Using bi-temporal ALS and NFI-based time-series data to account for large-scale aboveground carbon dynamics: the showcase of mediterranean forests

ABSTRACT

New remote-sensed biomass change products will transform our capacity to monitor and validate large-scale carbon dynamic in the next decade. In this study, we evaluated the use of multitemporal Airborne Laser Scanning (ALS) and the Climate Change Initiative (CCI) BIOMASS spaceborne mission to estimate AGB dynamics in different Mediterranean forest over an 8-year period (2010–2018). To do so, we evaluated different maps to estimate change in AGB, specifically indirect approach using forest-type specific ALS-based AGB maps using i) countrywide ALS coverage at 25 m resolution (2010–2018) and ii) the global, 100-m resolution CCI maps version 3 (2010–2018). The change in AGB (ΔAGB) was mapped across the study region to compute dynamics by forest type. Our results suggest that the indirect approach using ALS-model-based produced more accurate estimates in change of AGB than CCI when we compared with the design-based AGB estimation using Spanish National Forest Inventory (SNFI) at strata level. The spatial representation of the AGB change indicated that ΔAGB-ALS changes by forest type had an overall gain in biomass at regional level. The ΔAGB total and net annual changes by year and area (ΔAGB, Mg ha⁻¹ year⁻¹) were closed to the values obtained using SNFI at strata level. This study demonstrates the feasibility of enhancing carbon sequestration and stock capacity in Mediterranean forest using multitemporal ALS data and the limitations of global AGB maps at Regional Scale.

KEYWORDS:

• LiDAR
• CCI biomass mission
• forest monitoring
• biomass stocks
• Spanish National Forest Inventory (SNFI)
• carbon sequestration

Acknowledgments

We acknowledge Vicente Sandoval and Elena Robla from National Forest Inventory Department. a research grant funded by the Foundation for Science and Technology (FCT), Portugal to Dr. Guerra (#CEECIND/02576/2022). We thank the Spanish National Forest Inventory (SNFI) from MAPA for making the ground data of the 3^th (SNFI-3) and 4^th round (SNFI-4) available to us to conduct the study.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding authors, Juan Guerra Hernandez, Alfonso Jurado-Varela and Vicente Sandoval-Altelarrea, upon reasonable request.

Author contributions

Conceptualization, JGH, AP, VS; methodology, JGH, FTS, SG; BB, AJV, VS, software JGH, AP, FTS; data curation, JGH, FTS, SG; BB, AJV, VS; model validation, JGH, FTS, SG; BB, AJV,VS; investigation, JGH, FTS, SG; BB, AJV,VS; writing – original draft preparation, JGH, FTS, SG; BB, AJV,VS; writing – review and editing, JGH, FTS, SG; BB, AJV,VS; visualization, JGH, FTS, SG; BB, AJV,VS; supervision, SSP, AM; project administration, JGH, AP,AJV, VS; and funding acquisition, JGH, AP, AJV,VS.

Additional information

Funding

The authors also thank to Forest Research Centre, a research unit funded by Fundação para a Ciência e aTecnologia I.P. (FCT), Portugal (UIDB/00239/2023). This research was supported by the project “Extensión del cuarto inventario forestal nacional mediante técnicas LiDAR para la gestión sostenible de los montes de Extremadura” from the Extremadura Forest Service (FEADER nº 1952SE1FR435) and research grant funded by the Foundation for Science and Technology (FCT), Portugal to Dr. Guerra- Hernández (#CEECIND/02576/2022). Project “Apoio à Contratação de Recursos Humanos Altamente Qualificados” (NORTE-06-3559-FSE-000045). under the PORTUGAL 2020 Partnership Agreement. ForestWISE - Collaborative Laboratory for Integrated Forest & Fire Management, was recognized as a CoLAB by the Fundação para a Ciência e a Tecnologia, I.P. (FCT).