IDENTIFYING AREAS AFFECTED BY FIRES IN SUMATRA BASED ON TIME SERIES OF REMOTELY SENSED FIRE HOTSPOTS AND SPATIAL MODELING
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Accepted
2 January 2019
Published
1 December 2018
Keywords:
-
Forest fire, active fire, peatland, agent-based model
Abstract
Wildfires threaten the environment not only at local scales, but also at wider scales. Rapid monitoring system to detect active wildfires has been provided by satellite remote sensing technology, particularly through the advancement on thermal infrared sensors. However, satellite-based fire hotspots data, even at relatively high temporal resolution of less than one-day revisit period, such as time series of fire hotspots collected from TERRA and AQUA MODIS, do not tell exactly if they are fire ignitions or fire escapes, since other factors like wind, slope, and fuel biomass significantly drive the fire spread. Meanwhile, a number of biophysical fire simulation models have been developed, as tools to understand the roles of biophysical factors on the spread of wildfires. Those models explicitly incorporate effects of slope, wind direction, wind speed, and vegetative fuel on the spreading rate of surface fire from the ignition points across a fuel bed, based on either field or laboratory experiments. Nevertheless, none of those models have been implemented using real time fire data at relatively large extent areas. This study is aimed at incorporating spatially explicit time series data of weather (i.e. wind direction and wind speed), remotely sensed fuel biomass and remotely sensed fire hotspots, as well as incorporating more persistent biophysical factors (i.e. terrain), into an agent-based fire spread model, in order to identify fire ignitions within time series of remotely sensed fire hotspots.
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[2] Balbi, J-H., Rossi, J-L., Marcelli, T., Chatelon, F-J., 2010. Physical modeling of surface fire under nonparallel wind and slope conditions. Combust. Sci. and Tech. 182, 922-939.
[3] Balbi, J-H., Rossi, J-L., Marcelli, T., Santoni, P-A., 2007. A 3D physical real-time model of surface fires across fuel beds. Combust. Sci. and Tech. 179, 2511-2537.
[4] Ballard CE, McIntyre N, Wheater HS, Holden J, Wallage ZE. 2011. Hydrological modelling of drained blanket peatland. Journal of Hydrology 407: 81–93.doi: 10.1016/j.jhydrol.2011.07.005.
[5] Cole LES, Bhagwat SA and Willis KJ 2015 Long-term disturbance dynamics and resilience of tropical peat swamp forests. J. Ecol 103 16–30
[6] Condro, A.A. 2017. A 3D Voxel-based Model of Peatland Hydrology. Undergraduate thesis. Department of Geophysics and Meteorology, Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Bogor, Indonesia (In Press)
[7] Costanza R. 1989. Model goodness of fit: a multiple resolution procedure. Ecological Modeling 47: 199–215.Evans TP and Moran EF 2002 Spatial integration of social and biophysical factors related to landcover change. Popul Dev Rev 28 165–186
[8] Fajri, M.N., 2016. Penindakan pelaku pembakaran hutan dan lahan dengan pendekatan undang-undang pemberantasan tindak pidana korupsi. Integritas 2 (1), 43-68.
[9] Giglio, L., Schroeder, W., Justice, C.O., 2016. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31-41.
[10] Hayasaka H, Takahashi H, Limin H, Yulianti N, Usup A. 2016. Peat fire occurence. Di dalam: Osaki M, Tsuji N, editor. Tropical Peatland Ecosystems. Tokyo (JP): Springer.
[11] Holden J, Kirkby MJ, Lane SN, Milledge DG, Brookes CJ, Holden V, McDonald AT. 2008. Overland flow velocity and roughness properties in peatlands. Water Resources Research 44(6): 1–11.doi:10.1029/2007WR006052
[12] Huijnen, V., Wooster, M. J., Kaiser, J. W., Gaveau, D. L. A. , Flemming, J., Parrington, M., Inness, A., Murdiyarso, D., Main, B., van Weele, M., 2016. Fire carbon emissions over maritime Southeast Asia in 2015 largest since 1997. Sci. Rep. 6 (26886).
[13] Kuenzer, C., Zhang, J., Dech, S., 2016. Thermal infrared remote sensing: principles and theoretical background. In: Thenkabail, P.S. (Ed.), Remote Sensing Handbook Volume I: Remotely Sensed Data Characterization, Classification, and Accuracies. CRC Press, Boca Raton, FL, USA.
[14] Mandel, J., Amram, S., Beezley, J. D., Kelman, G., Kochanski, A. K., Kondratenko1, V. Y., Lynn, B. H., Regev, B., Vejmelka, M., 2014. Recent advances and applications of WRF–SFIRE. Nat. Hazards Earth Syst. Sci.14, 2829–2845.
[15] Monedero, S., Ramirez, J., Molina-Terrén, D., Cardil, A., 2017. Simulating wildfires backwards in time from the final fire perimeter in point-functional fire models. Environ. Model. Softw. 92, 163-168.
[16] Morandini, F., Silvani, X., Honore´, D., Boutin, G., Susset, A., Vernet, R., 2014. Slope effects on the fluid dynamics of a fire spreading across a fuel bed: PIV measurements and OH* chemiluminescence imaging. Exp. Fluids 55 (1788), 1-12.
[17] Panda, S.S., Rao, M.N., Thenkabail, P.S., Fitzerald, J.E., 2016. Remote sensing systems - platforms and sensors: aerial, satellite, UAV, optical, radar, and LiDAR. In: Thenkabail, P.S. (Ed.), Remote Sensing Handbook Volume I: Remotely Sensed Data Characterization, Classification, and Accuracies. CRC Press, Boca Raton, FL, USA.
[18] Page SE, Rieley JO, Banks CJ. Global and regional importance of the tropical peatland carbon pool. Global Change Biology 2010; 17:798818.
[19] Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, Wagener T. 2016. Sensitivity analysis of environmental models: a systematic review with practical workflow. Environmental Modelling & Software 79: 214–232.
[20] Piao S, Gang JF, Ciais P, Peylin P, Huang Y, Sitch S, Wang T. The carbon balance of terrestrial ecosystems in China. Nature 2009; 458:1009-13.
[21] Prakash, A., Kuenzer, C., 2016. Remote sensing–based mapping and monitoring of coal fires. In: Thenkabail, P.S. (Ed.), Remote Sensing Handbook Volume III: Remote Sensing of Water Resources, Disasters, and Urban Studies. CRC Press, Boca Raton, FL, USA.
[22] Schroeder, W., Oliva, P., Giglio, L., Quayle, B., Lorenz, E., Morelli, F., 2016. Active fire detection using Landsat-8/OLI data. Remote Sens. Environ. 185, 210-220.
[23] Setiawan Y, Pawitan H, Prasetyo LB, Lubis MI, Parlindungan M and Nurdiana A 2016 Characterizing spatial distribution and environments of sumatran peat swamp area using 250 M multi-temporal MODIS data. Proc Env Sci 33 117–127
[24] Vadrevu, K.P., Lasko, K., 2016. Satellite-derived nitrogen dioxide variations from biomass burning in a subtropical evergreen forest, Northeast India. In: Thenkabail, P.S. (Ed.), Remote Sensing Handbook Volume III: Remote Sensing of Water Resources, Disasters, and Urban Studies. CRC Press, Boca Raton, FL, USA.
[25] Van Loon AF. 2015. Hydrological drought explained. WIREs Water 2015.doi: 10.1002/wat2.1085
[26] Van Noordwijk M, Farida A, Suyamto D, Lusiana B, Khasanah N. 2003. Spatial variability of rainfall governs river flow and reduces effects of land use change at landscape scale: GenRiver and SpatRain simulations. Di dalam: Post DA, editor. Modsim 2003: International Congress on Modelling and Simulation; 2003 Jul 14–17; Townsville, Australia. Townsville (AU): CSIRO.hlm 572–577.
[27] Wilensky, U., 1999. NetLogo (http://ccl.northwestern.edu/netlogo/). Center for Connected Learning and Computer-Based Modeling (CCL). Northwestern University, Evanston, IL, USA.
[28] Wilensky U dan Rand W. 2015. An Introduction to Agent-Based Modeling. Massachusetts (US): The MIT Press.
[29] Xie Z, Di Z, Luo Z, Ma Q. 2012. A quasi-three-dimensional variably saturated groundwater flow model for climate modeling. Journal of Hydrometeorology 13(1): 27 – 46.doi:10.1175/jhm-d-10-05019.1.
Authors
SetiawanY., PrasetyoL. B., PawitanH., PermatasariP. A., SuyamtoD. and WijayantoA. K. (2018) “IDENTIFYING AREAS AFFECTED BY FIRES IN SUMATRA BASED ON TIME SERIES OF REMOTELY SENSED FIRE HOTSPOTS AND SPATIAL MODELING”, Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management). Bogor, ID, 8(3), pp. 420-427. doi: 10.29244/jpsl.8.3.420-427.
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