Self-Organizing Maps in Earth Observation Data Cubes Analysis
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Earth Observation (EO) Data Cubes infrastructures model analysis-ready data generated from remote sensing images as multidimensional cubes (space, time and properties), especially for satellite image time series analysis. These infrastructures take advantage of big data technologies and methods to store, process and analyze the big amount of Earth observation satellite images freely available nowadays. Recently, EO Data Cubes infrastructures and satellite image time series analysis have brought new opportunities and challenges for the Land Use and Cover Change (LUCC) monitoring over large areas. LUCC have caused a great impact on tropical ecosystems, increasing global greenhouse gases emissions and reducing the planet’s biodiversity. This paper presents the utility of Self-Organizing Maps (SOM) neural network method in the process to extract LUCC information from EO Data Cubes infrastructures, using image time series analysis. Most classification techniques to create LUCC maps from satellite image time series are based on supervised learning methods. In this context, SOM is used as a method to assess land use and cover samples and to evaluate which spectral bands and vegetation indexes are best suitable for the separability of land use and cover classes. A case study is described in this work and shows the potential of SOM in this application.
KeywordsSelf-Organizing Maps Earth Observation Data Cubes Analysis Satellite image time series Land Use and Cover Changes
- 4.FAO: Sepal repository (2018). https://github.com/openforis/sepal. Accessed 14 Dec 2018
- 9.Siam L (2013) Soft supervised self-organizing mapping (3SOM) for improving land cover classification with MODIS time-series. PhD thesis, Michigan State University, MichiganGoogle Scholar
- 14.Udelhoven T, Stellmes M, Rodes A (2015) Assessing rainfall-EVI relationships in the Okavango catchment employing MODIS time series data and distributed lag models. In: Revealing land surface dynamics. Remote sensing time series. Springer, Cham, pp 225–245Google Scholar
- 16.Camara G, Simoes R, Andrade P, Maus V, Sanchez A, Assis L, Santos L, Ywata A, Maciel A, Vinhas L, Ferreira K, Queiroz G (2018) Sits e-sensing/sits: Version 1.12.5, December 2018. https://doi.org/10.5281/zenodo.1974065