Biology subjects

Giannetti, F.

Publications and source records attributed to Giannetti, F..

3 recordsLinked to original sources

bioRxiv2024

deadtrees.earth - An Open-Access and Interactive Database for Centimeter-Scale Aerial Imagery to Uncover Global Tree Mortality Dynamics

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, not standardized and not spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map tree mortality over time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Drones provide a cost-effective source of training data by capturing high-resolution orthophotos of tree mortality events at sub-centimeter resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than a thousand centimeter-resolution orthophotos, covering already more than 300,000 ha, of which more than 58,000 ha are fully annotated. This community-sourced and rigorously curated dataset shall serve as a foundation for a global initiative to gather comprehensive reference data. In concert with Earth observation data and machine learning it will serve to uncover tree mortality patterns from local to global scales. This will provide the foundation to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. Thus, the open and interactive nature of deadtrees.earth together with the collective effort of the community is meant to continuously increase our capacity to uncover and understand tree mortality patterns.

ecology↗

bioRxiv2024

Stand Age and Climate Change Effects on Carbon Increments and Stock Dynamics

Carbon assimilation and wood production are influenced by environmental conditions and endogenous factors, such as species auto-ecology, age, and hierarchical position within the forest structure. Disentangling the intricate relationships between those factors is more pressing than ever due to climate changes pressure. We employed the 3D-CMCC-FEM model to simulate undisturbed forests of different ages under four climate change scenarios (plus one no climate change) from five Earth System Models. In this context, carbon stocks and increment were simulated via total car-bon woody stocks and mean annual increment, which depends mainly on climate trends. We find greater differences among different age cohorts under the same scenario than in different climate scenarios under the same age class. Increasing temperature and changes in precipitation patterns led to a decline in above-ground biomass in spruce stands, espe-cially in the older age classes. On the contrary, the results show that beech forests at DK-Sor will maintain and even increase C-storage rates under most RCP scenarios. Scots pine forests show an intermediate behavior with a stable stock capacity over time and in different scenarios but with decreasing mean volume annual increment. These results confirm current observations worldwide that indicate a stronger climate-related decline in conifers forests than in broadleaves.

ecology↗

bioRxiv2023

Regional estimates of gross primary production applying the process-based model 3D-CMCC-FEM vs. multiple datasets

Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of both carbon fluxes and stocks in forests, accounting for ecophysiological, climate and geographical variability. Yet, their predictive capacity should be demonstrated not only at the stand-level but also in the context of large spatial and temporal heterogeneity. For the first time, we apply a stand scale process-based model (3D-CMCC-FEM) in a spatially explicit manner at 1 km spatial resolution in a Mediterranean region in southern Italy. Specifically, we developed a methodology to initialize the model that comprehends the use of spatial information derived from the integration of remote sensing (RS) data, the national forest inventory data and regional forest maps to characterize structural features of the main forest species. Gross primary production (GPP) is simulated over the period 2005-2019 and the multiyear predictive capability of the model in simulating GPP is evaluated both aggregated as at species-level by means of independent multiple data sources based on different RS-based products. We show that the model is able to reproduce most of the spatial ([~]2800 km2) and temporal (32 years in total) patterns of the observed GPP at both seasonal, annual and interannual time scales, even at the species-level. These new very promising results open the possibility of applying the 3D-CMCC- FEM confidently and robustly to investigate the forests behavior under climate and environmental variability over large areas across the highly variable ecological and bio- geographical heterogeneity of the Mediterranean region. Key PointsO_LIWe apply a process-based forest model on a regular grid at 1 km spatial resolution in a Mediterranean region. C_LIO_LIInitial forest state is estimated using spatially explicit input data derived from remote sensing and national forest inventory data. C_LIO_LIThe 3D-CMCC-FEM shows comparably estimates in simulating both spatial and temporally the gross primary production, when compared to independent satellite-based products. C_LI

ecology↗