Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest
Abstract
:1. Introduction
2. Data and Methods
2.1. Study Area and Field Data Collection
2.2. Remote Sensing Datasets
2.3. Development of the Prediction Models
2.4. Evaluation of Models
3. Results
3.1. Optimization of Regression Model Parameters
3.2. Predictive Performance of the RF Models
3.3. Importance of Predictor Variables
4. Discussion
4.1. AGB and Corg Estimation Accuracy and Efficiency of Sensors
4.2. The Effect of Treatments and Plot Size on Model Performance and Importance of Spectral Variables
5. Conclusions
- Regarding RF parameters, different Ntrees impacted model errors, notably in non-normalized treatments, enhancing RF model precision. Thus, optimized RF models provide more accurate estimates. OOB estimates served effectively for validation, with average prediction errors within the limits found in validation sets in reference studies. This result is useful in light of wetland data collection challenges.
- Normalized sample data treatments enhanced RF model accuracy for AGB and Corg prediction. Estimation errors decrease as S2 model plot size increased, indicating smaller plots may compromise estimate reliability with S2.
- Utilizing S2 and PS sensors underscored the value of medium spatial resolution satellite data for enhancing estimate accuracy and high-resolution data for delineating AGB and Corg spatial variability, respectively, in wetlands. Sensor performances were close; however, S2 was more efficient.
- The RF method, employing the combination of VI CO2Flux and S2’s SWIR, blue, green, and RE bands 6 and 7 as predictors, excelled in AGB and Corg prediction. Leveraging an ML algorithm with VI and bands indicative of carbon fluxes and biomass changes proved beneficial, and these predictors serve as spectral indicators of these ecological functions.
- In addition to optimizing the parameters of the RF model, optimizing the input set of AGB and Corg collected in the field, i.e., evaluating normalization and plot sizes, has contributed to more accurate estimates. This approach holds promise for improved monitoring of the ecological processes of AGB and Corg storage in wetlands and for contributing to the understanding of these ecosystems as carbon sinks, vital for offsetting emissions and meeting national and global GHG reduction targets.
- We encourage future work that compares the effects of different plot sizes, sample data normalization methods, sensors, and VIs in RF models and other ML approaches on the accuracy of AGB and Corg estimates in marshes, as well as in other wetlands with emergent herbaceous vegetation, such as salt marshes and peatlands. This will contribute to the continued advancement of knowledge on improving the modeling of AGB and Corg in wetlands.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Sensor | March/2018 | August/2018 | November/2018 |
---|---|---|---|
Sentinel-2A | March 11 | August 28 | November 16 |
PlanetScope | March 13 | August 17 | November 21 |
Field data collection | March 14 | August 17 | November 22 |
Vegetation Indices | Equation | References |
---|---|---|
NDVI—Normalized Difference | [48] | |
NDAVI—Aquatic by Normalized Difference | [49] | |
WAVI—Adjusted to Water | [50] | |
sPRI—Photochemical Reflectance | [51] | |
CO2Flux1—Integrated | [52,53] | |
CO2Flux2—Integrated NDAVI | [39] |
Treatments | Legend | |
---|---|---|
Group 1 | Group 2 | |
SV | SVNL | Sample values obtained with a 50 × 50 cm sampler (SV); plot area equal to the sampler (0.25 m2); the same in NL |
SV1m2 | SV1m2NL | Sample values estimated based on the plot area of 1 m2 (SV1 m2); the same in NL |
SVPA | SVPANL | Sample values estimated based on plot area equal to the sensor pixel (SVPA), PS (3 m2) and S2 (20 m2); the same in NL |
AGB | |||||||
---|---|---|---|---|---|---|---|
Group | Sensor | Treatment | R2 | RMSE | RMSE% | RMSE OOB | RMSE OOB% |
G1 | S2 | SV | 0.85 | 21.46 | 12.35 | 39.60 | 22.75 |
SV1m2 | 0.85 | 87.55 | 12.65 | 157.26 | 22.58 | ||
SVPA | 0.85 | 34,246.41 | 12.33 | 58,938.28 | 20.98 | ||
PS | SV | 0.83 | 22.89 | 13.19 | 62.74 | 35.93 | |
SV1m2 | 0.86 | 85.19 | 12.31 | 163.32 | 23.49 | ||
SVPA | 0.84 | 804.33 | 12.81 | 1502.88 | 23.67 | ||
G2 | S2 | SVNL | 0.85 | 0.12 | 2.37 | 0.21 | 4.04 |
SV1m2NL | 0.83 | 0.13 | 1.95 | 0.22 | 3.34 | ||
SVPANL | 0.87 | 0.11 | 0.91 | 0.21 | 1.71 | ||
PS | SVNL | 0.85 | 0.12 | 2.37 | 0.22 | 4.24 | |
SV1m2NL | 0.85 | 0.12 | 1.83 | 0.21 | 3.17 | ||
SVPANL | 0.85 | 0.12 | 1.37 | 0.21 | 2.41 | ||
Corg | |||||||
G1 | S2 | SV | 0.89 | 7.41 | 10.39 | 16.17 | 19.71 |
SV1m2 | 0.79 | 41.83 | 14.39 | 57.38 | 22.41 | ||
SVPA | 0.84 | 14,228.77 | 12.50 | 24,846.43 | 21.73 | ||
PS | SV | 0.85 | 8.79 | 12.26 | 16.54 | 21.83 | |
SV1m2 | 0.84 | 36.51 | 12.71 | 63.21 | 21.88 | ||
SVPA | 0.84 | 318.91 | 12.27 | 573.27 | 23.02 | ||
G2 | S2 | SVNL | 0.86 | 0.12 | 2.73 | 0.21 | 5.08 |
SV1m2NL | 0.85 | 0.12 | 2.09 | 0.23 | 4.06 | ||
SVPANL | 0.86 | 0.12 | 1.00 | 0.20 | 1.70 | ||
PS | SVNL | 0.86 | 0.11 | 1.49 | 0.21 | 2.72 | |
SV1m2NL | 0.83 | 0.13 | 2.26 | 0.21 | 3.69 | ||
SVPANL | 0.85 | 0.12 | 2.67 | 0.21 | 5.02 |
AGB | |||||
---|---|---|---|---|---|
Sensor | Treatment | μObs | μPred | μOOB | |
G1 | S2 | SV | 172.61 | 173.78 | 174.05 |
SV1m2 | 658.32 | 660.43 | 664.10 | ||
SVPA | 276,178.96 | 277,714.74 | 280,909.88 | ||
PS | SV | 172.61 | 173.51 | 174.64 | |
SV1m2 | 658.32 | 660.09 | 663.16 | ||
SVPA | 6214.03 | 6278.38 | 6349.69 | ||
G2 | S2 | SVNL | 5.102 | 5.103 | 5.106 |
SV1m2NL | 6.488 | 6.502 | 6.504 | ||
SVPANL | 12.480 | 12.489 | 12.500 | ||
PS | SVNL | 5.102 | 5.107 | 5.116 | |
SV1m2NL | 6.488 | 6.493 | 6.497 | ||
SVPANL | 8.686 | 8.687 | 8.693 | ||
Corg | |||||
G1 | S2 | SV | 71.54 | 71.31 | 72.15 |
SV1m2 | 273.82 | 278.26 | 278.69 | ||
SVPA | 114,456.71 | 113,874.3 | 114,321.65 | ||
PS | SV | 71.54 | 71.69 | 71.87 | |
SV1m2 | 273.82 | 274.76 | 276.37 | ||
SVPA | 2575.28 | 2599.82 | 2625.71 | ||
G2 | S2 | SVNL | 4.223 | 4.222 | 4.221 |
SV1m2NL | 5.61 | 5.616 | 5.634 | ||
SVPANL | 11.601 | 11.605 | 11.616 | ||
PS | SVNL | 4.223 | 4.236 | 4.253 | |
SV1m2NL | 5.61 | 5.618 | 5.636 | ||
SVPANL | 7.807 | 7.814 | 7.824 |
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Belloli, T.F.; de Arruda, D.C.; Guasselli, L.A.; Cunha, C.S.; Korb, C.C. Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest. Land 2025, 14, 616. https://doi.org/10.3390/land14030616
Belloli TF, de Arruda DC, Guasselli LA, Cunha CS, Korb CC. Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest. Land. 2025; 14(3):616. https://doi.org/10.3390/land14030616
Chicago/Turabian StyleBelloli, Tássia Fraga, Diniz Carvalho de Arruda, Laurindo Antonio Guasselli, Christhian Santana Cunha, and Carina Cristiane Korb. 2025. "Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest" Land 14, no. 3: 616. https://doi.org/10.3390/land14030616
APA StyleBelloli, T. F., de Arruda, D. C., Guasselli, L. A., Cunha, C. S., & Korb, C. C. (2025). Modeling Wetland Biomass and Aboveground Carbon: Influence of Plot Size and Data Treatment Using Remote Sensing and Random Forest. Land, 14(3), 616. https://doi.org/10.3390/land14030616