Field-independent carbon mapping and quantification in forest plantation through remote sensing

ABSTRACT

Quantifying the carbon-stocking contribution of forest plantations is a crucial but challenging and expensive process, usually performed through field analysis. For this reason, plantations’ carbon storage is often calculated and reported using generic and inaccurate functions relying exclusively on tree species and plantation age. This study introduces a new field-independent (FI) method for forest plantations’ carbon quantification and mapping through automatic analysis of Sentinel-2 data. The study area is a Guatemalan forest plantation of 20 hectares, for which we constructed a reference dataset measuring in the field the diameter and the height of all trees within 20 randomly selected plots (10-meter radius). The CO2 equivalent absorbed by the plantation was first estimated using ground data and a design-based (DB) approach. Then, to obtain CO2 equivalent estimates but also maps, we used both ground and Sentinel-2 data to compare a standard model-assisted (MA) approach relying on Random Forests with the FI approach. Our results demonstrate that the FI method provides carbon stock statistics comparable to those obtained using DB and MA methods and more accurate maps. Accordingly, the RMSE obtained using the FI method was 34% while that obtained by the MA method – exploiting random forest algorithm – was greater (RMSE = 39%). The 95% confidence interval estimates of the CO2 stored in the plantation were 100 ± 18 MgC ha−1 and 102 ± 8 MgC ha−1, for DB and MA respectively. Using the FI method, the CO2 ranged between 89 and 117 Mg C ha−1, all values within the DB confidence interval. In addition, the FI map was surprisingly consistent with the MA-derived map, making our approach a valid alternative for monitoring plantation status and carbon storage when ground data are not available.

KEYWORDS:

• Sentinel-2
• forest
• Google Earth Engine
• machine learning
• estimates
• confidence intervals

Acknowledgments

This study was partially supported by the following projects:

1. MULTIFOR “Multi-scale observations to predict Forest response to pollution and climate change” PRIN 2020 Research Project of National Relevance funded by the Italian Ministry of University and Research (prot. 2020E52THS);

2. SUPERB “Systemic solutions for upscaling of urgent ecosystem restoration for forest-related biodiversity and ecosystem services” H2020 project funded by the European Commission, number 101,036,849 call LC-GD-7-1-2020;

3. EFINET “European Forest Information Network” funded by the European Forest Institute, Network Fund G-01-2021.

4. FORWARDS: the forestward observatory to secure resilience of European forests (Project 101,084,481).

5. PNRR, funded by the Italian Ministry of University and Research, Missione 4 Componente 2, “Dalla ricerca all’impresa”, Investimento 1.4, Project CN00000033.

Disclosure statement

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

Author contributions

Conceptualization, S.F., G.C.; methodology, S.F., E.V., G.D.; software, E.V, SF; validation, S.F., E.V. and G.D.; formal analysis, E.V., G.D., S.F., C.B.; data curation, E.V., G.D., S.F; writing – original draft preparation, S.F., G.D., E.V., G.C., C.M.; writing – review and editing, S.F., G.D., E.V., C.B., G.C., C.M., C.Z., G.C.; supervision, G.C., S.F.; project administration, S.F., G.C. All authors have read and agreed to the published version of the manuscript

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.