1. Proceedings
  2. I3M
  3. 2022
  4. SESDE
  5. 10.46354/i3m.2022.sesde.006

Visual Change Detection in Multi-Temporal Vegetation Transects of Alpine Plants

  • a Sebastian Pritz , 
  • b Christoph Praschl, 
  • c Roland Kaiser, 
  • d Gerald Adam Zwettler 
  • a,b,d,Research Group Advanced Information Systems and Technology, Research and Development Department, University of Applied Sciences Upper Austria, Softwarepark 11, Hagenberg, 4232, Austria
  • a Department for Medical and Bioinformatics, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Softwarepark 11, Hagenberg, 4232, Austria
  • c ENNACON environment nature consulting KG, Altheim 13, 5143 Feldkirchen bei Mattighofen, Austria
  • cDepartment Environment and Biodiversity, Paris-Lodron University Salzburg, Hellbrunner Str. 34, 5020 Salzburg, Austria
  • a,d Department of Software Engineering, School of Informatics, Communications and Media, University of Applied Sciences Upper Austria, Softwarepark 11, Hagenberg, 4232, Austria
Cite as
Pritz S., Praschl C., Kaiser R., and Zwettler G. (2022).,Visual Change Detection in Multi-Temporal Vegetation Transects of Alpine Plants. Proceedings of the 10th International Workshop on Simulation for Energy, Sustainable Development & Environment (SESDE 2022). , 006 . DOI: https://doi.org/10.46354/i3m.2022.sesde.006

Abstract

Due to the apparent effects of climate change on the Earth’s ecosystems, it is more important than ever to monitor flora and fauna in affected regions, e.g. mountain areas above the tree line. In the alpine ecosystem, and not just there, Vegetation plays a fundamental role and is the subject of this study. The work aims to develop algorithms for recognising small stature alpine plants from close range top view images. Ideally, automated assessment algorithms of the plant cover should objectively help scientists observe and interpret
the state of the plant ecosystem over a long time series. Therefore, the aim in this respect was to derive visualisations that accurately describe plant growth and displacement (translocation). Additionally, recording changes in biodiversity was an intent. This work uses multi-temporal data comprising RGB images and multi-label masks to accomplish the aforementioned task. The evaluated methods involve mask comparison, optical flow estimation, detection of individual plants, and descriptive statistical analysis of image feature properties. Tests on the given data set show that all methods but the optical flow estimation have great potential. The mask comparison method captured plant growth and translocation most satisfactory. Individual plant detection and statistical analysis further helped to evaluate changes in biodiversity. When combined, the proposed methods give an immediate overview about relevant changes in the multi-temporal transects, which has not been done before for close-distance images of alpine plants.
Due to the apparent effects of climate change on the Earth’s ecosystems, it is more important than ever to monitor flora and fauna in affected regions, e.g. mountain areas above the tree line. In the alpine ecosystem, and not just there, Vegetation plays a fundamental role and is the subject of this study. The work aims to develop algorithms for recognising small stature alpine plants from close range top view images. Ideally, automated assessment algorithms of the plant cover should objectively help scientists observe and interpret
the state of the plant ecosystem over a long time series. Therefore, the aim in this respect was to derive visualisations that accurately describe plant growth and displacement (translocation). Additionally, recording changes in biodiversity was an intent. This work uses multi-temporal data comprising RGB images and multi-label masks to accomplish the aforementioned task. The evaluated methods involve mask comparison, optical flow estimation, detection of individual plants, and descriptive statistical analysis of image feature properties. Tests on the given data set show that all methods but the optical flow estimation have great potential. The mask comparison method captured plant growth and translocation most satisfactory. Individual plant detection and statistical analysis further helped to evaluate changes in biodiversity. When combined, the proposed methods give an immediate overview about relevant changes in the multi-temporal transects, which has not been done before for close-distance images of alpine plants.

References

  1. Bao, W., Lai, W.-S., Ma, C., Zhang, X., Gao, Z., and Yang, M.-H. (2019). Depth-aware video frame interpolation. In IEEE Conf. on Computer Vision and Pattern Recognition.
  2. Beauchemin, S. S. and Barron, J. L. (1995). The computation of optical flow. ACM Comput. Surv., 27:433–467
  3. Bouguet, J.-Y. (2000). Pyramidal implementation of the lucas kanade feature tracker description of the algorithm.
    OpenCV Document, Intel, Microprocessor Research Labs, 1.
  4. Canny, J. (1986). A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE
    Transactions on, PAMI-8:679 – 698.
  5. Dellepiane, S. G. and Angiati, E. (2012). A new method for cross-normalization and multitemporal visualization of sar images for the detection of flooded areas. IEEE Trans. on Geoscience and Remote Sensing, 50:2765–2779.
  6. Dellepiane, S. G. and Angiati, E. (2012). A new method for cross-normalization and multitemporal visualization of sar images for the detection of flooded areas. IEEE Trans. on Geoscience and Remote Sensing, 50:2765–2779.
  7. Eberl, T. and Kaiser, R. (2019). Langzeitmonitoring von ökosystemprozessen im nationalpark hohe tauern. modul 02: Botanisch / vegetationskundliche analysen. methoden-handbuch
  8. Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, volume 96, pages 226–231. AAAI Press
  9. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, volume 96, pages 226–231. AAAI Press
  10. Feng, W., Sui, H., Tu, J., Huang, W., and Sun, K. (2018). A novel change detection approach based on visual saliency and random forest from multi-temporal highresolution remote-sensing images. International Journal
    of Remote Sensing, 39(22):7998–8021
  11. Fleet, D. and Weiss, Y. (2006). Optical Flow Estimation. Springer US, Boston, MA
  12. Fleet, D. and Weiss, Y. (2006). Optical Flow Estimation. Springer US, Boston, MA
  13. Hasler, D. and Suesstrunk, S. (2003). Measuring colourfulness in natural images. Proceedings of SPIE - The International Society for Optical Engineering, 5007:87–95.
  14. Klimeš, L., Klimešová, J., Hendriks, R., and van Groenendael, J. (1997). The ecology and evolution of clonal plants,
    chapter Clonal plant architectures: a comparative analysis of form and function, pages 1–29. Backhuys Publishers.
  15. Klimeš, L., Klimešová, J., Hendriks, R., and van Groenendael, J. (1997). The ecology and evolution of clonal plants,
    chapter Clonal plant architectures: a comparative analysis of form and function, pages 1–29. Backhuys Publishers.
  16. Körner, C. and Hiltbrunner, E. (2021). Why is the alpine flora comparatively robust against climatic warming?
    Diversity, 13:1–15.
  17. Lucas, B. D. and Kanade, T. (1981). An Iterative Image Registration Technique with an Application to Stereo Vision.
    IJCAI’81. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
  18. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In In 5-th Berkeley Symposium on Mathematical Statistics and Probability,volume 1, pages 281–297
  19. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In In 5-th Berkeley Symposium on Mathematical Statistics and Probability, volume 1, pages 281–297
  20. Partsinevelos, P., Nikolakaki, N., Psillakis, P., Miliaresis, G., and Xanthakis, M. (2015). Landcover change modeling through visualization and classification enhancement of multi-temporal imagery. Global NEST (under publication)
  21. Preim, B. and Botha, C. (2013). Visual computing for medicine: Theory, algorithms, and applications: Second edition. Visual Computing for Medicine: Theory, Algorithms, and Applications: Second Edition, pages 1–812.
  22. Rajagopalan, S., Karwoski, R., and Robb, R. (2003). Shapebased interpolation of porous and tortuous binary objects. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2003, volume 2879, pages 957–958. Springer Berlin Heidelberg.
  23. Rensink, R. A. (2002). Change detection. Annual Review of Psychology, 53(1):245–277. PMID: 11752486.
  24. Sandifer, P. A., Sutton-Grier, A. E., and Ward, B. P. (2015). Exploring connections among nature, biodiversity, ecosystem services, and human health and well-being: Opportunities to enhance health and biodiversity conservation. Ecosystem services, 12:1–15
  25. Schlaffer, S., Matgen, P., Hollaus, M., and Wagner, W. (2015). Flood detection from multi-temporal sar data using harmonic analysis and change detection. International Journal of Applied Earth Observation and Geoinformation, 38:15–24.
  26. Schlaffer, S., Matgen, P., Hollaus, M., and Wagner, W. (2015). Flood detection from multi-temporal sar data
    using harmonic analysis and change detection. International Journal of Applied Earth Observation and Geoinformation, 38:15–24.
  27. Shukla, P., Skea, J., Calvo Buendia, E., Masson-Delmotte, V., Pörtner, H., Roberts, D., Zhai, P., Slade, R., Connors, S., Van Diemen, R., et al. (2019). Ipcc, 2019: Climate change and land: an ipcc special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Press.
  28. Spruyt, V., Ledda, A., and Philips, W. (2013). Sparse optical flow regularization for real-time visual tracking. In 2013 IEEE International Conference on Multimedia and Expo (ICME), pages 1–6.
  29. Szeliski, R. (2021). Computer Vision: Algorithms and Applications. Springer.
  30. Tomowski, D., Klonus, S., Ehlers, M., Michel, U., and Reinartz, P. (2010). Change visualization through a texture-based analysis approach for disaster applications. In ISPRS Proceedings, pages 1–6
  31. Varghese, A., Gubbi, J., Ramaswamy, A., and Balamuralidhar, P. (2018). Changenet: A deep learning architecture
    for visual change detection. In Proc. of the European Conf. on Computer Vision (ECCV) Workshops.
  32. Xu, D., Zhang, H., Wang, Q., and Bao, H. (2005). Poisson shape interpolation. In Proceedings of the 2005 ACM Symposium on Solid and Physical Modeling, SPM ’05, page 267–274, New York, NY, USA. Association for Computing Machinery
  33. Yu, Z., Cao, Z.-G., Wu, X., Bai, X., Qin, Y., Zhuo, W., Xiao, Y., Zhang, X., and Xue, H. (2013). Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage. Agricultural and Forest Meteorology, s 174–175:65–84
  34. Zhang, T. and Suen, C. (1984). A fast parallel algorithm for thinning digital patterns. Commun. ACM, 27:236–239.
  35. Zhou, H., Yuan, Y., and Shi, C. (2009). Object tracking using sift features and mean shift. Computer Vision and Image Understanding, 113:345–352.