Abstract
We present a study to improve automation and accuracy on a Woody Savannah burned areas’ classification process through the use of Machine Learning (ML) classification models. The reference method for this is to extract polygons from images through segmentation and identify changes in polygons extracted from images taken from the same area but in different times through manual labeling. However, not all differences correspond to burned areas: there are also deforestation, change in crops, and clouds. Our objective is to identify the changed areas caused by fire. We propose an approach that employs polygons’ attributes for classification and evaluation in order to identify changes caused by fire. This paper presents the more relevant classifier models to the problem, highlighting Random Forest and an Ensemble model, that achieved better results. The developed approach is validated over a study area in the Brazilian Woody Savannah against reference data derived from classifications manually done by experts. The results indicate enhancement of the methods used so far, and will eventually be applied to more data from different areas and biomes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Che Alhadi, A., Deraman, A., Abdul Jalil, M.M., Wan Yussof, W.N.J., Mohamed, A.A.: An ensemble similarity model for short text retrieval. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10404, pp. 20–29. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62392-4_2
de Andrade, R.N., Bittencourt, O., Morelli, F., Santos, R.: Classificação semiautomática de áreas queimadas com o uso de redes neurais. In: XVIII Brazilian Symposium on Geoinformatics - GeoInfo 2017, pp. 92–97 (2017)
Bowman, D., et al.: Fire in the earth system. Science 324 (2009)
Chuvieco, E., Martín, M.: Cartografía de grandes incendios forestales en la península ibérica a partir de imágenes noaa-avhrr. Serie Geográfica 7 (1998)
Instituto Nacional de Pesquisas Espaciais (INPE): Programa de monitoramento de queimadas. http://www.inpe.br/queimadas/portal. Accessed 28 Jan 2018
Katagis, T., Gitas, I., Toukiloglou, P., Veraverbeke, S., Goossens, R.: Trend analysis of medium- and coarse-resolution time series image data for burned area mapping in a mediterranean ecosystem. Int. J. Wildland Fire (2014)
Key, C., Benson, N.: Landscape assessment: ground measure of severity, the composite burn index; and remote sensing of severity, the normalized burn ratio. In: FIREMON: Fire Effects Monitoring and Inventory System, pp. 1–51 (2006)
Li, J., Roy, D.: A global analysis of sentinel-2A, sentinel-2B and landsat-8 data revisit intervals and implications for terrestrial monitoring. Remote Sens. 9 (2017)
Liu, J., Heiskanen, J., Maeda, E.E., Pellikka, P.K.: Burned area detection based on Landsat time series in savannas of Southern Burkina Faso. Int. J. Appl. Earth Obs. Geoinf. 64, 210–220 (2018)
Smith, A.M.S., Drake, N., Wooster, M.J., Hudak, A.T., Holden, Z.A., Gibbons, C.J.: Production of Landsat ETM+ reference imagery of burned areas within Southern African Savannahs: comparison of methods and application to MODIS. Int. J. Remote Sens. 28, 2753–2775 (2007)
McFeeters, S.: The use of normalized difference water index (NDWI) in the delineation of open water features. Int. J. Remote Sens. 17, 1425–1432 (1996)
Melchiori, A.E., Setzer, A.W., Morelli, F., Libonati, R., Cândido, P.d.A., Jesús, S.C.d.: A Landsat-TM/OLI Algorithm for Burned Areas in the Brazilian Cerrado: Preliminary Results, pp. 1302–1311. Imprensa da Universidade de Coimbra (2014)
Ministério do Planejamento, Orçamento e Gestão (MPOG): Plano plurianual 2016–2019: Desenvolvimento, produtividade e inclusão social. http://www.planejamento.gov.br/assuntos/planeja/plano-plurianual/relatorio-objetivos.pdf. Accessed 12 Sept 2017
Mithal, V., Nayak, G., Khandelwal, A., Kumar, V., Nemani, R., Oza, N.C.: Mapping burned areas in tropical forests using a novel machine learning framework. Remote Sens. 10 (2018)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Pereira, A.A., et al.: Burned area mapping in the Brazilian savanna using a one-class support vector machine trained by active fires. Remote Sens. 9(11) (2017)
Pinty, B., Verstraete, M.: GEMI: a non-linear index to monitor global vegetation from satellites. Vegetation 101, 15–20 (1992)
Pivello, V.: The use of fire in the cerrado and amazonian rainforests of Brazil: past and present. Fire Ecol. 7, 24–39 (2011)
Plazas, J.E., López, I.D., Corrales, J.C.: A tool for classification of cacao production in colombia based on multiple classifier systems. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10405, pp. 60–69. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-62395-5_5
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6, 21–45 (2006)
Rouse Jr., J.W., Haas, R.H., Schell, J.A., Deering, D.W.: Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ. 351, 309 (1974)
Trigg, S., Flasse, S.: An evaluation of different bi-spectral spaces for discriminating burned shrub-savannah. Int. J. Remote Sens. 22, 2641–2647 (2001)
U.S. Geological Survey (USGS): Usgs science data lifecycle. https://earthexplorer.usgs.gov. Accessed 18 Oct 2018
Wolpert, D.H.: Stacked generalization. Neural Netw. 5(2), 241–259 (1992)
Acknowledges
This study was supported by National Council for Scientific and Technological Development (CNPq)/Coordination of Associated Laboratories (CLA/INPE) (no.300587/2017-1).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Bittencourt, O.O., Morelli, F., dos Santos Júnior, C.A., Santos, R. (2019). Evaluating Classification Models in a Burned Areas’ Detection Approach. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11622. Springer, Cham. https://doi.org/10.1007/978-3-030-24305-0_43
Download citation
DOI: https://doi.org/10.1007/978-3-030-24305-0_43
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24304-3
Online ISBN: 978-3-030-24305-0
eBook Packages: Computer ScienceComputer Science (R0)