Skip to main content
SpringerLink
Log in

Measuring loblolly pine crowns with drone imagery through deep learning

  • Original Paper
  • Published:
Journal of Forestry Research Aims and scope Submit manuscript

Abstract

In modeling forest stand growth and yield, crown width, a measure for stand density, is among the parameters that allows for estimating stand timber volumes. However, accurately measuring tree crown size in the field, in particular for mature trees, is challenging. This study demonstrated a novel method of applying machine learning algorithms to aerial imagery acquired by an unmanned aerial vehicle (UAV) to identify tree crowns and their widths in two loblolly pine plantations in eastern Texas, USA. An ortho mosaic image derived from UAV-captured aerial photos was acquired for each plantation (a young stand before canopy closure, a mature stand with a closed canopy). For each site, the images were split into two subsets: one for training and one for validation purposes. Three widely used object detection methods in deep learning, the Faster region-based convolutional neural network (Faster R-CNN), You Only Look Once version 3 (YOLOv3), and single shot detection (SSD), were applied to the training data, respectively. Each was used to train the model for performing crown recognition and crown extraction. Each model output was evaluated using an independent test data set. All three models were successful in detecting tree crowns with an accuracy greater than 93%, except the Faster R-CNN model that failed on the mature site. On the young site, the SSD model performed the best for crown extraction with a coefficient of determination (R2) of 0.92, followed by Faster R-CNN (0.88) and YOLOv3 (0.62). As to the mature site, the SSD model achieved a R2 as high as 0.94, follow by YOLOv3 (0.69). These deep leaning algorithms, in particular the SSD model, proved to be successfully in identifying tree crowns and estimating crown widths with satisfactory accuracy. For the purpose of forest inventory on loblolly pine plantations, using UAV-captured imagery paired with the SSD object detention application is a cost-effective alternative to traditional ground measurement.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Abadi M, Agarwal A, Barham P, Brevdo E, Chen ZF, Citro C, Corrado GS, Davis A, Dean J, Devin M, Ghemawat S, Goodfellow I, Harp A, Irving G, Isard M, Jia YQ, Kaiser L, Kudlur M, Levenberg J, Man D, Monga R, Moore S, Murray D, Shlens J, Steiner B, Sutskever I, Tucker P, Vanhoucke V, Vasudevan V, Vinyals O, Warden P, Wicke M, Yu Y, Zheng XQ (2016) Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467

  • Agisoft L (2014) Agisoft PhotoScan user manual: professional edition. Agisoft LLC, St Petersburg, Russia (Google Scholar)

  • Amarasekara H, Denne M (2002) Effects of crown size on wood characteristics of Corsican pine in relation to definitions of juvenile wood, crown formed wood and core wood. Forestry 75(1):51–61

    Article  Google Scholar 

  • Bertsimas D, Sim M (2004) The price of robustness. Oper Res 52(1):35–53

    Article  Google Scholar 

  • Chubey MS, Franklin SE, Wulder MA (2006) Object-based analysis of Ikonos-2 imagery for extraction of forest inventory parameters. Photogramm Eng Remote Sens 72(4):383–394

    Article  Google Scholar 

  • Davis J, Goadrich M (2006) The relationship between Precision-Recall and ROC curves. In: Cohen W, Moore A (eds) Proceedings of the 23rd international conference on machine learning, pp 233–240

  • Dobbertin M (2005) Tree growth as indicator of tree vitality and of tree reaction to environmental stress: a review. Eur J Forest Res 124(4):319–333

    Article  Google Scholar 

  • Erfanifard Y, Behnia N, Moosavi V (2014) Tree crown delineation on UltraCam-D aerial imagery with SVM classification technique optimized by Taguchi method in Zagros woodlands. Int J Image Data Fusion 5(4):300–314

    Google Scholar 

  • Everingham M, Eslami SA, Van Gool L, Williams CK, Winn J, Zisserman A (2015) The pascal visual object classes challenge: a retrospective. Int J Comput Vis 111(1):98–136

    Article  Google Scholar 

  • Girshick R (2015) Fast r-cnn. In: Proceedings of the 2015 IEEE international conference on computer vision, pp 1440–1448. https://doi.org/https://doi.org/10.1109/ICCV.2015.169

  • Girshick R, Donahue J, Darrell T, Malik J (2014) Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the 2014 IEEE conference on computer vision and pattern recognition, pp 580–587. https://doi.org/https://doi.org/10.1109/CVPR.2014.81

  • Gomes MF, Maillard P, Deng HW (2018) Individual tree crown detection in sub-meter satellite imagery using Marked Point Processes and a geometrical-optical model. Remote Sens Environ 211:184–195

    Article  Google Scholar 

  • Goutte C, Gaussier E (2005) A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Losada DE, Fernández-Luna JM (eds) Advances in information retrieval. ECIR 2005. Lecture notes in computer science, vol 3408. Springer, Berlin, Heidelberg, pp. 345–359. https://doi.org/https://doi.org/10.1007/978-3-540-31865-1_25

  • Harikumar A, Bovolo F, Bruzzone L (2017) An internal crown geometric model for conifer species classification with high-density lidar data. IEEE Trans Geosci Remote Sens 55(5):2924–2940

    Article  Google Scholar 

  • He KM, Zhang XY, Ren SQ, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition, pp 770–778. https://doi.org/https://doi.org/10.1109/CVPR.2016.90

  • He R, Wu X, Sun ZN, Tan TN (2018) Wasserstein CNN: learning invariant features for nir-vis face recognition. ITPAM 41(7):1761–1773

    Google Scholar 

  • Howard AG, Zhu ML, Chen B, Kalenichenko D, Wang WJ, Weyand T, Andreetto M, Adam H (2017) Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

  • Ke YH, Quackenbush LJ (2011) A review of methods for automatic individual tree-crown detection and delineation from passive remote sensing. Int J Remote Sens 32(17):4725–4747

    Article  Google Scholar 

  • Kumar A, Kim J, Lyndon D, Fulham M, Feng DG (2016) An ensemble of fine-tuned convolutional neural networks for medical image classification. IEEE J Biomed Health 21(1):31–40

    Article  Google Scholar 

  • LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  CAS  Google Scholar 

  • Lee H, Slatton KC, Roth BE, Cropper W Jr (2010) Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests. Int J Remote Sens 31(1):117–139

    Article  Google Scholar 

  • Li WJ, He CH, Fu HH, Zheng JP, Dong RM, Xia MC, Yu L, Luk W (2019) A real-time tree crown detection approach for large-scale remote sensing images on FPGAs. Remote Sens 11(9):1025

    Article  Google Scholar 

  • Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft coco: Common objects in context, In: Fleet D, Pajdla T, Schiele B, Tuytelaars T (eds) Computer vision—ECCV 2014. ECCV 2014. Lecture notes in computer science, vol 8693. Springer, Cham, pp 740–755. https://doi.org/https://doi.org/10.1007/978-3-319-10602-1_48

  • Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) SSD: Single shot multibox detector. In: Leibe B, Matas J, Sebe N, Welling M (eds) Computer vision—ECCV 2016. ECCV 2016. Lecture notes in computer science, vol 9905. Springer, Cham, pp 21–37. https://doi.org/https://doi.org/10.1007/978-3-319-46448-0_2

  • Naveed F, Hu BX, Wang JG, Hall GB (2019) Individual tree crown delineation using multispectral LiDAR data. Sensors 19(24):5421

    Article  Google Scholar 

  • Neubeck A, Van Gool L (2006) Efficient non-maximum suppression. In: Proceedings of the 18th international conference on pattern recognition (ICPR'06): IEEE, pp 850–855

  • Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  • Perez L, Wang JS (2017) The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621

  • Popescu SC, Wynne RH, Nelson RF (2003) Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomass. Can J Remote Sens 29(5):564–577

    Article  Google Scholar 

  • Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767

  • Redmon J, Divvala S, Girshick R, Farhadi A (2016) You only look once: unified, real-time object detection. In: Proceedings of the 2016 IEEE conference on computer vision and pattern recognition, pp. 779–788

  • Ren SQ, He KM, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems—Volume 1 (NIPS’15). MIT Press, Cambridge, MA, USA, pp 91–99

  • Ren Y, Zhu CR, Xiao SP (2018) Small object detection in optical remote sensing images via modified faster R-CNN. Appl Sci 8(5):813

    Article  Google Scholar 

  • Sandler M, Howard A, Zhu ML, Zhmoginov A, Chen LC (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: Proceedings of the 2018 IEEE conference on computer vision and pattern recognition, pp 4510–4520.

  • Santos AAd, Marcato Junior J, Araújo MS, Di Martini DR, Tetila EC, Siqueira HL, Aoki C, Eltner A, Matsubara ET, Pistori H (2019) Assessment of CNN-based methods for individual tree detection on images captured by RGB cameras attached to UAVs. Sensors 19(16):3595

    Article  Google Scholar 

  • Wagner FH, Ferreira MP, Sanchez A, Hirye MC, Zortea M, Gloor E, Phillips OL, de Souza Filho CR, Shimabukuro YE, Aragão LE (2018) Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images. ISPRS J Photogramm Remote Sens 145:362–377

    Article  Google Scholar 

  • Wu B, Yu BL, Wu QS, Huang Y, Chen ZQ, Wu JP (2016) Individual tree crown delineation using localized contour tree method and airborne LiDAR data in coniferous forests. Int J Appl Earth Obs Geoinf 52:82–94

    Article  Google Scholar 

  • Wu HF, Huang Q, Wang DQ, Gao LF (2018) A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals. J Electromyogr Kinesiol 42:136–142

    Article  Google Scholar 

  • Zhou YH, Wang LW, Jiang K, Xue LF, An F, Chen BQ, Yun T (2020) Individual tree crown segmentation based on aerial image using superpixel and topological features. J Appl Remote Sens 14(2):022210

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Engineering, Zhejiang A & F University, Lin’an, 311300, Zhejiang, People’s Republic of China

    Xiongwei Lou, Yanxiao Huang, Luming Fang & Laibang Yang

  2. Key Laboratory of State Forestry and Grassland Administration On Forestry Sensing Technology and Intelligent Equipment, Zhejiang A & F University, Lin’an, 311300, Zhejiang, People’s Republic of China

    Xiongwei Lou & Yanxiao Huang

  3. Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Zhejiang A & F University, Lin’an, 311300, Zhejiang, People’s Republic of China

    Luming Fang

  4. College of Forestry and Agriculture, Stephen F. Austin State University, Nacogdoches, TX, 75962, USA

    Yuhui Weng & I.-K.uai Hung

  5. Jiyang College of Zhejiang A & F University, Zhuji, 311800, Zhejiang, People’s Republic of China

    Siqi Huang

  6. College of Forestry and Biotechnology, Zhejiang A & F University, Lin’an, 311300, Zhejiang, People’s Republic of China

    Haili Gao

Authors
  1. Xiongwei Lou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yanxiao Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luming Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Siqi Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Haili Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Laibang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuhui Weng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. I.-K.uai Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuhui Weng.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Project funding: The work was supported by the McIntire-Stennis program and East Texas Pine Plantation Research Project at Stephen F. Austin State University. Part of the research was also supported by Zhejiang Provincial Key Science and Technology Project (2018C02013).

The online version is available at http://www.springerlink.com.

Corresponding editor: Tao Xu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lou, X., Huang, Y., Fang, L. et al. Measuring loblolly pine crowns with drone imagery through deep learning. J. For. Res. 33, 227–238 (2022). https://doi.org/10.1007/s11676-021-01328-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11676-021-01328-6

Keywords

Search

Navigation