Proceedings of International Conference on Applied Innovation in IT  ·  2025/04/26  ·  Vol. 13  ·  Issue 1  ·  pp. 87–93
A Novel Approach for Rapid Detection of Forest Degradation and Diseases Through Anomaly Analysis of Sentinel-2 Spectral Data
Sofiia Drozd, Nataliia Kussul and Hanna Yailymova
📄 Download PDF DOI: 10.25673/119219
Abstract
Forest degradation is an ongoing global issue, with significant environmental impacts that necessitate efficient monitoring and management. This paper presents a simple yet effective method for detecting forest degradation using freely available Sentinel-2 satellite data and an anomaly detection approach. The aim of this study was to develop an accessible and reliable technique that could match the performance of more complex algorithms while using minimal computational resources. The research focused on spectral bands with 10-20 m resolution and vegetation indices (NDVI, NDMI, GCI, PSSRa) to analyze forest damage in the Harz region. The method involved identifying anomalies in the spectral data relative to randomly selected reference points from healthy forest areas, which were verified with high-resolution imagery from Google Earth Pro. The results demonstrated that specific Sentinel-2 bands, particularly B3 and B5, were the most informative for detecting damaged forests, while vegetation indices were less effective. By analyzing anomalies in these bands, we successfully tracked forest degradation from 2020 to 2024, revealing a significant increase in damage between 2020 and 2021, with a total of 68.1 thousand hectares of forest lost by 2024. The theoretical relevance of this study lies in the development of a cost-effective and straightforward method for forest monitoring, while the practical relevance is evident in its potential for large-scale forest management and conservation. This method provides an efficient tool for monitoring forest health with minimal data requirements and computational effort, offering a promising solution for forest managers and conservationists worldwide.
Keywords
Satellite Data Anomaly Detection Sentinel-2 Forest Degradation.
References
  1. G. F. Watch, "Harz, Sachsen-Anhalt, Germany Deforestation Rates Statistics — GFW," [Online]. Available: https://www.globalforestwatch.org/dashboards/country/DEU/13/7/?category=forest-change.
  2. J. Wang, T. W. Sammis, V. P. Gutschick, M. Gebremichael, S. O. Dennis, and R. E. Harrison, "Review of satellite remote sensing use in forest health studies," Open Geogr. J, vol. 3, no. 1, pp. 28-42, 2010.
  3. F. Knutzen, P. Averbeck, C. Barrasso, L. M. Bouwer, B. Gardiner, J. M. Grünzweig, S. Hänel, K. Haustein, M. R. Johannessen, S. Kollet et al., "Impacts on and damage to European forests from the 2018–2022 heat and drought events," Natural Hazards and Earth System Sciences, vol. 25, no. 1, pp. 77-117, 2025.
  4. M. Lange, S. Preidl, A. Reichmuth, M. Heurich, and D. Doktor, "A continuous tree species-specific reflectance anomaly index reveals declining forest condition between 2016 and 2022 in Germany," Remote Sensing of Environment, vol. 312, p. 114323, 2024.
  5. A. Mujetahid, M. Nursaputra, and A. S. Soma, "Monitoring illegal logging using google earth engine in Sulawesi Selatan Tropical Forest, Indonesia," Forests, vol. 14, no. 3, p. 652, 2023.
  6. N. Kussul, S. Drozd, H. Yailymova, A. Shelestov, G. Lemoine, and K. Deininger, "Assessing damage to agricultural fields from military actions in Ukraine: An integrated approach using statistical indicators and machine learning," International Journal of Applied Earth Observation and Geoinformation, vol. 125, p. 103562, 2023.
  7. L. Shumilo, N. Kussul, and M. Lavreniuk, "U-Net model for logging detection based on the Sentinel-1 and Sentinel-2 data," in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, pp. 4680-4683, 2021.
  8. J. Zhang, S. Cong, G. Zhang, Y. Ma, Y. Zhang, and J. Huang, "Detecting pest-infested forest damage through multispectral satellite imagery and improved UNet++," Sensors, vol. 22, no. 19, p. 7440, 2022.
  9. R. Hall, G. Castilla, J. White, B. Cooke, and R. Skakun, "Remote sensing of forest pest damage: A review and lessons learned from a Canadian perspective," The Canadian Entomologist, vol. 148, no. S1, pp. S296-S356, 2016.
  10. M. Decuyper, R. O. Chávez, M. Lohbeck, J. A. Lastra, N. Tsendbazar, J. Hackländer, M. Herold, and T.-G. Vågen, "Continuous monitoring of forest change dynamics with satellite time series," Remote Sensing of Environment, vol. 269, p. 112829, 2022.
  11. F. Haghighian, S. Yousefi, and S. Keesstra, "Identifying tree health using sentinel-2 images: a case study on Tortrix viridana L. infected oak trees in Western Iran," Geocarto International, vol. 37, no. 1, pp. 304-314, 2022.
  12. M. Piragnolo, F. Pirotti, C. Zanrosso, E. Lingua, and S. Grigolato, "Responding to large-scale forest damage in an alpine environment with remote sensing, machine learning, and web-GIS," Remote Sensing, vol. 13, no. 8, p. 1541, 2021.
  13. F. Giannetti, M. Pecchi, D. Travaglini, S. Francini, G. D’Amico, E. Vangi, C. Cocozza, and G. Chirici, "Estimating VAIA windstorm damaged forest area in Italy using time series Sentinel-2 imagery and continuous change detection algorithms," Forests, vol. 12, no. 6, p. 680, 2021.
  14. T. Molnár and G. Király, "Forest disturbance monitoring using cloud-based Sentinel-2 satellite imagery and machine learning," Journal of imaging, vol. 10, no. 1, p. 14, 2024.
  15. N. S. Podoprigorova, G. A. Savchenko, K. R. Rabcevich, A. I. Kanev, A. V. Tarasov, and A. N. Shikohov, "Forest damage segmentation using machine learning methods on satellite images," in International Conference on Neuroinformatics, pp. 380-388, 2023.
  16. D. John and C. Zhang, "An attention-based U-Net for detecting deforestation within satellite sensor imagery," International Journal of Applied Earth Observation and Geoinformation, vol. 107, p. 102685, 2022.
  17. J. Anwander, M. Brandmeier, S. Paczkowski, T. Neubert, and M. Paczkowska, "Evaluating Different Deep Learning Approaches for Tree Health Classification Using High-Resolution Multispectral UAV Data in the Black Forest, Harz Region, and Göttinger Forest," Remote Sensing, vol. 16, no. 3, p. 561, 2024.
  18. UNEP-WCMC and IUCN, "Protected planet: The world database on protected areas (wdpa)," 2025, [Online]. Available: https://www.protectedplanet.net.
  19. EU Forest Observatory, [Online]. Available: https://forest-observatory.ec.europa.eu/forest/rmap.
  20. Radmin, "Sentinel-2 data and vegetation indices," [Online]. Available: http://www.eo4geo.eu/training/sentinel-2-dataand-vegetation-indices/.

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