Biomass Prediction Using Sentinel-2 Imagery and an Artificial Neural Network in the Amazon/Cerrado Transition Region
Abstract
:1. Introduction
2. Materials and Methods
2.1. Regional Setting
2.2. Dendrometric Variables of the Inventory
2.3. Forest Biomass
2.4. Sentinel-2 Imagery
2.5. Vegetation Indices
- Normalized Difference Vegetation Index (NDVI)
- Enhanced Vegetation Index (EVI)
- Enhanced Vegetation Index 2 (EVI 2)
- GNDV (Green Normalized Difference Vegetation Index)
- AFRI (Aerosol Free Vegetation Index)
- SAVI (Soil-Adjusted Vegetation Index)
- MSAVI (Modified Soil-Adjusted Vegetation Index)
- MSAVIaf (Modified Soil-Adjusted Vegetation Index aerosol free
- NDRE (Normalized Difference Red Edge Index)
2.6. Correlation Analysis
2.7. Modeling of the Artificial Neural Network (ANN)
3. Results
3.1. Vegetation Inventory
3.2. Correlation Analysis of Biomass and Vegetation Indices
3.3. Biomass Modeling
3.4. Statistical Analysis
3.5. Analyzing the Spatial Distribution of Biomass
4. Discussion
4.1. Forest Biomass and Land Use and Land Cover
4.2. Selection of Independent Variables (Vegetation Indices)
4.3. Training the Neural Networks
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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ID Sentinel-2A, Sensor MSI | Data |
---|---|
20160807T135257_T22LBL | 7 August 2016 |
20180802T135108_T22LCL | 2 August 2018 |
20200801T135115_T22LBL | 1 August 2020 |
20200803T134216_T22LCL | 3 August 2020 |
20210813T134211_T22LDL | 13 August 2021 |
Cerrado Plots | Forest Plots | ||||||||
---|---|---|---|---|---|---|---|---|---|
Statistics | DBH | Ht | WD | AGB | Statistics | DBH | Ht | WD | AGB |
Minimum | 10.0 | 4.0 | 0.41 | 14.35 | Minimum | 10.0 | 10.0 | 0.20 | 66.56 |
Maximum | 39.0 | 13.0 | 0.84 | 23.49 | Maximum | 93.2 | 30.0 | 1.09 | 331.38 |
Mean | 13.81 | 6.64 | 0.66 | 18.38 | Mean | 19.12 | 13.99 | 0.67 | 146.86 |
Variance | 15.38 | 1.65 | 0.01 | 10.97 | Variance | 105.19 | 17.5 | 0.019 | 2572.76 |
Deviation | 3.92 | 1.28 | 0.10 | 3.31 | Deviation | 10.25 | 4.18 | 0.14 | 50.72 |
CV (%) | 28.4 | 19.33 | 15.43 | 18.02 | CV (%) | 53.64 | 29.9 | 20.4 | 34.54 |
Vegetation Indices | Average |
---|---|
AFRI | 0.564 |
EVI | 0.571 |
GNDVI | 0.569 |
EVI2 | 0.392 |
MSAVIaf | 0.324 |
MSAVI | 0.545 |
NDRE | 0.515 |
NDVI | 0.697 |
SAVI | 0.408 |
AFRI | EVI | EVI2 | GNDVI | MSAVIaf | MSAVI | NDRE | NDVI | SAVI | Biomass | |
---|---|---|---|---|---|---|---|---|---|---|
AFRI | 1 | |||||||||
EVI | 0.887 ** | 1 | ||||||||
EVI2 | 0.875 ** | 0.963 ** | 1 | |||||||
GNDVI | 0.897 ** | 0.902 ** | 0.951 ** | 1 | ||||||
MSAVIaf | 0.948 ** | 0.963 ** | 0.969 ** | 0.950 ** | 1 | |||||
MSAVI | 0.868 ** | 0.975 ** | 0.933 ** | 0.866 ** | 0.942 ** | 1 | ||||
NDRE | 0.954 ** | 0.853 ** | 0.882 ** | 0.921 ** | 0.922 ** | 0.825 ** | 1 | |||
NDVI | 0.972 ** | 0.909 ** | 0.902 ** | 0.907 ** | 0.943 ** | 0.887 ** | 0.971 ** | 1 | ||
SAVI | 0.831 ** | 0.853 ** | 0.889 ** | 0.863 ** | 0.907 ** | 0.850 ** | 0.786 ** | 0.820 ** | 1 | |
Biomass | 0.469 * | 0.443 * | 0.532 ** | 0.621 ** | 0.555 ** | 0.404 * | 0.509 ** | 0.466 * | 0.594 ** | 1 |
ANN | Architecture | Activation | Activation | Adjustment | Validation | Test | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Nº of Cycles | RMSE% | R | RMSE% | R | RMSE% | R | ||||||
Hidden | Output | |||||||||||
1 | MLP 3-12-1 | 860 | Tang | Tang | 18.09 | 0.93 | 15.76 | 0.94 | 15.92 | 0.94 | ||
2 | MLP 3-11-1 | 1630 | Logistic | Exponential | 19.44 | 0.92 | 16.09 | 0.93 | 16.18 | 0.93 | ||
3 | MLP 3-8-1 | 910 | Logistic | Identity | 19.77 | 0.92 | 16.41 | 0.93 | 16.92 | 0.93 | ||
4 | MLP 3-13-1 | 950 | Tang | Exponential | 19.53 | 0.92 | 16.62 | 0.93 | 16.91 | 0.93 | ||
5 | MLP 3-11-1 | 670 | Logistic | Identity | 20.19 | 0.91 | 17.91 | 0.91 | 17.12 | 0.91 | ||
ANN | Predictor variables | Neurons per layer | Adjust | |||||||||
Input | Hidden | Output | TI | SI | AI | Algorithm | ||||||
1 | AFRI, EVI, GNDVI | 3 | 12 | 1 | 0.08 | 0.08 | 0.09 | BFGS | ||||
2 | AFRI, EVI, GNDVI | 3 | 9 | 1 | 0.10 | 0.11 | 0.12 | BFGS | ||||
3 | AFRI, EVI, GNDVI | 3 | 5 | 1 | 0.10 | 0.12 | 0.13 | BFGS | ||||
4 | AFRI, EVI, GNDVI | 3 | 13 | 1 | 0.11 | 0.13 | 0.10 | BFGS | ||||
5 | AFRI, EVI, GNDVI | 3 | 7 | 1 | 0.13 | 0.15 | 0.17 | BFGS |
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Faria, L.D.d.; Matricardi, E.A.T.; Marimon, B.S.; Miguel, E.P.; Junior, B.H.M.; Oliveira, E.A.d.; Prestes, N.C.C.d.S.; Carvalho, O.L.F.d. Biomass Prediction Using Sentinel-2 Imagery and an Artificial Neural Network in the Amazon/Cerrado Transition Region. Forests 2024, 15, 1599. https://doi.org/10.3390/f15091599
Faria LDd, Matricardi EAT, Marimon BS, Miguel EP, Junior BHM, Oliveira EAd, Prestes NCCdS, Carvalho OLFd. Biomass Prediction Using Sentinel-2 Imagery and an Artificial Neural Network in the Amazon/Cerrado Transition Region. Forests. 2024; 15(9):1599. https://doi.org/10.3390/f15091599
Chicago/Turabian StyleFaria, Luana Duarte de, Eraldo Aparecido Trondoli Matricardi, Beatriz Schwantes Marimon, Eder Pereira Miguel, Ben Hur Marimon Junior, Edmar Almeida de Oliveira, Nayane Cristina Candido dos Santos Prestes, and Osmar Luiz Ferreira de Carvalho. 2024. "Biomass Prediction Using Sentinel-2 Imagery and an Artificial Neural Network in the Amazon/Cerrado Transition Region" Forests 15, no. 9: 1599. https://doi.org/10.3390/f15091599