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.
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.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
Image ID: UMD/hansen/global_forest_change_2015_v1_3; Bands: ’last_50’, ’last_40’, ’last_30’.
- 2.
In GEE, rectangles are represented by the minimum and maximum corners as the list of four numbers in the order xMin, yMin, xMax, yMax. In our case \( (xMin, yMin, xMax, yMax) = (110.2, -3, 113.5, -1.5)\).
- 3.
As reported in [1, Appendix A. SI Table 3], the overall accuracy, sensitivity (recall), and precision of the produced map are equal to 0.911, 0.969, and 0.84, respectively.
- 4.
References
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
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
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
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)
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
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
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
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
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
Mucherino, A., Papajorgji, P.J., Pardalos, P.M.: Data Mining in Agriculture, 1st edn. Springer Publishing Company, Incorporated (2009)
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
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
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Baklanov, A., Khachay, M., Pasynkov, M. (2019). Fully Convolutional Neural Networks for Mapping Oil Palm Plantations in Kalimantan. In: Battiti, R., Brunato, M., Kotsireas, I., Pardalos, P. (eds) Learning and Intelligent Optimization. LION 12 2018. Lecture Notes in Computer Science(), vol 11353. Springer, Cham. https://doi.org/10.1007/978-3-030-05348-2_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-05348-2_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05347-5
Online ISBN: 978-3-030-05348-2
eBook Packages: Computer ScienceComputer Science (R0)