Mediterranean forests are one of the main biodiversity reservoirs in the world and play a fundamental role providing essential ecosystem services. However, these ecosystems, shaped by centuries of interaction with humans, are now among the most vulnerable to the effects of climate change. Increasing drought frequency, heat waves, and the spread of plant pathogens, particularly Phytophthora species, are profoundly altering forest structure, functionality, and regeneration, contributing to widespread forest decline. This thesis adopts an integrated and multidisciplinary approach combining field and laboratory investigations with advanced remote sensing and ecological modelling to investigate the drivers of forest decline and mortality, and to identify indicators for early diagnosis and adaptive management. In wild olive (Olea europaea var. sylvestris) stands of central Sardinia, ten coexisting Phytophthora taxa were identified and associated with progressive decline and high mortality. Pathogenicity tests showed that nine taxa significantly reduced root growth, with P. inundata and P. oleae being the most aggressive. Local-scale species distribution models highlighted Crown Height Model (CHM) and ΔNDVI as the most influential predictors, followed by distance from roads and watercourses. During the extreme summer drought of 2024, extensive canopy browning and mortality affected holm oak (Quercus ilex) and Mediterranean scrub across Sardinia. Sentinel-2 imagery and machine learning mapped over 110,000 ha of symptomatic areas, with SPI-12 emerging as the main driver. In the Mediterranean-type Jarrah Forest of Western Australia, airborne hyperspectral data outperformed multispectral data for species and decline classification, while deep learning enabled accurate tree crown segmentation. Overall, this thesis provides a multiscale framework supporting early detection and informed management of Mediterranean forests under accelerating climate change
Integrated remote sensing and ecological modeling of drought- and pathogen-induced forest decline in Mediterranean ecosystems
SATTA, GABRIELE GIUSEPPE ANTONIO
2026
Abstract
Mediterranean forests are one of the main biodiversity reservoirs in the world and play a fundamental role providing essential ecosystem services. However, these ecosystems, shaped by centuries of interaction with humans, are now among the most vulnerable to the effects of climate change. Increasing drought frequency, heat waves, and the spread of plant pathogens, particularly Phytophthora species, are profoundly altering forest structure, functionality, and regeneration, contributing to widespread forest decline. This thesis adopts an integrated and multidisciplinary approach combining field and laboratory investigations with advanced remote sensing and ecological modelling to investigate the drivers of forest decline and mortality, and to identify indicators for early diagnosis and adaptive management. In wild olive (Olea europaea var. sylvestris) stands of central Sardinia, ten coexisting Phytophthora taxa were identified and associated with progressive decline and high mortality. Pathogenicity tests showed that nine taxa significantly reduced root growth, with P. inundata and P. oleae being the most aggressive. Local-scale species distribution models highlighted Crown Height Model (CHM) and ΔNDVI as the most influential predictors, followed by distance from roads and watercourses. During the extreme summer drought of 2024, extensive canopy browning and mortality affected holm oak (Quercus ilex) and Mediterranean scrub across Sardinia. Sentinel-2 imagery and machine learning mapped over 110,000 ha of symptomatic areas, with SPI-12 emerging as the main driver. In the Mediterranean-type Jarrah Forest of Western Australia, airborne hyperspectral data outperformed multispectral data for species and decline classification, while deep learning enabled accurate tree crown segmentation. Overall, this thesis provides a multiscale framework supporting early detection and informed management of Mediterranean forests under accelerating climate change| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.14242/356336
URN:NBN:IT:UNISS-356336