Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 3, pp. 136-151

Identification of logged and windthrow areas from Sentinel-2 satellite images using the U-net convolutional neural network and factors affecting its accuracy

A.I. Kanev 1 , A.V. Tarasov 2 , A.N. Shikhov 2 , N.S. Podoprigorova 1 , F.A. Safonov 1 
1 Bauman Moscow State Technical University, Moscow, Russia
2 Perm State University, Perm, Russia
Accepted: 25.04.2023
DOI: 10.21046/2070-7401-2023-20-3-136-151
The results of detection (segmentation) of forest disturbances (logged and windthrow areas) based on Sentinel-2 satellite images with convolutional neural networks of U-net architecture in different regions of the European territory of Russia and the Urals are presented. The volume of the training sample was over 17 thousand objects. Overall, both logged and windthrow areas are detected with satisfactory accuracy (the average F-measure is over 0.5). At the same time, substantial differences in detection accuracy were found depending on the characteristics of both disturbances themselves and the affected forest cover. Thus, the maximum accuracy was achieved for tornado-induced windthrow areas, due to their geometric features. The dependence of windthrow detection accuracy on the species composition of damaged forests is not obvious and requires clarification; at the same time, the average area of damaged forest sites has a substantial effect on it. The maximum F-measure calculated for logged areas detected on test pairs of Sentinel-2 images reaches 0.80, which is substantially higher than in previously published studies with the U-net model. The maximum accuracy is typical for large clear-cuts in mixed and dark coniferous forests, while selective logged areas in deciduous forests are characterized by lowest one. The accuracy for wintertime and summertime pairs of images is substantially higher than for multi-seasonal pairs. Also, the accuracy strongly varies for different types of logged areas. Thus, forest roads on summertime images are detected with lowest producer’s accuracy, while logged areas on wintertime images are detected with highest one.
Keywords: logged areas, windthrow areas, Sentinel-2 data, convolutional neural networks, U-net, forest species composition
Full text

References:

  1. Bartalev S. A., Egorov V. A., Krylov A. M., Stytsenko F. V., Khovratovich T. S., The evaluation of possibilities to assess forest burnt severity using multi-spectral satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2010, Vol. 7, No. 3, pp. 215–225. (in Russian).
  2. Bartalev S. A. Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shabanov N. V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data, Moscow: IKI RAS, 2016, 208 p. (in Russian).
  3. Koroleva N. V., Ershov D. V., Assessment of an error of determination of areas of windfalls using space images of high spatial resolution of Landsat-TM, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9. No. 1, pp. 80–86 (in Russian).
  4. Krylov A. M., Vladimirova N. A., Remote monitoring of forest based on satellite imagery data, Geomatika, 2011, No. 3, pp. 53–58 (in Russian).
  5. Tarasov A. V., Operativnoe kartografirovanie narushenii lesnogo pokrova na osnove sputnikovykh dannykh s vysokim prostranstvenno-vremennym razresheniem: Diss. kand. tekhn. nauk (Operational mapping of forest cover disturbances based on satellite images with high spatial and temporal resolution, Cand. techn. sci. thesis), Perm, 2021, 135 p. (in Russian).
  6. Tarasov A. V., Shikhov A. N., Shabalina T. V., Detection of forest disturbances in Sentinel-2 images with convolutional neural networks, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 3, pp. 51–64 (in Russian), DOI: 10.21046/2070-7401-2021-18-3-51-64.
  7. Khovratovich T. S., Bartalev S. A., Kashnitskii A. V., Forest change detection based on sub-pixel estimation of crown cover density using bitemporal satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 4, pp. 102–110 (in Russian), DOI: 10.21046/2070-7401-2019-16-4-102-110.
  8. Shikhov A. N., Chernokulskii A. V., Azhigov I. O., Spatio-temporal distribution and origins of windthrow events in the forest zone of Western Siberia in 2001–2020, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 3, pp. 186–202 (in Russian), DOI: 10.21046/2070-7401-2022-19-3-186-202.
  9. Allen T. R., Kupfer J. A., Spectral response and spatial pattern of Fraser fir mortality and regeneration, Great Smoky Mountains, USA, Plant Ecology, 2001, Vol. 156, Issue 1, pp. 59–74, DOI: 10.1023/A:1011948906647.
  10. Baumann M., Ozdogan M., Wolter P. T., Krylov A., Vladimirova N., Radeloff V. C., Landsat remote sensing of forest windfall disturbance, Remote Sensing of Environment, 2014, Vol. 143, pp. 171–179, DOI: 10.1016/j.rse.2013.12.020.
  11. Cocke A. E., Fulé P. Z., Crouse J. E., Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data, Intern. J. Wildland Fire, 2005, Vol. 14(2), pp. 189–198.
  12. Coppin P. R., Bauer M. E., Processing of multi-temporal Landsat TM imagery to optimize extraction of forest cover change features, IEEE Trans. Geoscience and Remote Sensing, 1994, Vol. 32, pp. 918–927.
  13. Hansen M. C., Potapov P. V., Moore R., Hancher M., Turubanova S. A., Tyukavina A., Thau D., Stehman S. V., Goetz S. J., Loveland T. R., Kommareddy A., Egorov A., Chini L., Justice C. O., Townshend J. R. G., High-Resolution Global Maps of 21st-Century Forest Cover Change, Science, 2013, Vol. 342(6160), pp. 850–853, DOI: 10.1126/science.1244693.
  14. Hardisky M. A., Klemas V., Smart R. M., The influence of soil salinity, growth form, leaf moisture on the spectral radiance of Spartina alterniflora canopies, Photogrammetric Engineering and Remote Sensing, 1983, Vol. 49, pp. 77–83.
  15. Isaienkov K., Yushchuk M., Khramtsov V., Seliverstov O., Deep Learning for Regular Change Detection in Ukrainian Forest Ecosystem with Sentinel-2, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2021, Vol. 14, pp. 364–376.
  16. Kislov D. E., Korznikov K. A., Automatic windthrow detection using very-high-resolution satellite imagery and deep learning, Remote Sensing, 2020, Vol. 12(7), Art. No. 1145, DOI: 10.3390/rs12071145.
  17. Kislov D. E., Korznikov K. A., Altman J., Vozmishcheva A. S., Krestov P. V., Extending deep learning approaches for forest disturbance segmentation on very high-resolution satellite images, Remote Sensing in Ecology and Conservation, 2021, Vol. 7(3), pp. 355–368, DOI: 10.1002/rse2.194.
  18. Larabi M., Liu Q., Wang Y., Convolutional neural network features based change detection in satellite images, Proc. 1st Intern. Workshop Pattern Recognition, RRPR 2016, Dec. 4, 2016, Cancún, Mexico, 2016, Art. No. 100110W.
  19. Mou L., Bruzzone L., Zhu X. X., Learning spectral-spatial features via a recurrent convolutional neural network for change detection in multispectral imagery, IEEE Trans. Geoscience and Remote Sensing, 2019, Vol. 57(2), pp. 924–935.
  20. Nielsen A. A., Conradsen K., Simpson J. J., Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: New approaches to change detection studies, Remote Sensing of Environment, 1998, Vol. 64(1), pp. 1–19.
  21. Potapov P. V., Turubanova S. A., Tyukavina A., Krylov A. M., McCarty J. L., Radeloff V. C., Hansen M. C., Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive, Remote Sensing of Environment, 2015, Vol. 159, pp. 28–43, https://doi.org/10.1016/j.rse.2014.11.027.
  22. Potapov P., Li X., Hernandez-Serna A., Tyukavina A., Hansen M. C., Kommareddy A., Pickens A., Turubanova S., Tang H., Silva C. E., Armston J., Dubayah R., Blair J. B., Hofton M., Mapping global forest canopy height through integration of GEDI and Landsat data, Remote Sensing of Environment, 2021, Vol. 253, Art. No. 112165, DOI: 10.1016/j.rse.2020.112165.
  23. Rodriguez-Galiano V. F., Ghimire B., Rogan J., Chica-Olmo M., Rigol-Sanchez J. P., An assessment of the effectiveness of a random forest classifier for land-cover classification, ISPRS J. Photogrammetry and Remote Sensing, 2012, Vol. 67(1), pp. 93–104, DOI: 10.1016/j.isprsjprs.2011.11.002.
  24. Ronneberger O., Fischer P., Brox T., U-Net: Convolutional networks for biomedical image segmentation, arXiv: e-print service, arXiv:1505.04597, 2015, 8 p., https://arxiv.org/pdf/1505.04597.pdf.
  25. Scharvogel D., Brandmeier M., Weis M., A Deep Learning Approach for Calamity Assessment Using Sentinel-2 Data, Forests, 2020, Vol. 11(2), Art. No. 1239, 21 p., DOI: 10.3390/f11121239.
  26. Shikhov A. N., Chernokulsky A. V., Azhigov I. O., Semakina A. V., A satellite-derived database for stand-replacing windthrow events in boreal forests of European Russia in 1986–2017, Earth System Science Data, 2020, Vol. 12, pp. 3489–3513, DOI: 10.5194/essd-12-3489-2020.
  27. Xie F., Gao Q., Jin C., Zhao F., Hyperspectral image classification based on superpixel pooling convolutional neural network with transfer learning, Remote Sensing, 2021, Vol. 13(5), pp. 1–17.
  28. Wang F., Xu Y. J., Comparison of remote sensing change detection techniques for assessing hurricane damage to forests, Environmental Monitoring and Assessment, 2010, Vol. 162, pp. 311–326.