Recuperación de la vegetación posincendio y separabilidad espectral en ecosistemas de sabana amazónica mediante series temporales de teledetección y mediciones de carga de combustibles

Autores/as

DOI:

https://doi.org/10.18172/cig.6889

Palabras clave:

áreas quemadas, Landsat, Sentinel 2, sabanas tropicales

Resumen

El monitoreo y la comprensión de las respuestas de la vegetación al fuego en los ecosistemas de sabana amazónica siguen siendo un desafío científico muy importante para mejorar las prácticas de manejo del paisaje en estas áreas. En este sentido, el presente estudio analiza la dinámica de la separabilidad espectral, así como el proceso de recuperación posincendio de la vegetación, en relación con experimentos de fuego realizados en ecosistemas de sabana abierta del Parque Nacional Campos Amazônicos (Brasil). Para este propósito, se procesó y analizó un conjunto de datos armonizado de Landsat y Sentinel-2. También se generaron series temporales del Índice de Vegetación de Diferencia Normalizada (NDVI) y del Índice Normalizado de Quema 2 (NBR2) a partir de este mismo conjunto de datos para el período 2019–2023, evaluándose en combinación con mediciones in situ de carga de combustibles finos. Se calcularon M-Statistics y diferencia absoluta media comparando datos de parcelas quemadas y no quemadas, considerando diferentes tratamientos de estacionalidad del fuego (fuegos de inicio de estación seca – EDS; fuegos de mitad de estación seca – MDS) y tiempo desde el último incendio (combustible de 2 años; combustible de 3 años; y fuegos con combustible de 10 años o más). El uso combinado de Sentinel-2 y Landsat resultó en una disponibilidad de imágenes libres o parcialmente libres de nubes ≈0,6 veces mayor que la obtenida al usar únicamente imágenes de Landsat. El potencial del NBR2 se destacó, generando valores estadísticamente significativos de diferencia absoluta media al comparar incendios EDS y MDS, así como al comparar áreas con combustible de 2 años frente a las de 3 años o de 10 años o más. La información satelital y de campo coincidió en la detección de una rápida respuesta de la vegetación al fuego en estos ecosistemas, demostrando que las condiciones similares a las observadas antes del incendio se alcanzaron después de tres estaciones lluviosas. Los resultados refuerzan el potencial de los conjuntos de datos armonizados de teledetección Landsat y Sentinel-2 para evaluar y monitorear áreas afectadas por incendios en los ecosistemas de sabana amazónica, aportando significado ecológico y estableciendo conexiones entre los datos de teledetección y los de campo.

Descargas

Los datos de descargas todavía no están disponibles.

Biografía del autor/a

Daniel Borini Alves, Universidade Estadual Vale do Acaraú

Complete affiliationn:

Universidade Estadual Vale do Acaraú, Programa de Pós-Graduação em Geografia, Avenida John Sanford, 1845, 62030-000, Sobral, Ceará, Brazil

Antonio Laffayete Pires da Silveira, Universidade Federal de Rondônia

Universidade Federal de Rondônia, Programa de Pós Graduação em Conservação e Uso de Recursos Naturais, Departamento de Biologia, BR 364, Km 9.5, 76801-059, Porto Velho, Brazil.

Bruno Contursi Cambraia, Instituto Chico Mendes de Conservação da Biodiversidade

Instituto Chico Mendes de Conservação da Biodiversidade (ICMBio), Gerencia Regional 3 – Centro-Oeste, Rua 2 Quadra 30 Lote 36, nº 45/49, 74013-020, Goiânia, Goiás, Brazil.

José Falcão Sobrinho, Universidade Estadual Vale do Acaraú

Universidade Estadual Vale do Acaraú, Programa de Pós-Graduação em Geografia, Avenida John Sanford, 1845, 62030-000, Sobral, Ceará, Brazil

Thiago Sanna Freire Silva, University of Stirling

University of Stirling, Department of Biological and Environmental Sciences, FK9 4LA, Stirling, Scotland

Fernando Pérez-Cabello, Universidad de Zaragoza

University of Zaragoza, Department of Geography and Spatial Management, Geoforest-IUCA Group, C/ Pedro Cerbuna 12, 50009 Zaragoza, Spain

Citas

Ab’Saber, A., 1977. Espaços ocupados pela expansão dos climas secos na América do Sul, por ocasião dos períodos glaciais quaternários. Paleoclimas 3, 1-19.

Ab’Saber, A.N., 2003. Os domínios de natureza no Brasil: potencialidades paisagísticas. Ateliê Editorial, São Paulo.

Alberton, B., Torres, R. da S., Cancian, L.F., Borges, B.D., Almeida, J., Mariano, G.C., Santos, J. dos, Morellato, L.P.C., 2017. Introducing digital cameras to monitor plant phenology in the tropics: applications for conservation. Perspectives in Ecology and Conservation 15, 82-90. https://doi.org/10.1016/j.pecon.2017.06.004

Alcaras, E., Costantino, D., Guastaferro, F., Parente, C., Pepe, M., 2022. Normalized Burn Ratio Plus (NBR+): A New Index for Sentinel-2 Imagery. Remote Sensing 14, 1-19. https://doi.org/10.3390/rs14071727

Alves, D.B., Alvarado, S.T., 2019. Variação espaço-temporal da ocorrência do fogo nos biomas brasileiros com base na análise de produtos de sensoriamento remoto. Geografia 44, 321-345. https://doi.org/10.5016/geografia.v44i2.15119

Alves, D.B., Fidelis, A., Pérez-Cabello, F., Alvarado, S.T., Conciani, D.E., Cambraia, B.C., Silveira, A.L.P. da, Silva, T.S.F., 2022. Impact of image acquisition lag-time on monitoring short-term postfire spectral dynamics in tropical savannas: the Campos Amazônicos Fire Experiment. Journal of Applied Remote Sensing 16, 1-24. https://doi.org/10.1117/1.jrs.16.034507

Alves, D.B., Montorio Llovería, R., Pérez-Cabello, F., Vlassova, L., 2018. Fusing Landsat and MODIS data to retrieve multispectral information from fire-affected areas over tropical savannah environments in the Brazilian Amazon. International Journal of Remote Sensing 39, 1-23. https://doi.org/10.1080/01431161.2018.1479790

Alves, D.B., Pérez-Cabello, F., 2017. Multiple remote sensing data sources to assess spatio-temporal patterns of fire incidence over Campos Amazônicos Savanna Vegetation Enclave (Brazilian Amazon). Science of the Total Environment 601-602, 142-158. https://doi.org/10.1016/j.scitotenv.2017.05.194

Alves, D.B., Pérez-Cabello, F., Rodrigues Mimbrero, M., 2015. Land-use and land-cover dynamics monitored by NDVI multitemporal analysis in a selected southern Amazonian area (Brazil) for the last three decades. ISPRS - The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3, 329-335. https://doi.org/10.5194/isprsarchives-XL-7-W3-329-2015

Andersen, A.N., Cook, G.D., Williams, R.J., 2003. Fire in Tropical Savannas: The Kapalga Experiment. Springer-Verlag, New York, USA.

Archibald, S., Lehmann, C.E.R., Belcher, C.M., Bond, W.J., Bradstock, R.A., Daniau, A.L., Dexter, K.G., Forrestel, E.J., Greve, M., He, T., Higgins, S.I., Hoffmann, W.A., Lamont, B.B., McGlinn, D.J., Moncrieff, G.R., Osborne, C.P., Pausas, J.G., Price, O., Ripley, B.S., Rogers, B.M., Schwilk, D.W., Simon, M.F., Turetsky, M.R., Van Der Werf, G.R., Zanne, A.E., 2018. Biological and geophysical feedbacks with fire in the Earth system. Environmental Research Letters 13. https://doi.org/10.1088/1748-9326/aa9ead

Asner, G.P., 2001. Cloud cover in Landsat observations of the Brazilian Amazon. International Journal of Remote Sensing 22, 3855-3862. https://doi.org/10.1080/01431160010006926

Belda, S., Pipia, L., Morcillo-Pallarés, P., Rivera-Caicedo, J.P., Amin, E., De Grave, C., Verrelst, J., 2020. DATimeS: A machine learning time series GUI toolbox for gap-filling and vegetation phenology trends detection. Environmental Modelling & Software 127, 104666. https://doi.org/10.1016/j.envsoft.2020.104666

Berra, E.F., Fontana, D.C., Yin, F., Breunig, F.M., 2024. Harmonized Landsat and Sentinel-2 Data with Google Earth Engine. Remote Sensing 16. https://doi.org/10.3390/rs16152695

Bolognesi, S.F., Pasolli, E., Belfiore, O.R., De Michele, C., D’Urso, G., 2020. Harmonized landsat 8 and sentinel-2 time series data to detect irrigated areas: An application in Southern Italy. Remote Sensing 12. https://doi.org/10.3390/RS12081275

Bousquet, E., Mialon, A., Rodriguez-Fernandez, N., Mermoz, S., Kerr, Y., 2022. Monitoring post-fire recovery of various vegetation biomes using multi-wavelength satellite remote sensing. Biogeosciences 19, 3317-3336. https://doi.org/10.5194/bg-19-3317-2022

Burton, C., Lampe, S., Kelley, D.I., Thiery, W., Hantson, S., Christidis, N., Gudmundsson, L., Forrest, M., Burke, E., Chang, J., Huang, H., Ito, A., Kou-Giesbrecht, S., Lasslop, G., Li, W., Nieradzik, L., Li, F., Chen, Y., Randerson, J., Reyer, C.P.O., Mengel, M., 2024. Global burned area increasingly explained by climate change. Nature Climate Change 14, 1186-1192. https://doi.org/10.1038/s41558-024-02140-w

Carneiro Filho, A., 1993. Cerrados amazônicos: fósseis vivos? Algumas reflexões. Revista do Instituto Geológico 14, 63-68. https://doi.org/10.5935/0100-929X.19930010

Carvalho, W.D. de, Mustin, K., 2017. The highly threatened and little known Amazonian savannahs. Nature Ecology & Evolution 1, 0100. https://doi.org/10.1038/s41559-017-0100

Cheney, P., Sullivan, A., 1997. Grassfires: Fuel, Weather and Fire Behaviour. CSIRO, Melbourne, Australia.

Chuvieco, E., Martín, M.P., Palacios, A., 2002. Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination. International Journal of Remote Sensing 23, 5103-5110. https://doi.org/10.1080/01431160210153129

Claverie, M., Ju, J., Masek, J.G., Dungan, J.L., Vermote, E.F., Roger, J.C., Skakun, S. V., Justice, C., 2018. The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment 219, 145-161. https://doi.org/10.1016/j.rse.2018.09.002

Claverie, M., Masek, J.G., Ju, J., Dungan, J.L., 2017. Harmonized Landsat-8 Sentinel 2 (HLS) Product User’s Guide. https://doi.org/10.13140/RG.2.2.33017.26725

Collins, L., Trouvé, R., Baker, P.J., Cirulus, B., Nitschke, C.R., Nolan, R.H., Smith, L., Penman, T.D., 2023. Fuel reduction burning reduces wildfire severity during extreme fire events in south-eastern Australia. Journal of Environmental Management 343, 118171. https://doi.org/10.1016/j.jenvman.2023.118171

Coutinho, L.M., 1990. Fire in the Ecology of the Brazilian Cerrado, in: Goldammer, J.G. (Ed.), Fire in the Tropical Biota. Springer, Berlin, pp. 82-105. https://doi.org/10.1007/978-3-642-75395-4_6

Devries, B., Pratihast, A.K., Verbesselt, J., Kooistra, L., Herold, M., 2016. Characterizing Forest Change Using Community-Based Monitoring Data and Landsat Time Series. PLoS One 3, 1-25. https://doi.org/10.1371/journal.pone.0147121

ESA, European Space Agency, 2024. Copernicus Global Digital Elevation Model. https://doi.org/https://doi.org/10.5069/G9028PQB

Fontaine, J.B., Westcott, V.C., Enright, N.J., Lade, J.C., Miller, B.P., 2012. Fire behaviour in south-western Australian shrublands: evaluating the influence of fuel age and fire weather. International Journal of Wildland Fire 21, 385. https://doi.org/10.1071/WF11065

Fornacca, D., Ren, G., Xiao, W., 2018. Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China. Remote Sensing 10, 1196. https://doi.org/10.3390/rs10081196

Frantz, D., 2019. FORCE-Landsat + Sentinel-2 analysis ready data and beyond. Remote Sensing 11. https://doi.org/10.3390/rs11091124

Fritsch, F.N., Carlson, R.E., 1980. Monotone Piecewise Cubic Interpolation. SIAM Journal on Numerical Analysis 17, 238-246.

Fuentes, I., Lopatin, J., Galleguillos, M., Ceballos-Comisso, A., Eyheramendy, S., Carrasco, R., 2024. Is the change deforestation? Using time-series analysis of satellite data to disentangle deforestation from other forest degradation causes. Remote Sensing Applications: Society and Environment 35, 101210. https://doi.org/10.1016/j.rsase.2024.101210

Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., Michaelsen, J., 2015. The climate hazards infrared precipitation with stations — a new environmental record for monitoring extremes. Science Data 2, 150066. https://doi.org/10.1038/sdata.2015.66

Furley, P.A., Rees, R.M., Ryan, C.M., Saiz, G., 2008. Savanna burning and the assessment of long-term fire experiments with particular reference to Zimbabwe. Progress in Physical Geography 32, 611-634. https://doi.org/10.1177/0309133308101383

Gitas, I., Mitri, G., Veraverbeke, S., Polychronaki, A., 2012. Advances in Remote Sensing of Post-Fire Vegetation Recovery Monitoring - A Review, in: Fatoyinbo, L. Remote Sensing of Biomass - Principles and Applications. InTech. https://doi.org/10.5772/20571

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202, 18-27. https://doi.org/10.1016/j.rse.2017.06.031

Haffer, J., Prance, G.T., 2002. Impulsos climáticos da evolução na Amazônia durante o Cenozóico: sobre a teoria dos refúgios da diferenciação biótica. Estudos Avançados 12. https://doi.org/10.1590/S0103-40142002000300014

Halsey, R.W., Keeley, J.E., Wilson, K., 2009. Fuel age and fire spread: natural conditions versus opportunities for fire suppression. U.S. Forest Service.

Hardesty, J., Myers, R., Fulks, W., 2005. Fire, ecosystems and people: a preliminary assessment of fire as a global conservation issue. Fire Management 22, 78-87.

Harris, S., Veraverbeke, S., Hook, S., 2011. Evaluating spectral indices for assessing fire severity in chaparral ecosystems (Southern California) using modis/aster (MASTER) airborne simulator data. Remote Sensing 3, 2403-2419. https://doi.org/10.3390/rs3112403

Higgins, S.I., Bond, W.J., February, E.C., Bronn, A., Euston-Brown, D.I.W., Enslin, B., Govender, N., Rademan, L., O’Regan, S., Potgieter, A.L.F., Scheiter, S., Sowry, R., Trollope, L., Trollope, W.S.W., 2007. Effects of four decades of fire manipulation on woody vegetation structure in savanna. Ecology 88, 1119-1125. https://doi.org/10.1890/06-1664

Hill, M.J., 2013. Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect. Remote Sensing of Environment 137, 94-111. https://doi.org/10.1016/j.rse.2013.06.004

Hoffmann, W.A., Adasme, R., Haridasan, M., De Carvalho, M.T., Geiger, E.L., Pereira, M.A.B., Gotsch, S.G., Franco, A.C., 2009. Tree topkill, not mortality, governs the dynamics of savanna-forest boundaries under frequent fire in central Brazil. Ecology 90, 1326-1337. https://doi.org/10.1890/08-0741.1

IBGE, Instituto Brasileiro de Geografia e Estatística, 2012. Manual técnico da vegetação brasileira. IBGE, Rio de Janeiro.

ICMBio, Instituto Chico Mendes de Conservação da Biodiversidade, 2016. Plano de Manejo - Parque Nacional dos Campos Amazônicos. Ministério do Meio Ambiente (MMA), Brasília, Brazil.

JT, Jupyter Team, 2015. Jupyter Notebook Project.

Kaufman, Y.J., Remer, L.A., 1994. Detection of forests using mid-IR reflectance: an application for aerosol studies. IEEE Transactions on Geoscience and Remote Sensing. 32, 672-683.

Key, C.H., Benson, N.C., 2006. Landscape assessment (LA): Sampling and analysis methods, in: Lutes, D.C., Keane, R.E., Caratti, J.F., Key, C.H., Benson, N.C., Sutherland, S., Gangi, L.J. (Eds.), FIREMON: Fire Effects Monitoring and Inventory System. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USA, pp. 1-55.

Lasaponara, R., 2006. Estimating spectral separability of satellite derived parameters for burned areas mapping in the Calabria region by using SPOT-Vegetation data. Ecological Modelling 6, 265-270. https://doi.org/10.1016/j.ecolmodel.2006.02.025

Laurentino, J. de S., 2023. Regeneração do estrato herbáceo após queimas experimentais no Parque Nacional dos Campos Amazônicos. Masther’s Dissertation, Universidade Estadual do Maranhão, Brasil.

Lhermitte, S., Verbesselt, J., Verstraeten, W.W., Veraverbeke, S., Coppin, P., 2011. Assessing intra-annual vegetation regrowth after fire using the pixel based regeneration index. ISPRS Journal of Photogrammetry and Remote Sensing 66, 17-27. https://doi.org/10.1016/j.isprsjprs.2010.08.004

MAPBIOMAS, 2023. Projeto MapBiomas – Coleção 9.0 da série anual de mapas de cobertura e uso de solo do Brasil. Available at: http://mapbiomas.org/ (last access: 18/03/25).

Marengo, J.A., Liebmann, B., Kousky, V.E., Filizola, N.P., Wainer, I.C., 2001. Onset and end of the rainy season in the Brazilian Amazon Basin. Journal of Climate 14, 833-852. https://doi.org/10.1175/1520-0442(2001)014<0833:OAEOTR>2.0.CO;2

Miranda, H.S., 2010. Efeitos do regime do fogo sobre a estrutura de comunidades de Cerrado: Projeto Fogo, 2ed ed. IBAMA, Brasília, Brasil.

Miranda, H.S., Silva, E.P.R., Miranda, A.C., 1996. Comportamento do fogo em queimadas de campo sujo, in: Miranda, H.S., Saito, C.H., Souza, D.B.F. (Eds.), Impacto de Queimadas Em Áreas de Cerrado e Restinga. Universidade de Brasília, Brasilia, Brazil, pp. 1-10.

Morton, D.C., DeFries, R.S., Nagol, J., Souza Jr., C.M., Kasischke, E.S., Hurtt, G.C., Dubayah, R., 2011. Mapping canopy damage from understory fires in Amazon forests using annual time series of Landsat and MODIS data. Remote Sensing of Environment 115, 1706-1720. https://doi.org/10.1016/j.rse.2011.03.002

Motta, P.E.F., Curi, N., Franzmeier, D.F., 2002. Relation of soils and geomorphic surfaces in the Brazilian Cerrado, in: Oliveira, P.S., Marquis, R.J. (Eds.), The Cerrados of Brazil: Ecology and Natural History of a Neotropical Savanna. Columbia University Press, New York, EUA, pp. 13-32.

Murphy, S., 2020. Atmospheric correction of a (single) Sentinel 2 image. Available at: https://github.com/samsammurphy/gee-atmcorr-S2 (last access: 01/03/2025)

Nguyen, M.D., Baez-Villanueva, O.M., Bui, D.D., Nguyen, P.T., Ribbe, L., 2020. Harmonization of Landsat and Sentinel 2 for crop monitoring in drought prone areas: Case studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon). Remote Sensing 12, 1-18. https://doi.org/10.3390/rs12020281

Oliveira, U., Soares-Filho, B., Bustamante, M., Gomes, L., Ometto, J.P., Rajão, R., 2022. Determinants of Fire Impact in the Brazilian Biomes. Frontiers in Forests and Global Change 5, 1-12. https://doi.org/10.3389/ffgc.2022.735017

Oliveira, U., Soares-Filho, B., de Souza Costa, W.L., Gomes, L., Bustamante, M., Miranda, H., 2021. Modeling fuel loads dynamics and fire spread probability in the Brazilian Cerrado. Forest Ecology and Management 482, 118889. https://doi.org/10.1016/j.foreco.2020.118889

Othman, M.A., Ash’Aari, Z.H., Aris, A.Z., Ramli, M.F., 2018. Tropical deforestation monitoring using NDVI from MODIS satellite: A case study in Pahang, Malaysia. IOP Conference Series: Earth and Environmental Science 169. https://doi.org/10.1088/1755-1315/169/1/012047

Pacheco, A. da P., da Silva Junior, J.A., Ruiz-Armenteros, A.M., Henriques, R.F.F., de Oliveira Santos, I., 2023. Analysis of Spectral Separability for Detecting Burned Areas Using Landsat-8 OLI/TIRS Images under Different Biomes in Brazil and Portugal. Forests 14. https://doi.org/10.3390/f14040663

Pausas, J.G., Bond, W.J., 2020. On the Three Major Recycling Pathways in Terrestrial Ecosystems. Trends in Ecology & Evolution 35, 767-775. https://doi.org/10.1016/j.tree.2020.04.004

Pereira Júnior, A.C., Oliveira, S.L.J., Pereira, J.M.C., Turkman, M.A.A., 2014. Modelling fire frequency in a Cerrado savanna protected area. PLoS One 9. https://doi.org/10.1371/journal.pone.0102380

Pérez-Cabello, F., Montorio, R., Alves, D.B., 2021. Remote sensing techniques to assess post-fire vegetation recovery. Current Opinion in Environmental Science & Health 21, 100251. https://doi.org/10.1016/j.coesh.2021.100251

Pettorelli, N., Vik, J.O., Mysterud, A., Gaillard, J.M., Tucker, C.J., Stenseth, N.C., 2005. Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution 20, 503-510. https://doi.org/10.1016/j.tree.2005.05.011

Pineda Valles, H.E., Nunes, G.M., Berlinck, C.N., Gonçalves, L.G., Ribeiro, G.H.P. de M., 2023. Use of Remotely Piloted Aircraft System Multispectral Data to Evaluate the Effects of Prescribed Burnings on Three Macrohabitats of Pantanal, Brazil. Remote Sensing 15, 2934. https://doi.org/10.3390/rs15112934

Pivello, V.R., 2011. The use of fire in the cerrado and amazonian rainforests of Brazil: past and present. Fire Ecology 7, 24-39. https://doi.org/10.4996/fireecology.0701024

Pivello, V.R., Vieira, I., Christianini, A. V., Ribeiro, D.B., da Silva Menezes, L., Berlinck, C.N., Melo, F.P.L., Marengo, J.A., Tornquist, C.G., Tomas, W.M., Overbeck, G.E., 2021. Understanding Brazil’s catastrophic fires: Causes, consequences and policy needed to prevent future tragedies. Perspectives in Ecology and Conservation 19, 233-255. https://doi.org/10.1016/j.pecon.2021.06.005

Poortinga, A., Tenneson, K., Shapiro, A., Nquyen, Q., San Aung, K., Chishtie, F., Saah, D., 2019. Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification. Remote Sensing 11, 831. https://doi.org/10.3390/rs11070831

Prance, G.T., 1996. Islands in Amazonia. Philosophical Transactions of the Royal Society B: Biological Sciences 351, 823-833. https://doi.org/10.1098/rstb.1996.0077

Ratter, J.A., Bridgewater, S., Ribeiro, J.F., 2003. Analysis of the floristic composition of the Brazilian cerrado vegetation: comparison of the woody vegetation of 376 areas. Edinburgh Journal of Botany 57-109. https://doi.org/10.1017/S0960428603000064

RCT, R Core Team, 2019. R: A Language and Environment for Statistical Computing.

Rissi, M.N., Baeza, M.J., Gorgone-Barbosa, E., Zupo, T., Fidelis, A., 2017. Does season affect fire behaviour in the Cerrado? International Journal of Wildland Fire 26, 427-433. https://doi.org/10.1071/wf14210

Rouse, J.W., Hass, R.H., Schell, J.A., Deering, D.W., 1974. Monitoring vegetation systems in the great plains with ERTS, in: Freden, S.C., Mercanti, E.P., Becker, M.A. (Eds.), Third Earth Resources Technology Satellite (ERTS) Symposium. NASA Goddard Space Flight Center, Washington, DC, United States, pp. 309-317. https://doi.org/citeulike-article-id:12009708

Rowan, G.S.L., Kalacska, M., Inamdar, D., Arroyo-Mora, J.P., Soffer, R., 2021. Multi-Scale Spectral Separability of Submerged Aquatic Vegetation Species in a Freshwater Ecosystem. Frontiers in Environmental Science 9, 1-22. https://doi.org/10.3389/fenvs.2021.760372

Roy, D.P., Lewis, P., Schaaf, C., Devadiga, S., Boschetti, L., 2006. The Global Impact of Clouds on the Production of MODIS Bidirectional Reflectance Model-Based Composites for Terrestrial Monitoring. IEEE Geoscience and Remote Sensing Letters 3, 452-456. https://doi.org/10.1109/LGRS.2006.875433

Roy, D.P., Zhang, H.K., Ju, J., Gomez-Dans, J.L., Lewis, P.E., Schaaf, C.B., Sun, Q., Li, J., Huang, H., Kovalskyy, V., 2016. A general method to normalize Landsat reflectance data to nadir BRDF adjusted reflectance. Remote Sensing of Environment 176, 255-271. https://doi.org/10.1016/j.rse.2016.01.023

Sano, E.E., Ferreira, L.G., Asner, G.P., Steinke, E.T., 2007. Spatial and temporal probabilities of obtaining cloud‐free Landsat images over the Brazilian tropical savanna. International Journal of Remote Sensing 28, 2739-2752. https://doi.org/10.1080/01431160600981517

Scheffler, D., Frantz, D., Segl, K., 2020. Spectral harmonization and red edge prediction of Landsat-8 to Sentinel-2 using land cover optimized multivariate regressors. Remote Sensing of Environment 241, 111723. https://doi.org/10.1016/j.rse.2020.111723

Scheffler, D., Hollstein, A., Diedrich, H., Segl, K., Hostert, P., 2017. AROSICS: An automated and robust open-source image co-registration software for multi-sensor satellite data. Remote Sensing 9. https://doi.org/10.3390/rs9070676

Schmidt, I.B., Eloy, L., 2020. Fire regime in the Brazilian Savanna: Recent changes, policy and management. Flora 268, 151613. https://doi.org/10.1016/j.flora.2020.151613

Senande-Rivera, M., Insua-Costa, D., Miguez-Macho, G., 2022. Spatial and temporal expansion of global wildland fire activity in response to climate change. Nature Communications 13, 1-9. https://doi.org/10.1038/s41467-022-28835-2

Simon, M.F., Grether, R., de Queiroz, L.P., Skema, C., Pennington, R.T., Hughes, C.E., 2009. Recent assembly of the Cerrado, a neotropical plant diversity hotspot, by in situ evolution of adaptations to fire. Proceedings of the National Academy of Sciences 106, 20359-20364. https://doi.org/10.1073/pnas.0903410106

Soenen, S.A., Peddle, D.R., Coburn, C.A., 2005. SCS+C: A modified sun-canopy-sensor topographic correction in forested terrain. IEEE Transactions on Geoscience and Remote Sensing 43, 2148-2159. https://doi.org/10.1109/TGRS.2005.852480

Torres, J., Gonçalves, J., Marcos, B., Honrado, J., 2018. Indicator-based assessment of post-fire recovery dynamics using satellite NDVI time-series. Ecological Indicators 89, 199-212. https://doi.org/10.1016/j.ecolind.2018.02.008

van Wilgen, B.W., Govender, N., Biggs, H.C., 2007. The contribution of fire research to fire management: a critical review of a long-term experiment in the Kruger National Park, South Africa. International Journal of Wildland Fire 16, 519. https://doi.org/10.1071/WF06115

Veraverbeke, S., Lhermitte, S., Verstraeten, W.W., Goossens, R., 2011. A time-integrated MODIS burn severity assessment using the multi-temporal differenced normalized burn ratio (dNBR MT). International Journal of Applied Earth Observation and Geoinformation 13, 52-58. https://doi.org/10.1016/j.jag.2010.06.006

Williams, P.R., Congdon, R.A., Grice, A.C., Clarke, P.J., 2003. Fire-related cues break seed dormancy of six legumes of tropical eucalypt savannas in north-eastern Australia. Austral Ecology 28, 507-514. https://doi.org/10.1046/j.1442-9993.2003.01307.x

Wilson, R.T., 2013. Py6S: A Python interface to the 6S radiative transfer model. Computers and Geosciences 51, 166–171. https://doi.org/10.1016/j.cageo.2012.08.002

Wulder, M.A., Hermosilla, T., White, J.C., Hobart, G., Masek, J.G., 2021. Augmenting Landsat time series with Harmonized Landsat Sentinel-2 data products: Assessment of spectral correspondence. Science of Remote Sensing 4, 100031. https://doi.org/10.1016/j.srs.2021.100031

Publicado

2025-09-18

Cómo citar

1.
Alves DB, Laffayete Pires da Silveira A, Cambraia BC, Falcão Sobrinho J, Silva TSF, Pérez-Cabello F. Recuperación de la vegetación posincendio y separabilidad espectral en ecosistemas de sabana amazónica mediante series temporales de teledetección y mediciones de carga de combustibles. CIG [Internet]. 18 de septiembre de 2025 [citado 8 de octubre de 2025];. Disponible en: https://publicaciones.unirioja.es/ojs/index.php/cig/article/view/6889

Número

Sección

Número especial: Incendios forestales: riesgo, consecuencias y avances tecnológicos en su análisis y gestión

Datos de los fondos