Intercomparison of Burned Area Products and Its Implication for Carbon Emission Estimations in the Amazon
Abstract
:1. Introduction
2. Burned Area Detection by Remote Sensing
3. Study Area
4. Materials and Methods
4.1. Burned Area Products
Name | Developer | Scale | Time Span | Sensors/Inputs | Spatial Resolution | References |
---|---|---|---|---|---|---|
TREES | TREES—INPE | Regional (Brazilian Amazon) | 2006–2016 | MODIS | 250 m | [7,43,46] |
MCD64A1 c6 | NASA | Global | 2000–present | MODIS (surface reflectance and active fires) | 500 m | [17] |
GABAM | Institute of Remote Sensing and Digital Earth—Chinese Academy of Sciences | Global | 2000, 2005, 2010, 2015 and 2018 | Landsat 8 OLI | 30 m | [36] |
Fire_cci v.5.0 | ESA | Global | 2001–2016 | MOD09GQ (surface reflectance) MOD09GA (quality flags) MCD14ML (active fires) | 250 m | [55] |
Burned Area Product | Overall Accuracy | Omission Error | Commission Error | Validation Method Summary | References |
---|---|---|---|---|---|
TREES | |||||
Forest areas | 0.9920 | 0 | 0.1600 | Point-based validation. Stratified random sample of 300 points’ distributed over burned and unburned forest on Landsat images for Mato Grosso state, 2010. The points are verified by experienced interpreters. | [28] |
Non-forest areas | 0.9630 | 0.4852 | 0.1067 | ||
MCD64A1 c6 | |||||
Global | 0.9970 | 0.7260 | 0.4020 | Globally distributed reference dataset from 1 March 2014 to 19 March 2015, consisting of high-resolution reference maps derived from 1116 Landsat images visually interpreted. These independent reference data were selected using a stratified random sampling approach that allows for the probability sampling of Landsat data in both time and space. | [41] |
Tropical forests | 0.9940 | 0.9060 | 0.6350 | ||
GABAM | 0.9392 | 0.3013 | 0.1317 | It considered 80 validation sites globally, from where it acquired data from Landsat 8, CBERS-4 MUX and Gaofen-1 WFV. The reference burned areas were mapped with a semi-automatic classification method and refined with the manual edition. | [36] |
Fire_cci v5.0 | 0.9972 | 0.7090 | 0.5123 | Stratified random sample of 1200 pairs of Landsat images, covering the whole globe from 2003 to 2014. | [55] |
4.2. Committed Gross Carbon Emission Estimation
4.3. Total and Regional Analysis
5. Results
5.1. Vector Approach: Intercomparison of Total Burned Area
5.2. Vector Approach: Impact on Committed Gross Carbon Emissions Estimates
5.3. Matrix Approach: Statistical and Spatial Intercomparison
Study Area | AC 1 | AM 2 | MA 3 | MT 4 | PA 5 | RO 6 | |
---|---|---|---|---|---|---|---|
Total | |||||||
TREES × MCD64A1 | 0.408 | 0.194 | 0.316 | 0.458 | 0.459 | 0.406 | 0.451 |
TREES × Fire_cci | 0.483 | 0.114 | 0.610 | 0.437 | 0.529 | 0.491 | 0.395 |
TREES × GABAM | 0.467 | 0.470 | 0.495 | 0.395 | 0.529 | 0.416 | 0.544 |
MCD64A1 × Fire_cci | 0.507 | 0.784 | 0.389 | 0.369 | 0.544 | 0.493 | 0.583 |
MCD64A1 × GABAM | 0.450 | 0.164 | 0.376 | 0.489 | 0.468 | 0.474 | 0.465 |
Fire_cci × GABAM | 0.414 | 0.073 | 0.463 | 0.254 | 0.555 | 0.387 | 0.408 |
Non-forest | |||||||
TREES × MCD64A1 | 0.428 | 0.242 | 0.390 | 0.474 | 0.464 | 0.421 | 0.456 |
TREES × Fire_cci | 0.505 | 0.153 | 0.653 | 0.459 | 0.543 | 0.510 | 0.413 |
TREES × GABAM | 0.449 | 0.276 | 0.420 | 0.436 | 0.484 | 0.424 | 0.513 |
MCD64A1 × Fire_cci | 0.533 | 0.798 | 0.454 | 0.396 | 0.553 | 0.520 | 0.607 |
MCD64A1 × GABAM | 0.472 | 0.651 | 0.406 | 0.489 | 0.445 | 0.484 | 0.480 |
Fire_cci × GABAM | 0.480 | 0.670 | 0.513 | 0.312 | 0.563 | 0.445 | 0.443 |
Forest | |||||||
TREES × MCD64A1 | 0.515 | 0.291 | 0.433 | 0.674 | 0.551 | 0.507 | 0.507 |
TREES × Fire_cci | 0.542 | 0.151 | 0.580 | 0.699 | 0.572 | 0.543 | 0.425 |
TREES × GABAM | 0.513 | 0.519 | 0.504 | 0.608 | 0.583 | 0.451 | 0.514 |
MCD64A1 × Fire_cci | 0.573 | 0.790 | 0.464 | 0.614 | 0.600 | 0.548 | 0.578 |
MCD64A1 × GABAM | 0.493 | 0.202 | 0.382 | 0.673 | 0.527 | 0.498 | 0.463 |
Fire_cci × GABAM | 0.446 | 0.090 | 0.408 | 0.546 | 0.553 | 0.418 | 0.386 |
6. Discussion
7. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Bush, M.; Silman, M.; McMichael, C.; Saatchi, S. Fire, climate change and biodiversity in Amazonia: A Late-Holocene perspective. Philos. Trans. R. Soc. B Biol. Sci. 2008, 363, 1795–1802. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Cochrane, M. A Fire science for rainforests. Nature 2003, 421, 913–919. [Google Scholar] [CrossRef] [PubMed]
- Pausas, J.G.; Keeley, J.E. A Burning Story: The Role of Fire in the History of Life. Bioscience 2009, 59, 593–601. [Google Scholar] [CrossRef] [Green Version]
- Gatti, L.V.; Gloor, M.; Miller, J.B.; Doughty, C.E.; Malhi, Y.; Domingues, L.G.; Basso, L.S.; Martinewski, A.; Correia, C.S.C.; Borges, V.F.; et al. Drought sensitivity of Amazonian carbon balance revealed by atmospheric measurements. Nature 2014, 506, 76–80. [Google Scholar] [CrossRef]
- Aragão, L.E.O.C.; Anderson, L.O.; Fonseca, M.G.; Rosan, T.M.; Vedovato, L.B.; Wagner, F.H.; Silva, C.V.J.; Silva Junior, C.H.L.; Arai, E.; Aguiar, A.P.; et al. 21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 2018, 9, 1–12. [Google Scholar] [CrossRef]
- De Azevedo, T.R.; Costa Junior, C.; Brandão Junior, A.; Cremer, M.d.S.; Piatto, M.; Tsai, D.S.; Barreto, P.; Martins, H.; Sales, M.; Galuchi, T.; et al. SEEG initiative estimates of Brazilian greenhouse gas emissions from 1970 to 2015. Sci. Data 2018, 5, 180045. [Google Scholar] [CrossRef] [Green Version]
- Anderson, L.O.; Aragão, L.E.O.C.; Gloor, M.; Arai, E.; Adami, M.; Saatchi, S.S.; Malhi, Y.; Shimabukuro, Y.E.; Barlow, J.; Berenguer, E.; et al. Disentangling the contribution of multiple land covers to fire-mediated carbon emissions in Amazonia during the 2010 drought. Glob. Biogeochem. Cycles 2015, 29, 1739–1753. [Google Scholar] [CrossRef] [Green Version]
- Brasil Decreto No 9.578, de 22 de Novembro de 2018. Consolida atos Normativos Editados pelo Poder Executivo Federal que Dispõem Sobre o Fundo Nacional sobre Mudança do Clima, de que Trata a Lei no 12.114, de 9 de Dezembro de 2009, e a Política Nacional sobre Mudança do Clima, de que trata a Lei nº 12.187, de 29 de Dezembro de 2009; Diário Oficial da União: Brasília, Brasil, 2018.
- Aragão, L.E.O.; Malhi, Y.; Barbier, N.; Lima, A.; Shimabukuro, Y.; Anderson, L.; Saatchi, S. Interactions between rainfall, deforestation and fires during recent years in the Brazilian Amazonia. Philos. Trans. R. Soc. B Biol. Sci. 2008, 363, 1779–1785. [Google Scholar] [CrossRef]
- Silva Junior, C.; Aragão, L.; Fonseca, M.; Almeida, C.; Vedovato, L.; Anderson, L. Deforestation-Induced Fragmentation Increases Forest Fire Occurrence in Central Brazilian Amazonia. Forests 2018, 9, 305. [Google Scholar] [CrossRef] [Green Version]
- Marengo, J.A.; Tomasella, J.; Alves, L.M.; Soares, W.R.; Rodriguez, D.A. The drought of 2010 in the context of historical droughts in the Amazon region. Geophys. Res. Lett. 2011, 38. [Google Scholar] [CrossRef] [Green Version]
- Jiménez-Muñoz, J.C.; Mattar, C.; Barichivich, J.; Santamaría-Artigas, A.; Takahashi, K.; Malhi, Y.; Sobrino, J.A.; Schrier, G. van der Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. Sci. Rep. 2016, 6, 33130. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Li, W.; Fu, R.; Dickinson, R.E. Rainfall and its seasonality over the Amazon in the 21st century as assessed by the coupled models for the IPCC AR4. J. Geophys. Res. 2006, 111, D02111. [Google Scholar] [CrossRef] [Green Version]
- Cox, P.M.; Harris, P.P.; Huntingford, C.; Betts, R.A.; Collins, M.; Jones, C.D.; Jupp, T.E.; Marengo, J.A.; Nobre, C.A. Increasing risk of Amazonian drought due to decreasing aerosol pollution. Nature 2008, 453, 212–215. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Silva Junior, C.H.L.; Anderson, L.O.; Silva, A.L.; Almeida, C.T.; Dalagnol, R.; Pletsch, M.A.J.S.; Penha, T.V.; Paloschi, R.A.; Aragão, L.E.O.C. Fire Responses to the 2010 and 2015/2016 Amazonian Droughts. Front. Earth Sci. 2019, 7, 1–16. [Google Scholar] [CrossRef]
- Silva, C.V.J.; Aragão, L.E.O.C.; Barlow, J.; Espirito-Santo, F.; Young, P.J.; Anderson, L.O.; Berenguer, E.; Brasil, I.; Foster Brown, I.; Castro, B.; et al. Drought-induced Amazonian wildfires instigate a decadal-scale disruption of forest carbon dynamics. Philos. Trans. R. Soc. B Biol. Sci. 2018, 373, 20180043. [Google Scholar] [CrossRef] [Green Version]
- 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. 2018, 217, 72–85. [Google Scholar] [CrossRef]
- Pettinari, M.L.; Chuvieco, E. ESA CCI ECV Fire Disturbance: D3.3.3 Product User Guide—MODIS, Version 1.0. 2018. Available online: https://www.esa-fire-cci.org/documents (accessed on 25 October 2020).
- Shimabukuro, Y.E.; Miettinen, J.; Beuchle, R.; Grecchi, R.C.; Simonetti, D.; Achard, F. Estimating Burned Area in Mato Grosso, Brazil, Using an Object-Based Classification Method on a Systematic Sample of Medium Resolution Satellite Images. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 4502–4508. [Google Scholar] [CrossRef]
- Penha, T.V.; Körting, T.S.; Fonseca, L.M.G.; Silva Júnior, C.H.L.; Pletsch, M.A.J.S.; Anderson, L.O.; Morelli, F. Burned Area Detection in the Brazilian Amazon using Spectral Indices and GEOBIA. Rev. Bras. Cartogr. 2020, 72, 253–269. [Google Scholar] [CrossRef]
- Mouillot, F.; Schultz, M.G.; Yue, C.; Cadule, P.; Tansey, K.; Ciais, P.; Chuvieco, E. Ten years of global burned area products from spaceborne remote sensing—A review: Analysis of user needs and recommendations for future developments. Int. J. Appl. Earth Obs. Geoinf. 2014, 26, 64–79. [Google Scholar] [CrossRef] [Green Version]
- Humber, M.L.; Boschetti, L.; Giglio, L.; Justice, C.O. Spatial and temporal intercomparison of four global burned area products. Int. J. Digit. Earth 2019, 12, 460–484. [Google Scholar] [CrossRef]
- Padilla, M.; Stehman, S.V.; Ramo, R.; Corti, D.; Hantson, S.; Oliva, P.; Alonso-Canas, I.; Bradley, A.V.; Tansey, K.; Mota, B.; et al. Comparing the accuracies of remote sensing global burned area products using stratified random sampling and estimation. Remote Sens. Environ. 2015, 160, 114–121. [Google Scholar] [CrossRef] [Green Version]
- Ruiz, J.; Lázaro, J.; Cano, I.; Leal, P. Burned Area Mapping in the North American Boreal Forest Using Terra-MODIS LTDR (2001–2011): A Comparison with the MCD45A1, MCD64A1 and BA GEOLAND-2 Products. Remote Sens. 2014, 6, 815–840. [Google Scholar] [CrossRef] [Green Version]
- Tsela, P.; Wessels, K.; Botai, J.; Archibald, S.; Swanepoel, D.; Steenkamp, K.; Frost, P. Validation of the Two Standard MODIS Satellite Burned-Area Products and an Empirically-Derived Merged Product in South Africa. Remote Sens. 2014, 6, 1275–1293. [Google Scholar] [CrossRef] [Green Version]
- Padilla, M.; Stehman, S.; Litago, J.; Chuvieco, E. Assessing the Temporal Stability of the Accuracy of a Time Series of Burned Area Products. Remote Sens. 2014, 6, 2050–2068. [Google Scholar] [CrossRef] [Green Version]
- 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. 2017, 9, 1131. [Google Scholar] [CrossRef] [Green Version]
- Anderson, L.O.; Cheek, D.; Aragao, L.E.; Andere, L.; Duarte, B.; Salazar, N.; Lima, A.; Duarte, V.; Arai, E. Development of a Point-based Method for Map Validation and Confidence Interval Estimation: A Case Study of Burned Areas in Amazonia. J. Remote Sens. GIS 2017, 6. [Google Scholar] [CrossRef]
- Pereira, J.M.C.; Mota, B.; Privette, J.L.; Caylor, K.K.; Silva, J.M.N.; Sá, A.C.L.; Ni-Meister, W. A simulation analysis of the detectability of understory burns in miombo woodlands. Remote Sens. Environ. 2004, 93, 296–310. [Google Scholar] [CrossRef]
- Roy, D.P.; Boschetti, L. Southern Africa validation of the MODIS, L3JRC, and GlobCarbon burned-area products. IEEE Trans. Geosci. Remote Sens. 2009, 47, 1032–1044. [Google Scholar] [CrossRef]
- Chuvieco, E.; Mouillot, F.; van der Werf, G.R.; San Miguel, J.; Tanase, M.; Koutsias, N.; García, M.; Yebra, M.; Padilla, M.; Gitas, I.; et al. Historical background and current developments for mapping burned area from satellite Earth observation. Remote Sens. Environ. 2019, 225, 45–64. [Google Scholar] [CrossRef]
- Pereira, J.M.C.; Sá, A.C.L.; Sousa, A.M.O.; Silva, J.M.N.; Santos, T.N.; Carreiras, J.M.B. Spectral characterisation and discrimination of burnt areas. In Remote Sensing of Large Wildfires; Springer: Berlin/Heidelberg, Germany, 1999; pp. 123–138. [Google Scholar]
- Pleniou, M.; Koutsias, N. Relationships between vegetation indices and different burn and vegetation ratios: A multi-scale approach applied in a fire affected area. In Proceedings of the SPIE; Hadjimitsis, D.G., Themistocleous, K., Michaelides, S., Papadavid, G., Eds.; SPIE: Paphos, Cyprus, 2013; Volume 8795, p. 87951I. [Google Scholar] [CrossRef]
- Boschetti, M.; Stroppiana, D.; Brivio, P.A. Mapping Burned Areas in a Mediterranean Environment Using Soft Integration of Spectral Indices from High-Resolution Satellite Images. Earth Interact. 2010, 14, 1–20. [Google Scholar] [CrossRef]
- Chuvieco, E.; Martín, M.P.; Palacios, A. Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. Int. J. Remote Sens. 2002, 23, 5103–5110. [Google Scholar] [CrossRef]
- Long, T.; Zhang, Z.; He, G.; Jiao, W.; Tang, C.; Wu, B.; Zhang, X.; Wang, G.; Yin, R. 30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine. Remote Sens. 2019, 11, 489. [Google Scholar] [CrossRef] [Green Version]
- Mithal, V.; Nayak, G.; Khandelwal, A.; Kumar, V.; Nemani, R.; Oza, N. Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework. Remote Sens. 2018, 10, 69. [Google Scholar] [CrossRef] [Green Version]
- Pleniou, M.; Koutsias, N. Sensitivity of spectral reflectance values to different burn and vegetation ratios: A multi-scale approach applied in a fire affected area. ISPRS J. Photogramm. Remote Sens. 2013, 79, 199–210. [Google Scholar] [CrossRef]
- Silva, J.M.N.; Cadima, J.F.C.L.; Pereira, J.M.C.; Grégoire, J.-M. Assessing the feasibility of a global model for multi-temporal burned area mapping using SPOT-VEGETATION data. Int. J. Remote Sens. 2004, 25, 4889–4913. [Google Scholar] [CrossRef]
- Melchiorre, A.; Boschetti, L. Global Analysis of Burned Area Persistence Time with MODIS Data. Remote Sens. 2018, 10, 750. [Google Scholar] [CrossRef] [Green Version]
- Boschetti, L.; Roy, D.P.; Giglio, L.; Huang, H.; Zubkova, M.; Humber, M.L. Global validation of the collection 6 MODIS burned area product. Remote Sens. Environ. 2019, 235, 111490. [Google Scholar] [CrossRef]
- Bastarrika, A.; Alvarado, M.; Artano, K.; Martinez, M.; Mesanza, A.; Torre, L.; Ramo, R.; Chuvieco, E. BAMS: A Tool for Supervised Burned Area Mapping Using Landsat Data. Remote Sens. 2014, 6, 12360–12380. [Google Scholar] [CrossRef] [Green Version]
- Anderson, L.O.; Eduardo, L.; De Aragão, C.; De Lima, A. Detecção de cicatrizes de áreas queimadas baseada no modelo linear de mistura espectral e imagens índice de vegetação utilizando dados multitemporais do sensor MODIS/TERRA no estado do Mato Grosso, Amazônia brasileira. Acta Amaz. 2005, 35, 445–456. [Google Scholar] [CrossRef] [Green Version]
- Lima, A.; Silva, T.S.F.; de Aragão, L.E.O.e.C.; de Feitas, R.M.; Adami, M.; Formaggio, A.R.; Shimabukuro, Y.E. Land use and land cover changes determine the spatial relationship between fire and deforestation in the Brazilian Amazon. Appl. Geogr. 2012, 34, 239–246. [Google Scholar] [CrossRef]
- Cardozo, F.; Pereira, G.; Shimabukuro, Y.; Moraes, E. Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest. Remote Sens. 2014, 6, 8002–8025. [Google Scholar] [CrossRef] [Green Version]
- Shimabukuro, Y.E.; Duarte, V.; Arai, E.; Freitas, R.M.; Lima, A.; Valeriano, D.M.; Brown, I.F.; Maldonado, M.L.R. Fraction images derived from Terra Modis data for mapping burnt areas in Brazilian Amazonia. Int. J. Remote Sens. 2009, 30, 1537–1546. [Google Scholar] [CrossRef]
- Shimabukuro, Y.E.; Beuchle, R.; Grecchi, R.C.; Achard, F. Assessment of forest degradation in Brazilian Amazon due to selective logging and fires using time series of fraction images derived from Landsat ETM+ images. Remote Sens. Lett. 2014, 5, 773–782. [Google Scholar] [CrossRef]
- 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. 2012, 117. [Google Scholar] [CrossRef]
- Artés, T.; Oom, D.; de Rigo, D.; Durrant, T.H.; Maianti, P.; Libertà, G.; San-Miguel-Ayanz, J. A global wildfire dataset for the analysis of fire regimes and fire behaviour. Sci. Data 2019, 6, 296. [Google Scholar] [CrossRef]
- 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. 2013, 118, 317–328. [Google Scholar] [CrossRef] [Green Version]
- Andela, N.; Morton, D.C.; Giglio, L.; Paugam, R.; Chen, Y.; Hantson, S.; van der Werf, G.R.; Randerson, J.T. The Global Fire Atlas of individual fire size, duration, speed, and direction. Earth Syst. Sci. Data Discuss. 2019, 1–28. [Google Scholar] [CrossRef] [Green Version]
- Shi, Y.; Matsunaga, T.; Yamaguchi, Y. High-Resolution Mapping of Biomass Burning Emissions in Three Tropical Regions. Environ. Sci. Technol. 2015, 49, 10806–10814. [Google Scholar] [CrossRef]
- Fanin, T.; van der Werf, G.R. Relationships between burned area, forest cover loss, and land cover change in the Brazilian Amazon based on satellite data. Biogeosciences 2015, 12, 6033–6043. [Google Scholar] [CrossRef] [Green Version]
- Chen, Y.; Morton, D.C.; Andela, N.; van der Werf, G.R.; Giglio, L.; Randerson, J.T. A pan-tropical cascade of fire driven by El Niño/Southern Oscillation. Nat. Clim. Chang. 2017, 7, 906–911. [Google Scholar] [CrossRef]
- Chuvieco, E.; Lizundia-Loiola, J.; Pettinari, M.L.; Ramo, R.; Padilla, M.; Tansey, K.; Mouillot, F.; Laurent, P.; Storm, T.; Heil, A.; et al. Generation and analysis of a new global burned area product based on MODIS 250m reflectance bands and thermal anomalies. Earth Syst. Sci. Data 2018, 10, 2015–2031. [Google Scholar] [CrossRef] [Green Version]
- Lizundia-Loiola, J.; Otón, G.; Ramo, R.; Chuvieco, E. A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data. Remote Sens. Environ. 2020, 236, 111493. [Google Scholar] [CrossRef]
- INPE. Projeto de Monitoramento do Desmatamento na Amazônia Brasileira por Satélite (PRODES); INPE: São José dos Campos, Brazil, 2017. [Google Scholar]
- Brando, P.M.; Nepstad, D.C.; Balch, J.K.; Bolker, B.; Christman, M.C.; Coe, M.; Putz, F.E. Fire-induced tree mortality in a neotropical forest: The roles of bark traits, tree size, wood density and fire behavior. Glob. Chang. Biol. 2012, 18, 630–641. [Google Scholar] [CrossRef]
- IPCC. Fifth Assessment Report of the Intergovernmental Panel on Climate Change; IPCC: New York, NY, USA, 2013. [Google Scholar]
- Bivand, R.; Rundel, C. Rgeos: Interface to Geometry Engine—Open Source (‘GEOS’) 2018. Available online: https://rdrr.io/cran/rgeos/ (accessed on 24 November 2020).
- R Core Team. R: A Language and Environment for Statistical Computing 2020; R Core Team: Vienna, Austria, 2020; Available online: https://www.R-project.org/ (accessed on 30 October 2020).
- Hijmans, R.J. Raster: Geographic Data Analysis and Modeling. 2017. Available online: https://rdrr.io/cran/raster/ (accessed on 24 November 2020).
- Smirnov, N.V. Estimate of deviation between empirical distribution functions in two independent samples. Bull. Math. Univ. Moscou 1939, 2, 3–14. [Google Scholar]
- Visser, H.; de Nijs, T. The Map Comparison Kit. Environ. Model. Softw. 2006, 21, 346–358. [Google Scholar] [CrossRef]
- RIKS BV. Map Comparison Kit 3: User Manual; RIKS BV: Maastricht, The Netherlands, 2013. [Google Scholar]
- RIKS BV. MCK Reader: Methods of the Map Comparison Kit; RIKS BV: Maastricht, The Netherlands, 2011. [Google Scholar]
- Alonso-Canas, I.; Chuvieco, E. Global burned area mapping from ENVISAT-MERIS and MODIS active fire data. Remote Sens. Environ. 2015, 163, 140–152. [Google Scholar] [CrossRef]
- Giglio, L.; Schroeder, W.; Justice, C.O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 2016, 178, 31–41. [Google Scholar] [CrossRef] [Green Version]
- Justice, C.; Giglio, L.; Korontzi, S.; Owens, J.; Morisette, J.; Roy, D.; Descloitres, J.; Alleaume, S.; Petitcolin, F.; Kaufman, Y. The MODIS fire products. Remote Sens. Environ. 2002, 83, 244–262. [Google Scholar] [CrossRef]
- Meddens, A.J.H.; Kolden, C.A.; Lutz, J.A.; Abatzoglou, J.T.; Hudak, A.T. Spatiotemporal patterns of unburned areas within fire perimeters in the northwestern United States from 1984 to 2014. Ecosphere 2018, 9, e02029. [Google Scholar] [CrossRef]
- Morton, D.C.; DeFries, R.S.; Nagol, J.; Souza, C.M.; Kasischke, E.S.; Hurtt, G.C.; Dubayah, R. Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data. Remote Sens. Environ. 2011, 115, 1706–1720. [Google Scholar] [CrossRef] [Green Version]
- Rodrigues, J.A.; Libonati, R.; Pereira, A.A.; Nogueira, J.M.P.; Santos, F.L.M.; Peres, L.F.; Santa Rosa, A.; Schroeder, W.; Pereira, J.M.C.; Giglio, L.; et al. How well do global burned area products represent fire patterns in the Brazilian Savannas biome? An accuracy assessment of the MCD64 collections. Int. J. Appl. Earth Obs. Geoinf. 2019, 78, 318–331. [Google Scholar] [CrossRef]
- Campanharo, W.; Lopes, A.; Anderson, L.; da Silva, T.; Aragão, L. Translating Fire Impacts in Southwestern Amazonia into Economic Costs. Remote Sens. 2019, 11, 764. [Google Scholar] [CrossRef] [Green Version]
- Da Silva, S.S.; Fearnside, P.M.; de Alencastro Graça, P.M.L.; Brown, I.F.; Alencar, A.; de Melo, A.W.F. Dynamics of forest fires in the southwestern Amazon. For. Ecol. Manag. 2018, 424, 312–322. [Google Scholar] [CrossRef]
- Silva, S.S.; Anderson, L.O.; Costa, J.G.; Souza, F.S.C.; Nascimento, E.S.; Silva, I.S.; Pereira, M.P.; Silva, F.V.; Almeida, M.R.N.; Xaud, H.A.M.; et al. Relatório Executivo: Queimadas 2019—Acre; UFAC: Cruzeiro do Sul, Acre, Brasil, 2020; 31p. [Google Scholar] [CrossRef]
- Barlow, J.; Berenguer, E.; Carmenta, R.; França, F. Clarifying Amazonia’s burning crisis. Glob. Chang. Biol. 2020, 26, 319–321. [Google Scholar] [CrossRef] [Green Version]
- Giglio, L.; Descloitres, J.; Justice, C.O.; Kaufman, Y.J. An Enhanced Contextual Fire Detection Algorithm for MODIS. Remote Sens. Environ. 2003, 87, 273–282. [Google Scholar] [CrossRef]
- 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. 2014, 143, 85–96. [Google Scholar] [CrossRef]
- Otón, G.; Pettinari, M.L. ESA CCI ECV Fire Disturbance: D3.3.4 Product User Guide—LTDR, Version 1.0. 2019. Available online: https://www.esa-fire-cci.org/documents (accessed on 1 November 2020).
- Hawbaker, T.J.; Vanderhoof, M.K.; Beal, Y.; Takacs, J.D.; Schmidt, G.L.; Falgout, J.T.; Williams, B.; Fairaux, N.M.; Caldwell, M.K.; Picotte, J.J.; et al. Mapping burned areas using dense time-series of Landsat data. Remote Sens. Environ. 2017, 198, 504–522. [Google Scholar] [CrossRef]
- Diniz, C.G.; Souza, A.A.d.A.; Santos, D.C.; Dias, M.C.; da Luz, N.C.; de Moraes, D.R.V.; Maia, J.S.A.; Gomes, A.R.; Narvaes, I.d.S.; Valeriano, D.M.; et al. DETER-B: The New Amazon Near Real-Time Deforestation Detection System. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2015, 8, 3619–3628. [Google Scholar] [CrossRef]
- Longo, M.; Keller, M.; Dos-Santos, M.N.; Leitold, V.; Pinagé, E.R.; Baccini, A.; Saatchi, S.; Nogueira, E.M.; Batistella, M.; Morton, D.C. Aboveground biomass variability across intact and degraded forests in the Brazilian Amazon. Glob. Biogeochem. Cycles 2016, 30, 1639–1660. [Google Scholar] [CrossRef] [Green Version]
- Chave, J.; Réjou-Méchain, M.; Búrquez, A.; Chidumayo, E.; Colgan, M.S.; Delitti, W.B.C.; Duque, A.; Eid, T.; Fearnside, P.M.; Goodman, R.C.; et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob. Chang. Biol. 2014, 20, 3177–3190. [Google Scholar] [CrossRef]
- Kauffman, J.B.; Cummings, D.L.; Ward, D.E. Relationships of Fire, Biomass and Nutrient Dynamics along a Vegetation Gradient in the Brazilian Cerrado. J. Ecol. 1994, 82, 519. [Google Scholar] [CrossRef]
- Cochrane, M.A.; Schulze, M.D. Fire as a Recurrent Event in Tropical Forests of the Eastern Amazon: Effects on Forest Structure, Biomass, and Species Composition1. Biotropica 1999, 31, 2–16. [Google Scholar] [CrossRef]
- Haugaasen, T.; Barlow, J.; Peres, C.A. Surface wildfires in central Amazonia: Short-term impact on forest structure and carbon loss. For. Ecol. Manag. 2003, 179, 321–331. [Google Scholar] [CrossRef]
- Barlow, J.; Peres, C.A.; Lagan, B.O.; Haugaasen, T. Large tree mortality and the decline of forest biomass following Amazonian wildfires. Ecol. Lett. 2002, 6, 6–8. [Google Scholar] [CrossRef]
- Alencar, A.; Nepstad, D.; Diaz, M.C.V. Forest understory fire in the Brazilian Amazon in ENSO and non-ENSO years: Area burned and comitted carbon missions. Earth Interact. 2006, 10, 1–17. [Google Scholar] [CrossRef]
- Alencar, A.A.; Nepstad, D.; Moutinho, P. Carbon emissions associated with forest fires in Brazil. In Tropical Deforestation and Climate Change; IPAM: Belém, Portugal, 2005; p. 132. [Google Scholar]
- Barbosa, R.I.; Fearnside, P.M. Incêndios na Amazônia Brasileira: Estimativa da emissão de gases do efeito estufa pela queima de diferentes ecossistemas de Roraima na passagem do evento “El Nino” (1997/98). Acta Amaz. 1999, 29, 513–534. [Google Scholar] [CrossRef] [Green Version]
- Brinck, K.; Fischer, R.; Groeneveld, J.; Lehmann, S.; Dantas De Paula, M.; Pütz, S.; Sexton, J.O.; Song, D.; Huth, A. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nat. Commun. 2017, 8, 14855. [Google Scholar] [CrossRef] [Green Version]
- Baccini, A.; Goetz, S.J.; Walker, W.S.; Laporte, N.T.; Sun, M.; Sulla-Menashe, D.; Hackler, J.; Beck, P.S.A.; Dubayah, R.; Friedl, M.A.; et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Chang. 2012, 2, 182–185. [Google Scholar] [CrossRef]
- Coutinho, A.C.; Almeida, C.; Venturieri, A.; Esquerdo, J.C.D.M.; Silva, M. Uso e cobertura da terra nas áreas desflorestadas da Amazônia Legal, TerraClass; Embrapa: Brasília, Brazil; INPE: Belém, Portugal, 2013; ISBN 9788570351807. [Google Scholar]
- Rosan, T.M. Estimativa de Emissões de CO2 por Desmatamento e Degradação Florestal Utilizada como Subsidio para Definição de Municípios Prioritários para Monitoramento e Controle; Instituto Nacional de Pesquisas Espaciais—INPE: São José dos Campos SP, Brasil, 2017. [Google Scholar]
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Pessôa, A.C.M.; Anderson, L.O.; Carvalho, N.S.; Campanharo, W.A.; Junior, C.H.L.S.; Rosan, T.M.; Reis, J.B.C.; Pereira, F.R.S.; Assis, M.; Jacon, A.D.; et al. Intercomparison of Burned Area Products and Its Implication for Carbon Emission Estimations in the Amazon. Remote Sens. 2020, 12, 3864. https://doi.org/10.3390/rs12233864
Pessôa ACM, Anderson LO, Carvalho NS, Campanharo WA, Junior CHLS, Rosan TM, Reis JBC, Pereira FRS, Assis M, Jacon AD, et al. Intercomparison of Burned Area Products and Its Implication for Carbon Emission Estimations in the Amazon. Remote Sensing. 2020; 12(23):3864. https://doi.org/10.3390/rs12233864
Chicago/Turabian StylePessôa, Ana Carolina M., Liana O. Anderson, Nathália S. Carvalho, Wesley A. Campanharo, Celso H. L. Silva Junior, Thais M. Rosan, João B. C. Reis, Francisca R. S. Pereira, Mauro Assis, Aline D. Jacon, and et al. 2020. "Intercomparison of Burned Area Products and Its Implication for Carbon Emission Estimations in the Amazon" Remote Sensing 12, no. 23: 3864. https://doi.org/10.3390/rs12233864