Fire analysis in the Caatinga environment from Landsat-8 images, enhanced vegetation index and analysis by the main components
DOI:
https://doi.org/10.5902/1980509843818Keywords:
Vegetation Cover, Fires, Remote Sensing and StatisticsAbstract
Fires generate negative environmental and socioeconomic impacts that directly and indirectly influence the Earth's regional and global climate changes. Forest fires and fires play a relevant ecological role as they affect the local biodiversity, soil properties and water supply. The Caatinga biome has a high level of degradation of human and natural activities, being extremely affected by fires that burn predominantly due to human activities. Remote orbital sensing, as it presents specific spatial, spectral and temporal characteristics, is an essential technological alternative in monitoring areas affected by fire on the Earth's surface. This work aimed to analyze, in a spatial, spectral and temporal scope, the behavior of a fire in a Caatinga environment from the multivariate statistical analysis of Landsat-8 Images data, Enhanced Vegetation Index and Analysis by theMajor Components. The quantification of characteristics of vegetation derived from the spectral index provides a better assessment of the physical condition of the earth's surface under the effects of fire. Remote sensing techniques and multivariate statistics were used to assess the spectral behavior of wildfires in the Caatinga biome. The results of the Kolmogorov-Smirnov Normality Test showed a significance level of 5%. The integration of the statistical methods of Simple Linear Regression and Analysis by the Principal Components enabled important diagnoses in the estimates and/or relationships between the random variables. The multivariate technique allowed 94% of the data variation to be assessed. The maps resulting from the tested methodology represent an important improvement in mapping the distribution of vegetation. This study generates indications for future scientific research related to the management of space concerning to vulnerability and recovery of vegetation landscapes from the semi-arid climate under fire situations generated by burnings.
Downloads
References
ABOELGHAR, M. et al. Retrieving leaf area index from SPOT4 satellite data. The Egyptian Journal of Remote Sensing and Space Science, [s. l.], v. 13, n. 2, p. 121-127, 2010.
ALEXANDRIS, N.; KOUTSIAS, N.; GUPTA, S. Remote sensing of burned areas via PCA, Part 2: SVD-based PCA using MODIS and Landsat data. Open Geospatial Data, Software and Standards, [s. l.], v. 2, n. 21, 2017. DOI: https://doi.org/10.1186/s40965-017-0029-0
BOWMAN, D. M. et al. Fire in the earth system. Science, New York, v. 324, p. 481-484, 2009.
CAMPOS, S. et al. Closure and partitioning of the energy balance in a preserved area of a Brazilian seasonally dry tropical forest. Agricultural and Forest Meteorology, Amsterdam, v. 271, p. 398-412, 2019.
CHAVEZ JUNIOR, P. S. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, New York, v. 24, p. 459-479, 1988.
CHUVIECO, E. Teledeteccion Ambiental. La observacion de la Tierra desde el espacio. Barcelona: Editorial Ariel, 2010. 528 p.
COMPANHIA DE PESQUISA DE RECURSOS MINERAIS (Brasil). Serviço Geológico do Brasil. Projeto cadastro de fontes de abastecimento por água subterrânea. Diagnóstico do município de Floresta, Estado de Pernambuco. [S. l.], 2005. 33 p.
CORDER, G. W.; FOREMAN, D I. Nonparametric statistics for non-statisticians: a step-by-step approach.[S. l.]:John Wiley & Sons, 2011. 264 p.
DENG, J. et al. A methodology to monitor urban expansion and green space change using a time series ofmulti-sensor spot and sentinel-2a images. Remote Sensing, Basel, v. 11, n. 10, p. 1-21, 2019.
FERNANDES, P. M. et al. Bottom-up variables govern large-fire size in Portugal. Ecosystems, New York, v. 19, p. 1362-1375, 2016.
FORMIGONI, M. H.; XAVIER, A. C.; LIMA, J. S. S. Análise Temporal da Vegetação na Região do Nordeste através de dados EVI do MODIS. Ciência Florestal, Santa Maria, v. 21, n. 1, p. 1-8, 2011.
GAITANI, N. et al. High-resolution spectral mapping of urban thermal properties with unmanned aerial vehicles. Building Environment, Oxford, v. 121, p. 215-224, 2017.
GALVÃO, L. S. et al. On intra-annual EVI variability in the dry season of tropical forest: a case study with MODIS and hyperspectral data. Remote Sensing of Environment, New York, v. 115, n. 9, p. 2350-2359, 2011.
GAO, X. et al. Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment, New York, v. 74, n. 3, p. 609-620, 2000.
HUETE, A. R. et al. Amazon rainforests green up with sunlight in dry season. Geophysical Research Letters, Medford, v. 33, n. L06405, 2006.
HUETE, A. R. et al. A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sensing of Environment, New York, v. 59, n. 3, p. 440-451, 1997.
IBGE. Metadados2016. [S. l.], 2016. Disponível em: ftp://geoftp.ibge.gov.br. Acesso em: 21 jan. 2020.
INTERGOVERNMENTAL PANEL ON CLIMATE CHANGE. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2013. 1535 p.
ISLAM, T. U. Ranking of normality tests: an appraisal through skewed alternative space. Symmetry, [s. l.], v. 11, n. 7, p. 1-14, 2019.
KAISER, H. F. The varimax criterion for analytic rotation in fator analysis. Psychometrika, New York, v. 23, n. 3, p. 187- 200, 1958.
KOUTSIAS, N.; MALLINIS, G.; KARTERIS, M. A forward/backward principal component analysis of Landsat-7 ETM+ data to enhance the spectral signal of burnt surfaces. ISPRS Journal of Photogrammetry and Remote Sensing, Amsterdam, v. 64, n. 1, p. 37-46. DOI: doi:10.1016/j.isprsjprs. 2008.06.004
LIZUNDIA-LOIOLA, J.; PETTINARI, M. L.; CHUVIECO, E. Temporal Anomalies in Burned Area Trends: Satellite Estimations of the Amazonian 2019 Fire Crisis. Remote Sensing, Basel, v. 12, n. 151, p. 1-8, 2020.
MACHADO, G. C. Efeitos dos incêndios florestais na vegetação do cerrado utilizando dados do sensor MODIS. 2015. Dissertação (Mestrado em Engenharia Florestal) - Universidade de Brasília, Departamento de Engenharia Florestal, Brasília, 2015.
MARIANO, D. A. et al. Use of remote sensing indicators to assess effects of drought and human induced land degradation on ecosystem health in Northeastern Brazil. Remote Sensing of Environment, New York, v. 213, p. 129-143, 2018.
MISHRA, P. et al. Descriptive statistics and normality tests for statistical data. Annals of Cardiac Anaesthesia, [s. l.], v. 22, n. 1, p. 67–72, 2019.
MOLINA, D. M. Origins of Arson in Northwestern Spain. Fire Management Notes, Washington, v. 57, n. 3, p. 18-23, 1997.
PONZONI, F. J.; SHIMABUKURO, Y. E. Sensoriamento remoto no estudo da vegetação. São José dos Campos: Parêntese, 2010. 127 p.
THENKABAIL, P. S.; LYON, J. G.; HUETE, A. Hyper spectral remote sensing of vegetation. Boca Raton: CRC Press, 2016. 782 p.
UNITED STATES GEOLOGICAL SURVEY. Heart Explorer Data System. Base de dados 2019. USA, 2019. Disponível em: https://earthexplorer.usgs.gov/. Acesso em: 22 nov. 2019.
WALLACE, C. S. A.; WEBB, R. H.; THOMAS, K. A. Estimated distribution of perennial vegetation cover in the Mojave desert using MODIS-EVI data. GI Science and Remote Sensing, USA, v. 45, n. 2, p. 167-187, 2008.
