Burned area mapping in Different Data Products for the Southwest of the Brazilian Amazon

Conteúdo do artigo principal

Débora Joana Dutra
https://orcid.org/0000-0003-3748-5622
Philip Martin Fearnside
https://orcid.org/0000-0003-3672-9082
Aurora Miho Yanai
https://orcid.org/0000-0003-2128-9547
Paulo Maurício Lima de Alencastro Graça
Ricardo Dalagnol
https://orcid.org/0000-0002-7151-8697
Ana Carolina Moreira Pessôa
https://orcid.org/0000-0003-3285-8047
Beatriz Figueiredo Cabral
https://orcid.org/0000-0002-7130-9385
Chantelle Burton
Christopher Jones
Richard Betts
Poliana Domingos Ferro
https://orcid.org/0000-0001-7702-077X
Daniel Alves Braga
https://orcid.org/0000-0002-5170-1902
Luiz Eduardo Oliveira e Cruz de Aragão
Liana Oighenstein Anderson
https://orcid.org/0000-0001-9545-5136

Resumo

Fires affect the Amazon rainforest and cause various socio-environmental problems. Analyses of forest fire dynamics supporting actions to combat and prevent forest fires. However, many studies have reported discrepancies in the quantification of fire, especially in the tropics. We evaluated four operational products for estimating burned areas (MAPBIOMAS, MCD64A1, GABAM, and GWIS) in a part of the southwestern Brazilian Amazon. We used the year 2019 as a reference to assess the relative performance of each product through stratification by forest and non-forest areas. Statistical (Kolmogorov–Smirnov test) and geospatial analyses were performed using fuzzy similarity analysis and mapping of burned areas for forest and non-forest classes. The four products showed a divergence of up to 90.6% in the total area burned. MAPBIOMAS was the product with the largest area burned (3379 km²), and MCD64A1 detected the smallest area (325 km²). MAPBIOMAS and GABAM generally overestimates burn scars in forest areas compared to MCD64A1 and GWIS. Factors that influence the mapping of burned areas include cloud shadow, the spatial resolution of sensors, and external noises (drought and decomposition of bamboo forests). We highlight the importance of field validation when mapping imagery to differentiate the truly burned areas from targets with similar spectral behavior.

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DUTRA, D. J.; FEARNSIDE, P. M.; YANAI, A. M.; GRAÇA, P. M. L. de A.; DALAGNOL, R.; PESSÔA, A. C. M.; CABRAL, B. F.; BURTON, C.; JONES, C.; BETTS, R.; FERRO, P. D.; BRAGA, D. A.; ARAGÃO, L. E. O. e C. de; ANDERSON, L. O. Burned area mapping in Different Data Products for the Southwest of the Brazilian Amazon. Revista Brasileira de Cartografia, [S. l.], v. 75, 2023. DOI: 10.14393/rbcv75n0a-68393. Disponível em: https://seer.ufu.br/index.php/revistabrasileiracartografia/article/view/68393. Acesso em: 20 dez. 2024.
Seção
Seção Especial "Brazilian Symposium on GeoInformatics - GEOINFO 2023"

Referências

ALENCAR, A. A. C. et al. Long-term Landsat-based monthly burned area dataset for the Brazilian biomes using deep learning. Remote Sensing, v. 4, n. 11 art. 2510, 2022. DOI: 10.3390/rs14112510

ALONSO-CANAS, I.; CHUVIECO, E. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. Remote Sensing of Environment, v. 163, p. 140–152, Jun. 2015. DOI: 10.1016/j.rse.2015.03.011

ALVARES, C. A. et al. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, v. 22, n. 6, p. 711–728, 1 Dec. 2013. DOI: 10.1127/0941-2948/2013/0507

ANDERSON, L. O. C. et al. Disentangling the contribution of multiple land covers to fire-mediated carbon emissions in Amazonia during the 2010 drought. Global Biogeochemical Cycles, v. 29, n. 10, p. 1739–1753, out. 2015. DOI: 10.1002/2014GB005008

ARAGÃO, L. E. O. C. et al. 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nature Communications, v. 9, n. 1, p. 1–12, 2018. DOI: 10.1038/s41467-017-02771-y

ARAGÃO, L. E. O. C. et al.. O desafio do Brasil para conter o desmatamento e as queimadas na Amazônia durante a pandemia por COVID-19 em 2020: implicações ambientais, sociais e sua governança, 1., no 1. São José dos Campos, 2020. DOI: 10.13140/RG.2.2.17256.49921

ARRUDA, V. L. S. et al. An alternative approach for mapping burn scars using Landsat imagery, Google Earth Engine, and deep learning in the Brazilian savanna. Remote Sensing Applications: Society and Environment, v. 22, art. 100472, 2021. DOI: 10.1016/j.rsase.2021.100472

ASSIS, L. F. F. G. et al. TerraBrasilis: A Spatial data analytics infrastructure for large-scale thematic mapping. ISPRS International Journal of Geo-Information, v. 8, n. 11, p. 513, 2019. DOI: 10.3390/ijgi8110513

BARLOW, J. et al. Clarifying Amazonia’s burning crisis. Global Change Biology, v. 26, n. 2, p. 319–321, 15 Feb. 2020. DOI: 10.1111/gcb.14872

BARLOW, J. et al. Transformando a Amazônia através de "arcos de restauração". Technical report. EMPRAPA. 2023.

BARNI, P. E. et al. Deforestation and forest fires in Roraima and their relationship with phytoclimatic regions in the northern Brazilian Amazon. Environmental Management, v. 55, n. 5, p. 1124–1138, 21 May 2015. DOI: 10.1007/s00267-015-0447-7

BERLINCK, C.; BATISTA, E. K. L. Good fire, bad fire: It depends on who burns. Flora Morphology, Distribution. Functional. Ecology of Plants, v. 268, art. 151610, 2020. DOI: 10.1016/j.flora.2020.151610

BIVAND, R.; RUNDEL, C. Rgeos: Interface to Geometry Engine—Open Source (‘GEOS’). Available at: <https://rdrr.io/cran/rgeos/>. Accessed: 30 jun. 2021.

BOSCHETTI, L. et al. Global validation of the collection 6 MODIS burned area product. Remote Sensing of Environment, v. 235, n. November, art. 111490, 2019. DOI: 10.1016/j.rse.2019.111490

BOSCHETTI, L. et al. GWIS national and sub-national fire activity data from the NASA MODIS Collection 6 Burned Area Product in Support of Policy Making, Carbon Inventories and Natural Resource Management, 2020. Available at: <https://gwis.jrc.ec.europa.eu/apps/country.profile/downloads>. Accessed: 6 jun. 2021.

BURTON, C. et al. El Niño driven changes in global fire 2015/16. Frontiers in Earth Science, v. 8, 10 Jun. 2020. DOI: 10.3389/feart.2020.00199

BUSH, M. et al. Fire, climate change and biodiversity in Amazonia: A Late-Holocene perspective. Philosophical Transactions of the Royal Society B: Biological Sciences, v. 363, n. 1498, p. 1795–1802, 27 May 2008. DOI: 10.1098/rstb.2007.0014

CAMPANHARO W. A. et al. Translating Fire Impacts in Southwestern Amazonia into Economic Costs. Remote Sensing.9 v.11,n.7764, 2019. DOI: 10.3390/rs11070764

CAMPANHARO, W. A. et al. Hospitalization due to fire-induced pollution in the Brazilian Legal Amazon from 2005 to 2018. Remote Sensing, v. 14, n. 1, art. 69, 2022. DOI: 10.3390/rs14010069

CARVALHO, A. L. et al. Bamboo-dominated forests of the southwest Amazon: detection, spatial extent, life cycle length and flowering waves. PloS one, v. 8, n. 1, p. e54852, 2013. DOI: 10.1371/journal.pone.0054852

CARVALHO, N. S. et al. Spatio-temporal variation in dry season determines the Amazonian fire calendar. Environmental Research Letters, v. 16, n. 12, art. 125009, 2021. DOI: 10.1088/1748-9326/ac3aa3

CHUVIECO, E. et al. Generation and analysis of a new global burned area product based on MODIS 250 m reflectance bands and thermal anomalies. Earth System Science Data, v. 10, n. 4, p. 2015–2031, 2018. DOI: 10.5194/essd-10-2015-2018

DA SILVA, S. S. et al. Increasing bamboo dominance in southwestern Amazon forests following intensification of drought-mediated fires. Forest Ecology and Management, v. 490, p. 119139, 2021. DOI: 10.1016/j.foreco.2021.119139

DALAGNOL, R. et al. Life cycle of bamboo in the southwestern Amazon and its relation to fire events. Biogeosciences, v. 15(20), p.6087–6104, 2018. DOI: 10.5194/bg-15-6087-2018

DE ANDRADE, D. F. C. et al. Forest resilience to fire in eastern Amazon depends on the intensity of pre-fire disturbance. Forest Ecology and Management, v. 472, art. 118258, Sept. 2020. DOI: 10.1016/j.foreco.2020.118258

DE MENDONÇA, M. J. C. et al. The economic cost of the use of fire in the Amazon. Ecological Economics, v. 49, n. 1, p. 89–105, May 2004. DOI: 10.1016/j.ecolecon.2003.11.011

DINAMICA EGO TEAM. Calc Reciprocal Similarity Map. Available at: <https://csr.ufmg.br/dinamica/dokuwiki/doku.php?id=calc_reciprocal_similarity_map>. Accessed: 5 jul. 2021.

DOMINGUES, M S and BERMANN, C. O arco de desflorestamento na Amazônia: da pecuária à soja. Ambiente & sociedade, v. 15, p. 1-22, 2012. DOI: 10.1590/S1414-753X2012000200002

DUTRA, D. J. et al. Comparison of regional scale burned area products for southwestern Brazilian Amazonia. 2022, São José dos Campos: GEOINFO 2022, 2022. p. 12.

DUTRA, D. J. et al. Fire dynamics in an emerging deforestation frontier in southwestern Amazonia, Brazil. Fire, v. 6, n. 1, p. 2, 21 Dec. 2023. DOI: https://doi.org/10.3390/fire6010002

FEARNSIDE, P. M. Amazon forest maintenance as a source of environmental services. Anais da Academia Brasileira de Ciências, v. 80, n. 1, p. 101–114, Mar. 2008.

FERRO, P D et al. Detecção de áreas queimadas baseado no modelo linear de mistura espectral aplicado em cubo de dados do CBERS-4 e CBERS -4A no oeste de Rondônia, Brasil. Simpósio Brasileiro de Sensoriamento Remoto. Florianópolis. 2023

GATTI, L. V. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature, v. 595, n. 7867, p. 388-393, 2021. DOI: 10.1038/s41586-021-03629-6

GEOINFO. GEOINFO. Available at: <http://www.geoinfo.info/geoinfo2022/index.php>.

GIGLIO, L. et al. The Collection 6 MODIS burned area mapping algorithm and product. Remote Sensing of Environment, v. 217, n. July, p. 72–85, 2018. DOI: 10.1016/j.rse.2018.08.005

HIJMANS, R. J. Raster: Geographic Data Analysis and Modeling. Available at: <https://rdrr.io/cran/raster/>. Accessed: 30 jun. 2021.

HUMBER, M. L. et al. Spatial and temporal intercomparison of four global burned area products. International Journal of Digital Earth, v. 12, n. 4, p. 460–484, 3 Apr. 2019. DOI: 10.1080/17538947.2018.1433727

JUSTICE, C. et al. The MODIS fire products. Remote Sensing of Environment, v. 83, n. 1–2, p. 244–262, Nov. 2002. DOI: 10.1016/S0034-4257(02)00076-7

KEY, C. H.; BENSON, N. C. Landscape assessment (LA). FIREMON: Fire effects monitoring and inventory system, 164, LA-1,2006.

KEY, C. H. Ecological and sampling constraints on defining landscape fire severity. Fire Ecology, v. 2, n. 2, p. 34-59, 2006.

LANGFORD, Z. et al. Wildfire mapping in interior Alaska using deep neural networks on imbalanced datasets. IEEE International Conference on Data Mining Workshops, ICDMW, v. 2018-Nov,, n. August 2019, p. 770–778, 2019. DOI: 10.1109/ICDMW.2018.00116

LAPOLA, D. M. et al. The drivers and impacts of Amazon Forest degradation. Science, 379(6630), eabp8622,2023. DOI: 10.1126/science.abp8622

LEITE-FILHO, A. T. et al. Modeling Environmental Dynamics with Dinamica EGO. Available at: <https://www.csr.ufmg.br/dinamica/dokuwiki/doku.php?id=guidebook_start>. Accessed: 5 jul. 2021b.

LEITE-FILHO, A T et al. Deforestation reduces rainfall and agricultural revenues in the Brazilian Amazon. Nature Communications, v. 12, n. 1, art 12, 2021a. DOI: 10.1038/s41467-021-22840-7

LEÃO, P H A et al. Contribuição de produtos de área queimada na Amazônia Maranhense: proposta de avaliações combinadas. In: Simpósio Brasileiro de Geoinformática, 23. (GEOINFO), 2022, On-line. São José dos Campos: INPE, 2022.

LONG, T. et al. 30-m resolution global annual burned area mapping based on Landsat images and Google Earth Engine. Remote Sensing, v. 11, n. 5, p. 1–30, 2019. DOI: 10.3390/rs11050489

MARASENI, T. N. et al. Savanna burning methodology for fire management and emissions reduction: a critical review of influencing factors. Carbon Balance Manag, v. 1, 2016. DOI: 10.1186/s13021-016-0067-4

MATAVELI, G. A. V. et al. Analysis of fire dynamics in the Brazilian savannas. Natural Hazards and Earth System Sciences Discussions, n. March, p. 1–27, 2017. DOI: 10.5194/nhess-2017-90, 2017

MATAVELI, G. A. V. et al. Relationship between biomass burning emissions and deforestation in Amazonia over the last two decades. Forests, v. 12, n. 9, p. 1–19, 2021a. DOI: 10.3390/f12091217

MATAVELI, G. A. V. et al. The emergence of a new deforestation hotspot in Amazonia. Perspectives in Ecology and Conservation, v. 19, n. 1, p. 33–36, 2021b. DOI: 10.1016/j.pecon.2021.01.002

MORTON, D. C. et al. Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data. Remote Sensing of Environment, v. 115, n. 7, p. 1706–1720, Jul. 2011. DOI: 10.1016/j.rse.2011.03.002

MOUILLOT, F. et al. Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments. International Journal of Applied Earth Observation and Geoinformation, v. 26, p. 64–79, Feb. 2014. DOI: 10.1016/j.jag.2013.05.014

PADILLA, M. et al. Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote Sensing of Environment, v. 160, p. 114–121, abr. 2015. DOI: 10.1016/j.rse.2015.01.005

PEKEL, J et al. Belward, High-resolution mapping of global surface water and its long-term changes. Nature, v. 540, p 418-422. 2016. DOI:10.1038/nature20584

PENHA, T. V. et al. Burned area detection in the Brazilian Amazon using spectral indices and GEOBIA. Revista Brasileira de Cartografia, v. 72, n. 2, p. 253–269, 2020. DOI: 10.14393/rbcv72n2-48726

PESSÔA, A. C. M. et al. Intercomparison of burned area products and its implication for carbon emission estimations in the Amazon. Remote Sensing, v. 12, n. 23, p. 3864, 25 Nov. 2020. DOI: 10.3390/rs12233864

PRENTICE, I. C. et al. Modeling fire and the terrestrial carbon balance. Global Biogeochemical Cycles, v. 25, n. 3, p. n/a-n/a, Sept. 2011. DOI: 10.1029/2010GB003906

QGIS. QGIS Geographic Information System. Available at: <https://qgis.org/pt_BR/site/>. Accessed: 6 jun. 2021.

R CORE TEAM. R. A Language and Environment for Statistical Computing 2020. Available at: <https://www.r-project.org/>. Accessed: 2 may 2021.

RODRIGUES, J. A. et al. How well do global burned area products represent fire patterns in the Brazilian Savannas biome? An accuracy assessment of the MCD64 collections. International Journal of Applied Earth Observation and Geoinformation, v. 78, p. 318–331, Jun. 2019. DOI: 10.1016/j.jag.2019.02.010

ROSSI, L. C. et al. Predation on artificial caterpillars following understorey fires in human‐modified Amazonian forests. Biotropica, v. 54, n. 3, p. 754-763, 2022. DOI: 10.1111/btp.13097

ROSSI, L. C. The effect of forest degradation on ecosystem services related to frugivory and insectivory promoted by birds and mammals in Amazonian forests. Thesis. Universidade Estadual Paulista. 2022.

ROY, D. P.; BOSCHETTI, L. Southern Africa validation of the MODIS, L3JRC, and GlobCarbon burned-area products. IEEE Transactions on Geoscience and Remote Sensing, v. 47, n. 4, p. 1032–1044, Apr. 2009. DOI: 10.1109/TGRS.2008.2009000

SAFI, Y.; BOUROUMI, A. Prediction of forest fires using artificial neural networks. Applied Mathematical Sciences, v. 7, n. 5–8, p. 271–286, 2013.

SHAKESBY, R. A.; DOERR, S. H. Wildfire as a hydrological and geomorphological agent. Earth-Sci. Rev., v. 74, p. 269–307, 2006. DOI: 10.1016/j.earscirev.2005.10.006

SHIMABUKURO, Y E et al. Mapping burned areas of Mato Grosso state Brazilian Amazon using multisensor datasets. Remote Sensing, v. 12, n. 22, p. 3827, 2020. DOI: 10.3390/rs12223827

SHIMABUKURO, Y. E. et al. Estimating burned area in Mato Grosso, Brazil, using an object-based classification method on a systematic sample of medium resolution satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 8, n. 9, p. 4502–4508, 2015. DOI: 10.1109/JSTARS.2015.2464097

SHIMABUKURO, Y. E. and SMIT, J. A. The least-squares mixing models to generate fraction images derived from remote sensing multispectral data. IEEE Transactions on Geoscience and Remote Sensing, v. 29, pp. 16–20, 1991. DOI: 10.1109/36.103288

SILVA JUNIOR, C. H. L. et al. Fire responses to the 2010 and 2015/2016 Amazonian droughts. Frontiers in Earth Science, v. 7, n. May, p. 1–16, 2019. DOI: 10.3389/feart.2019.00097

SILVEIRA, M. V. F. et al. Amazon fires in the 21st century: The year of 2020 in evidence. Global Ecology and Biogeography, n. September 2022. DOI: 10.1111/geb.13577

SMIRNOV, N. V. Estimate of deviation between empirical distribution functions in two independent samples. Bull. Math. Univ. Moscow, v. 2, p. 3–14, 1939.

SOARES-FILHO, B. et al. Simulating the response of land-cover changes to road paving and governance along a major Amazon highway: The Santarém-Cuiabá corridor. Global Change Biology, v. 10, n. 5, p. 745–764, 2004. DOI: 10.1111/j.1529-8817.2003.00769.x

WARMERDAM, F. The geospatial data abstraction library. Open source approaches in spatial data handling, p. 87-104, 2008.

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