Urban trees provide a wide range of ecosystem services (ESs), notably including carbon storage and sequestration, which are increasingly important in the context of climate change. Urban forests store carbon in woody biomass, sequester additional carbon during growth phases, and may release stored carbon back into the atmosphere after tree mortality. Moreover, urban trees influence local microclimates, air temperatures, and building energy consumption, indirectly affecting carbon emissions from urban energy systems.
The rapid growth of anthropogenic carbon emissions and ongoing urbanization have increased the need for reliable assessments of carbon storage in urban green infrastructure. Street trees, characterized by their linear and spatially dispersed distribution, require individual-level analysis, which is traditionally based on labor-intensive field surveys. Additionally, the United Nations Framework Convention on Climate Change’s (UNFCCC) Paris Agreement has set a target to ensure the global mean temperature increase below 2 °C by controlling carbon emission. With this policy concept, quantifying carbon storage and sequestration has gained significant attention as a vital regulating service provided by urban green spaces.
This study aims to evaluate the ES of carbon storage provided by urban parks in the Basilicata region (Southern Italy). The dataset includes 803 urban parks, covering a total area of 4.28 km2, corresponding to approximately 0.043% of the overall regional territory and about 0.12% of the region’s forested area. Individual park sizes exhibit a high degree of variability, ranging from 1.18 m2 to 1.62 km2. A median park size of 427 m2 highlights the prevalence of small urban green spaces across the region.
Two complementary approaches are employed: 1) satellite-derived aboveground biomass (AGB) products from ESA Earth observation data, and 2) terrestrial Light Detection and Ranging (LiDAR)-based measurements of tree structural parameters. LiDAR data are used to estimate Diameter at Breast Height (DBH), tree height (H), and AGB, which are subsequently converted into carbon stock using species-specific allometric equations with For-est model. A comparative analysis of these methods will access their consistency, plausibility, and effectiveness of remote sensing–based parameters at different spatial scales. The proposed methodology is demonstrated through a case study conducted in an urban area of the Basilicata region, Southern Italy.
Expected results include quantitative estimates of carbon storage and aboveground biomass, expressed in biophysical and monetary terms (EUR/ha/yr) for both approaches. The final result will include an assessment of the consistency and applicability of terrestrial LiDAR-derived and satellite-based biomass estimates for urban street trees.
By comparing these modeling approaches and their ES monetary outputs, the study aims to support informed decision-making by identifying appropriate methods for carbon stock assessment and enabling comparison of quantitative ES values.
This approach will serve as a foundation for future assessments of carbon sequestration.
