Scholarly record
HEXAGONAL-BASED GIS AND REMOTE SENSING FOR MONITORING FOREST ECOSYSTEMS AND DETECTING ANOMALOUS CHANGES
Abstract
This study presents a hexagonal-based geoinformation approach for monitoring forest ecosystems and detecting anomalous changes in Ukraine, where forest resources are under severe anthropogenic and military pressure. Using Sentinel-2 satellite imagery, vegetation indices were processed to classify vegetation dynamics and identify areas of significant degradation. A hexagonal spatial partitioning method was applied to improve spatial accuracy, reduce edge effects, and support the quantitative assessment of forest cover changes. Zhytomyr Oblast, one of the most forested and ecologically sensitive regions of Ukraine, served as a pilot study area. The analysis revealed over 40,000 cases of forest cover anomalies, with the highest intensity recorded in Ovruch district. The integration of remote sensing and GIS provides a powerful tool for ecological monitoring, particularly under conditions of limited field access due to military activities. Beyond methodological advances, the findings highlight critical environmental implications: forest degradation caused by illegal logging, fires, and war-related destruction undermines biodiversity conservation, carbon sequestration, and water regulation functions. The proposed approach supports the Sustainable Development Goals (SDG 13 Climate Action, SDG 15 Life on Land) by enabling informed decision-making for forest management, prioritizing restoration activities, and enhancing resilience of ecosystems. The results confirm the effectiveness of combining GIS, remote sensing, and hexagonal models for sustainable forestry and post-war ecological recovery.
Publication Impact Profile
Publication details
References7
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. https://doi.org/ 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
Chu, T., & Guo, X. (2014). Remote sensing techniques in monitoring post-fire effects and burn severity. Journal of Applied Remote Sensing, 8(1), 083598. DOI: 10.1117/1.JRS.8.083598
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
View or Download full articleAccess options
SWS access login
Login as SWS Scientific CommitteeLogin as SWS Scientific PartnerLogin as SWS AuthorAuthors and approved SWS contributors will read and export their own linked papers after identity matching by SWS profile, email and SGEM GlobalID.
For librarian assistance: [email protected]
Purchase Instant Access
- Article can be downloaded after successful payment.
- Article may be used according to SWS library access terms.
- Article cannot be redistributed.