Abstract
The Encontro das Águas State Park (EASP), renowned as the world’s largest refuge for Jaguars (Panthera onca), is located within the Brazilian portion of the Pantanal biome, and it covers a vast area of approximately 1,080 square kilometers. This ecologically rich region suffered significant devastation from extensive fires in 2020. Given that the ongoing monitoring of wildfires is a crucial task for the preservation of fauna and flora in legally protected environments such as the Pantanal biome, this paper investigates the catastrophic 2020 fire incidents in the EASP reserve through a fully automated methodology capable of detecting and characterizing fire-devastated areas. By taking updated and accurate data from the Google Earth Engine platform, our approach integrates a comprehensive collection of MODIS sensor images, spectral indices, and filtering processes to generate a spatial map of fire-affected areas in a given period of analysis. Specifically, given a surface reflectance and atmospheric corrected MODIS (MOD09Q/A1) image series, the NBR index is computed from each image and then processed through Savitzky-Golay filtering to remove noisy and missing data. Next, the \(\Delta \)NBR index is calculated for each consecutive pair of images so as to produce a frequency map of burned areas. In order to quantify and analyze the recent changes due to these successive wildfires that took place in this Pantanal portion, we focused on the devastating fire events that occurred in the EASP park from July to September 2020. The fire mappings were assessed and statistically validated using the kappa coefficient and significance tests computed through reference samples collected from official databases and visual inspection. The findings revealed that, tragically, 84% of the study area experienced at least one instance of fire during the three-month investigation period. The high temporal resolution of MODIS sensors proves to be extremely valuable in promptly and effectively detecting changes in land use.
Similar content being viewed by others
Code Availability
The code of the proposed framework is freely available at https://github.com/rogerionegri/firemap.
References
Alencar AAC et al (2022) Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning. Remote Sens 14(11):2510. https://doi.org/10.3390/rs14112510
Almeida-Filho R, Shimabukuro YE (2004) Cover: Monitoring biomass burning in the Brazilian Amazônia. Int J Remote Sens 25(24):5537–5541. https://doi.org/10.1080/0143116031000075143
Arisanty D et al (2022) Utilizing Sentinel-2 Data for Mapping Burned Areas in Banjarbaru Wetlands, South Kalimantan Province. Int J For Res 2022:1–12. https://doi.org/10.1155/2022/7936392
Associação C (2011) Cartilha: queimada controlada. https://www.terrabrasilis.org.br/ecotecadigital/pdf/queimada-controlada.pdf
Boucher JABC, Beaudoin ABD, Hébert CB, Guindon LB, Bauce ÉA (2016) Assessing the potential of the differenced Normalized Burn Ratio (dNBR) for estimating burn severity in eastern Canadian boreal forests. Int J Wildland Fire 26(1):32–45. https://doi.org/10.1071/WF15122
Brinkmann E, et al. (2019) Psychometric evaluation of a screening question for persistent depressive disorder. BMC Psychiatry 119(1). https://doi.org/10.1186/s12888-019-2100-0
Carvalho Júnior O, Guimarães R, Silva CS, Gomes R (2015) Standardized time-series and interannual phenological deviation: new techniques for burned-area detection using long-term MODIS-NBR dataset. Remote Sens 7(6):6950–6985. https://doi.org/10.3390/rs70606950
Chuvieco E, Martín MP, Palacios A (2002) Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. Int J Remote Sens 23(23):5103–5110. https://doi.org/10.1080/01431160210153129
Congalton RG, Green K (2009) Assessing the Accuracy of Remotely Sensed Data. CRC Press, Boca Raton
Copernicus (2020) Wildfires affected the Encontro das Aguas Park, Brazil. https://www.copernicus.eu/en/media/image-day-gallery/wildfires-affected-encontro-das-aguas-park-brazil. Image credit: European Union, Copernicus Sentinel-2 imagery
de Araújo FM, Ferreira LG, Arantes AE (2012) Distribution patterns of burned areas in the brazilian biomes: an analysis based on satellite data for the 2002-2010 period. Remote Sens 4(7):1929–1946. https://www.mdpi.com/2072-4292/4/7/1929. https://doi.org/10.3390/rs4071929
Dombi J, Dineva A (2020) Adaptive Savitzky-Golay filtering and its applications. Int J Adv Intell Paradigms 16(2):145–156. https://doi.org/10.1504/ijaip.2020.107011
Farhadi H, Ebadi H, Kiani, A (2023) Badi: a novel burned area detection index for sentinel-2 imagery using google earth engine platform. ISPRS Ann Photogramm Remote Sens Spat Inf Sci X-4/W1-2022, 179–186. https://isprs-annals.copernicus.org/articles/X-4-W1-2022/179/2023/. https://doi.org/10.5194/isprs-annals-X-4-W1-2022-179-2023
Filipponi F (2019) Exploitation of Sentinel-2 Time Series to Map Burned Areas at the National Level: A Case Study on the 2017 Italy Wildfires. Remote Sens 11(6):622. https://doi.org/10.3390/rs11060622
Gao B (1996) NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266. https://doi.org/10.1016/S0034-4257(96)00067-3
Garcia LC et al (2021) Record-breaking wildfires in the world’s largest continuous tropical wetland: Integrative fire management is urgently needed for both biodiversity and humans. J Environ Manag 293:112870. https://doi.org/10.1016/j.jenvman.2021.112870
Giglio L, van der Werf GR, Randerson JT, Collatz GJ, Kasibhatla P (2006) Global estimation of burned area using MODIS active fire observations. Atmos Chem Phys 6(4):957–947. https://acp.copernicus.org/articles/6/957/2006/. https://doi.org/10.5194/acp-6-957-2006
Hardtke LA, Blanco PD, Valle HF, Metternicht GI, Sione WF (2015) Semi-automated mapping of burned areas in semi-arid ecosystems using MODIS time-series imagery. Int J Appl Earth Obs Geoinformation 38:25–35. https://doi.org/10.1016/j.jag.2014.11.011
Herawati H, Santoso H (2011) Tropical forest susceptibility to and risk of fire under changing climate: A review of fire nature, policy and institutions in Indonesia. For Policy Econ 13(4):227–233. https://www.sciencedirect.com/science/article/pii/S1389934111000189. https://doi.org/10.1016/j.forpol.2011.02.006
IBGE (2018) IBGE retrata cobertura natural dos biomas do paiís de 2000 a 2018. https://agenciadenoticias.ibge.gov.br/agencia-sala-de-imprensa/2013-agencia-de-noticias/releases/28943-ibge-retrata-cobertura-natural-dos-biomas-do-pais-de-2000-a-2018
INPE (2021) Banco de Dados de queimadas. http://www.inpe.br/queimadas/bdqueimadas
ISA (2018) Parque Estadual Encontro das Águas. https://uc.socioambiental.org/pt-br/arp/4253
Jankauskaite G, Delegido J (2022) Assessing fire impacts on the Pantanal wetland using Sentinel-2 imagery. https://doi.org/10.21203/rs.3.rs-1729338/v1
Júnior OC, et al. (2012) Combining noise-adjusted principal components transform and median filter techniques for denoising modis temporal signatures. Braz J Geophys 30(2):147–157. https://doi.org/10.22564/rbgf.v30i2.88
Keeley JE, Syphard, AD (2021) Large California wildfires: 2020 fires in historical context. Fire Ecol 17(22). https://doi.org/10.1186/s42408-021-00111-5
Key C, Benson N (1999) The Normalized Burn Ratio, a Landsat TM radiometric index of burn severity incorporating multi-temporal differencing. US Geological Survey, p. 2000
Key C, Benson N (2006) Landscape Assessment: Ground measure of severity, the Composite Burn Index; and Remote sensing of severity, the Normalized Burn Ratio LA 1-51 (-, 2006)
Kraemer HC, Kupfer DJ, Clarke DE, Narrow WE, Regier DA (2012) DSM-5: how reliable is reliable enough? Am J Psychiatry 169(1):13–15. https://doi.org/10.1176/appi.ajp.2011.11010050
Leal Filho W, Azeiteiro UM, Salvia AL, Fritzen B, Libonati R (2021) Fire in Paradise: Why the Pantanal is burning. Environ Sci Policy 123:31–34
Li X, Shen R, Chen R (2020) Improving time series reconstruction by fixing invalid values and its fidelity evaluation. IEEE Access 8:7558–7572. https://doi.org/10.1109/access.2019.2962757
Libonati R et al (2022) Assessing the role of compound drought and heatwave events on unprecedented 2020 wildfires in the Pantanal. Environ Res Lett 17(1):015005. https://doi.org/10.1088/1748-9326/ac462e
Libonati R, DaCamara CC, Peres LF, Carvalho LASd, Garcia LC (2020) Rescue Brazil’s burning Pantanal wetlands. Nature 588(7836):217–219
Liu R, Shang R, Liu Y, Lu X (2017) Global evaluation of gap-filling approaches for seasonal NDVI with considering vegetation growth trajectory, protection of key point, noise resistance and curve stability. Remote Sens Environ 189:164–179. https://doi.org/10.1016/j.rse.2016.11.023
Liu J, Maeda EE, Wang D, Heiskanen J (2021) Sensitivity of Spectral Indices on Burned Area Detection using Landsat Time Series in Savannas of Southern Burkina Faso. Remote Sens 13(13). https://www.mdpi.com/2072-4292/13/13/2492. https://doi.org/10.3390/rs13132492
Liu S, Zheng Y, Dalponte M, Tong X (2020) A novel fire index-based burned area change detection approach using Landsat-8 OLI data. Eur J Remote Sens 53(1):104–112. https://www.sciencedirect.com/science/article/pii/S0034425711002343. https://doi.org/10.1080/22797254.2020.1738900
Lizundia-Loiola J, Otón G, Ramo R, Chuvieco E (2020) A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data. Remote Sens Environ 236:111493. https://doi.org/10.1016/j.rse.2019.111493
Luz AEO, et al. (2022) Mapping Fire Susceptibility in the Brazilian Amazon Forests Using Multitemporal Remote Sensing and Time-Varying Unsupervised Anomaly Detection. Remote Sens 14(10). https://doi.org/10.3390/rs14102429
Magalhães Neto Nd, Evangelista H (2022) Human Activity Behind the Unprecedented 2020 Wildfire in Brazilian Wetlands (Pantanal). Front Environ Sci 10:1–15. https://doi.org/10.3389/fenvs.2022.888578
MapBiomas (2021) Projeto MapBiomas - Mapeamento das áreas queimadas no Brasil (Coleção 6). https://mapbiomas.org/colecoes-mapbiomas-1?cama_set_language=pt-BR
Marengo JA, Cunha AP, Cuartas LA, Deusdará Leal KR, Broedeli E, Seluchi ME, et al (2021) Extreme Drought in the Brazilian Pantanal in 2019–2020: Characterization, Causes, and Impacts. Frontiers in Water 3. https://doi.org/10.3389/frwa.2021.639204
Marques JF et al (2021) Fires dynamics in the Pantanal: Impacts of anthropogenic activities and climate change. Journal of Environmental Management 299:113586
Mather PM, Koch M (2011) Computer Processing of Remotely-Sensed Images: An Introduction 3 edn (Wiley 2011)
Melchiorre A, Boschetti L (2018) Global analysis of burned area persistence time with MODIS data. Remote Sens 10(5):750. https://doi.org/10.3390/rs10050750
MMA (2021) Biomas: Pantanal. https://antigo.mma.gov.br/biomas/pantanal.html
MT-BRAZIL (2002) Mato Grosso State Decree #4881 of December 22th 2004: the Encontro das Águas State Park creation. https://documentacao.socioambiental.org/ato_normativo/UC/3553_20180618_195535.pdf
NASA (2021) Moderate Resolution Imaging Spectroradiometer. http://modis.gsfc.nasa.gov/
Nascimento ES, Lopes AAF, Marra AB, Pinto MRE, VÁgula D, Silva EA, Galo ML (2023) Detecção de queimadas e análise do impacto do fogo na vegetação natural do Parque Estadual Encontro das Águas, pantanal mato-grossense, pp 1490-1493. https://proceedings.science/sbsr-2023/trabalhos/deteccao-de-queimadas-e-analise-do-impacto-do-fogo-na-vegetacao-natural-do-parqu
Pereira, AA et al. (2017) Burned Area Mapping in the Brazilian Savanna Using a One-Class Support Vector Machine Trained by Active Fires. Remote Sens 9(11) https://www.mdpi.com/2072-4292/9/11/1161. https://doi.org/10.3390/rs9111161
Pereira O, Ferreira L, Pinto F, Baumgarten L (2018) Assessing pasture degradation in the brazilian cerrado based on the analysis of MODIS NDVI time-series. Remote Sens 10(11):1761. https://doi.org/10.3390/rs10111761
Pletch MA, et al. (2021) The 2020 Brazilian Pantanal fires. Anais da Academia Brasileira de Ciências 93(3). https://doi.org/10.1590/0001-3765202120210077
Ren J, Campbell J, Shao Y (2017) Estimation of SOS and EOS for Midwestern US corn and soybean crops. Remote Sens 9(7):722. https://doi.org/10.3390/rs9070722
Rodríguez Mega E (2020) Apocalyptic fires are ravaging the world’s largest tropical wetland. Nature 586:20–21. https://doi.org/10.1038/d41586-020-02716-4
Roy D, Boschetti L, Trigg S (2006) Remote sensing of fire severity: assessing the performance of the normalized burn ratio. IEEE Geosci Remote Sens Lett 3(1):112–116. https://doi.org/10.1109/LGRS.2005.858485
Schafer RW (2011) What Is a Savitzky-Golay Filter? (Lecture Notes). IEEE Signal Proc Mag 28(4):111–117. https://doi.org/10.1109/MSP.2011.941097
Schmid M, Rath D, Diebold U (2022) Why and How Savitzky-Golay Filters Should Be Replaced. ACS Meas Sci 2(2):185–196. https://doi.org/10.1021/acsmeasuresciau.1c00054
Silgueiro VdF, Souza COCFd, Muller EO, Silva CJd (2021) Dimensions of the 2020 wildfire catastrophe in the Pantanal wetland: the case of the municipality of Poconé, Mato Grosso, Brazil. Res Soc Dev 10(15):e08101522619. https://doi.org/10.33448/rsd-v10i15.22619. https://rsdjournal.org/index.php/rsd/article/view/22619
Sobrino JA, Llorens R, Fernández C, Fernández-Alonso, JM. Vega, JA (2019) Relationship between Soil Burn Severity in Forest Fires Measured In Situ and through Spectral Indices of Remote Detection. Forests 10(5). https://doi.org/10.3390/f10050457
Souza A, et al (2013) Classificação climática e balanço Hídrico climatológico no estado de mato grosso. Nativa 1(1):34–43. https://doi.org/10.14583/2318-7670.v01n01a07
Tortato FR, Izzo TJ (2017) Advances and barriers to the development of jaguar-tourism in the Brazilian Pantanal. Perspect Ecol Conserv 15(1):61–63. https://doi.org/10.1016/j.pecon.2017.02.003
Trigg S, Flasse S (2001) An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah. Int J Remote Sens 22(13):2641–2647. https://doi.org/10.1080/01431160110053185
USGS (2023) MODIS/Terra Surface Reflectance 8-Day L3 Global 250 m SIN Grid. https://lpdaac.usgs.gov/products/mod09q1v061/
Vasilakos C, Tsekouras GE, Palaiologou P, Kalabokidis K (2018) Neural-Network Time-Series Analysis of MODIS EVI for Post-Fire Vegetation Regrowth. ISPRS Int J Geo-Inf 7(11):420. https://doi.org/10.3390/ijgi7110420
Veraverbeke S, Harris S, Hook S (2011) Evaluating spectral indices for burned area discrimination using MODIS/ASTER (MASTER) airborne simulator data. Remote Sens Environ 115(10):2702–2709. https://www.sciencedirect.com/science/article/pii/S0034425711002343. https://doi.org/10.1016/j.rse.2011.06.010
Williamson GJ, Ellis TM, Bowman DMJS (2022) Double-Differenced dNBR: Combining MODIS and Landsat Imagery to Map Fine-Grained Fire MOSAICS in Lowland Eucalyptus Savanna in Kakadu National Park. Northern Australia. Fire 5(5):160. https://doi.org/10.3390/fire5050160
WWF-Brasil (2021) Retrospectiva 2020: Pantanal teve recordes histoóricos de queimadas. https://www.wwf.org.br/?77589/Retrospectiva-2020-Pantanal-teve-recordes-historicos-de-queimadas
WWF-Brasil (n.d.) Áreas prioritárias: Pantanal. https://www.wwf.org.br/natureza_brasileira/areas_prioritarias/pantanal/
Xue J, Su B (2017) Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications. J Sensors 2017, 1–17. https://www.hindawi.com/journals/js/2017/1353691/. https://doi.org/10.1155/2017/1353691
Funding
This research was funded by the São Paulo Research Foundation (FAPESP), grants 2016/24185-8, 2021/01305-6 and 2021/03328-3, and National Council for Scientific and Technological Development (CNPq), grants 316228/2021-4 and 305220/2022-5.
Author information
Authors and Affiliations
Contributions
Larissa M. P. Parra, Fabrícia C. Santos, Rogeério G. Negri, Adriano Bressane and Wallace Casaca wrote the main manuscript text; Rogério G. Negri implemented the codes; Larissa M. P. Parra, Fabrícia C. Santos, Rogério G. Negri, Adriano Bressane, Marilaine Colnago, Maurício A. Dias and Wallace Casaca analyzed the results. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Conflicts of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by: H. Babaie.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Parra, L.M.P., Santos, F.C., Negri, R.G. et al. Assessing the impacts of catastrophic 2020 wildfires in the Brazilian Pantanal using MODIS data and Google Earth Engine: A case study in the world’s largest sanctuary for Jaguars. Earth Sci Inform 16, 3257–3267 (2023). https://doi.org/10.1007/s12145-023-01080-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-023-01080-x