Avaliação Temporal da Dinâmica de Regeneração da Vegetação em Áreas Queimadas no Pantanal
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Abstract
The Pantanal, one of the most conserved biomes in Brazil, has been suffering from alterations in the hydrological cycle, fires, and the expansion of agribusiness. The constant and continuous fires in this biome cause immeasurable environmental damage due to its characteristic long dry periods with low humidity and high temperature. In this sense, the present study analyzed the dynamics of post-burn vegetation regeneration and biomass production in the Pantanal biome by means of derivatives of temporal (seasonal) profiles of the NDVI (Normalized Vegetation Index) vegetation index and the NBR (Normalized Burn Ratio Index) burn index. For mapping and identification of burned areas, an algorithm implemented in Google Earth Engine was used, and MOD14A2 active fire products derived from MODIS sensor images. Seven burned areas were selected in September/2016 and the Landsat8 Surface Reflectance Tier1 data collection from April 2013 to September 2020 was used to calculate the indices. According to the analyses, two areas were not able to recover after the fire event. However, in all areas the indices returned to values similar to those found in the year before the fire. In addition, the rainfall of the location was a determining factor in defining the effect of the burn and biomass production, with areas in locations with higher rainfall being the least affected by the effect of the burns. Based on the results of this study, the indices used proved to be efficient in identifying burned areas and in classifying the regeneration potential of these areas.
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References
ALCARAZ, D.; PARUELO, J.; CABELLO, J. Identification of current ecosystem functional types in the Iberian Peninsula. Global Ecology and Biogeography, [S. l.], v. 15, n. 2, p. 200–212, 2006. DOI: 10.1111/j.1466-822X.2006.00215.x.
ANTUNES, J. F. G.; LAMPARELLI, R. A. C.; RODRIGUES, L. H. A. Assessing of the sugarcane cultivation dynamics in São Paulo state by MODIS data temporal profiles. Engenharia Agrícola, [S. l.], v. 35, n. 6, p. 1127–1136, 2015.
BAR, S.; PARIDA, B. R.; PANDEY, A.C. Landsat-8 and Sentinel-2 based Forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment, [S. l.], v. 18, p. 100324, 2020.
COLLINS, L.; MCCARTHY, G.; MELLOR, A.; NEWELL, G.; SMITH, L. Training data requirements for fire severity mapping using Landsat imagery and random forest. Remote Sensing of Environment, [S. l.], v. 245, p. 111839, 2020.
CROWLEY, M. A.; CARDILLE, J. A.; WHITE, J. C.; WULDER, M. A. Multi-sensor, multi-scale, Bayesian data synthesis for mapping within-year wildfire progression. Remote sensing letters, [S. l.], v. 10, n. 3, p. 302–311, 2019.
CUEVAS-GONZÁLEZ, M.; GERARD, F.; BALZTER, H.; RIAÑO, D. Analysing forest recovery after wildfire disturbance in boreal Siberia using remotely sensed vegetation indices. Global Change Biology, [S. l.], v. 15, n. 3, p. 561–577, 2009. DOI: 10.1111/j.1365-2486.2008.01784.x.
DA SILVA, A. B. N.; BELTRÃO, N. E. S.; SANTOS, L. B. Utilizando imagens Sentinel-2 e índices espectrais para análise de severidade em áreas queimadas de origem antrópica: um estudo no sudeste da Amazônia. Revista Brasileira de Geografia Física, v. 16, n. 01, p. 489-504, 2023.
EMADIZADEH, A.; AZIZI, Z. Detection and Diagnosis of Fire Areas in Golestan Forests Using Landsat Satellite Images. Journal of Radar and Optical Remote Sensing, [S. l.], v. 2, n. 4, p. 7–20, 2020.
ESCUIN, S. NAVARRO, S.; FERNÁNDEZ P. Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images, International Journal of Remote Sensing, 29:4, 1053-1073, 2008. DOI: 10.1080/01431160701281072
GARCÍA-LLAMAS, P.; SUÁREZ-SEOANE, S.; FERNÁNDEZ-GUISURAGA, J. M.; FERNÁNDEZ-GARCÍA, V.; FERNÁNDEZ-MANSO, A.; QUINTANO, C.; TABOADA, A.; MARCOS, E; CALVO, L. Evaluation and comparison of Landsat 8, Sentinel-2 and Deimos-1 remote sensing indices for assessing burn severity in Mediterranean fire-prone ecosystems. International Journal of Applied Earth Observation and Geoinformation, 80, 137-144, 2019.
GORELICK, N.; HANCHER, M.; DIXON, M.; ILYUSHCHENKO, S.; THAU, D.; MOORE, R. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote sensing of Environment, [S. l.], v. 202, p. 18–27, 2017.
GUIMARÃES, A. M. B. M. Estudo da dinâmica da vegetação após incêndio. O caso do Parque Natural de Sintra-Cascais após grande incêndio de agosto de 2000. Universidade Técnica de Lisboa, 2009.
KEY, C. H.; BENSON, N. C. Measuring and remote sensing of burn severity: the CBI and NBR. Poster abstract/Eds. LF Neuenschwander, KC Ryan. In: PROC. JOINT FIRE SCIENCE CONF. AND WORKSHOP. V. II. BOISE, 15-17 JUNE 1999, Anais... [s.l: s.n.] p. 284.
LE PAGE, M.; ZRIBI, M. Analysis and predictability of drought in Northwest Africa using optical and microwave satellite remote sensing products. Scientific reports, v. 9, n. 1, p. 1466, 2019.
LI, X.; SONG, K.; LIU, G. Wetland Fire Scar Monitoring and Its Response to Changes of the Pantanal Wetland. Sensors, [S. l.], v. 20, n. 15, p. 4268, 2020. DOI: 10.3390/s20154268.
LLOYD, D. A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery. International Journal of Remote Sensing, [S. l.], v. 11, n. 12, p. 2269–2279, 1990. DOI: 10.1080/01431169008955174.
MALLINIS, G.; MITSOPOULOS, I.; CHRYSAFI, I. Evaluating and comparing Sentinel 2A and Landsat-8 Operational Land Imager (OLI) spectral indices for estimating fire severity in a Mediterranean pine ecosystem of Greece. GIScience & Remote Sensing, [S. l.], v. 55, n. 1, p. 1–18, 2018.
MARENGO, J. A., CUNHA, A. P., CUARTAS, L. A., LEAL, K. R. D., BROEDEl, E., SELUCHI, M. E., MICHELIN, M.; BAIÃO, C. F. P.; ÂNGULO, E. C.; ALMEIDA, E. K.; KAZMIERCKZAK, M. L.; MATEUS, N. P. A.; SILVA, R. C.; BENDER, F. Extreme drought in the Brazilian Pantanal in 2019–2020: characterization, causes, and impacts. Frontiers in Water, 3, v. 3, p. 13, 2021.
MONDAL, P. Quantifying surface gradients with a 2-band Enhanced Vegetation Index (EVI2). Ecological Indicators, [S. l.], v. 11, n. 3, p. 918–924, 2011.
MORAES, E. C.; MATAVELI, G. A.; SANTOS, P. R.; OLIVEIRA, B. S. Estudo da dinâmica de queimada no bioma Pantanal no período de 2002 a 2015. In: Simpósio Brasileiro De Sensoriamento Remoto. Santos (SP). Anais..., [S. l.], v. 18, 2017, p. 3423–3430, 2017.
NOVAIS, M. DA S., LATORRACA, E. D. B., DE CAMPOS, A. C. B. R., DA CRUZ TEIXEIRA, N., DE ALMEIDA, G. A. G.; MARCILIO, N. D. S. A. DA SILVA NOVAIS, Mariane et al. Análise de severidade de áreas queimadas no município de Poconé–MT. Research, Society and Development, v. 11, n. 5, p. e6311527882-e6311527882, 2022. DOI: http://dx.doi.org/10.33448/rsd-v11i5.27882http://dx.doi.org/10.33448/rsd-v11i5.27882
PETTORELLI, N.; VIK, J. O.; MYSTERUD, A.; GAILLARD, J. M.; TUCKER, C. J.; STENSETH, N. C. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution, [S. l.], v. 20, n. 9, p. 503–510, 2005. DOI: 10.1016/j.tree.2005.05.011.
ROUSE, J. W.; HAAS, R. H.; SCHELL, J. A.; DEERING, D. W. Monitoring vegetation systems in the great plains with ERTS. In: Third ERTS Symposium, Washignton, DC, Proceedings…, NASA SP-351, NASA, Washignton, DC, v. 1, p. 309-317, 1973.
SEGER, C. D.; BATISTA, A. C.; TETTO, A. F.; GIONGO ALVES, M. V.; SOARES, R. V.; BIONDI, D. Incremento da biomassa aérea da vegetação dos campos naturais do Pparaná em período pós-queima. FLORESTA, [S. l.], v. 46, n. 1, p. 93, 2016. DOI: 10.5380/rf.v46i1.43381.
SORIANO, B. M. A.; NARCISO, M. G. Sistema de alerta de risco de incêndio para o Pantanal. Embrapa Pantanal-Capítulo em livro científico (ALICE), [S. l.], 2020.
TEODORO, A.; AMARAL, A. A statistical and spatial analysis of Portuguese forest fires in summer 2016 considering Landsat 8 and Sentinel 2A data. Environments, [S. l.], v. 6, n. 3, p. 36, 2019.
TORRES, J.; GONÇALVES, J.; BRUNO, M.; JOÃO, H. Indicator-based assessment of post-fire recovery dynamics using satellite NDVI time-series. Ecological Indicators, [S. l.], v. 89, p. 199–212, 2018.
USGS. Landsat 8 Collection 1 Land Surface Reflectance Code Product Guide. Disponível em: https://www.usgs.gov/media/files/landsat-8-collection-1-land-surface-reflectance-code-product-guide. Acesso em: 2020.
VIANA, D. R.; ALVALÁ, R. C dos S. Vegetation index performance for the pantanal region during both dry and rainy seasons. Geografia, v. 36, p. 143-158, 2011.