Clasificación de imágenes Landsat-8 en la Demarcación Hidrográfica del Segura

  1. Rodríguez-Valero, M. I. 1
  2. Alonso-Sarria, F. 1
  1. 1 Universidad de Murcia
    info

    Universidad de Murcia

    Murcia, España

    ROR https://ror.org/03p3aeb86

Journal:
Revista de teledetección: Revista de la Asociación Española de Teledetección

ISSN: 1133-0953

Year of publication: 2019

Issue: 53

Pages: 33-44

Type: Article

DOI: 10.4995/RAET.2019.11016 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista de teledetección: Revista de la Asociación Española de Teledetección

Abstract

This work presents a cartography of land uses in the Segura Hydrographic Demarcation obtained by classifying 2017 Landsat 8 images. The classification was carried out using two classifiers: Maximum Likelihood (ML) and Random Forest (RF). Training areas were obtained from historical high resolution imagery until 2016. Prior to classification, a cross validation analysis of the training areas was carried out to determine which of them may have undergone a change of use between 2016 and 2017. The results obtained with ML and RF, both with the original set of training areas and with the one obtained eliminating the problem, are compared to determine the best option. In the case of ML, the results improve after eliminating the changing training areas, from 77.7% to 81.4%; however, with RF this improvement is not so important, going from 84.1% to 85.1%. Therefore, it can be concluded that, with both methods, the classification is more exact when the modified training areas are used and, although the results obtained are quite acceptable for both ML and RF, the latter performs a more accurate classification.

Bibliographic References

  • Ayala, R., Menenti, M. 2001. Metodología para la búsqueda del mejor clasificador de imágenes de satélite. Teledetección, Medio Ambiente y Cambio Global, 469-472.
  • Berberoglu, S., Curran, P.J., Lloyd, C.D., Atkinson, P.M. 2007. Texture classification of Mediterranean land cover. International Journal of Applied Earth Observation and Geoinformation, 9(3), 322-334. https://doi.org/10.1016/j.jag.2006.11.004
  • Berk, R.A. 2016. Statistical learning from a regression perspective. Springer. https://doi.org/10.1007/978- 3-319-44048-4 Breiman, L. 2001. Random Forests Machine Learning 45(1), 5-32. https://doi. org/10.1023/A:1010933404324
  • Cánovas, F., Alonso, F., Gomariz, F. 2016. Modificación del algoritmo Random Forest para su empleo en clasificación de imágenes de Teledetección. Aplicaciones de las Tecnologías de la Información Geográfica (TIG) para el desarrollo económico sostenible, 359-368.
  • Chávez, P.S. 1996. Image-Based Atmospheric Corrections - Revisited and Improved. Photogrammetric Engineering and Remote Sensing, 62, 1025-1036.
  • Chen, X., Zhao, H., Li, P., Yin, Z. 2006: Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment, 104, 133-146. https://doi.org/10.1016/j.rse.2005.11.016
  • Chiarito, G., Chiarito, E. 2015. Evaluación de temperaturas urbanas de acuerdo al uso del suelo: Rosario. Avances en Energías Renovables y Medio Ambiente, 19, 93-102.
  • Chuvieco, E. 1996. Fundamentos de Teledetección Espacial. Madrid: Rialp. Tercera Edición Revisada.
  • Chuvieco, E. 2010. Teledetección Ambiental. La observación de la Tierra desde el espacio. Barcelona: Ariel Ciencia.
  • Congedo, L. 2016. Semi-Automatic Classification Plugin Documentation. Último acceso: 14 de mayo, 2018, de https://doi.org/10.13140/RG.2.2.29474.02242/1
  • Efron, B., Hastie, T. 2018. Computer Age Statistical Inference Cambridge University Press.
  • Ezzine, H., Bouziane, A., Ouazar, D. 2014. Seasonal comparisons of meteorological and agricultural droughtindices in Morocco using open short timeseries data. International Journal of Applied Earth Observation and Geoinformation, 26(1), 36-48. https://doi.org/10.1016/j.jag.2013.05.005
  • García, P, Pérez, M.E., García, J.M., Redondo, M.M., Sanz, J.J., Navarro, A. 2014. Sellado de suelos a partir de teledetección y SIG: estudio en el Tajo medio-alto. Dpto. de Análisis Geográfico Regional y Geografía Física. Universidad Complutense de Madrid.
  • Gomariz, F., Alonso, F., Cánovas, F. 2017. Improving Classification Accuracy of MultiTemporal Landsat Images by Assessing the Use of Different Algorithms, Textural and Ancillary Information for a Mediterranean Semiarid Area from 2000 to 2015. Remote Sensing, 9(10), 1058. https://doi.org/10.3390/rs9101058
  • Landis, J.R., Koch, G.G. 1977. The measurement of observer agreement for categorical data. Biometrics, 33(1), 159-174. https://doi.org/10.2307/2529310
  • Maxwell, A.E., Warner T.A., Fang, F. 2018. Implementation of machine-learning classification in remote sensing: an applied review, International Journal of Remote Sensing, 39(9), 2784-2817. https://doi.org/10.1080/01431161.2018.1433343
  • Moran, M.S., Jackson, R.D., Slater, P.N., Teillet, P.M. 1992. Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output. Remote Sensing of Environment, 41(2-3), 169-184. https://doi.org/10.1016/0034-4257(92)90076-V
  • Pérez, Ll., Delegido, J., Rivera, J.P., Verrelst, J. 2015. Análisis de métodos de validación cruzada para la obtención robusta de parámetros biofísicos. Revista de Teledetección, 44, 55-65. https://doi.org/10.4995/raet.2015.4153
  • Santana, L.M., Escobar, L., Capote, P. 2010. Estimación de un índice de calidad ambiental urbano, a partir de imágenes de satélite. Revista de Geografía Norte Grande, 45, 77-95. https://doi.org/10.4067/S0718- 34022010000100006
  • Wang, Q.N., Tenhunen, J.D. 2004. Vegetation mapping with multitemporal NDVI in North Eastern China Transect (NECT). International Journal of Applied Earth Observations and Geoinformation, 6(1), 17- 31. https://doi.org/10.1016/j.jag.2004.07.002
  • Xu, H. 2006. Modification of Normalized Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery. International Journal of Remote Sensing, 27, 3025-3033. https://doi.org/10.1080/01431160600589179
  • Zhou, Z.H. 2012. Ensemble methods. Foundations and algorithms CRC Prss. https://doi.org/10.1201/b12207
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