Skip to main content
SpringerLink
Log in

Classification of an Agrosilvopastoral System Using RGB Imagery from an Unmanned Aerial Vehicle

  • Conference paper
  • First Online:
Progress in Artificial Intelligence (EPIA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11804))

Included in the following conference series:

Abstract

This paper explores the usage of unmanned aerial vehicles (UAVs) to acquire remotely sensed very high-resolution imagery for classification of an agrosilvopastoral system in a rural region of Portugal. Aerial data was obtained using a low-cost UAV, equipped with an RGB sensor. Acquired imagery undergone a photogrammetric processing pipeline to obtain different data products: an orthophoto mosaic, a canopy height model (CHM) and vegetation indices (VIs). A superpixel algorithm was then applied to the orthophoto mosaic, dividing the images into different objects. From each object, different features were obtained based in its maximum, mean, minimum and standard deviation. These features were extracted from the different data products: CHM, VIs, and color bands. Classification process – using random forest algorithm – classified objects into five different classes: trees, low vegetation, shrubland, bare soil and infrastructures. Feature importance obtained from the training model showed that CHM-driven features have more importance when comparing to those obtained from VIs or color bands. An overall classification accuracy of 86.4% was obtained.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

References

  1. Torresan, C., et al.: Forestry applications of UAVs in Europe: a review. Int. J. Remote Sens. 38, 2427–2447 (2017). https://doi.org/10.1080/01431161.2016.1252477

    Article  Google Scholar 

  2. Castaldi, F., Pelosi, F., Pascucci, S., Casa, R.: Assessing the potential of images from unmanned aerial vehicles (UAV) to support herbicide patch spraying in maize. Precision Agric. 18, 76–94 (2017). https://doi.org/10.1007/s11119-016-9468-3

    Article  Google Scholar 

  3. von Bueren, S.K., Burkart, A., Hueni, A., Rascher, U., Tuohy, M.P., Yule, I.J.: Deploying four optical UAV-based sensors over grassland: challenges and limitations. Biogeosciences 12, 163–175 (2015). https://doi.org/10.5194/bg-12-163-2015

    Article  Google Scholar 

  4. Pádua, L., et al.: UAS, sensors, and data processing in agroforestry: a review towards practical applications. Int. J. Remote Sens. 38, 2349–2391 (2017). https://doi.org/10.1080/01431161.2017.1297548

    Article  Google Scholar 

  5. Matese, A., et al.: Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture. Remote Sens. 7, 2971–2990 (2015). https://doi.org/10.3390/rs70302971

    Article  Google Scholar 

  6. Shakhatreh, H., et al.: Unmanned aerial vehicles: a survey on civil applications and key research challenges. arXiv:1805.00881 [cs] (2018)

  7. Belgiu, M., Drăguţ, 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

    Article  Google Scholar 

  8. Feng, Q., Liu, J., Gong, J.: UAV remote sensing for urban vegetation mapping using random forest and texture analysis. Remote Sens. 7, 1074–1094 (2015). https://doi.org/10.3390/rs70101074

    Article  Google Scholar 

  9. Akar, Ö.: The rotation forest algorithm and object-based classification method for land use mapping through UAV images. Geocarto Int. 33(5). https://www.tandfonline.com/doi/abs/10.1080/10106049.2016.1277273

    Article  Google Scholar 

  10. Ma, L., et al.: Evaluation of feature selection methods for object-based land cover mapping of unmanned aerial vehicle imagery using random forest and support vector machine classifiers. IJGI 6, 51 (2017). https://doi.org/10.3390/ijgi6020051

    Article  Google Scholar 

  11. Melville, B., Lucieer, A., Aryal, J.: Classification of lowland native grassland communities using hyperspectral unmanned aircraft system (UAS) imagery in the tasmanian midlands. Drones 3, 5 (2019). https://doi.org/10.3390/drones3010005

    Article  Google Scholar 

  12. Nevalainen, O., et al.: Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens. 9, 185 (2017). https://doi.org/10.3390/rs9030185

    Article  Google Scholar 

  13. Michez, A., Piégay, H., Lisein, J., Claessens, H., Lejeune, P.: Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environ. Monit. Assess. 188, 146 (2016). https://doi.org/10.1007/s10661-015-4996-2

    Article  Google Scholar 

  14. Goodbody, T.R.H., Coops, N.C., Hermosilla, T., Tompalski, P., Crawford, P.: Assessing the status of forest regeneration using digital aerial photogrammetry and unmanned aerial systems. Int. J. Remote Sens. 39, 5246–5264 (2018). https://doi.org/10.1080/01431161.2017.1402387

    Article  Google Scholar 

  15. Russo, R.O.: Agrosilvopastoral systems: a practical approach toward sustainable agriculture. J. Sustain. Agric. 7, 5–16 (1996). https://doi.org/10.1300/J064v07n04_03

    Article  Google Scholar 

  16. Nair, P.K.R.: Classification of agroforestry systems. Agroforest Syst. 3, 97–128 (1985). https://doi.org/10.1007/BF00122638

    Article  Google Scholar 

  17. Pádua, L., et al.: UAS-based imagery and photogrammetric processing for tree height and crown diameter extraction. In: Proceedings of the International Conference on Geoinformatics and Data Analysis, pp. 87–91. ACM, New York (2018). https://doi.org/10.1145/3220228.3220241

  18. Dandois, J.P., Olano, M., Ellis, E.C.: Optimal altitude, overlap, and weather conditions for computer vision UAV estimates of forest structure. Remote Sens. 7, 13895–13920 (2015). https://doi.org/10.3390/rs71013895

    Article  Google Scholar 

  19. Motohka, T., Nasahara, K.N., Oguma, H., Tsuchida, S.: Applicability of green-red vegetation index for remote sensing of vegetation phenology. Remote Sens. 2, 2369–2387 (2010). https://doi.org/10.3390/rs2102369

    Article  Google Scholar 

  20. Marques, P., et al.: UAV-based automatic detection and monitoring of chestnut trees. Remote Sens. 11, 855 (2019). https://doi.org/10.3390/rs11070855

    Article  Google Scholar 

  21. Bendig, J., et al.: Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 39, 79–87 (2015). https://doi.org/10.1016/j.jag.2015.02.012

    Article  Google Scholar 

  22. Tucker, C.J.: Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8, 127–150 (1979). https://doi.org/10.1016/0034-4257(79)90013-0

    Article  Google Scholar 

  23. Zarco-Tejada, P.J., et al.: Assessing vineyard condition with hyperspectral indices: leaf and canopy reflectance simulation in a row-structured discontinuous canopy. Remote Sens. Environ. 99, 271–287 (2005). https://doi.org/10.1016/j.rse.2005.09.002

    Article  Google Scholar 

  24. Popescu, S.C.: Estimating biomass of individual pine trees using airborne lidar. Biomass Bioenerg. 31, 646–655 (2007). https://doi.org/10.1016/j.biombioe.2007.06.022

    Article  Google Scholar 

  25. Smith, A.R.: Color gamut transform pairs. In: Proceedings of the 5th Annual Conference on Computer Graphics and Interactive Techniques - SIGGRAPH 1978, pp. 12–19. ACM Press (1978). https://doi.org/10.1145/800248.807361

  26. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34, 2274–2282 (2012). https://doi.org/10.1109/TPAMI.2012.120

    Article  Google Scholar 

  27. Crommelinck, S., Bennett, R., Gerke, M., Koeva, M.N., Yang, M.Y., Vosselman, G.: SLIC superpixels for object delineation from UAV data. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. 4, 9–16 (2017). https://doi.org/10.5194/isprs-annals-IV-2-W3-9-2017

    Article  Google Scholar 

Download references

Acknowledgments

This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project «POCI-01-0145-FEDER-006961»  and by National Funds through the FCT – Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013. The research activities of Luís Pádua were funded by the Portuguese Foundation for Science and Technology (SFRH/BD/139702/2018).

Author information

Authors and Affiliations

  1. School of Science and Technology, University of Trás-os-Montes e Alto Douro, 5000-801, Vila Real, Portugal

    Luís Pádua, Nathalie Guimarães, Telmo Adão, Pedro Marques, Emanuel Peres, António Sousa & Joaquim J. Sousa

  2. Centre for Robotics in Industry and Intelligent Systems (CRIIS), INESC Technology and Science (INESC-TEC), Porto, 4200-465, Portugal

    Luís Pádua, Telmo Adão, Emanuel Peres, António Sousa & Joaquim J. Sousa

Authors
  1. Luís Pádua
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nathalie Guimarães
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Telmo Adão
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Pedro Marques
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Emanuel Peres
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. António Sousa
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Joaquim J. Sousa
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luís Pádua .

Editor information

Editors and Affiliations

  1. INESC-TEC, University of Trás-os-Montes and Alto Douro, Vila Real, Portugal

    Paulo Moura Oliveira

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. LIACC/UP, University of Porto, Porto, Portugal

    Luís Paulo Reis

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Pádua, L. et al. (2019). Classification of an Agrosilvopastoral System Using RGB Imagery from an Unmanned Aerial Vehicle. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11804. Springer, Cham. https://doi.org/10.1007/978-3-030-30241-2_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30241-2_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30240-5

  • Online ISBN: 978-3-030-30241-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation