Abstract
The research on space-based tropical smouldering peatlands poses challenges due to their low temperature and background object environments. This study focuses on analyzing smoldering pixels detected by the Tropical Peatland Combustion Algorithm (ToPeCAl) applied to Landsat-8 imagery in Indonesian peatlands. A modification to the contextual test for selecting fire pixels is proposed. Instead of using the high confidence level typically employed for selecting flaming pixels, a low confidence level is suggested to identify smoldering pixels from their background pixels. The validation of smoldering pixels is conducted using atmospheric products from multi-sensor satellites. The findings reveal that applying the low confidence level on the contextual test results in an accuracy of 75% for smoldering detection, similar to previous research with ground check validation. A dynamic statistical threshold value is utilized from background pixels within a moving window to validate smoldering pixels using atmospheric data, taking into consideration factors such as wind velocity, spatial resolution, and time of acquisition between sensors. Approximately 50–70% of the selected smoldering pixels show higher values of Aerosol Optical Depth from Moderate Resolution Imaging Spectroradiometer (MODIS-AOD), Carbon Monoxide from TROPOspheric Monitoring Instrument (TROPOMI-CO), and Absorbing Aerosol Index from Second-generation Global Imager (SGLI-AAI) compared to their mean background pixel values. The study reveals a significant correlation (up to 0.8) between TROPOMI-CO and ground measurement of AOD over smouldering peatlands. These insights have the potential to aid disaster management agencies in effectively controlling smoldering fires and minigating the risk associated with smoldering peatlands.
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References
Arjasakusuma S, Kusuma SS, Vetrita Y et al (2022) Monthly burned-area mapping using multi-sensor integration of Sentinel-1 and Sentinel-2 and machine learning: case study of 2019’s fire events in South Sumatra Province, Indonesia. Remote Sens Appl Soc Environ 27:100790. https://doi.org/10.1016/j.rsase.2022.100790
Baetens L, Desjardins C, Hagolle O (2019) Validation of Copernicus Sentinel-2 cloud masks obtained from MAJA, Sen2Cor, and FMask processors using reference cloud masks generated with a supervised active learning procedure. Remote Sens 11:433. https://doi.org/10.3390/rs11040433
Berman JJ (2016) Understanding your data. In: Berman JJ (ed) Data simplification: taming information with open source tools. Morgan Kaufmann, Boston, pp 135–187
Bertschi I, Yokelson RJ, Ward DE et al (2003) Trace gas and particle emissions from fires in large diameter and belowground biomass fuels. J Geophys Res Atmos 108:8472. https://doi.org/10.1029/2002JD002100
Cole LES, Bhagwat SA, Willis KJ (2019) Fire in the swamp forest: palaeoecological insights into natural and human-induced burning in intact tropical peatlands. Front For Glob Change. https://doi.org/10.3389/ffgc.2019.00048
Crippa P, Castruccio S, Archer-Nicholls S et al (2016) Population exposure to hazardous air quality due to the 2015 fires in equatorial Asia. Sci Rep 6:37074. https://doi.org/10.1038/srep37074
da Junior CA, Lima S, Teodoro M et al (2022) Fires drive long-term environmental degradation in the Amazon basin. Remote Sens 14:338. https://doi.org/10.3390/rs14020338
Gaveau DLA, Descals A, Salim MA et al (2021) Refined burned-area mapping protocol using Sentinel-2 data increases estimate of 2019 indonesian burning. Earth Syst Sci Data 13:5353–5368. https://doi.org/10.5194/essd-13-5353-2021
GCOM Project Team (2018) GCOM-C “SHIKISAI” data users handbook, 1st edn. Japan Aerospace Exploration Agency, Tokyo
Getis A, Ord JK (2008) The analysis of spatial association by use of distance statistics. In: Luc A, Rey S (eds) Perspectives on spatial data analysis. Springer, Berlin, pp 127–145
Giglio L, Csiszar I, Restás Á et al (2008) Active fire detection and characterization with the advanced spaceborne thermal emission and reflection radiometer (ASTER). Remote Sens Environ 112:3055–3063. https://doi.org/10.1016/j.rse.2008.03.003
Giglio L, Schroeder W, Justice CO (2016) The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens Environ 178:31–41. https://doi.org/10.1016/j.rse.2016.02.054
Giles DM, Sinyuk A, Sorokin MG et al (2019) Advancements in the Aerosol Robotic Network (AERONET) Version 3 database – automated near-real-time quality control algorithm with improved cloud screening for sun photometer aerosol optical depth (AOD) measurements. Atmos Meas Tech 12:169–209. https://doi.org/10.5194/amt-12-169-2019
Hall JV, Zhang R, Schroeder W et al (2019) Validation of GOES-16 ABI and MSG SEVIRI active fire products. Int J Appl Earth Obs Geoinf 83:101928. https://doi.org/10.1016/j.jag.2019.101928
Hayasaka H, Takahashi H, Limin SH et al (2016) Peat fire occurrence. In: Osaki M, Tsuji N (eds) Tropical peatland ecosystems. Springer Japan, Tokyo, pp 377–395
Hird A (2019) Congo Basin vs the Amazon: a tale of two forest fires. RFI. https://www.rfi.fr/en/africa/20190827-africa-forest-fire-amazon-deforestation. Accessed 7 Oct 2021
Hu Y, Fernandez-Anez N, Smith TEL, Rein G (2018) Review of emissions from smouldering peat fires and their contribution to regional haze episodes. Int J Wildl Fire 27:293–312. https://doi.org/10.1071/WF17084
Huijnen V, Wooster MJ, Kaiser JW et al (2016) Fire carbon emissions over maritime Southeast Asia in 2015 largest since 1997. Sci Rep 6:1–8. https://doi.org/10.1038/srep26886
Jenner L (2018) Papua and Papua New Guinea experience high numbers of wildfires. NASA. https://www.nasa.gov/image-feature/goddard/2018/papua-and-papua-new-guinea-experience-high-numbers-of-wildfires. Accessed 10 Oct 2020
Koplitz SN, Mickley LJ, Marlier ME et al (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. Environ Res Lett 11:94023. https://doi.org/10.1088/1748-9326/11/9/094023
Kumar SS, Roy DP (2018) Global operational land imager Landsat-8 reflectance-based active fire detection algorithm. Int J Digit Earth 11:154–178. https://doi.org/10.1080/17538947.2017.1391341
Landgraf J, de Brugh J, Scheepmaker RA et al (2022) Algorithm theoretical baseline document for Sentinel-5 precursor: carbon monoxide total column retrieval. https://sentinel.esa.int/documents/247904/2476257/Sentinel-5P-TROPOMI-ATBD-Carbon-Monoxide-Total-Column-Retrieval.pdf. Accessed 6 Oct 2020
Li X, Wang J, Song W et al (2014) Automatic smoke detection in MODIS satellite data based on K-means clustering and fisher linear discrimination. Photogramm Eng Remote Sens 80:971–982. https://doi.org/10.14358/PERS.80.10.971
Li X, Song W, Lian L, Wei X (2015) Forest fire smoke detection using back-propagation neural network based on MODIS data. Remote Sens 7:4473–4498. https://doi.org/10.3390/rs70404473
Lu X, Zhang X, Li F et al (2021) Detection of fire smoke plumes based on aerosol scattering using viirs data over global fire-prone regions. Remote Sens 13:1–22. https://doi.org/10.3390/rs13020196
Magro C, Nunes L, Gonçalves OC et al (2021) Atmospheric trends of CO and CH4 from extreme wildfires in Portugal using Sentinel-5P TROPOMI level-2 data. Fire. https://doi.org/10.3390/fire4020025
McFeeters SK (1996) The use of the normalized difference Water Index (NDWI) in the delineation of open water features. Int J Remote Sens 17:1425–1432. https://doi.org/10.1080/01431169608948714
Mishra AK, Lehahn Y, Rudich Y, Koren I (2015) Co-variability of smoke and fi re in the Amazon basin. Atmos Environ 109:97–104. https://doi.org/10.1016/j.atmosenv.2015.03.007
Mukai S, Sano I, Nakata M (2019) Algorithms for the classification and characterization of aerosols: utility verification of near-UV satellite observations. J Appl Remote Sens 13:1. https://doi.org/10.1117/1.jrs.13.014527
Mukai S, Sano I, Nakata M (2021) Improved algorithms for remote sensing-based aerosol retrieval during extreme biomass burning events. Atmosphere (Basel) 12:403. https://doi.org/10.3390/atmos12030403
Murdiyarso D, Lilleskov E, Kolka R (2019) Tropical peatlands under siege: the need for evidence-based policies and strategies. Mitig Adapt Strateg Glob Change 24:493–505. https://doi.org/10.1007/s11027-019-9844-1
Murphy SW, de Souza Filho CR, Wright R et al (2016) HOTMAP: global hot target detection at moderate spatial resolution. Remote Sens Environ 177:78–88. https://doi.org/10.1016/j.rse.2016.02.027
Nakata M, Sano I, Mukai S, Kokhanovsky A (2022) Characterization of wildfire smoke over complex terrain using satellite observations, ground-based observations, and meteorological models. Remote Sens 14:2344. https://doi.org/10.3390/rs14102344
Nasi R (2019) It’s not just the Amazon: we must also protect Congo basin peatlands from fire. Truthout. https://truthout.org/articles/its-not-just-the-amazon-we-must-also-protect-congo-basin-peatlands-from-fire. Accessed 7 Oct 2021
Obregón M, Rodrigues G, Costa MJ et al (2019) Validation of ESA Sentinel-2 L2A aerosol optical thickness and columnar water vapour during 2017–2018. Remote Sens 11:1649. https://doi.org/10.3390/rs11141649
Osaki M, Nursyamsi D, Noor M et al (2016a) Peatland in Indonesia. In: Osaki M, Tsuji N (eds) Tropical peatland ecosystems. Springer Japan, Tokyo, pp 49–58
Osaki M, Setiadi B, Takahashi H, Evri M (2016b) Peatland in Kalimantan. In: Osaki M, Tsuji N (eds) Tropical peatland ecosystems. Springer Japan, Tokyo, pp 91–112
Rabelo ERC, Veras CAG, Carvalho JA et al (2004) Log smoldering after an amazonian deforestation fire. Atmos Environ 38:203–211. https://doi.org/10.1016/j.atmosenv.2003.09.065
Rein G (2009) Smouldering combustion phenomena in science and technology. Int Rev Chem Eng 1:3–18
Rein G (2013) Smouldering fires and natural fuels. Fire phenomena and the earth system: an interdisciplinary guide to fire science. Wiley-Blackwell, Chichester, pp 15–33
Rein G (2016) Smouldering combustion. In: Hurley MJ, Gottuk D, Hall JR et al (eds) SFPE handbook of fire protection engineering, 5th edn. Springer, New York, pp 1–3493
Rieley JO, Page SE (2016) Tropical peatland of the world. In: Osaki M, Tsuji N (eds) Tropical peatland ecosystems. Springer, Tokyo, pp 3–32
Santoso M, Huang X, Prat-Guitart N et al (2019) Smouldering fires and soils. In: Pereira P et al (eds) Fire effects on soil properties. CSIRO Publishing, Victoria, pp 203–216
Santoso MA, Christensen EG, Amin HMF et al (2022) GAMBUT field experiment of peatland wildfires in Sumatra: from ignition to spread and suppression. Int J Wildl Fire 31:949–966
Schroeder W, Oliva P, Giglio L, Csiszar IA (2014) The new VIIRS 375m active fire detection data product: Algorithm description and initial assessment. Remote Sens Environ 143:85–96. https://doi.org/10.1016/j.rse.2013.12.008
Schroeder W, Oliva P, Giglio L et al (2016) Active fire detection using Landsat-8/OLI data. Remote Sens Environ 185:210–220. https://doi.org/10.1016/j.rse.2015.08.032
Shiodera S, Atikah TD, Apandi I et al (2016) Peat-fire impact on forest structure in peatland of Central Kalimantan. In: Osaki M, Tsuji N (eds) Tropical peatland ecosystems. Springer, Tokyo, pp 197–212
Shukla BP, Pal PK (2009) Automatic smoke detection using satellite imagery: preparatory to smoke detection from insat— 3D. Int J Remote Sens 30:9–22. https://doi.org/10.1080/01431160802226059
Smith TEL, Evers S, Yule CM, Gan JY (2018) In situ tropical peatland fire emission factors and their variability, as determined by field measurements in Peninsula Malaysia. Glob Biogeochem Cycl 32:18–31. https://doi.org/10.1002/2017GB005709
Sofan P, Bruce D, Jones E, Marsden J (2019) Detection and validation of tropical peatland flaming and smouldering using Landsat-8 SWIR and TIRS bands. Remote Sens 11:465. https://doi.org/10.3390/rs11040465
Sofan P, Bruce D, Jones E et al (2020) Applying the tropical peatland combustion algorithm to Landsat-8 operational land imager (OLI) and Sentinel-2 multi spectral instrument (MSI) imagery. Remote Sens 12:1–37. https://doi.org/10.3390/rs12233958
Sönmez I, Tekeli AE, Erdi E et al (2014) Validation of the Meteosat Second Generation (MSG) fire monitoring product using ground observations over Turkey. Arab J Geosci 7:3389–3398. https://doi.org/10.1007/s12517-013-1039-6
Spencer RS, Levy RC, Remer LA et al (2019) Exploring aerosols near clouds with high-spatial-resolution aircraft remote sensing during SEAC4RS. J Geophys Res Atmos 124:2148–2173. https://doi.org/10.1029/2018JD028989
Stockwell CE, Jayarathne T, Cochrane MA et al (2016) Field measurements of trace gases and aerosols emitted by peat fires in Central Kalimantan, Indonesia, during the 2015 El Niño. Atmos Chem Phys 16:11711–11732. https://doi.org/10.5194/acp-16-11711-2016
Tanpipat V, Honda K, Nuchaiya P (2009) Modis hotspot validation over Thailand. Remote Sens 1:1043–1054. https://doi.org/10.3390/rs1041043
Torres B, Toledano C, Berjón A et al (2013) Measurements on pointing error and field of view of Cimel-318 sun photometers in the scope of AERONET. Atmos Meas Tech 6:2207–2220. https://doi.org/10.5194/amt-6-2207-2013
Tosca MG, Randerson JT, Zender CS et al (2011) Dynamics of fire plumes and smoke clouds associated with peat and deforestation fires in Indonesia. J Geophys Res Atmos 116:1–14. https://doi.org/10.1029/2010JD015148
Urbanski S (2014) Wildland fire emissions, carbon, and climate: emission factors. For Ecol Manag 317:51–60. https://doi.org/10.1016/j.foreco.2013.05.045
Vadrevu KP, Lasko K, Giglio L, Justice C (2014) Analysis of southeast asian pollution episode during June 2013 using satellite remote sensing datasets. Environ Pollut 195:245–256. https://doi.org/10.1016/j.envpol.2014.06.017
van Marle MJE, Field RD, van der Werf GR et al (2017) Fire and deforestation dynamics in Amazonia (1973–2014): fire dynamics in Amazonia (1973–2014). Glob Biogeochem Cycl 31:24–38. https://doi.org/10.1002/2016GB005445
Veefkind JP, Aben I, McMullan K et al (2012) TROPOMI on the ESA Sentinel-5 precursor: a GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applications. Remote Sens Environ 120:70–83. https://doi.org/10.1016/j.rse.2011.09.027
Verhegghen A, Eva H, Ceccherini G et al (2016) The potential of Sentinel satellites for burnt area mapping and monitoring in the Congo basin forests. Remote Sens 8:986. https://doi.org/10.3390/rs8120986
Wickramasinghe C, Wallace L, Reinke K, Jones S (2020) Intercomparison of Himawari-8 AHI-FSA with MODIS and VIIRS active fire products. Int J Digit Earth 13:457–473. https://doi.org/10.1080/17538947.2018.1527402
Williamson GJ, Price OF, Henderson SB, Bowman DMJS (2013) Satellite-based comparison of fire intensity and smoke plumes from prescribed fires and wildfires in south-eastern Australia. Int J Wildl Fire 22:121–129. https://doi.org/10.1071/WF11165
Wooster MJ, Gaveau DLA, Salim MA et al (2018) New tropical peatland gas and particulate emissions factors indicate 2015 indonesian fires released far more particulate matter (but less methane) than current inventories imply. Remote Sens 10:495. https://doi.org/10.3390/rs10040495
Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27:3025–3033. https://doi.org/10.1080/01431160600589179
Xu H, Zhang G, Zhou Z et al (2022) Development of a novel burned-area subpixel mapping (BASM) workflow for fire scar detection at subpixel level. Remote Sens 14:3546. https://doi.org/10.3390/rs14153546
Yilmaz OS, Acar U, Sanli FB et al (2023) Mapping burn severity and monitoring CO content in Türkiye’s 2021 wildfires, using Sentinel-2 and Sentinel-5P satellite data on the GEE platform. Earth Sci Inform 16:221–240. https://doi.org/10.1007/s12145-023-00933-9
Yoshida M, Murakami H (2021) Algorithm theoretical basis document of aerosol properties for GCOM-C/SGLI. GCOM. https://suzaku.eorc.jaxa.jp/GCOM_C/data/ATBD/ver3/V3ATBD_A3AB_ARNP_MYoshida_20211117.pdf. Accessed 10 Oct 2020
Zhao TX, Ackerman S, Guo W (2010) Dust and smoke detection for multi-channel imagers. Remote Sens 2:2347–2368. https://doi.org/10.3390/rs2102347
Zhao L, Liu J, Peters S et al (2022) Investigating the impact of using IR bands on early fire smoke detection from Landsat imagery with a lightweight CNN model. Remote Sens 14:3047. https://doi.org/10.3390/rs14133047
World Bank (2019) Indonesian economic quarterly: investing in people. The World Bank. https://documents1.worldbank.org/curated/en/622281575920970133/pdf/Indonesia-Economic-Quarterly-Investing-in-People.pdf. Accessed 9 Oct 2020
Acknowledgements
This paper is a part of the study activities entitled “Mapping Tropical Peatland fires using Remote Sensing Satellite Data”. This study was funded by the Research Organisation for Aeronautics and Space of the National Research and Innovation Agency (BRIN). We would like to thank the reviewers for their constructive comments on our manuscript.
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Sofan, P., Chulafak, G.A., Yulianto, F. et al. Assessing space-based smoldering peatland in the tropics with atmospheric products from multi-sensor satellites. Model. Earth Syst. Environ. 10, 465–481 (2024). https://doi.org/10.1007/s40808-023-01793-4
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DOI: https://doi.org/10.1007/s40808-023-01793-4