Spatial correlates of forest and land fires in Indonesia
Z. D. Tan
A C , L. R. Carrasco B and D. Taylor A
+ Author Affiliations
- Author Affiliations
A Department of Geography, National University of Singapore, 1 Arts Link, Kent Ridge, Singapore 117570.
B Department of Biological Sciences, National University of Singapore, 16 Science Drive 4, Singapore 117558.
C Corresponding author: tzdienle@u.nus.edu
International Journal of Wildland Fire 29(12) 1088-1099 https://doi.org/10.1071/WF20036
Submitted: 18 March 2020 Accepted: 30 August 2020 Published: 29 September 2020
Journal Compilation © IAWF 2020 Open Access CC BY
Abstract
Biomass fires in Indonesia emit high levels of greenhouse gases and particulate matter, key contributors to global climate change and poor air quality in south-east Asia. In order to better understand the drivers of biomass fires across Indonesia over multiple years, we examined the distribution and probability of fires in Sumatra, Kalimantan (Indonesian Borneo) and Papua (western New Guinea) over four entire calendar years (2002, 2005, 2011 and 2015). The 4 years of data represent years with El Niño and La Niña conditions and high levels of data availability in the study region. Generalised linear mixed-effects models and zero-inflated negative binomial models were used to relate fire hotspots and a range of spatial predictor data. Geographic differences in occurrences of fire hotspots were evident. Fire probability was greatest in mixed-production agriculture lands and in deeper, degraded peatlands, suggesting anthropogenic activities were strong determinants of burning. Drought conditions in El Niño years were also significant. The results demonstrate the importance of prioritising areas of high fire probability, based on land use and other predisposing conditions, in effective fire management planning.
Keywords: biomass burning, climate change, fire hotspot, haze, south-east Asia.
References
Abood SA, Lee JSH, Burivalova Z, Garcia-Ulloa J, Koh LP (2015) Relative contributions of the logging, fiber, oil palm, and mining industries to forest loss in Indonesia. Conservation Letters 8, 58–67.| Relative contributions of the logging, fiber, oil palm, and mining industries to forest loss in Indonesia.Crossref | GoogleScholarGoogle Scholar |
Aldrian E, Dwi Susanto R (2003) Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature. International Journal of Climatology 23, 1435–1452.
| Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature.Crossref | GoogleScholarGoogle Scholar |
Arumingtyas L (2019) BRG kembangkan sistem pemantauan muka air gambut [BRG develops a peat surface water monitoring system]. Mongabay. Available at https://www.mongabay.co.id/2019/01/28/brg-kembangkan-sistem-pemantauan-muka-air-gambut/ [Verified 3 September 2020] [In Indonesian]
Astuti R (2020) Fixing flammable forest: the scalar politics of peatland governance and restoration in Indonesia. Asia Pacific Viewpoint 61, 283–300.
| Fixing flammable forest: the scalar politics of peatland governance and restoration in Indonesia.Crossref | GoogleScholarGoogle Scholar |
Avitabile V, Herold M, Heuvelink GBM, Lewis SL, Phillips OL, Asner GP, Armston J, Asthon P, Banin L, Bayol N, Berry NJ, Boeckx P, de Jong BHJ, DeVries B, Girardin C, Kearsley E, Lindsell J, Lopez-Gonzalez G, Lucas R, Malhi Y, Morel A, Mitchard ETA, Nagy L, Qie L, Quinones M, Ryan CM, Slik F, Sunderland TCH, Vaglio Laurin G, Valentini R, Verbeeck H, Wijaya A, Willcock S (2016) An integrated pan-tropical biomass map using multiple reference datasets. Global Change Biology 22, 1406–1420.
| An integrated pan-tropical biomass map using multiple reference datasets.Crossref | GoogleScholarGoogle Scholar | 26499288PubMed |
Bartoń K (2018) MuMIn: Multi-Model Inference. R package version 1.42.1. The R Foundation. Available at https://CRAN.R-project.org/package=MuMIn [Verified 3 September 2020]
Bates D, Maechler M, Bolker B, Walker S (2015) Fitting linear mixed-effects models using lme4. Journal of Statistical Software 67, 1–48.
| Fitting linear mixed-effects models using lme4.Crossref | GoogleScholarGoogle Scholar |
Bolker B, Brooks M, Garnder B, Lennert C, Minami M (2012) Owls example: a zero-inflated, generalized linear mixed-model for count data. Available at https://groups.nceas.ucsb.edu/non-linear-modeling/projects/owls/WRITEUP/owls.pdf/@@download [Verified 3 September 2020]
Brando PM, Paolucci L, Ummenhofer CC, Ordway EM, Hartmann H, Cattau ME, Rattis L, Medjibe V, Coe MT, Balch J (2019) Droughts, wildfires, and forest carbon cycling: a pantropical synthesis. Annual Review of Earth and Planetary Sciences 47, 555–581.
| Droughts, wildfires, and forest carbon cycling: a pantropical synthesis.Crossref | GoogleScholarGoogle Scholar |
Brier GW (1950) Verification of forecasts expressed in terms of probability. Monthly Weather Review 78, 1–3.
| Verification of forecasts expressed in terms of probability.Crossref | GoogleScholarGoogle Scholar |
Brooks ME, Kristensen K, van Benthem KJ, Magnusson A, Berg CW, Nielsen A, Skaug HJ, Mächler M, Bolker BM (2017) glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modelling. The R Journal 9, 378–400.
| glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modelling.Crossref | GoogleScholarGoogle Scholar |
Cai W, Borlace S, Lengaigne M, van Rensch P, Collins M, Vecchi G, Timmermann A, Santoso A, McPhaden MJ, Wu L, England MH, Wang G, Guilyardi E, Jin F (2014) Increasing frequency of extreme El Niño events due to greenhouse gas warming. Nature Climate Change 4, 111–116.
| Increasing frequency of extreme El Niño events due to greenhouse gas warming.Crossref | GoogleScholarGoogle Scholar |
Cattau ME, Harrison ME, Shinyo I, Tungau S, Uriarte M, DeFries R (2016) Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan, Indonesia. Global Environmental Change 39, 205–219.
| Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan, Indonesia.Crossref | GoogleScholarGoogle Scholar |
Center for International Earth Science Information Network (CIESIN) (2018) Gridded population of the world, version 4 (GPWv4): population density adjusted to match 2015 revision UN WPP country totals, Revision 11. (NASA Socioeconomic Data and Applications Center (SEDAC), Columbia University: Palisades, NY, USA) Available at https://doi.org/10.7927/H4F47M65 [Verified 3 September 2020]
Christman ZJ, Rogan J (2012) Error propagation in raster data integration. Photogrammetric Engineering and Remote Sensing 78, 617–624.
| Error propagation in raster data integration.Crossref | GoogleScholarGoogle Scholar |
Crawley MJ (2013) Statistical modelling. In ‘The R Book’. (Ed. MJ Crawley) pp. 388–448. (John Wiley & Sons Ltd: Chichester, UK)
Database of Global Administrative Areas (2018). Indonesia. Available at: https://gadm.org/download_country_v3.html [Verified 18 September 2020].
De Groot WJ, Field RD, Brady MA, Roswintiarti O, Mohamad M (2007) Development of the Indonesian and Malaysian fire danger rating systems. Mitigation and Adaptation Strategies for Global Change 12, 165–180.
| Development of the Indonesian and Malaysian fire danger rating systems.Crossref | GoogleScholarGoogle Scholar |
Dennis RA, Mayer J, Applegate G, Chokkalingam U, Colfer CJP, Kurniawan I, Lachowski H, Maus P, Permana RP, Ruchiat Y, Stolle F, Suyanto , Tomich TP (2005) Fire, people and pixels: linking social science and remote sensing to understand underlying causes and impacts of fires in Indonesia. Human Ecology 33, 465–504.
| Fire, people and pixels: linking social science and remote sensing to understand underlying causes and impacts of fires in Indonesia.Crossref | GoogleScholarGoogle Scholar |
Dixon B, Earls J (2009) Resample or not?! Effects of resolution of DEMs in watershed modelling. Hydrological Processes 23, 1714–1724.
| Resample or not?! Effects of resolution of DEMs in watershed modelling.Crossref | GoogleScholarGoogle Scholar |
Dowdy AJ, Field RD, Spessa AC (2016) Seasonal forecasting of fire weather based on a new global fire weather database. In ‘Proceedings of the 5th international fire behavior and fuels conference,’ 11–15 April 2016, Melbourne, Australia. pp. 458-463. (International Association of Wildland Fire: Missoula, Montana, USA) Available at http://www.iawfonline.org/wp-content/uploads/2018/02/5th-Internatonal-Fire-Behavior-and-Fuel-Conference-Proceedings-Final-updated-1.9.2017-web.pdf [Verified 3 September 2020]
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Diversity & Distributions 17, 43–57.
| A statistical explanation of MaxEnt for ecologists.Crossref | GoogleScholarGoogle Scholar |
ESRI (2014) ArcGIS. Available at http://www.esri.com/software/arcgis/ [Verified 3 September 2020]
Evers S, Yule CM, Padfield R, O’Reilly P, Varkkey H (2017) Keep wetlands wet: the myth of sustainable development of tropical peatlands – implications for policies and management. Global Change Biology 23, 534–549.
| Keep wetlands wet: the myth of sustainable development of tropical peatlands – implications for policies and management.Crossref | GoogleScholarGoogle Scholar | 27399889PubMed |
Fanin T, Van Der Werf GR (2017) Precipitation – fire linkages in Indonesia (1997–2015). Biogeosciences 14, 3995–4008.
| Precipitation – fire linkages in Indonesia (1997–2015).Crossref | GoogleScholarGoogle Scholar |
Field RD, Spessa AC, Aziz NA, Camia A, Cantin A, Carr R, de Groot WJ, Dowdy AJ, Flannigan MD, Manomaiphiboon K, Pappenberger E, Tanpipat V, Wang X (2015) Development of a global fire weather database. Natural Hazards and Earth System Sciences 15, 1407–1423.
| Development of a global fire weather database.Crossref | GoogleScholarGoogle Scholar |
Field RD, van der Werf GR, Fanin T, Fetzer EJ, Fuller R, Jethva H, Levy R, Livesey NJ, Luo M, Torres O, Worden HM (2016) Indonesian fire activity and smoke pollution in 2015 show persistent non-linear sensitivity to El Niño-induced drought. Proceedings of the National Academy of Sciences of the United States of America 113, 9204–9209.
| Indonesian fire activity and smoke pollution in 2015 show persistent non-linear sensitivity to El Niño-induced drought.Crossref | GoogleScholarGoogle Scholar | 27482096PubMed |
Gaveau DLA, Salim MA, Hergoualc’h K, Locatelli B, Sloan S, Wooster M, Marlier ME, Molidena E, Yaen H, DeFries R, Verchot L, Murdiyarso D, Nasi R, Holmgren P, Sheil D (2014) Major atmospheric emissions from peat fires in south-east Asia during non-drought years: evidence from the 2013 Sumatran fires. Scientific Reports 4, 1–7.
Giglio L, Schroeder W, Hall JV, Justice CO (2018) MODIS Collection 6 Active Fire Product User's Guide Revision B. (NASA) Available at http://modis-fire.umd.edu/files/MODIS_C6_Fire_User_Guide_B.pdf [Verified 18 September 2020]
Goldammer JGG (2016) Fire management in tropical forests. In ‘Tropical forestry handbook’. (Eds L Pansel, M Köhl) pp. 2659–2710. (Springer: Berlin, Heidelberg, Germany)
Goldstein JE (2016) Knowing the subterranean: land grabbing, oil palm, and divergent expertise in Indonesia’s peat soil. Environment & Planning A 48, 754–770.
| Knowing the subterranean: land grabbing, oil palm, and divergent expertise in Indonesia’s peat soil.Crossref | GoogleScholarGoogle Scholar |
Greenpeace (2014) Concessions datasets: palm oil, selective logging (IUPHHK-HA) and wood fiber (IUPHHK-HTI). Available at http://www.greenpeace.org/seasia/id/Global/seasia/Indonesia/Code/Forest-Map/en/data.htm [Verified 25 November 2019]
Griscom BW, Busch J, Cook-Patton SC, Ellis PW, Funk J, Leavitt SM, Lomax G, Turner WR, Chapman M, Engelmann J, Gurwick NP, Landis E, Lawrence D, Malhi Y, Schindler Murray L, Navarrete D, Roe S, Scull S, Smith P, Streck C, Walker WS, Worthington T (2020) National mitigation potential from natural climate solutions in the tropics. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 375, 20190126
| National mitigation potential from natural climate solutions in the tropics.Crossref | GoogleScholarGoogle Scholar | 31983330PubMed |
Gutiérrez-Vélez VH, Uriarte M, Defries R, Pinedo-Vasquez M, Fernandes K, Ceccato P, Baethgen W, Padoch C (2014) Land-cover change interacts with drought severity to change fire regimes in western Amazonia. Ecological Applications 24, 1323–1340.
| Land-cover change interacts with drought severity to change fire regimes in western Amazonia.Crossref | GoogleScholarGoogle Scholar | 29160657PubMed |
Hamner B, Frasco M (2018) Metrics: evaluation metrics for machine learning. R package version 0.1.4. The R Foundation. Available at https://CRAN.R-project.org/package=Metrics [Verified 3 September 2020]
Hendon HH (2003) Indonesian rainfall variability: impacts of ENSO and local sea–air interaction. Journal of Climate 16, 1775–1790.
| Indonesian rainfall variability: impacts of ENSO and local sea–air interaction.Crossref | GoogleScholarGoogle Scholar |
Heyer JP, Power MJ, Field RD, van Marle JE (2018) The impacts of recent drought on fire, forest loss, and regional smoke emissions in lowland Bolivia. Biogeosciences 15, 4317–4331.
| The impacts of recent drought on fire, forest loss, and regional smoke emissions in lowland Bolivia.Crossref | GoogleScholarGoogle Scholar |
Hoscilo A, Page SE, Tansey KJ, Rieley JO (2011) Effect of repeated fires on land-cover change on peatland in southern Central Kalimantan, Indonesia, from 1973 to 2005. International Journal of Wildland Fire 20, 578–588.
| Effect of repeated fires on land-cover change on peatland in southern Central Kalimantan, Indonesia, from 1973 to 2005.Crossref | GoogleScholarGoogle Scholar |
Jarvis A, Reuter HI, Nelson A, Guevara E 2008 Hole-filled SRTM for the Globe, version 4. Centre for International Tropical Agriculture. Available at http://srtm.csi.cgiar.org [Verified 3 September 2020]
Koplitz SN, Mickley LJ, Marlier ME, Buonocore JJ, Kim PS, Liu T, Sulprizio MP, DeFries RS, Jacob DJ, Schwartz J (2016) Public health impacts of the severe haze in Equatorial Asia in September–October 2015: demonstration of a new framework for informing fire management strategies to reduce downwind smoke exposure. Environmental Research Letters 11, 094023
| Public health impacts of the severe haze in Equatorial Asia in September–October 2015: demonstration of a new framework for informing fire management strategies to reduce downwind smoke exposure.Crossref | GoogleScholarGoogle Scholar |
Lehner B (2013) HydroSHEDS technical documentation version 1.2. World Wildlife Fund US. Available at https://www.hydrosheds.org/images/inpages/HydroSHEDS_TechDoc_v1_2.pdf [Verified 18 September 2020]
Lehner B, Verdin K, Jarvis A (2008) New global hydrography derived from spaceborne elevation data. Eos 89, 93–94.
| New global hydrography derived from spaceborne elevation data.Crossref | GoogleScholarGoogle Scholar |
Leifeld J, Menichetti L (2018) The underappreciated potential of peatlands in global climate change mitigation strategies. Nature Communications 9, 1071
| The underappreciated potential of peatlands in global climate change mitigation strategies.Crossref | GoogleScholarGoogle Scholar | 29540695PubMed |
Lilleskov E, McCullough K, Hergoualc’h K, Torres DC, Chimner R, Murdiyarso D, Kolka R, Bourgeau-Chavez L, Hribljan J, Pasquel JA, Wayson C (2019) Is Indonesian peatland loss a cautionary tale for Peru? A two-country comparison of the magnitude and causes of tropical peatland degradation. Mitigation and Adaptation Strategies for Global Change 24, 591–623.
| Is Indonesian peatland loss a cautionary tale for Peru? A two-country comparison of the magnitude and causes of tropical peatland degradation.Crossref | GoogleScholarGoogle Scholar |
Margono BA, Potapov PV, Turubanova S, Stolle F, Hansen MC (2014) Primary forest cover loss in Indonesia over 2000–2012. Nature Climate Change 4, 730–735.
| Primary forest cover loss in Indonesia over 2000–2012.Crossref | GoogleScholarGoogle Scholar |
Miettinen J, Hooijer A, Shi C, Tollenaar D, Vernimmen R, Liew SC, Malins C, Page SE (2012a) Extent of industrial plantations on south-east Asian peatlands in 2010 with analysis of historical expansion and future projections. Global Change Biology. Bioenergy 4, 908–918.
| Extent of industrial plantations on south-east Asian peatlands in 2010 with analysis of historical expansion and future projections.Crossref | GoogleScholarGoogle Scholar |
Miettinen J, Shi C, Tan WJ, Liew SC (2012b) 2010 land cover map of insular south-east Asia in 250-m spatial resolution. Remote Sensing Letters 3, 11–20.
| 2010 land cover map of insular south-east Asia in 250-m spatial resolution.Crossref | GoogleScholarGoogle Scholar |
Miettinen J, Shi C, Liew SC (2016) 2015 land cover map of south-east Asia at 250-m spatial resolution. Remote Sensing Letters 7, 701–710.
| 2015 land cover map of south-east Asia at 250-m spatial resolution.Crossref | GoogleScholarGoogle Scholar |
Miettinen J, Shi C, Liew SC (2017) Fire distribution in peninsular Malaysia, Sumatra and Borneo in 2015 with special emphasis on peatland fires. Environmental Management 60, 747–757.
| Fire distribution in peninsular Malaysia, Sumatra and Borneo in 2015 with special emphasis on peatland fires.Crossref | GoogleScholarGoogle Scholar | 28674917PubMed |
Ministry of Environment and Forestry (2015) SiPongi – karhutla monitoring sistem. Available at http://sipongi.menlhk.go.id/home/main [Verified 3 September 2020] [In Indonesian]
Nakagawa S, Johnson PCD, Schielzeth H (2017) The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. Journal of the Royal Society Interface 14, 20170213
| The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded.Crossref | GoogleScholarGoogle Scholar |
NASA GISS (2019) Global fire weather database. Available at https://data.giss.nasa.gov/impacts/gfwed/ [Verified 3 September 2020]
Nelson A (2008) Estimated travel time to the nearest city of 50 000 or more people in year 2000. Global Environment Monitoring Unit – Joint Research Centre of the European Commission. Available at https://forobs.jrc.ec.europa.eu/products/gam/ [Verified 3 September 2020]
Ni’mah NLK, Herdiansyah H, Soesilo TEB, Mutia EF (2018) Strategy for increasing the participation of masyarakat peduli api in forest fire control. IOP Conference Series: Earth and Environmental Science 126, 012148
| Strategy for increasing the participation of masyarakat peduli api in forest fire control.Crossref | GoogleScholarGoogle Scholar |
Null J (2018) El Niño and La Niña Years and intensities. Golden Gate Weather Services. Available at https://ggweather.com/enso/oni.htm [Verified 3 September 2020]
Page SE, Hooijer A (2016) In the line of fire: the peatlands of south-east Asia. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 371, 20150176
| In the line of fire: the peatlands of south-east Asia.Crossref | GoogleScholarGoogle Scholar | 27216508PubMed |
Pan X, Chin M, Ichoku CM, Field RD (2018) Connecting Indonesian fires and drought with the type of El Niño and phase of the Indian Ocean Dipole during 1979–2016. Journal of Geophysical Research, D, Atmospheres 123, 7974–7988.
| Connecting Indonesian fires and drought with the type of El Niño and phase of the Indian Ocean Dipole during 1979–2016.Crossref | GoogleScholarGoogle Scholar |
Parker RJ, Boesch H, Wooster MJ, Moore DP, Webb AJ, Gaveau D, Murdiyaso D (2016) Atmospheric CH4 and CO2 enhancements and biomass burning emission ratios derived from satellite observations of the 2015 Indonesian fire plumes. Atmospheric Chemistry and Physics 16, 10111–10131.
| Atmospheric CH4 and CO2 enhancements and biomass burning emission ratios derived from satellite observations of the 2015 Indonesian fire plumes.Crossref | GoogleScholarGoogle Scholar |
Peng CJ, Lee KL, Ingersoll GM (2002) An introduction to logistic regression analysis and reporting. The Journal of Educational Research 96, 3–14.
| An introduction to logistic regression analysis and reporting.Crossref | GoogleScholarGoogle Scholar |
Price OF, Bradstock R (2014) Countervailing effects of urbanization and vegetation extent on fire frequency on the wildland urban interface: disentangling fuel and ignition effects. Landscape and Urban Planning 130, 81–88.
| Countervailing effects of urbanization and vegetation extent on fire frequency on the wildland urban interface: disentangling fuel and ignition effects.Crossref | GoogleScholarGoogle Scholar |
Reid JS, Xian P, Hyer EJ, Flatau MK, Ramirez EM, Turk FJ, Samson CR, Zhang C, Fukuda EM, Maloney ED (2012) Multi-scale meteorological conceptual analysis of observed active fire hotspot activity and smoke optical depth in the Maritime Continent. Atmospheric Chemistry and Physics 12, 2117–2147.
| Multi-scale meteorological conceptual analysis of observed active fire hotspot activity and smoke optical depth in the Maritime Continent.Crossref | GoogleScholarGoogle Scholar |
Sheil D, Casson A, Meijaard E, van Noordwijk M, Gaskell J, Sunderland-Groves J, Wertz K, Kanninen M (2009) ‘The impacts and opportunities of oil palm in south-east Asia: what do we know and what do we need to know?’ (Center for International Forestry Research: Bogor, Indonesia)
Simorangkir D (2007) Fire use: is it really the cheaper land preparation method for large-scale plantations? Mitigation and Adaptation Strategies for Global Change 12, 147–164.
| Fire use: is it really the cheaper land preparation method for large-scale plantations?Crossref | GoogleScholarGoogle Scholar |
Sing T, Sanders O, Beerenwinkel N, Lengauer T (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21, 3940–3941.
| ROCR: visualizing classifier performance in R.Crossref | GoogleScholarGoogle Scholar | 16096348PubMed |
Sloan S, Locatelli B, Wooster MJ, Gaveau DLA (2017) Fire activity in Borneo driven by industrial land conversion and drought during El Niño periods, 1982–2010. Global Environmental Change 47, 95–109.
| Fire activity in Borneo driven by industrial land conversion and drought during El Niño periods, 1982–2010.Crossref | GoogleScholarGoogle Scholar |
Spessa AC, Field RD, Pappenberger F, Langner A, Englhart S, Weber U, Stockdale FS, Kaiser JW, Moore J (2015) Seasonal forecasting of fire over Kalimantan, Indonesia. Natural Hazards and Earth System Sciences 15, 429–442.
| Seasonal forecasting of fire over Kalimantan, Indonesia.Crossref | GoogleScholarGoogle Scholar |
Stolle F, Chomitz KM, Lambin EF, Tomich TP (2003) Land use and vegetation fires in Jambi Province, Sumatra, Indonesia. Forest Ecology and Management 179, 277–292.
| Land use and vegetation fires in Jambi Province, Sumatra, Indonesia.Crossref | GoogleScholarGoogle Scholar |
Sumarga E (2017) Spatial indicators for human activities may explain the 2015 fire hotspot distribution in Central Kalimantan, Indonesia. Tropical Conservation Science 10, 1–9.
| Spatial indicators for human activities may explain the 2015 fire hotspot distribution in Central Kalimantan, Indonesia.Crossref | GoogleScholarGoogle Scholar |
Supari FT, Salimun E, Aldrian E, Sopaheluwakan A, Liew J (2018) ENSO modulation of seasonal rainfall and extremes in Indonesia. Climate Dynamics 51, 2559–2580.
| ENSO modulation of seasonal rainfall and extremes in Indonesia.Crossref | GoogleScholarGoogle Scholar |
Syphard AD, Radeloff VC, Hawbaker TJ, Stewart SI (2009) Conservation threats due to human-caused increases in fire frequency in Mediterranean-climate ecosystems. Conservation Biology 23, 758–769.
| Conservation threats due to human-caused increases in fire frequency in Mediterranean-climate ecosystems.Crossref | GoogleScholarGoogle Scholar | 22748094PubMed |
Sze JS, Jefferson , Lee JSH (2019) Evaluating the social and environmental factors behind the 2015 extreme fire event in Sumatra, Indonesia. Environmental Research Letters 14, 015001
| Evaluating the social and environmental factors behind the 2015 extreme fire event in Sumatra, Indonesia.Crossref | GoogleScholarGoogle Scholar |
Tan-Soo J, Pattanayak SK (2019) Seeking natural capital projects: forest fires, haze, and early life exposure in Indonesia. Proceedings of the National Academy of Sciences of the United States of America 116, 5239–5245.
| Seeking natural capital projects: forest fires, haze, and early life exposure in Indonesia.Crossref | GoogleScholarGoogle Scholar | 30782799PubMed |
Tansey K, Beston J, Hoscilo A, Page SE, Hernández CUP (2008) Relationship between MODIS fire hot spot count and burned area in a degraded tropical peat swamp forest in central Kalimantan, Indonesia. Journal of Geophysical Research, D, Atmospheres 113, D23112
| Relationship between MODIS fire hot spot count and burned area in a degraded tropical peat swamp forest in central Kalimantan, Indonesia.Crossref | GoogleScholarGoogle Scholar |
Thung PH (2018) A case study on the persistence of shifting cultivation in the context of post-2015 anti-haze regulation in West Kalimantan. Human Ecology 46, 197–205.
| A case study on the persistence of shifting cultivation in the context of post-2015 anti-haze regulation in West Kalimantan.Crossref | GoogleScholarGoogle Scholar |
Vayda AP (2006) Causal explanation of Indonesian forest fires: concepts, applications, and research priorities. Human Ecology 34, 615–635.
| Causal explanation of Indonesian forest fires: concepts, applications, and research priorities.Crossref | GoogleScholarGoogle Scholar |
Vetrita V, Cochrane MA (2020) Fire frequency and related land-use and land-cover changes in Indonesia’s peatlands. Remote Sensing 12, 5
| Fire frequency and related land-use and land-cover changes in Indonesia’s peatlands.Crossref | GoogleScholarGoogle Scholar |
Wahyunto S, Ritung dan H, Suparto (2003) Maps of area of peatland distribution and carbon content in Sumatra, 1990–2002 (Bogor). Wetlands International - Indonesia Programme & Wildlife Habitat Canada (WHC) Available at http://www.wetlands.or.id/PDF/buku/Atlas%20Sebaran%20Gambut%20Sumatera.pdf [Verified 3 September 2020]
Wahyunto S, Ritung dan H, Suparto (2004) Maps of area of peatland distribution and carbon content in Kalimantan, 2000–2002 (Bogor). Wetlands International - Indonesia Programme & Wildlife Habitat Canada (WHC) Available at http://www.wetlands.or.id/PDF/buku/Atlas%20Sebaran%20Gambut%20Kalimantan.pdf [Verified 3 September 2020]
Wahyunto HB, Bekti B, Widiastuti F (2006) Maps of peatland distribution, area and carbon content in Papua, 2000–2001 (Bogor). Wetlands International - Indonesia Programme & Wildlife Habitat Canada (WHC) Available at http://www.wetlands.or.id/PDF/buku/Atlas%20Sebaran%20Gambut%20Papua.pdf [Verified 3 September 2020]
Weiss DJ, Nelson A, Gibson HS, Temperley W, Peedell S, Lieber A, Hancher M, Poyart E, Belchior S, Fullman N, Mappin B, Dalrymple U, Rozier J, Lucas TCD, Howes RE, Tusting LS, Kang SY, Cameron E, Bisanzio D, Battle KE, Bhatt S, Gething PW (2018) A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553, 333–336.
| A global map of travel time to cities to assess inequalities in accessibility in 2015.Crossref | GoogleScholarGoogle Scholar | 29320477PubMed |
World Bank (2016) The cost of fire: an economic analysis of Indonesia’s 2015 fire crisis. Indonesia Sustainable Landscapes Knowledge Note 1. (Jakarta, Indonesia). Available at http://pubdocs.worldbank.org/en/643781465442350600/Indonesia-forest-fire-notes.pdf [Verified]
Zuur AF, Ieno EN, Walker N, Saveliev AA, Smith GM (2009) ‘Mixed-effect models and extensions in ecology with R.’ (Springer-Verlag: New York, NY, USA)
