Forest Dynamics Monitoring Through Novel Remote Sensing Techniques
Date Issued
May 31, 2026
Advisor
Abstract
This PhD thesis develops, applies, and benchmarks novel multi-sensor remote sensing methodologies for monitoring forest dynamics in semi-arid Mediterranean ecosystems, with the Paphos Forest, Cyprus, as the primary study area. The thesis is organised around three interacting axes of forest dynamics: long-term internal change, abiotic disturbance, and biotic disturbance and integrates the full Landsat archive (1984–2024), Sentinel-2 MSI, Sentinel-1 C-band SAR, and ancillary climate and ecological datasets within a cloud-native Google Earth Engine framework. Four research studies are presented: multi-decadal canopy growth dynamics from Landsat trend analysis (1991–2022); climate-mediated species transitions among Pinus brutia, Quercus alnifolia, and the endemic Cedrus brevifolia (1984–2024) modelled through multinomial logistic regression with climate–elevation interactions; detection of forest disturbance from dust events (2015–2019) using the BFAST family of change-detection algorithms; and a transferable framework for mapping pine processionary moth canopy stress across four European pine forests. The work demonstrates that integrating multi-decadal optical and SAR archives with rigorous statistical modelling enables operationally relevant forest-dynamics monitoring across abiotic and biotic disturbance regimes.
Τύπος PhD Dissertation
Τύπος PhD Dissertation
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