Advertisement

Fully Convolutional Neural Networks for Mapping Oil Palm Plantations in Kalimantan

  • Artem BaklanovEmail author
  • Michael Khachay
  • Maxim Pasynkov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11353)

Abstract

This research is motivated by the global warming problem, which is likely influenced by human activity. Fast-growing oil palm plantations in the tropical belt of Africa, Southeast Asia and parts of Brazil lead to significant loss of rainforest and contribute to the global warming by the corresponding decrease of carbon dioxide absorption. We propose a novel approach to monitoring of the development of such plantations based on an application of state-of-the-art Fully Convolutional Neural Networks (FCNs) to solve Semantic Segmentation Problem for Landsat imagery.

Keywords

Remote sensing Mapping Landsat Fully convolutional neural network 

References

  1. 1.
    Austin, K., Mosnier, A., Pirker, J., McCallum, I., Fritz, S., Kasibhatla, P.: Shifting patterns of oil palm driven deforestation in Indonesia and implications for zero-deforestation commitments. Land Use Policy 69, 41–48 (2017).  https://doi.org/10.1016/j.landusepol.2017.08.036, http://www.sciencedirect.com/science/article/pii/S0264837717301552
  2. 2.
    Chong, K.L., Kanniah, K.D., Pohl, C., Tan, K.P.: A review of remote sensing applications for oil palm studies. Geo-Spat. Inf. Sci. 20(2), 184–200 (2017).  https://doi.org/10.1080/10095020.2017.1337317
  3. 3.
    Fu, G., Liu, C., Zhou, R., Sun, T., Zhang, Q.: Classification for high resolution remote sensing imagery using a fully convolutional network. Remote Sens. 9(5) (2017).  https://doi.org/10.3390/rs9050498, http://www.mdpi.com/2072-4292/9/5/498
  4. 4.
    Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Jose Garcia-Rodriguez, V.: A review on deep learning techniques applied to semantic segmentation. Manuscript 1 (2017)Google Scholar
  5. 5.
    Gaveau, D.L.A., et al.: Rapid conversions and avoided deforestation: examining four decades of industrial plantation expansion in Borneo. Sci. Rep. 6 (2016).  https://doi.org/10.1038/srep32017
  6. 6.
    Gutiérrez-Vélez, V.H., DeFries, R.: Annual multi-resolution detection of land cover conversion to oil palm in the Peruvian Amazon. Remote Sens. Environ. 129, 154–167 (2013).  https://doi.org/10.1016/j.rse.2012.10.033, http://www.sciencedirect.com/science/article/pii/S003442571200421X
  7. 7.
    Hansen, M.C., et al.: High-resolution global maps of 21st-century forest cover change. Science 342(6160), 850–853 (2013).  https://doi.org/10.1126/science.1244693, http://science.sciencemag.org/content/342/6160/850
  8. 8.
    Huang, Z., Pan, Z., Lei, B.: Transfer learning with deep convolutional neural network for SAR target classification with limited labeled data. Remote Sens. 9(9) (2017).  https://doi.org/10.3390/rs9090907, http://www.mdpi.com/2072-4291/9/9/907
  9. 9.
    Lee, J.S.H., Wich, S., Widayati, A., Koh, L.P.: Detecting industrial oil palm plantations on Landsat images with Google Earth Engine. Remote Sens. Appl.: Soc. Environ. 4, 219–224 (2016).  https://doi.org/10.1016/j.rsase.2016.11.003, https://www.sciencedirect.com/science/article/pii/S235293851630129X
  10. 10.
    Mucherino, A., Papajorgji, P.J., Pardalos, P.M.: Data Mining in Agriculture, 1st edn. Springer Publishing Company, Incorporated (2009)CrossRefGoogle Scholar
  11. 11.
    Nooni, I., Duker, A., Van Duren, I., Addae-Wireko, L., Osei Jnr, E.: Support vector machine to map oil palm in a heterogeneous environment. Int. J. Remote Sens. 35(13), 4778–4794 (2014).  https://doi.org/10.1080/01431161.2014.930201
  12. 12.
    Petersen, R., et al.: Mapping tree plantations with multispectral imagery: preliminary results for seven tropical countries. Technical report, World Resources Institute (2016). www.wri.org/publication/mapping-tree-plantations
  13. 13.
    Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 640–651 (2017).  https://doi.org/10.1109/TPAMI.2016.2572683CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Research University Higher School of EconomicsSt. PetersburgRussia
  2. 2.International Institute for Applied Systems Analysis (IIASA)LaxenburgAustria
  3. 3.Krasovsky Institute of Mathematics and MechanicsEkaterinburgRussia
  4. 4.Ural Federal UniversityEkaterinburgRussia

Personalised recommendations