Scholarly record
MULTI-SENSOR FOREST MONITORING : INTEGRATING SENTINEL-1 SAR AND SENTINEL-2 OPTICAL DATA WITH HEXAGONAL SPATIAL PARTITIONING
Abstract
This paper presents an extended multi-sensor geoinformation framework for monitoring forest ecosystems in Ukraine under conditions restricted field access. Building upon previously validated hexagonal GIS and NDVI-based remote sensing methodology applied to Zhytomyr Oblast (2021–2024), the current study incorporates an expanded Sentinel-2 time series through 2025 and introduces a theoretical framework for integrating Sentinel-1 Synthetic Aperture Radar (SAR) data to overcome the critical limitation of optical cloud cover. The hexagonal spatial partitioning model continues to serve as the primary analytical framework for spatial aggregation, anomaly localization, and quantitative assessment of forest disturbance intensity. The extended dataset reveals persistent and intensifying degradation patterns in high-risk zones previously identified, including Ovruch district , with emerging evidence of partial canopy recovery in stabilized areas. The proposed SAR integration methodology addresses cloud-contamination gaps in optical imagery through coherence-based change detection and backscatter analysis, enabling continuous all-weather monitoring.
Publication details
References13
Chaskovskyi, O. H., & Hrynyk, H. H. (2020). Assessment of forest cover loss in the Ukrainian Carpathians using remote methods based on open-source satellite data. Scientific Bulletin of UNFU, 30(1), 66-73. DOI: 10.36930/40300111
Melnyk, O., Manko, P., & Brunn, A. (2023). Remote sensing methods for estimating tree species of forests in the Volyn region, Ukraine. Frontiers in Forests and Global Change, 6, 1041882. DOI: 10.3389/ffgc.2023.1041882
Kovalchuk, I. (2023). Application of remote sensing for natural resource monitoring in Ukraine. Scientific Journal NATIVA, 11(2), 153-158. DOI: 10.31413/nativa.v11i2.18355
Senf, C., Pflugmacher, D., Hostert, P., & Seidl, R. (2018). Detecting and quantifying forest disturbance severity in Europe using Sentinel-2 time series. Remote Sensing, 10(11), 1794. DOI: 10.3390/rs10111794
Hansen, M. C., Potapov, P. V., Moore, R., et al. (2013). High-resolution global maps of 21st-century forest cover change. Science, 342(6160), 850-853. DOI: 10.1126/science.1244693
Reiche, J., Hamunyela, E., Verbesselt, J., et al. (2018). A Bayesian approach to combine Landsat and ALOS PALSAR time series for near real-time deforestation detection. Remote Sensing, 10(5), 764. DOI: 10.3390/rs10050764
Zhu, Z., & Woodcock, C. E. (2014). Continuous change detection and classification of land cover using all available Landsat data. Remote Sensing of Environment, 144, 152-171. DOI: 10.1016/j.rse.2014.01.011
Banskota, A., Kayastha, N., Falkowski, M. J., et al. (2014). Forest monitoring using Landsat time series data: A review. Canadian Journal of Remote Sensing, 40(5), 362-384. DOI: 10.1080/07038992.2014.987376
Verbesselt, J., Zeileis, A., & Herold, M. (2012). Near real-time disturbance detection using satellite image time series. Remote Sensing of Environment, 123, 98-108. DOI: 10.1016/j.rse.2012.02.022
Tomchenko, O., Khyzhniak, A., Sheviakina, N., et al. (2023). Assessment and monitoring of fires caused by the war in Ukraine on landscape scale. Journal of Landscape Ecology, 16(2), 76-97. DOI: 10.2478/jlecol-2023-0011
Reiche, J., Verhoeven, R., Verbesselt, J., et al. (2016). Combining satellite data for better tropical forest monitoring. Nature Climate Change, 6(2), 120-122. DOI: 10.1038/nclimate2919
Rosen, P. A., & Kumar, R. (2023). SAR coherence-based forest change detection: A review of methods and applications. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 150-167. DOI: 10.1016/j.isprsjprs.2022.12.011
Tanase, M. A., Villard, L., Pitar, D., et al. (2015). Synthetic aperture radar sensitivity to forest changes: A simulations-based study for the Romanian forests. Science of the Total Environment, 512, 481-491. DOI: 10.1016/j.scitotenv.2015.01.065