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International Conference on Pattern Recognition Applications and Methods

ICPRAM 2020: Pattern Recognition Applications and Methods pp 64–86Cite as

A Preliminary Study on Tree-Top Detection and Deep Learning Classification Using Drone Image Mosaics of Japanese Mixed Forests

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Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12594))

Abstract

Tree counting and classification tasks in forestry are often addressed by costly, in terms of labour and money, field surveys carried on manually by forestry experts. Consequently, computer vision techniques have been used to automatically detect tree tops and classify them in terms of species or plant health status. The success of the algorithms are highly dependent on the data, and most significantly in its quantity and in the number of challenges it presents. In this work we used Unmanned Aerial Vehicles to acquired extremely challenging data from natural Japanese mixed forests. In a first step, six common clustering algorithms were used for tree top detection. Furthermore, we also assessed the usability of five different deep learning architectures to classify tree tops corresponding to trees in different degrees of affectation from a parasite infestation. Data covering an area of 40 ha are used in extensive experiments resulting in a detection accuracy of over 80% with high location accuracy and up to 90% with lower accuracy. Classification results produced by our algorithms reached error rates as low as 0.096 for classification. Data acquisition and runtime considerations show that this techniques is useful to process real forest data.

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References

  1. Agisoft: Agisoft metashape 1.5.5, professional edition. http://www.agisoft.com/downloads/installer/. Accessed 19 Aug 2019

  2. Aliero, M., Bunza, M., Al-Doksi, J.: The usefulness of unmanned airborne vehicle (UAV) imagery for automated palm oil tree counting. J. For. 1 (2014)

    Google Scholar 

  3. Allen, C.D., Breshears, D.D.: Drought-induced shift of a forest–woodland ecotone: rapid landscape response to climate variation. Proc. Natl. Acad. Sci. 95(25), 14839–14842 (1998)

    Article  Google Scholar 

  4. Anderegg, W.R.L., Anderegg, L.D.L., Kerr, K.L., Trugman, A.T.: Widespread drought-induced tree mortality at dry range edges indicates that climate stress exceeds species’ compensating mechanisms. Glob. Chang. Biol. 25(11), 3793–3802 (2019)

    Article  Google Scholar 

  5. Banu, T.P., Borlea, G.F., Banu, C.M.: The use of drones in forestry. J. Environ. Sci. Eng. 5 (2016)

    Google Scholar 

  6. Bezdek, J.C., Ehrlich, R., Full, W.: FCM: the fuzzy C-means clustering algorithm. Comput. Geosci. 10(2), 191–203 (1984)

    Article  Google Scholar 

  7. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools 25, 120–125 (2000)

    Google Scholar 

  8. Chen, I.C., Hill, J.K., Ohlemüller, R., Roy, D.B., Thomas, C.D.: Rapid range shifts of species associated with high levels of climate warming. Science 333(6045), 1024–1026 (2011)

    Article  Google Scholar 

  9. Crimmins, S.M., Dobrowski, S.Z., Greenberg, J.A., Abatzoglou, J.T., Mynsberge, A.R.: Changes in climatic water balance drive downhill shifts in plant species’ optimum elevations. Science 331(6015), 324–327 (2011)

    Article  Google Scholar 

  10. Csillik, O., Cherbini, J., Johnson, R., Lyons, A., Kelly, M.: Identification of citrus trees from unmanned aerial vehicle imagery using convolutional neural networks. Drones 2(4), 39 (2018)

    Article  Google Scholar 

  11. Danielsson, P.E., Seger, O.: Generalized and separable sobel operators. In: Machine Vision for Three-Dimensional Scenes, pp. 347–379. Elsevier (1990)

    Google Scholar 

  12. Díaz-Varela, R.A., De la Rosa, R., León, L., Zarco-Tejada, P.J.: High-resolution airborne UAV imagery to assess olive tree crown parameters using 3D photo reconstruction: application in breeding trials. Remote Sens. 7(4), 4213–4232 (2015)

    Article  Google Scholar 

  13. Diez, Y., Kentsch, S., Lopez-Caceres, M.L., Nguyen, H.T., Serrano, D., Roure, F.: Comparison of algorithms for tree-top detection in drone image mosaics of Japanese mixed forests. In: Proceedings of the ICPRAM 2020. INSTICC, SciTePress (2020)

    Google Scholar 

  14. Diez, Y., Suzuki, T., Vila, M., Waki, K.: Computer vision and deep learning tools for the automatic processing of Wasan documents. In: Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, pp. 757–765. INSTICC, SciTePress (2019)

    Google Scholar 

  15. Erikson, M., Olofsson, K.: Comparison of three individual tree crown detection methods. Mach. Vis. Appl. 16(4), 258–265 (2005)

    Article  Google Scholar 

  16. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the KKD, vol. 96, pp. 226–231. AAAI Press (1996)

    Google Scholar 

  17. Falkowski, M.J., Smith, A.M., Gessler, P.E., Hudak, A.T., Vierling, L.A., Evans, J.S.: The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. Can. J. Remote. Sens. 34(sup2), S338–S350 (2008)

    Article  Google Scholar 

  18. Frayer, W.E., Furnival, G.M.: Forest survey sampling designs: a history. J. For. 97(12), 4–10 (1999)

    Google Scholar 

  19. Gambella, F., et al.: Forest and UAV: a bibliometric review. Contemp. Eng. Sci. 9, 1359–1370 (2016)

    Article  Google Scholar 

  20. García, E., et al.: Breast MRI and X-ray mammography registration using gradient values. Med. Image Anal. 54, 76–87 (2019)

    Article  Google Scholar 

  21. Gougeon, F.A.: A crown-following approach to the automatic delineation of individual tree crowns in high spatial resolution aerial images. Can. J. Remote Sens. 21(3), 274–284 (1995)

    Article  Google Scholar 

  22. Grotti, M., Chianucci, F., Puletti, N., Fardusi, M.J., Castaldi, C., Corona, P.: Spatio-temporal variability in structure and diversity in a semi-natural mixed oak-hornbeam floodplain forest. Ecol. Indic. 104, 576–587 (2019)

    Article  Google Scholar 

  23. Guerra-Hernández, J., Cosenza, D.N., Rodriguez, L.C.E., Silva, M., Tomé, M., Díaz-Varela, R.A., Gonzáez-Ferreiro, E.: Comparison of ALS- and UAV(SfM)-derived high-density point clouds for individual tree detection in eucalyptus plantations. Int. J. Remote Sens. 39(15–16), 5211–5235 (2018)

    Article  Google Scholar 

  24. Haralick, R.M., Shapiro, L.G.: Computer and Robot Vision, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1992)

    Google Scholar 

  25. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015)

    Google Scholar 

  26. Hirschmugl, M., Ofner, M., Raggam, J., Schardt, M.: Single tree detection in very high resolution remote sensing data. Remote Sens. Environ. 110(4), 533–544 (2007). ForestSAT Special Issue

    Google Scholar 

  27. Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269 (2016)

    Google Scholar 

  28. Iandola, F.N., Moskewicz, M.W., Ashraf, K., Han, S., Dally, W.J., Keutzer, K.: SqueezeNet: Alexnet-level accuracy with 50x fewer parameters and \(<\)1mb model size. CoRR abs/1602.07360 (2016)

    Google Scholar 

  29. Katoh, M., Gougeon, F.A.: Improving the precision of tree counting by combining tree detection with crown delineation and classification on homogeneity guided smoothed high resolution (50 cm) multispectral airborne digital data. Remote Sens. 4(5), 1411–1424 (2012)

    Article  Google Scholar 

  30. Ke, Y., Quackenbush, L.J.: A comparison of three methods for automatic tree crown detection and delineation from high spatial resolution imagery. Int. J. Remote Sens. 32(13), 3625–3647 (2011)

    Article  Google Scholar 

  31. Kelly, A.E., Goulden, M.L.: Rapid shifts in plant distribution with recent climate change. Proc. Natl. Acad. Sci. 105(33), 11823–11826 (2008)

    Article  Google Scholar 

  32. Kentsch, S., Lopez Caceres, M.L., Serrano, D., Roure, F., Diez, Y.: Computer vision and deep learning techniques for the analysis of drone-acquired forest images, a transfer learning study. Remote Sens. 12(8), 1287 (2020)

    Article  Google Scholar 

  33. Korpela, I., et al.: Single-tree forest inventory using lidar and aerial images for 3D treetop positioning, species recognition, height and crown width estimation. In: Proceedings of IAPRS, vol. 36 ( 2007)

    Google Scholar 

  34. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1, NIPS 2012, pp. 1097–1105. Curran Associates Inc. (2012)

    Google Scholar 

  35. Larsen, M., Eriksson, M., Descombes, X., Perrin, G., Brandtberg, T., Gougeon, F.A.: Comparison of six individual tree crown detection algorithms evaluated under varying forest conditions. Int. J. Remote Sens. 32(20), 5827–5852 (2011)

    Article  Google Scholar 

  36. Lenoir, J., Gégout, J.C., Marquet, P.A., de Ruffray, P., Brisse, H.: A significant upward shift in plant species optimum elevation during the 20th century. Science 320(5884), 1768–1771 (2008)

    Article  Google Scholar 

  37. Li, W., Fu, H., Yu, L., Cracknell, A.: Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sens. 9(1), 22 (2017)

    Article  Google Scholar 

  38. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982)

    Article  MathSciNet  Google Scholar 

  39. Lopez, L., Hayashida, P., Mori, P., Koyama, P., Ashitani, P., Nobori, P.Y.: 8th forest plan. Internal Report (2014)

    Google Scholar 

  40. Mubin, N.A., Nadarajoo, E., Shafri, H.Z.M., Hamedianfar, A.: Young and mature oil palm tree detection and counting using convolutional neural network deep learning method. Int. J. Remote Sens. 40(19), 7500–7515 (2019)

    Article  Google Scholar 

  41. Natesan, S., Armenakis, C., Vepakomma, U.: ResNet-based tree species classification using UAV images. ISPRS Int. Arch. Photogramm. Remote. Sens. Spat. Inf. Sci. XLII-2/W13, 475–481 (2019)

    Google Scholar 

  42. Onishi, M., Ise, T.: Automatic classification of trees using a UAV onboard camera and deep learning. ArXiv abs/1804.10390 (2018)

    Google Scholar 

  43. Paneque-Gálvez, J., McCall, M.K., Napoletano, B.M., Wich, S.A., Koh, L.P.: Small drones for community-based forest monitoring: an assessment of their feasibility and potential in tropical areas. Forests 5(6), 1481–1507 (2014)

    Article  Google Scholar 

  44. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  45. Pinz, A.: Final results of the vision expert system VES: finding trees in aerial photographs. Wissensbasierte Mustererkennung. OCG-Schriftenreihe 49, 90–111 (1989)

    Google Scholar 

  46. Pitkänen, J.: Individual tree detection in digital aerial images by combining locally adaptive binarization and local maxima methods. Can. J. For. Res. 31(5), 832–844 (2001)

    Article  Google Scholar 

  47. Pont, D., Kimberley, M.O., Brownlie, R.K., Sabatia, C.O., Watt, M.S.: Calibrated tree counting on remotely sensed images of planted forests. Int. J. Remote Sens. 36(15), 3819–3836 (2015)

    Article  Google Scholar 

  48. Pouliot, D., King, D., Bell, F., Pitt, D.: Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration. Remote Sens. Environ. 82(2), 322–334 (2002)

    Article  Google Scholar 

  49. Richardson, J.J., Moskal, L.M.: Strengths and limitations of assessing forest density and spatial configuration with aerial lidar. Remote Sens. Environ. 115(10), 2640–2651 (2011)

    Article  Google Scholar 

  50. Rizeei, H.M., Shafri, H.Z.M., Mohamoud, M., Pradhan, B., Kalantar, B.: Oil palm counting and age estimation from worldview-3 imagery and LiDAR data using an integrated OBIA height model and regression analysis. J. Sens. 2018, 2536327:1–2536327:14 (2018)

    Google Scholar 

  51. Roure, F., Lladó, X., Salvi, J., Diez, Y.: GridDS: a hybrid data structure for residue computation in point set matching. Mach. Vis. Appl. 30(2), 291–307 (2019)

    Article  Google Scholar 

  52. Santoro, F., Tarantino, E., Figorito, B., Gualano, S., D’Onghia, A.M.: A tree counting algorithm for precision agriculture tasks. Int. J. Digit. Earth 6(1), 94–102 (2013)

    Article  Google Scholar 

  53. Shafri, H.Z.M., Hamdan, N., Saripan, M.I.: Semi-automatic detection and counting of oil palm trees from high spatial resolution airborne imagery. Int. J. Remote Sens. 32(8), 2095–2115 (2011)

    Article  Google Scholar 

  54. Shimada, T.: State of Japan’s forests and forest management 2nd country report of Japan to the Montreal process. Forestry Agency, Japan, October 2009

    Google Scholar 

  55. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2015)

    Google Scholar 

  56. Srestasathiern, P., Rakwatin, P.: Oil palm tree detection with high resolution multi-spectral satellite imagery. Remote Sens. 6(10), 9749–9774 (2014)

    Article  Google Scholar 

  57. Torres-Sánchez, J., López-Granados, F., Serrano, N., Arquero, O., Peña, J.M.: High-throughput 3-D monitoring of agricultural-tree plantations with unmanned aerial vehicle (UAV) technology. PLoS ONE 10, 1–20 (2015)

    Google Scholar 

  58. Torresan, C., et al.: Forestry applications of UAVs in Europe: a review. Int. J. Remote Sens. 38(8–10), 2427–2447 (2017)

    Article  Google Scholar 

  59. Van Rossum, G., Drake Jr., F.L.: Python tutorial. Centrum voor Wiskunde en Informatica Amsterdam, The Netherlands (1995)

    Google Scholar 

  60. Vauhkonen, J., et al.: Comparative testing of single-tree detection algorithms under different types of forest. For. Int. J. For. Res. 85(1), 27–40 (10 2011)

    Google Scholar 

  61. Weinstein, B.G., Marconi, S., Bohlman, S., Zare, A., White, E.: Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks. Remote Sens. 11(11) (2019)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Science, Yamagata University, Yamagata, Japan

    Yago Diez & Koma Moritake

  2. Faculty of Agriculture, Yamagata University, Yamagata, Japan

    Sarah Kentsch, Maximo Larry Lopez Caceres & Ha Trang Nguyen

  3. Eurecat, Centre Tecnològic de Catalunya, Barcelona, Spain

    Daniel Serrano & Ferran Roure

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  1. Yago Diez
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  2. Sarah Kentsch
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  3. Maximo Larry Lopez Caceres
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  4. Koma Moritake
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  5. Ha Trang Nguyen
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  6. Daniel Serrano
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  7. Ferran Roure
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Correspondence to Yago Diez .

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Editors and Affiliations

  1. Sapienza Università di Roma, Roma, Italy

    Maria De Marsico

  2. ICAR, Consiglio Nazionale delle Ricerche, Naples, Napoli, Italy

    Gabriella Sanniti di Baja

  3. Instituto de Telecomunicações, Lisbon, Portugal

    Ana Fred

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Diez, Y. et al. (2020). A Preliminary Study on Tree-Top Detection and Deep Learning Classification Using Drone Image Mosaics of Japanese Mixed Forests. In: De Marsico, M., Sanniti di Baja, G., Fred, A. (eds) Pattern Recognition Applications and Methods. ICPRAM 2020. Lecture Notes in Computer Science(), vol 12594. Springer, Cham. https://doi.org/10.1007/978-3-030-66125-0_5

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