Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil
Abstract
:1. Introduction
- What is the overall performance of the FM? Does LULC play an important role in the FM accuracy?
- Does the size of the burned area (BA) influence the FM accuracy? Is the FM influenced by BA found in the surroundings of a central ABI pixel grid?
- What is the FM potential considering a sequence of positive fire indications? What is its agreement with the MODIS and VIIRS datasets?
- Assuming that we have a certain number of consecutive AF detections from the FM, what is the fire reality in the remaining data over MATOPIBA?
2. Data
2.1. Reference Satellites: MODIS and VIIRS Active Fire Data
2.2. GOES-16 ABI Imagery
2.3. Sentinel-2 Imagery
3. Methods
3.1. Data Split
3.2. Data Processing and Experiments
3.2.1. Algorithms and Hyperparameters Optimization
3.2.2. Lag and Machine Learning Algorithm Selection
3.3. Final Model Development and Assessment
4. Results
4.1. Overall Performance of the FM
4.2. FM Performance Regarding Burned Areas Mapping
4.3. What Is the FM Potential When Considering a Consecutive Sequence of Positive Predictions?
4.4. Fire Reality in the Remaining Data over MATOPIBA
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Bond, W.J.; Woodward, F.I.; Midgley, G.F. The global distribution of ecosystems in a world without fire. New Phytol. 2005, 165, 525–538. [Google Scholar] [CrossRef] [PubMed]
- Ivo, I.O.; Biudes, M.S.; Vourlitis, G.L.; Gomes, N.; Martim, C.C. Effect of fires on biophysical parameters, energy balance and evapotranspiration in a protected area in the Brazilian Cerrado. Remote. Sens. Appl. Soc. Environ. 2020, 19, 100342. [Google Scholar] [CrossRef]
- Lashof, D. The contribution of biomass burning to global warming: An integrated assessment. In Global Biomass Burning: Atmospheric, Climatic, and Biospheric Implications; Levine, J., Ed.; Massachusetts Institute of Technology Press: Williamsburg, VA, USA, 1991; pp. 441–444. [Google Scholar]
- Van der Werf, G.R.; Randerson, J.T.; Giglio, L.; Collatz, G.; Mu, M.; Kasibhatla, P.S.; Morton, D.C.; DeFries, R.; Jin, Y.V.; van Leeuwen, T.T. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. 2010, 10, 11707–11735. [Google Scholar] [CrossRef] [Green Version]
- Silva, P.R.D.S.; Ignotti, E.; de Oliveira, B.F.A.; Junger, W.L.; Morais, F.; Artaxo, P.; Hacon, S. High risk of respiratory diseases in children in the fire period in Western Amazon. Rev. Saude Publica 2016, 50, 1–11. [Google Scholar] [CrossRef] [Green Version]
- Uriarte, M.; Yackulic, C.B.; Cooper, T.; Flynn, D.; Cortes, M.; Crk, T.; Cullman, G.; McGinty, M.; Sircely, J. Expansion of sugarcane production in São Paulo, Brazil: Implications for fire occurrence and respiratory health. Agric. Ecosyst. Environ. 2009, 132, 48–56. [Google Scholar] [CrossRef]
- Aragão, L.E.O.C.; Silva Junior, C.H.L.; Anderson, L.O. 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; SEI/INPE: São José dos Campos, Brazil, 2020; Volume 1, p. 34. [CrossRef]
- Machado-Silva, F.; Libonati, R.; de Lima, T.F.M.; Peixoto, R.B.; de Almeida França, J.R.; Magalhães, M.d.A.F.M.; Santos, F.L.M.; Rodrigues, J.A.; DaCamara, C.C. Drought and fires influence the respiratory diseases hospitalizations in the Amazon. Ecol. Indic. 2020, 109, 105817. [Google Scholar] [CrossRef]
- Pivello, V.R.; Vieira, I.; Christianini, A.V.; Ribeiro, D.B.; da Silva Menezes, L.; Berlinck, C.N.; Melo, F.P.; Marengo, J.A.; Tornquist, C.G.; Tomas, W.M.; et al. Understanding Brazil’s catastrophic fires: Causes, consequences and policy needed to prevent future tragedies. Perspect. Ecol. Conserv. 2021, 19, 233–255. [Google Scholar] [CrossRef]
- Alencar, A.; Moutinho, P.; Arruda, V.; Silvério, D. The Amazon in Flames: Fire and Deforestation in 2019—And What’s to Come in 2020. 2020. Available online: https://ipam.org.br/wp-content/uploads/2020/04/NT3-Fire-2019.pdf (accessed on 3 February 2022).
- Bencherif, H.; Bègue, N.; Kirsch Pinheiro, D.; Du Preez, D.J.; Cadet, J.M.; da Silva Lopes, F.J.; Shikwambana, L.; Landulfo, E.; Vescovini, T.; Labuschagne, C.; et al. Investigating the Long-Range Transport of Aerosol Plumes Following the Amazon Fires (August 2019): A Multi-Instrumental Approach from Ground-Based and Satellite Observations. Remote. Sens. 2020, 12, 3846. [Google Scholar] [CrossRef]
- Ramos-Neto, M.B.; Pivello, V.R. Lightning fires in a Brazilian savanna National Park: Rethinking management strategies. Environ. Manag. 2000, 26, 675–684. [Google Scholar] [CrossRef]
- Fidelis, A. Is fire always the “bad guy”? Flora 2020, 268, 151611. [Google Scholar] [CrossRef]
- Miranda, H.S.; Bustamante, M.M.; Miranda, A.C.; Oliveira, P.; Marquis, R. The fire factor. In The Cerrados of Brazil: Ecology and Natural History of a Neotropical Savanna; Columbia University Press: New York, NY, USA, 2002; pp. 51–68. [Google Scholar]
- Klink, C.A.; Machado, R.B. A conservação do Cerrado brasileiro. Megadiversidade 2005, 1, 147–155. [Google Scholar]
- Pivello, V.R. The use of fire in the Cerrado and Amazonian rainforests of Brazil: Past and present. Fire Ecol. 2011, 7, 24–39. [Google Scholar] [CrossRef]
- Abreu, R.C.; Hoffmann, W.A.; Vasconcelos, H.L.; Pilon, N.A.; Rossatto, D.R.; Durigan, G. The biodiversity cost of carbon sequestration in tropical savanna. Sci. Adv. 2017, 3, e1701284. [Google Scholar] [CrossRef] [Green Version]
- Fidelis, A.; Alvarado, S.T.; Barradas, A.C.S.; Pivello, V.R. The Year 2017: Megafires and Management in the Cerrado. Fire 2018, 1, 49. [Google Scholar] [CrossRef] [Green Version]
- Miranda, H.; Neto, W.; Neves, B. Caracterização das queimadas de Cerrado. In Efeitos do Regime do Fogo Sobre a Estrutura de Comunidades de Cerrado: Resultados do Projeto Fogo; Miranda, H.S., Ed.; IBAMA: Brasília, Brazil, 2010; pp. 23–33. [Google Scholar]
- Schmidt, I.B.; Eloy, L. Fire regime in the Brazilian Savanna: Recent changes, policy and management. Flora 2020, 268, 151613. [Google Scholar] [CrossRef]
- Miranda, E.E.; Magalhães, L.A.; Carvalho, C.A. Nota Técnica: Proposta de Delimitação Territorial do MATOPIBA. 2014. Available online: https://www.infoteca.cnptia.embrapa.br/infoteca/handle/doc/1037313 (accessed on 5 November 2021).
- INPE, Instituto Nacional de Pesquisas Espaciais. TerraBrasilis, PRODES (Desmatamento). 2022. Available online: http://terrabrasilis.dpi.inpe.br/ (accessed on 22 February 2022).
- Soterroni, A.C.; Ramos, F.M.; Mosnier, A.; Fargione, J.; Andrade, P.R.; Baumgarten, L.; Pirker, J.; Obersteiner, M.; Kraxner, F.; Câmara, G.; et al. Expanding the Soy Moratorium to Brazil’s Cerrado. Sci. Adv. 2019, 5, eaav7336. [Google Scholar] [CrossRef] [Green Version]
- Marengo, J.A.; Jimenez, J.C.; Espinoza, J.C.; Cunha, A.P.; Aragão, L.E. Increased Climate Pressure on the New Agricultural Frontier in the Eastern Amazonia-Cerrado Transition Zone. Sci. Rep. 2021, 12, 457. [Google Scholar] [CrossRef]
- Pletsch, M.A.; Körting, T.S.; Morita, F.C.; Morelli, F.; Bittencourt, O.; Victorino, P.S. Using GOES-16 Time Series to characterize near real-time active fires in Cerrado. In Proceedings of the GEOINFO, São José dos Campos, Brazil, 12 November 2019; pp. 66–76. [Google Scholar]
- Wooster, M.J.; Roberts, G.J.; Giglio, L.; Roy, D.P.; Freeborn, P.H.; Boschetti, L.; Justice, C.; Ichoku, C.; Schroeder, W.; Davies, D.; et al. Satellite remote sensing of active fires: History and current status, applications and future requirements. Remote. Sens. Environ. 2021, 267, 112694. [Google Scholar] [CrossRef]
- Schmit, T.J.; Gunshor, M.M.; Menzel, W.P.; Gurka, J.J.; Li, J.; Bachmeier, A.S. Introducing the next-generation Advanced Baseline Imager on GOES-R. Bull. Am. Meteorol. Soc. 2005, 86, 1079–1096. [Google Scholar] [CrossRef]
- Schmit, T.J.; Griffith, P.; Gunshor, M.M.; Daniels, J.M.; Goodman, S.J.; Lebair, W.J. A closer look at the ABI on the GOES-R series. Bull. Am. Meteorol. Soc. 2017, 98, 681–698. [Google Scholar] [CrossRef]
- Laney, D. 3D data management: Controlling data volume, velocity and variety. Meta Group Res. Note 2001, 6, 1. [Google Scholar]
- NASA, National Aeronautics and Space Administration. FIRMS—Fire Information for Resource Management System. 2021. Available online: https://firms.modaps.eosdis.nasa.gov/ (accessed on 10 November 2021).
- INPE, Instituto Nacional de Pesquisas Espaciais. Programa Queimadas. 2021. Available online: https://queimadas.dgi.inpe.br/queimadas/portal/ (accessed on 10 November 2021).
- 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] [PubMed] [Green Version]
- 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]
- Li, F.; Zhang, X.; Kondragunta, S.; Schmidt, C.C.; Holmes, C.D. A preliminary evaluation of GOES-16 active fire product using Landsat-8 and VIIRS active fire data, and ground-based prescribed fire records. Remote. Sens. Environ. 2020, 237, 111600. [Google Scholar] [CrossRef]
- 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]
- Schroeder, W.; Oliva, P.; Giglio, L.; Quayle, B.; Lorenz, E.; Morelli, F. Active fire detection using Landsat-8/OLI data. Remote. Sens. Environ. 2016, 185, 210–220. [Google Scholar] [CrossRef] [Green Version]
- Schroeder, W.; Giglio, L. NASA VIIRS Land Science Investigator Processing System (SIPS) Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m & 750 m Active Fire Products: Product User’s Guide Version 1.4; NASA: Washington, DC, USA, 2018.
- Cao, C.; Xiong, J.; Blonski, S.; Liu, Q.; Uprety, S.; Shao, X.; Bai, Y.; Weng, F. Suomi NPP VIIRS sensor data record verification, validation, and long-term performance monitoring. J. Geophys. Res. Atmos. 2013, 118, 11–664. [Google Scholar] [CrossRef]
- Csiszar, I.; Schroeder, W.; Giglio, L.; Ellicott, E.; Vadrevu, K.P.; Justice, C.O.; Wind, B. Active fires from the Suomi NPP Visible Infrared Imaging Radiometer Suite: Product status and first evaluation results. J. Geophys. Res. Atmos. 2014, 119, 803–816. [Google Scholar] [CrossRef]
- Schmit, T.; Lindstrom, S.; Gerth, J.; Gunshor, M. Applications of the 16 spectral bands on the Advanced Baseline Imager (ABI). J. Oper. Meteorol. 2018, 6, 33–46. [Google Scholar] [CrossRef]
- NOAA and NASA Geostationary Operational Environamental Satellite—R Series. Quick Guide: ABI Band 7. 2021. Available online: https://www.goes-r.gov/exit.html?http://cimss.ssec.wisc.edu/goes/OCLOFactSheetPDFs/ABIQuickGuide_Band07.pdf (accessed on 9 December 2021).
- Sentinel Hub. KSWIR—Short Wave Infrared RGB Composite. 2021. Available online: https://custom-scripts.sentinel-hub.com/sentinel-2/swir-rgb/ (accessed on 25 November 2021).
- Jain, P.; Coogan, S.C.; Subramanian, S.G.; Crowley, M.; Taylor, S.; Flannigan, M.D. A review of machine learning applications in wildfire science and management. Environ. Rev. 2020, 28, 478–505. [Google Scholar] [CrossRef]
- Bot, K.; Borges, J.G. A Systematic Review of Applications of Machine Learning Techniques for Wildfire Management Decision Support. Inventions 2022, 7, 15. [Google Scholar] [CrossRef]
- Breiman, L. Random forests. Mach. Learn. 2001, 45, 5–32. [Google Scholar] [CrossRef] [Green Version]
- Friedman, J.H. Greedy function approximation: A gradient boosting machine. Ann. Stat. 2001, 29, 1189–1232. [Google Scholar] [CrossRef]
- Ampomah, E.K.; Qin, Z.; Nyame, G. Evaluation of tree-based ensemble machine learning models in predicting stock price direction of movement. Information 2020, 11, 332. [Google Scholar] [CrossRef]
- Legendre, P.; Legendre, L. Numerical Ecology. In Developments in Environmental Modelling, 2nd ed.; Elsevier: Amsterdam, The Netherlands, 1998; Volume 20. [Google Scholar]
- Milanović, S.; Marković, N.; Pamučar, D.; Gigović, L.; Kostić, P.; Milanović, S.D. Forest fire probability mapping in eastern Serbia: Logistic regression versus random forest method. Forests 2021, 12, 5. [Google Scholar] [CrossRef]
- Higa, L.; Marcato Junior, J.; Rodrigues, T.; Zamboni, P.; Silva, R.; Almeida, L.; Liesenberg, V.; Roque, F.; Libonati, R.; Gonçalves, W.N.; et al. Active Fire Mapping on Brazilian Pantanal Based on Deep Learning and CBERS 04A Imagery. Remote. Sens. 2022, 14, 688. [Google Scholar] [CrossRef]
- de Almeida Pereira, G.H.; Fusioka, A.M.; Nassu, B.T.; Minetto, R. Active fire detection in Landsat-8 imagery: A large-scale dataset and a deep-learning study. ISPRS J. Photogramm. Remote. Sens. 2021, 178, 171–186. [Google Scholar] [CrossRef]
- Oliveira, U.; Soares-Filho, B.; de Souza Costa, W.L.; Gomes, L.; Bustamante, M.; Miranda, H. Modeling fuel loads dynamics and fire spread probability in the Brazilian Cerrado. For. Ecol. Manag. 2021, 482, 118889. [Google Scholar] [CrossRef]
- Vieira, R.M.D.S.P.; Tomasella, J.; Barbosa, A.A.; Polizel, S.P.; Ometto, J.P.H.B.; Santos, F.C.; da Cruz Ferreira, Y.; de Toledo, P.M. Land degradation mapping in the MATOPIBA region (Brazil) using remote sensing data and decision-tree analysis. Sci. Total. Environ. 2021, 782, 146900. [Google Scholar] [CrossRef]
- Arruda, F.V.D.; Sousa, D.G.D.; Teresa, F.B.; Prado, V.H.M.D.; Cunha, H.F.D.; Izzo, T.J. Trends and gaps of the scientific literature about the effects of fire on Brazilian Cerrado. Biota Neotrop. 2018, 18, 1–6. [Google Scholar] [CrossRef]
- Durigan, G.; Ratter, J.A. The need for a consistent fire policy for Cerrado conservation. J. Appl. Ecol. 2016, 53, 11–15. [Google Scholar] [CrossRef]
Metrics | Overall FM Assessment | FM Assessment by LULC | ||
---|---|---|---|---|
NF | SF | Gr | ||
True positives (real: fire, predicted: fire) | 6607 (40.60%) | 3906 (82.09%) | 1691 (24.98%) | 1010 (21.28%) |
False negatives (real: fire, predicted: non-fire) | 2468 (15.17%) | 419 (08.81%) | 1190 (17.58%) | 859 (18.10%) |
False positives (real: non-fire, predicted: fire) | 971 (05.96%) | 178 (03.74%) | 763 (11.27%) | 30 (00.63%) |
True negatives (real: non-fire, predicted: non-fire) | 6228 (38.27%) | 255 (05.36%) | 3125 (46.17%) | 2848 (60.00%) |
Fire prevalence on test data | 55.8% | 90.9% | 42.6% | 39.4% |
Accuracy rate | 78.9% | 87.5% | 71.1% | 81.3% |
Sensitivity | 72.8% | 90.3% | 58.7% | 54.0% |
Specificity | 86.5% | 58.9% | 80.4% | 99.0% |
Positive Predictive Value | 87.2% | 95.6% | 68.9% | 97.1% |
Negative Predictive Value | 71.6% | 37.8% | 72.4% | 76.8% |
(a) | FM Accuracy According to BA Mapping in the Central Pixel (km2) | ||||||||
---|---|---|---|---|---|---|---|---|---|
0–0.01 | 0.01–0.1 | 0.1–1.0 | >1.0 | ||||||
Classification | F | NF | F | NF | F | NF | F | NF | |
BA Mapping | F | 0.00% | 0.00% | 77.10% | 22.90% | 71.20% | 28.80% | 67.80% | 32.20% |
NF | 13.50% | 86.50% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | 0.00% | |
(b) | FM Accuracy According to BA Mapping in the Sorroundings (km2) | ||||||||
0–0.01 | 0.01–0.1 | 0.1–1.0 | >1.0 | ||||||
Classification | F | NF | F | NF | F | NF | F | NF | |
BA Mapping | F | 0.00% | 4.00% | 82.00% | 10.00% | 31.00% | 13.00% | 47.00% | 28.00% |
NF | 12.00% | 84.00% | 0.00% | 8.00% | 9.00% | 47.00% | 4.00% | 21.00% |
Metrics | Reference Satellites | Consecutive AF Detection | ||
---|---|---|---|---|
Naive | 15 | 125 | ||
True positives (real: fire, predicted: fire) | 32 (28.32%) | 58 (51.33%) | 56 (49.56%) | 51 (45.13%) |
False negatives (real: fire, predicted: non-fire) | 30 (26.55%) | 4 (3.54%) | 6 (5.31%) | 11 (9.73%) |
False positives (real: non-fire, predicted: fire) | 3 (2.65%) | 45 (39.82%) | 31 (27.43%) | 19 (16.82%) |
True negatives (real: non-fire, predicted: non-fire) | 48 (42.48%) | 6 (5.31%) | 20 (17.70%) | 32 (28.32%) |
Fire prevalence on test data | 55.76% | 55.76% | 55.76% | 55.76% |
Accuracy rate | 70.80% | 56.64% | 67.26% | 73.45% |
Sensitivity | 51.61% | 93.55% | 90.32% | 82.26% |
Specificity | 94.12% | 11.76% | 39.22% | 62.75% |
Positive Predictive Value | 91.43% | 56.31% | 64.37% | 72.86% |
Negative Predictive Value | 61.54% | 60.00% | 76.92% | 74.42% |
Reference Satellites | |||||
---|---|---|---|---|---|
TP | FN | FP | TN | ||
125 consecutive AF detections | TP | 22.10% | 23.00% | 0.00% | 0.00% |
FN | 6.20% | 3.50% | 0.00% | 0.00% | |
FP | 0.00% | 0.00% | 0.90% | 15.90% | |
TN | 0.00% | 0.00% | 1.90% | 26.50% |
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Pletsch, M.A.J.S.; Körting, T.S.; Morita, F.C.; Silva-Junior, C.H.L.; Anderson, L.O.; Aragão, L.E.O.C. Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil. Remote Sens. 2022, 14, 3141. https://doi.org/10.3390/rs14133141
Pletsch MAJS, Körting TS, Morita FC, Silva-Junior CHL, Anderson LO, Aragão LEOC. Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil. Remote Sensing. 2022; 14(13):3141. https://doi.org/10.3390/rs14133141
Chicago/Turabian StylePletsch, Mikhaela A. J. S., Thales S. Körting, Felipe C. Morita, Celso H. L. Silva-Junior, Liana O. Anderson, and Luiz E. O. C. Aragão. 2022. "Near Real-Time Fire Detection and Monitoring in the MATOPIBA Region, Brazil" Remote Sensing 14, no. 13: 3141. https://doi.org/10.3390/rs14133141