Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data
2.2.1. Field Dataset
2.2.2. Remote Sensing Dataset
2.3. Data Analyses
3. Results
3.1. Relationships between Initial and Post-Fire Biomass Stocks
3.2. Relationships between Spectral Indices and Structural Changes
3.3. Biomass Loss Modelling
4. Discussion
4.1. Post-Fire Biomass Losses Accounting for Spatial Variability
4.2. Spectral Indices Are Sensitive to Accumulated Tree Mortality
4.3. Spectral Indices’ Changes for Predicting Biomass Losses
4.4. Limitations of This Study
4.5. Implications for Future Studies
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Tree State | Size Class (DBH) | Mean BA (± s.d.) (m2 ha−1) | p-Value | ||
---|---|---|---|---|---|
Burned | Unburned | Welch’s t Test | Mann-Whitney U Test | ||
dead | 10− | 0.49 (±0.26) | 0.26 (±0.21) | 0.181 ns | 0.053 m |
20− | 0.32 (±0.31) | 0.33 (±0.28) | - | 0.888 ns | |
30− | 0.47 (±0.57) | 0.7 (±0.7) | - | 0.538 ns | |
40− | 0.16 (±0.29) | 1.3 (±0.37) | - | 0.001 * | |
50+ | 0.53 (±0.87) | 0.38 (±0.62) | - | 0.867 ns | |
all | 1.97 (±0.82) | 2.97 (±1) | 0.048 * | 0.067 m | |
live | 10− | 4.68 (±0.67) | 4.39 (±0.57) | 0.440 ns | 0.553 ns |
20− | 4.77 (±0.88) | 4.68 (±0.62) | 1.000 ns | 1.000 ns | |
30− | 4.2 (±1.41) | 4.5 (±1.49) | 0.665 ns | 0.682 ns | |
40− | 3.08 (±1.21) | 2.26 (±0.36) | 0.061 m | 0.083 m | |
50+ | 6.84 (±5.24) | 4.56 (±3.55) | - | 0.494 ns | |
all | 23.56 (±5.23) | 20.39 (±2.86) | - | 0.151 ns |
Post-Fire Interval | Predictors | R2 | RMSE | RMSE(%) |
---|---|---|---|---|
1-year | initial AGB | 0.65 | 0.42 | 80.3 |
initial AGB + ∆NBR + ∆GV + ∆Shade | 0.69 | 0.43 | 82.9 | |
initial AGB + ∆NPV + ∆GV + ∆Shade | 0.68 | 0.44 | 84.1 | |
∆NBR + ∆GV + ∆Shade | 0.65 | 0.44 | 84.2 | |
∆NBR + ∆NPV + ∆GV + ∆Shade | 0.65 | 0.44 | 85.0 | |
∆NPV + ∆GV + ∆Shade | 0.63 | 0.45 | 86.9 | |
2-year | initial AGB + ∆NBR + ∆GV + ∆Shade | 0.75 | 0.54 | 67.9 |
initial AGB + ∆NPV + ∆GV + ∆Shade | 0.75 | 0.54 | 68.1 | |
∆NBR + ∆GV + ∆Shade | 0.74 | 0.55 | 69.6 | |
∆NPV + ∆GV + ∆Shade | 0.74 | 0.55 | 69.7 | |
∆NBR + ∆NPV + ∆GV + ∆Shade | 0.74 | 0.57 | 71.0 | |
initial AGB | 0.60 | 0.64 | 81.0 | |
3-year | ∆NBR + ∆NPV + ∆GV + ∆Shade | 0.71 | 0.76 | 86.4 |
∆NBR + ∆GV + ∆Shade | 0.71 | 0.77 | 87.3 | |
∆NPV + ∆GV + ∆Shade | 0.71 | 0.78 | 88.1 | |
initial AGB + ∆NBR + ∆GV + ∆Shade | 0.70 | 0.80 | 90.9 | |
initial AGB + ∆NPV + ∆GV + ∆Shade | 0.71 | 0.81 | 91.8 | |
initial AGB | 0.55 | 0.87 | 98.8 |
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Pontes-Lopes, A.; Dalagnol, R.; Dutra, A.C.; de Jesus Silva, C.V.; de Alencastro Graça, P.M.L.; de Oliveira e Cruz de Aragão, L.E. Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data. Remote Sens. 2022, 14, 1545. https://doi.org/10.3390/rs14071545
Pontes-Lopes A, Dalagnol R, Dutra AC, de Jesus Silva CV, de Alencastro Graça PML, de Oliveira e Cruz de Aragão LE. Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data. Remote Sensing. 2022; 14(7):1545. https://doi.org/10.3390/rs14071545
Chicago/Turabian StylePontes-Lopes, Aline, Ricardo Dalagnol, Andeise Cerqueira Dutra, Camila Valéria de Jesus Silva, Paulo Maurício Lima de Alencastro Graça, and Luiz Eduardo de Oliveira e Cruz de Aragão. 2022. "Quantifying Post-Fire Changes in the Aboveground Biomass of an Amazonian Forest Based on Field and Remote Sensing Data" Remote Sensing 14, no. 7: 1545. https://doi.org/10.3390/rs14071545