Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest

Tássia Fraga Belloli (), Diniz Carvalho de Arruda, Laurindo Antonio Guasselli, Christhian Santana Cunha and Carina Cristiane Korb
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Tássia Fraga Belloli: State Center of Research in Remote Sensing and Meteorology, Federal University of Rio Grande do Sul, 9500 Bento Gonçalves Avenue, Porto Alegre 91501-970, RS, Brazil
Diniz Carvalho de Arruda: Institute Sustainable Development, Vale Technological Institute, 955 Boa Ventura da Silva Street, Belém 66055-090, PA, Brazil
Laurindo Antonio Guasselli: State Center of Research in Remote Sensing and Meteorology, Federal University of Rio Grande do Sul, 9500 Bento Gonçalves Avenue, Porto Alegre 91501-970, RS, Brazil
Christhian Santana Cunha: State Center of Research in Remote Sensing and Meteorology, Federal University of Rio Grande do Sul, 9500 Bento Gonçalves Avenue, Porto Alegre 91501-970, RS, Brazil
Carina Cristiane Korb: State Center of Research in Remote Sensing and Meteorology, Federal University of Rio Grande do Sul, 9500 Bento Gonçalves Avenue, Porto Alegre 91501-970, RS, Brazil

Land, 2025, vol. 14, issue 3, 1-22

Abstract: Wetlands are essential carbon sinks in the global ecosystem, absorbing CO 2 in their biomass and soils and mitigating global warming. Accurate aboveground biomass (AGB) and organic carbon (Corg) estimation are crucial for wetland carbon sink research. Remote sensing (RS) data effectively estimate and map AGB and Corg in wetlands using various techniques, but there is still room to improve the efficiency of machine learning (ML)-based approaches. This study examined how different sample data treatments and plot sizes impact a random forest model’s performance based on RS for AGB and Corg prediction. The model was trained with samples of emergent vegetation collected in a palustrine wetland in southern Brazil and spectral variables (single bands and vegetation indices—VIs) from medium- and high-resolution optical images from Sentinel-2 and PlanetScope, respectively. The treatments involved AGB and Corg values dimensioned for three different plot sizes (G1) and the same subjected to normalized natural logarithmic transformation—NL (G2). Therefore, six AGB and Corg models were created for each sensor. Models and sensor performance and spectral variable importance were compared. In our results, NL sample data RF models proved more accurate. Larger plots produced smaller prediction errors with S2 models, indicating the influence of plot size on the reliability of the estimate. S2 surpassed PS in AGB/Corg prediction, respectively—S2 (R 2 0.87; 0.89, RMSE OOB: between 19.7% and 22.7%); PS (R 2 0.86; 0.86, RMSE OOB: between 21% and 35.9%)—but PS was superior in mapping spatial variability. The VI CO 2 Flux and S2’s SWIR, blue, green, and RE bands 6 and 7 were more important for AGB/Corg prediction. The contribution of this study is the finding that in addition to optimizing RF model parameters, optimizing the AGB and Corg dataset collected in the field, i.e., evaluating normalization and plot sizes, is crucial to obtain more accurate estimates with RS- and ML-based models. This approach enhances AGB/Corg stock estimation in wetlands, and the highlighted predictors can act as spectral indicators of these ecological functions. These results have the potential to guide standardization in the collection and processing of input data for predictive models of AGB/Corg in wetlands, with the aim of ensuring consistent predictions in inventories and monitoring.

Keywords: improve prediction models; random forest regression; AGB spectral indicator; carbon stocks; marshes (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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