991306 [Google Scholar]
  3. H. Fang, S. Walton, E. Delahaye, J. Harris, D. A. Storchak, M. Chen, Categorical Colormap Optimization with Visualization Case Studies, IEEE Trans. Vis. Comput. Graph. 23, 871 (2017) https://doi.org/10.1109/TVCG.2016.2599214 [CrossRef] [PubMed] [Google Scholar]
  4. S. N. B. M. Said, E.-S. M. M. Zahran, S. Shams, Forest Fire Risk Assessment Using Hotspot Analysis in GIS, Open Civ. Eng. J. 11, 786 (2017) https://doi.org/10.2174/1874149501711010786 [CrossRef] [Google Scholar]
  5. V. V. Hnatushenko, V. V. Hnatushenko, D. K. Mozgovyi, V. V. Vasiliev, Satellite technology of the forest fires effects monitoring, Scientific Bulletin of National Mining University 70 (2016) ISSN 2071-2227 [Google Scholar]
  6. M. A. Matin, V. S. Chitale, M. S. R. Murthy, K. Uddin, B. Bajracharya, S. Pradhan, Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data, Int. J. Wildl. Fire 26, 276 (2017) https://doi.org/10.1071/WF16056 [CrossRef] [Google Scholar]
  7. P. Goymer, Forest vision, Nat. Ecol. Evol. 1, 1 (2017) https://doi.org/10.1038/s41559-017-0097 [CrossRef] [PubMed] [Google Scholar]
  8. K. V. Suresh Babu, A. Roy, P. R. Prasad, Forest fire risk modeling in Uttarakhand Himalaya using TERRA satellite datasets, Eur. J. Remote Sens. 49, 381 (2016) https://doi.org/10.5721/EuJRS20164921 [CrossRef] [Google Scholar]
  9. P. G. Curtis, C. M. Slay, N. L. Harris, A. Tyukavina, M. C. Hansen, Classifying drivers of global forest loss, Science 361, 1108 (2018) https://doi.org/10.1126/science.aau3445 [CrossRef] [Google Scholar]
  10. Y. Qin, T. Gartner, S. Minnemeyer, P. Reig, S. Sargent, Global Forest Watch Water Metadata Document (World Resources Institute, Washington D.C., 2016) http://agri.ckcest.cn/ass/NK005-20160919002.pdf [Google Scholar]
  11. M. O. Jackson, P. Tebaldi, A Forest Fire Theory of the Duration of a Boom and the Size of a Subsequent Bust, SSRN Electron. J. 1 (2019) https://doi.org/10.2139/ssrn.2263501 [Google Scholar]
  12. S. Tian, Y. Wang, T. Cai, Study on calculating methods of forest fire area for dynamic disaster assessment based on infrared image, in AOPC 2017 Optical Sensing and Imaging Technology and Applications, Proc. SPIE, Beijing, China, pp. 104625J (2017) https://doi.org/10.1117/12.2285847 [Google Scholar]
  13. A. T. Filicetti, S. E. Nielsen, Fire and forest recovery on seismic lines in sandy upland jack pine (Pinus banksiana) forests, For. Ecol. Manage. 421, 32 (2018) https://doi.org/10.1016/j.foreco.2018.01.027 [CrossRef] [Google Scholar]
  14. X. Cai, Improved Forest Fire Danger Rating Using Regression Kriging with the Canadian Precipitation Analysis (CaPA) System in Alberta, (University of Alberta, 2017) [Google Scholar]
  15. E. J. Belval, C. D. O’Connor, M. P. Thompson, M. S. Hand, The Role of Previous Fires in the Management and Expenditures of Subsequent Large Wildfires, Fire 2, 57 (2019) https://doi.org/10.3390/fire2040057 [CrossRef] [Google Scholar]
  16. B. Marielle, S. Couture, and S. M. Garcia, Insurance Demand against Forest Fire Risk: Empirical Analysis on French Private Forest Owners, SSRN Electron. J. 33, 0 (2012) https://doi.org/10.2139/ssrn.1800528 [Google Scholar]
  17. D. Sampurno, Spatiotemporal Analysis in Monitoring Landscape Dynamic Patterns in Tropical Peat Ecosystem (Study in Tebing Tinggi Island, Riau, Indonesia), J. Environ. Sci. Sustainable Dev. 2, 1 (2019) https://doi.org/10.7454/jessd.v2i1.33 [CrossRef] [Google Scholar]
  18. Y.N. Lukito, The urban forest project as an extension of landscape immersion and a way to support community engagement in the Ragunan Zoo, Jakarta, ASEAN J. Community Engagement 2, 2 (2018) https://doi.org/10.7454/ajce.v2i2.135 [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.