Mapping data on Indonesia’s worst forest and land fires of palm oil cultivation lands

Evi Frimawaty

doi:10.1051/e3sconf/202021105002

All issues

Volume 211 (2020)

E3S Web Conf., 211 (2020) 05002

References

Open Access

Issue		E3S Web Conf. Volume 211, 2020 The 1^st 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

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]
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]
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]
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]
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]
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]
P. Goymer, Forest vision, Nat. Ecol. Evol. 1, 1 (2017) https://doi.org/10.1038/s41559-017-0097 [CrossRef] [PubMed] [Google Scholar]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]
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]

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[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]