Skip to main content
SpringerLink
Log in

Application of Fully Convolutional Neural Networks to Mapping Industrial Oil Palm Plantations

  • Conference paper
  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11179))

Abstract

This research is motivated by sustainability problems of oil palm expansion. Fast-growing industrial Oil Palm Plantations (OPPs) 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 expansion of OPPs based on an application of state-of-the-art Fully Convolutional Neural Networks (FCNs) to solve Semantic Segmentation Problem for Landsat imagery. The proposed approach significantly outperforms per-pixel classification methods based on Random Forest using texture features, NDVI, and all Landsat bands. Moreover, the trained FCN is robust to spatial and temporal shifts of input data. The paper provides a proof of concept that FCNs as semi-automated methods enable OPPs mapping of entire countries and may serve for yearly detection of oil palm expansion.

This research was supported by Russian Science Foundation, grant no. 14-11-00109.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Object-oriented classifiers first perform image segmentation to merge pixels into objects and then apply classification based on the objects [19] (but not on an individual pixel as per-pixel classifiers do). Spectral-Color algorithms are trained using multi-spectral features of each pixel.

  2. 2.

    In GEE, rectangles are represented by the minimum and maximum corners as lists (xMin, yMin, xMax, yMax). The ROI is defined by \((110.3, -3, 113.6, -1.5)\).

  3. 3.

    An image from Landsat 8 has 11 bands, ‘B1’ – ‘B11’. NDVI stands for Normalized Difference Vegetation Index that varies between \(-1\) and 1 and utilizes the relationship between vegetation brightness in the red and infrared bands. Namely, absorption of red light (‘B4’) and reflection of infrared radiation (‘B5’) by plants allows to separate non-vegetation (e.g., water, bare soil, man-made objects) from living vegetation. Basically, NDVI serves as an index of photosynthesis in a pixel.

  4. 4.

    Implemented on the basis of https://github.com/shelhamer/fcn.berkeleyvision.org.

  5. 5.

    Caffe deep learning framework http://caffe.berkeleyvision.organized.

  6. 6.

    For models with features ‘G(10)’ included, we sampled 20000 random pixels due to computational constraints of GEE.

  7. 7.

    The training was performed in R using library ‘randomForest’ with all other default parameters except number of trees to grow was set to 300.

  8. 8.

    The image size is \(2048 \times 1688\) pixels; Lat.: 1.14868; Long.: 99.18078. The distance to the boundary of Kalimantan ROI is around 1300 km.

  9. 9.

    The image size is \(1336 \times 1069\) pixels; Lat.: \(-1.93356\); Long.: 111.21357.

References

  1. PASCAL VOC 2012. www.host.robots.ox.ac.uk/pascal/VOC/voc2012/

  2. 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

    Article  Google Scholar 

  3. Belgiu, M., Drãguţ, L.: Random forest in remote sensing: a review of applications and future directions. ISPRS J. Photogram. Remote Sens. 114, 24–31 (2016). https://doi.org/10.1016/j.isprsjprs.2016.01.011. http://www.sciencedirect.com/science/article/pii/S0924271616000265

    Article  Google Scholar 

  4. Carlson, K.M., Curran, L.M., Asner, G.P., Pittman, A.M., Trigg, S.N., Marion Adeney, J.: Carbon emissions from forest conversion by Kalimantan oil palm plantations. Nat. Clim. Change 3, 283–287 (2013). https://doi.org/10.1038/nclimate1702

    Article  Google Scholar 

  5. Chen, L., Kokkinos, I., Murphy, K., Yuille, A.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. 40, 834–848 (2018). https://doi.org/10.1109/TPAMI.2017.2699184

    Article  Google Scholar 

  6. Chong, K.L., Kanniah, K.D., Pohl, C., Tan, K.P.: A review of remote sensing applications for oil palm studies. Geo-spatial Inf. Sci. 20(2), 184–200 (2017). https://doi.org/10.1080/10095020.2017.1337317

    Article  Google Scholar 

  7. Conners, R.W., Trivedi, M.M., Harlow, C.A.: Segmentation of a high-resolution urban scene using texture operators. Comput. Vis. Graph. Image Process. 25(3), 273–310 (1984). https://doi.org/10.1016/0734-189X(84)90197-X

    Article  Google Scholar 

  8. Daliman, S., Rahman, S., Bakar, S., Busu, I.: Segmentation of oil palm area based on GLCM-SVM and NDVI. In: IEEE Region 10 Symposium, pp. 645–650 (2014). https://doi.org/10.1109/TENCONSpring.2014.6863113

  9. 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), 498 (2017). https://doi.org/10.3390/rs9050498. http://www.mdpi.com/2072-4292/9/5/498

    Article  Google Scholar 

  10. 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 

  11. 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

  12. Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Teh, Y.W., Titterington, M. (eds.) Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, Proceedings of Machine Learning Research, vol. 9, pp. 249–256. PMLR, Sardinia, 13–15 May 2010. http://proceedings.mlr.press/v9/glorot10a.html

  13. 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

    Article  Google Scholar 

  14. 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

    Article  Google Scholar 

  15. Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC–3, 610–621 (1973). https://doi.org/10.1109/TSMC.1973.4309314

    Article  Google Scholar 

  16. 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), 907 (2017). https://doi.org/10.3390/rs9090907. http://www.mdpi.com/2072-4292/9/9/907

    Article  Google Scholar 

  17. Kamiran, N., Sarker, M.L.R.: Exploring the potential of high resolution remote sensing data for mapping vegetation and the age groups of oil palm plantation. IOP Conf. Ser.: Earth Environ. Sci. 18(1), 012181 (2014). http://stacks.iop.org/1755-1315/18/i=1/a=012181

    Article  Google Scholar 

  18. 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

    Article  Google Scholar 

  19. Lu, D., Weng, Q.: A survey of image classification methods and techniques for improving classification performance. Int. J. Remote Sens. 28(5), 823–870 (2007). https://doi.org/10.1080/01431160600746456

    Article  Google Scholar 

  20. Mucherino, A., Papajorgji, P.J., Pardalos, P.M.: Data Mining in Agriculture, 1st edn. Springer, New York (2009). https://doi.org/10.1007/978-0-387-88615-2

    Book  MATH  Google Scholar 

  21. 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

    Article  Google Scholar 

  22. 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

  23. 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.2016.2572683

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National Research University Higher School of Economics, St. Petersburg, Russia

    Artem Baklanov

  2. International Institute for Applied Systems Analysis, Laxenburg, Austria

    Artem Baklanov

  3. Krasovsky Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay & Maxim Pasynkov

  4. Ural Federal University, Yekaterinburg, Russia

    Michael Khachay

Authors
  1. Artem Baklanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael Khachay
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Maxim Pasynkov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Artem Baklanov .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. University of Mannheim, Mannheim, Germany

    Goran Glavaš

  4. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  5. Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

  6. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  7. National Research University Higher School of Economics , Saint Petersburg, Russia

    Olessia Koltsova

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. Loria, Vandoeuvre lès Nancy, France

    Amedeo Napoli

  11. University of Hamburg, Hamburg, Germany

    Alexander Panchenko

  12. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  13. Ca Foscari University of Venice, Venice, Italy

    Marcello Pelillo

  14. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Baklanov, A., Khachay, M., Pasynkov, M. (2018). Application of Fully Convolutional Neural Networks to Mapping Industrial Oil Palm Plantations. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2018. Lecture Notes in Computer Science(), vol 11179. Springer, Cham. https://doi.org/10.1007/978-3-030-11027-7_16

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-11027-7_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-11026-0

  • Online ISBN: 978-3-030-11027-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation