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Integrating UAV and Sentinel-2 satellite data to map the health of a mediterranean open forest : the case study of Lefka Ori National Park in Crete, Greece
Salami, Ruth Adetutu (2025) Integrating UAV and Sentinel-2 satellite data to map the health of a mediterranean open forest : the case study of Lefka Ori National Park in Crete, Greece.
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| Abstract: | Monitoring forest health is essential for understanding ecosystem resilience, particularly in Mediterranean pine forests that are increasingly affected by drought and pest-related disturbances. This study evaluates the potential of integrating ultra-high resolution Unmanned Aerial Vehicle (UAV) data with medium resolution Sentinel-2 satellite imagery to improve vegetation health classification in open pine forests in West Crete, Greece. Field-based measurements of tree health, based on defoliation and discoloration, were used to classify individual trees into health categories. Vegetation indices (VIs), including Green Normalized Difference Vegetation Index (GNDVI), Normalised Difference Red Edge Index (NDRE), and Soil Adjusted Vegetation Index (SAVI), were derived from UAV multispectral data at the object (tree) and plot levels and compared with corresponding indices from Sentinel-2 imagery. Statistical analysis revealed that SAVI was most effective at distinguishing between tree health classes at the object level, while GNDVI and NDRE showed limited spectral separability. At the plot level, GNDVI exhibited a strong correlation (R² = 0.82) between UAV and Sentinel-2 data, indicating its potential as a reliable proxy for UAV-derived assessments. Correlation analysis also highlighted improved alignment between UAV-derived and Sentinel-2 vegetation health indices after applying soil masking techniques (on Sentinel-2 data using an NDVI threshold) and integrating Fractional Vegetation Cover (FVC) from UAV data. This approach helped address mixed-pixel effects in Sentinel-2 imagery, particularly in open-canopy forests. The findings underscore the value of UAV data in validating and enhancing Sentinel–2–based vegetation health assessments. The study concludes that leveraging UAV-derived metrics, especially plot-level FVC, significantly improves the accuracy and reliability of forest health classification from satellite imagery. This research presents an exploratory approach for multi-sensor integration, offering practical insights for forest monitoring programs in mediterranean ecosystems and similar forested landscapes. |
| Item Type: | Essay (Master) |
| Clients: | University of Twente, Netherlands |
| Faculty: | ITC: Faculty of Geo-information Science and Earth Observation |
| Subject: | 43 environmental science |
| Programme: | Geoinformation Science and Earth Observation MSc (75014) |
| Link to this item: | https://purl.utwente.nl/essays/107802 |
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