Abstract
MODIS vegetation indexes time series have been widely used to build land cover change maps on large scales. In this scope, to obtain good quality maps using supervised classification methods, it is crucial to select representative training samples of land cover change classes. In this paper, we evaluate two clustering methods, Hierarchical and Self-Organizing Map (SOM), to assess land cover samples of MODIS vegetation indexes time series. As we show, these techniques are suitable tools for assisting users to select representative land cover change samples from MODIS vegetation indexes time series. We present the accuracy of both methods for a case study in Ipiranga do Norte municipality in Mato Grosso state, Brazil.
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
References
Aguiar, D., Adami, M., Silva, W., Rudorff, B., Mello, M., Silva, J.: MODIS time series to assess pasture land. In: Geoscience and Remote Sensing Symposium (IGARSS) (2010)
Arvor, D., Meirelles, M., Dubreuil, V., Begue, A., Shimabukuro, Y.: Analyzing the agricultural transition in Mato Grosso, Brazil, using satellite-derived indices. Appl. Geogr. 22, 702–713 (2012)
Bagan, H., Wang, Q., Watanabe, M., Yang, Y., Ma, J.: Land cover classification from MODIS EVI time-series data using SOM neural network. Int. J. Remote Sens. 26, 4999–5012 (2005)
Maus, V., Camara, G., Cartaxo, R., Sanchez, A., Ramos, F., Ribeiro, G.: A time-weighted dynamic time warping method for land cover mapping. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens 20, 3729–3739 (2015)
Augustijn, E., Zurita-Milla, R.: Self-organizing maps as an approach to exploring spatiotemporal diffusion patterns. Int. J. Health Geogr. 12, 60–74 (2013)
Huete, A., Didan, K., Miura, T.: Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83(1), 195–213 (2002)
Xiao, X., Braswell, B., Zhang, Q., Boles, S., Frolking, S., Moore, B.: Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia. Remote Sens. Environ 84, 385–392 (2003)
Natita, W., Wiboonsak, W., Dusadee, S.: Appropriate learning rate and neighborhood function of self-organizing map (SOM) for specific classification over Southern Thailand. Int. J. Model. Optim 6, 61–65 (2016)
Silva, A.L., Peres, M.S., Boscarioli, C.: Introduo a Minerao de Dados: com Aplicaes em R (2016)
Sarda-Espinosa, A.: Comparing time-series clustering algorithms in R using the dtwclust package (2017). https://CRAN.R-project.org/package=dtwclust
Liao, T.: Clustering of time series data-a survey. J. Pattern Recogn. Soc. 8, 1857–1874 (2004)
Tatarinova, T., Schumitzky, A.: Nonlinear Mixture Models: A Bayesian Approach. Imperial College Press (2014)
Keogh, E., Ratanamahatana, C.: Exact indexing of dynamic time warping. Knowl. Inform. Syst. 7, 358–386 (2005)
Huete, A., Liu, H.Q., Batchily, K., Leeuwen, V.: A comparison of vegetation indices over a global set of TM images for EOS-MODIS. Remote Sens. Environ. 59, 440–451 (1997)
Vegetation Indices 16-Day L3 Global 250m. http://pdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13q
Bock, H.H.: Information and entropy in cluster analysis. In: Bozdogan, H., Sclove, S.L., Gupta, A.K., Haughton, D., Kitagawa, G., Ozaki, T., Tanabe, K. (eds.) Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach. Springer, Netherlands (1994). doi:10.1007/978-94-011-0800-3_4
Boles, S.H., Xiao, X., Liu, J., Zhang, Q., Sharav, M., Chen, S., Ojima, D.: Land cover characterization of temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sens. Environ. 90, 477–489 (2004)
Arbelaitz, O., Gurrutxaga, I., Muguerza, J., Perez, J.M., Perona, I.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243–256 (2013)
Tomašev, N., Radovanović, M.: Clustering evaluation in high-dimensional data. In: Celebi, M.E., Aydin, K. (eds.) Unsupervised Learning Algorithms. Springer International Publishing, Cham (2016). doi:10.1007/978-3-319-24211-8_4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Santos, L.A., Simoes, R.E.O., Ferreira, K.R., de Queiroz, G.R., Camara, G., Santos, R.D.C. (2017). Clustering Methods to Asses Land Cover Samples of MODIS Vegetation Indexes Time Series. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2017. ICCSA 2017. Lecture Notes in Computer Science(), vol 10409. Springer, Cham. https://doi.org/10.1007/978-3-319-62407-5_48
Download citation
DOI: https://doi.org/10.1007/978-3-319-62407-5_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62406-8
Online ISBN: 978-3-319-62407-5
eBook Packages: Computer ScienceComputer Science (R0)