in

Declining severe fire activity on managed lands in Equatorial Asia

  • Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356 (2017).

    CAS 
    Article 

    Google Scholar 

  • Sloan, S., Locatelli, B., Wooster, M. J. & Gaveau, D. L. A. Fire activity in Borneo driven by industrial land conversion and drought during El Niño periods, 1982–2010. Glob. Environ. Change 47, 95–109 (2017).

    Article 

    Google Scholar 

  • Kelley, D. I. et al. How contemporary bioclimatic and human controls change global fire regimes. Nat. Clim. Change 9, 690–96 (2019).

    Article 

    Google Scholar 

  • Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).

  • Ward, D. S., Shevliakova, E., Malyshev, S. & Rabin, S. Trends and variability of global fire emissions due to historical anthropogenic activities. Glob. Biogeochem. Cycles 32, 122–42 (2018).

    CAS 
    Article 

    Google Scholar 

  • Earl, N. & Simmonds, I. Spatial and temporal variability and trends in 2001–2016 global fire activity. J. Geophys. Res. Atmos. 123, 2524–36 (2018).

    Article 

    Google Scholar 

  • Giglio, L., Randerson, J. T. & van der Werf, G. R. Analysis of daily, monthly, and annual burned area using the fourth-generation Global Fire Emissions Database (GFED4). J. Geophys. Res. Biogeosci. 118, 317–28 (2013).

    Article 

    Google Scholar 

  • Doerr, S. H. & Santín, C. Global trends in wildfire and its impacts: perceptions versus realities in a changing world. Philos. Trans. R. Soc. B Biol. Sci. 371, 20150345 (2016).

    Article 

    Google Scholar 

  • van Lierop, P., Lindquist, E., Sathyapala, S. & Franceschini, G. Global forest area disturbance from fire, insect pests, diseases and severe weather events. Forest Ecol. Manag. 352, 78–88 (2015).

    Article 

    Google Scholar 

  • Zheng, B. et al. Increasing forest fire emissions despite the decline in global burned area. Sci. Adv. 7, eabh2646 (2021).

    Article 

    Google Scholar 

  • Andela, N. & van der Werf, G. R. Recent trends in African fires driven by cropland expansion and El Niño to La Niña transition. Nat. Clim. Change 4, 791–95 (2014).

    Article 

    Google Scholar 

  • Van der Werf, G. R. et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 10, 11707–35 (2010).

    Article 
    CAS 

    Google Scholar 

  • Balch, J. K. et al. Negative fire feedback in a transitional forest of southeastern Amazonia. Glob. Change Biol. 14, 2276–87 (2008).

    Article 

    Google Scholar 

  • Cochrane, M. A. & Laurance, W. F. Synergisms among fire, land use, and climate change in the Amazon. Ambio 37, 522–27 (2008).

    Article 

    Google Scholar 

  • Gaveau, D. L. A. et al. Major atmospheric emissions from peat fires in Southeast Asia during non-drought years: evidence from the 2013 Sumatran fires. Sci. Rep. 4, 6112 (2014).

  • Vadrevu, K. P. et al. Trends in vegetation fires in South and Southeast Asian countries. Sci. Rep. 9, 7422 (2019).

    Article 
    CAS 

    Google Scholar 

  • Sloan, S., Tacconi, L. & Cattau, M. E. Fire prevention in managed landscapes: recent successes and challenges in Indonesia. Mitig. Adapt. Strateg. Glob. Change 26, Article 32 (2021).

    Article 

    Google Scholar 

  • Gaveau, D. L. A., Descales, A., Salim, M. A., Shields, D. & Sloan, S. Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 Indonesian burning. Earth Syst. Sci. Data, https://doi.org/10.5194/essd-2021-113, (2021).

  • Randerson, J. T., Chen, Y., van der Werf, G. R., Rogers, B. M. & Morton, D. C. Global burned area and biomass burning emissions from small fires. J. Geophys. Res. Biogeosci. 117, G04012 (2012).

    Article 
    CAS 

    Google Scholar 

  • Field, R. D., van der Werf, G. R. & Shen, S. S. P. Human amplification of drought-induced biomass burning in Indonesia since 1960. Nat. Geosci. 2, 185–88 (2009).

    CAS 
    Article 

    Google Scholar 

  • Huijnen, V. et al. Fire carbon emissions over maritime Southeast Asia in 2015 largest since 1997. Sci. Rep. 6, 26886 (2016).

    CAS 
    Article 

    Google Scholar 

  • Tacconi, L. Preventing fires and haze in Southeast Asia. Nat. Clim. Change 6, 640–43 (2016).

    Article 

    Google Scholar 

  • Koplitz, S. N. et al. 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. Environ. Res. Lett. 11, 094023 (2016).

    Article 

    Google Scholar 

  • Kiely, L. et al. Air quality and health impacts of vegetation and peat fires in Equatorial Asia during 2004–2015. Environ. Res. Lett.15, 094054 (2020).

    Article 

    Google Scholar 

  • Crippa, P. et al. Population exposure to hazardous air quality due to the 2015 fires in Equatorial Asia. Sci. Rep. 6, 37074 (2016).

    CAS 
    Article 

    Google Scholar 

  • Glauber, A. J. & Gunawan, I. The Cost of Fire: An Economic Analysis of Indonesia’s 2015 Fire Crisis. (The World Bank, Washington, D.C., (2016).

  • Tan, Z. D., Carrasco, L. R. & Taylor, D. Spatial correlates of forest and land fires in Indonesia. Int. J. Wildland Fire 29, 1088–99 (2020).

    Article 

    Google Scholar 

  • Marlier, M. E. et al. Fire emissions and regional air quality impacts from fires in oil palm, timber, and logging concessions in Indonesia. Environ. Res. Lett. 10, 085005 (2015).

    Article 
    CAS 

    Google Scholar 

  • Vetrita, Y. & Cochrane, M. A. Fire frequency and related land-use and land-cover changes in Indonesia’s peatlands. Remote Sens. 12, 5 (2020).

  • Nikonovas, T., Spessa, A., Doerr, S. H., Clay, G. D. & Mezbahuddin, S. Near-complete loss of fire-resistant primary tropical forest cover in Sumatra and Kalimantan. Commun. Earth Environ. 1, 65 (2020).

    Article 

    Google Scholar 

  • Field, R. Biomass burning in Indonesia: Signs of Progress in 2019?, http://www.columbia.edu/~rf2426/index_files/20200128.Field.GSFC.NoOz.pdf, January, NASA Goddard Space Flight Center, (2019).

  • Watts, J. et al. Incentivising compliance: evaluating the effectiveness of targeted village incentives for reducing forest and peat fires. Forest Policy Econ. 108, 101956 (2019).

  • Wijedasa, L. et al. Carbon emissions from peat forests will continue to increase despite emission-reduction schemes. Glob. Change Biol. 24, 4598–613 (2018).

    Article 

    Google Scholar 

  • Sloan, S., Meyfroidt, P., Rudel, T. K. & Bongers, F. & Chazdon Robin, L. The forest transformation: Planted tree cover and regional dynamics of tree gains and losses. Glob. Environ. Change 59, 101988 (2019).

    Article 

    Google Scholar 

  • Albar, I., Jaya, I. N. S., Saharjo, B. H., Kuncahyo, B. & Vadrevu, K. P. Spatio-temporal analysis of land and forest fires in Indonesia using MODIS active fire dataset, in Land-Atmospheric Research Applications in South and Southeast Asia (eds K P Vadrevu et al.), p. 105-27 (Springer International Publishing, 2018).

  • Miettinen, J., Shi, C. & Liew, S. C. Fire distribution in Peninsular Malaysia, Sumatra and Borneo in 2015 with special emphasis on peatland fires. Environ. Manage. 60, 747–57 (2017).

    Article 

    Google Scholar 

  • Fanin, T. & van der Werf, G. R. Precipitation–fire linkages in Indonesia (1997–2015). Biogeosciences 14, 3995–4008 (2017).

    Article 

    Google Scholar 

  • Wiggins, E. B. et al. Smoke radiocarbon measurements from Indonesian fires provide evidence for burning of millennia-aged peat. Proc. Natl. Acad. Sci. USA 115, 12419 (2018).

    CAS 
    Article 

    Google Scholar 

  • Page, S. E. et al. The amount of carbon released from peat and forest fires in Indonesia during 1997. Nature 420, 61–65 (2002).

    CAS 
    Article 

    Google Scholar 

  • Lohberger, S., Stängel, M., Atwood, E. C. & Siegert, F. Spatial evaluation of Indonesia’s 2015 fire-affected area and estimated carbon emissions using Sentinel-1. Glob. Change Biol. 24, 644–54 (2018).

    Article 

    Google Scholar 

  • van der Werf, G. R. et al. Global fire emissions estimates during 1997–2016. Earth Syst. Sci. Data 9, 697–720 (2017).

    Article 

    Google Scholar 

  • Field, R. D. et al. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proc. Natl Acad. Sci. USA 113, 9204–09 (2016).

    CAS 
    Article 

    Google Scholar 

  • Austin, K. G. et al. Shifting patterns of oil palm driven deforestation in Indonesia and implications for zero-deforestation commitments. Land Use Policy 69, 41–48 (2017).

    Article 

    Google Scholar 

  • Pan, X., Chin, M., Ichoku, C. & Field, R. Connecting Indonesian fires and drought with the type of El Niño and phase of the Indian Ocean Dipole during 1979–2016. J. Geophys. Res. Atmos. 123, (2018).

  • van der Werf, G. R. et al. Climate regulation of fire emissions and deforestation in Equatorial Asia. Proc. Natl Acad. Sci. USA 105, 20350–55 (2008).

    Article 

    Google Scholar 

  • Wooster, M. J., Roberts, G., Perry, G. L. W. & Kaufman, Y. J. Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release. J. Geophys. Rese. Atmos. 110, (2005).

  • Spessa, A. et al. Seasonal forecasting of fires over Kalimantan, Indonesia. Nat. Hazards Earth Syst. Sci. 15, 429–42 (2015).

    Article 

    Google Scholar 

  • Siegert, F., Ruecker, G., Hinrichs, A. & Hoffmann, A. A. Increased damage from fires in logged forests during droughts caused by El Niño. Nature 414, 437–40 (2001).

    CAS 
    Article 

    Google Scholar 

  • Fernandes, K. et al. Heightened fire probability in Indonesia in non-drought conditions: the effect of increasing temperatures. Environ. Res. Lett. 12, 054002 (2017).

    Article 

    Google Scholar 

  • Herawati, H. & Santoso, H. Tropical forest susceptibility to and risk of fire under changing climate: a review of fire nature, policy and institutions in Indonesia. Forest Policy Econ. 13, 227–33 (2011).

    Article 

    Google Scholar 

  • Nepstad, D. et al. Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains. Science 344, 1118–23 (2014).

    CAS 
    Article 

    Google Scholar 

  • Dennis, R. A Review of Fire Projects In Indonesia, 1982-1998. (CIFOR, Bogor, Indonesia, 1999).

  • de Groot, W. J., Field, R. D., Brady, M. A., Roswintiarti, O. & Mohamad, M. Development of the Indonesian and Malaysian fire danger rating systems. Mitig. Adapt. Strateg. Glob. Change 12, 165 (2006).

    Article 

    Google Scholar 

  • Clough, Y. et al. Land-use choices follow profitability at the expense of ecological functions in Indonesian smallholder landscapes. Nat. Commun. 7, 13137 (2016).

    CAS 
    Article 

    Google Scholar 

  • Bissonnette, J.-F. & De Koninck, R. The return of the plantation? Historical and contemporary trends in the relation between plantations and smallholdings in Southeast Asia. J. Peasant Stud. 44, 918–38 (2017).

    Article 

    Google Scholar 

  • Gaveau, D. L. A. et al. Slowing deforestation in Indonesia follows declining oil palm expansion and lower oil prices. PLOS ONE 17, e0266178 (2022).

  • Svatoňová, T., Herák, D. & Kabutey, A. Financial profitability and sensitivity analysis of palm oil plantation in Indonesia. Acta Univ. Agric. Silvic. Mendelianae Brunensis 63, 1365–73 (2015).

    Article 

    Google Scholar 

  • Gaveau, D. L. A. et al. Rapid conversions and avoided deforestation: examining four decades of industrial plantation expansion in Borneo. Scientific Reports 6, (2016).

  • Simamora, A. P. Govt says no to converting peatland into plantations, The Jakarta Post. August (2010).

  • Satriastanti, F. E. Jokowi bans new oil palm and mining concessions, Mongabay.com April (2016).

  • Sloan, S., Edwards, D. P. & Laurance, W. F. Does Indonesia’s REDD+ moratorium on new concessions spare imminently-threatened forests? Conserv. Lett. 5, 222–31 (2012).

    Article 

    Google Scholar 

  • Busch, J. et al. Reductions in emissions from deforestation from Indonesia’s moratorium on new oil palm, timber, and logging concessions. Proc. Natl Acad Sci USA 112, 1328–33 (2015).

    CAS 
    Article 

    Google Scholar 

  • Forsyth, T. Public concerns about transboundary haze: a comparison of Indonesia, Singapore, and Malaysia. Glob. Environ. Change 25, 76–86 (2014).

    Article 

    Google Scholar 

  • Carbon Conservation. Fire Free Village Program – Review 2017. (Carbon Conservation, Singapore, (2017).

  • Gaveau, D. L. A. et al. Overlapping land claims limit the use of satellites to monitor no-deforestation committments and no-burning compliance. Conserv. Lett. 10, 257–64 (2017).

    Article 

    Google Scholar 

  • EarthData. MODIS Collection 6 Active-Fire Detections standard scientific data (MCD14ML), NASA EarthData, https://earthdata.nasa.gov/firms (2019).

  • Giglio, L., Schroeder, W. & Justice, C. O. The Collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).

    Article 

    Google Scholar 

  • Sloan, S., Cattau, M.E. Discrete Fire Events, their Severity, and their Ignitions, as Derived from MODIS MCD 14ML Active-Fire Detection Data for Indonesia, 2002-2019. Sean Sloan and Megan E. Cattau, Datadryad.org. (2022).

  • Cattau, M. E. et al. Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan, Indonesia. Glob. Environ. Change 39, 205–19 (2016).

    Article 

    Google Scholar 

  • Wooster, M. J., Perry, G. L. W. & Zoumas, A. Fire, drought and El Niño relationships on Borneo during the pre-MODIS era (1980–2000). Biogeosciences 9, 317–40 (2012).

    Article 

    Google Scholar 

  • Tansey, K., Beston, J., Hoscilo, A., Page, S. E. & Paredes Hernández, C. U. Relationship between MODIS fire hot spot count and burned area in a degraded tropical peat swamp forest in Central Kalimantan, Indonesia. J. Geophys. Res. 113, (2008).

  • Oom, D., Silva, P. C., Bistinas, I. & Pereira, J. M. C. Highlighting biome-specific sensitivity of fire size distributions to time-gap parameter using a new algorithm for fire event individuation. Remote Sens. 8, 663 (2016).

  • Schroeder, W. et al. Validation of GOES and MODIS active fire detection products using ASTER and ETM plus data. Remote Sens. Environ. 112, 2711–26 (2008).

    Article 

    Google Scholar 

  • Hantson, S., Padilla, M., Corti, D. & Chuvieco, E. Strengths and weaknesses of MODIS hotspots to characterize global fire occurrence. Remote Sens. Environ. 131, 152–59 (2013).

    Article 

    Google Scholar 

  • Tanpipat, V., Honda, K. & Nuchaiya, P. MODIS hotspot validation over Thailand. Remote Sens. 1, 1043–54 (2009).

    Article 

    Google Scholar 

  • Liew, S. C., Shen, C., Low, J., Lim, A. & Kwoh, L. K. The 24th Asian Conference on Remote Sensing and 2003 International Symposium on Remote Sensing (ACRS2003). p. 671-73 (Asian Association on Remote Sensing), November 3–7.

  • Fornacca, D., Ren, G. & Xiao, W. Performance of three MODIS fire products (MCD45A1, MCD64A1, MCD14ML), and ESA Fire_CCI in a mountainous area of northwest Yunnan, China, characterized by frequent small fires. Remote Sens. 9, 1131 (2017).

    Article 

    Google Scholar 

  • Schroeder, W., Oliva, P., Giglio, L. & Csiszar, I. A. The New VIIRS 375m active fire detection data product: algorithm description and initial assessment. Remote Sens. Environ. 143, 85–96 (2014).

    Article 

    Google Scholar 

  • Giglio, L., Boschetti, L., Roy, D. P., Humber, M. L. & Justice, C. O. The Collection 6 MODIS burned area mapping algorithm and product. Remote Sens. Environ. 217, 72–85 (2018).

    Article 

    Google Scholar 

  • Roy, D. P., Boschetti, L., Justice, C. O. & Ju, J. The Collection 5 MODIS burned area product — Global evaluation by comparison with the MODIS active fire product. Remote Sens. Environ. 112, 3690–707 (2008).

    Article 

    Google Scholar 

  • Miettinen, J., Langner, A. & Siegert, F. Burnt area estimation for the year 2005 in Borneo using multi-resolution satellite imagery. Int. J. Wildland Fire 16, 45–53 (2007).

  • Luo, R., Hui, D., Miao, N., Liang, C. & Wells, N. Global relationship of fire occurrence and fire intensity: a test of intermediate fire occurrence-intensity hypothesis. J. Geophys. Res. Biogeosci. 122, 1123–36 (2017).

    Article 

    Google Scholar 

  • Andela, N. et al. The Global Fire Atlas of individual fire size, duration, speed, and direction. Earth Syst. Sci. Data 11, 529–52 (2019).

    Article 

    Google Scholar 

  • Andela, N., Morton, D. C., Giglio, L. & Randerson, J. T. Global Fire Atlas with Characteristics of Individual Fires, 2003-2016, ORNL Distributed Active Archive Center, https://doi.org/10.3334/ORNLDAAC/1642, https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1642 (2019).

  • Field, R. D. & Shen, S. S. P. Predictability of carbon emissions from biomass burning in Indonesia from 1997 to 2006. J. Geophys. Res. Biogeosci. 113, G04024 (2008).

    Article 
    CAS 

    Google Scholar 

  • Fuller, D. O. & Murphy, K. The ENSO-fire dynamic in insular Southeast Asia. Clim. Change 74, 435–55 (2006).

    Article 

    Google Scholar 

  • Field, R. D. et al. Development of a global fire weather database. Nat. Hazards Earth Syst. Sci. 15, 1407–23 (2015).

    Article 

    Google Scholar 

  • Huffman, G. J. GPM IMERG Final Precipitation gridded data, L3 1 month 0.1 degree x 0.1 degree, version 06B. NASA Precipitation Processing System, Goddard Earth Sciences Data and Information Services Center (GES DISC). https://storm-pps.gsfc.nasa.gov/storm/; https://pmm.nasa.gov/data-access/downloads/gpm (2019).

  • Huffman, G. J. et al. The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeorol. 8, 38–55 (2007).

    Article 

    Google Scholar 

  • Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).

    Article 

    Google Scholar 

  • Hsu, J., Huang, W.-R., Liu, P.-Y. & Li, X. Validation of CHIRPS precipitation estimates over taiwan at multiple timescales. Remote Sens. 13, 254 (2021).

  • Rozante, J. R., Vila, D. A., Barboza Chiquetto, J., Fernandes, A. D. A. & Souza Alvim, D. Evaluation of TRMM/GPM blended daily products over Brazil. Remote Sens. 10, 882 (2018).

  • Prakash, S., Mitra, A. K., Pai, D. S. & AghaKouchak, A. From TRMM to GPM: how well can heavy rainfall be detected from space? Adv. Water Resour. 88, 1–7 (2016).

    Article 

    Google Scholar 

  • Ma, Q. et al. Performance evaluation and correction of precipitation data using the 20-year IMERG and TMPA precipitation products in diverse subregions of China. Atmos. Res. 249, 105304 (2021).

    Article 

    Google Scholar 

  • Nwachukwu, P. N., Satge, F., Yacoubi, S. E., Pinel, S. & Bonnet, M.-P. From TRMM to GPM: how reliable are satellite-based precipitation data across Nigeria? Remote Sens. 12, 3964 (2020).

  • Popovych, V. F. & Dunaieva, I. A. Assessment of the GPM IMERG and CHIRPS precipitation estimations for the steppe part of the Crimea. Meteorol. Hydrol. Water Manage 9, (2021).

  • Navarro, A. et al. Assessment of IMERG precipitation estimates over Europe. Remote Sens. 11, 2470 (2019).

  • Dezfuli, A. K. et al. Validation of IMERG precipitation in Africa. J. Hydrometeorol. 18, 2817–25 (2017).

    Article 

    Google Scholar 

  • Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap. (Chapman and Hall, Boca Raton, FL, USA, 1993).

  • Pérez-Hoyos, A., Rembold, F., Kerdiles, H. & Gallego, J. Comparison of global land cover datasets for cropland monitoring. Remote Sens. 9, 1118 (2017).

  • ESA. Annual land-cover product, 1992 to 2019/present, based on MERIS 300-m and ancillary SPOT, AVHRR, Sentinel-3 and PROB-V satellite data. European Space Agency (ESA) European Centre for Medium-Range Weather Forecasts (ECMFW) Copernicus Climate Change Service (C3S) Climate Change Initiative (CCI), https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=overview; http://maps.elie.ucl.ac.be/CCI/viewer/download.php; http://www.esa-landcover-cci.org/ (2020).

  • Defourny, P. Product User Guide and Specification: ICDR Land Cover 2016 to 2019 (Version 2.1.1 of ESA Coperninus Climate Change Intitiative Annual 300-m Land-Cover Classifications). (Universitie Catholique du Lovain, Louvain, Belgium, (2020).

  • Vetrita, Y. & Cochrane, M. A. Annual Burned Area from Landsat, Mawas, Central Kalimantan, Indonesia, 1997-2015, ORNL Distributed Active Archive Center, https://doi.org/10.3334/ORNLDAAC/1708, https://daac.ornl.gov/CMS/guides/Annual_Burned_Area_Maps.html; https://daac.ornl.gov/cgi-bin/dataset_lister.pl?p=33 (2019).


  • Source: Ecology - nature.com

    Selection, drift and community interactions shape microbial biogeographic patterns in the Pacific Ocean

    Global soil profiles indicate depth-dependent soil carbon losses under a warmer climate