Mapping Forest Growing Stock and Its Current Annual Increment Using Random Forest and Remote Sensing Data in Northeast Italy
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Data
2.2.1. Third Italian National Forest Inventory
2.2.2. Digital Elevation Models and Derivatives
2.2.3. Sentinel-2 Data
3. Methods
3.1. Random Forest Application
3.2. Validation
4. Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
for each target variable (i.e. GSV, CAI) # Definition of the equation formula to be used by the model eq <- as.formula(dependant variable ~ covariate1 + covariate 2 + … + covariate n) # Estimation of the RF model, activating the option to calculate partial dependant plots RF_model <- randomForest(eq, data = NFI data, ntree = 1000, importance = T) # Spatialization of the results estimating the dependent variable for the entire area (i.e. at each cell of the grid 23 × 23 m covering all the forest area) predict (RF_model, grid) # LOO evaluation for each raw in the NFI database
LOO_rf <- randomForest(eq, data = NFI data except the raw, ntree = 1000)
predict(LOO_rf, raw) end for Calculating the parameters RMSE, RMSE% and R2 confronting the predicted values of the dependent variable using the model trained on the subsample which excludes that raw and the actual value of the target end for |
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Type | Variable | Year Reference | Resolution (m) |
---|---|---|---|
Digital Elevation Models and Derivatives from Lidar | Elevation [median] | Original LiDAR collected between 2017 and 2020 | 10 |
Slope [median] | 10 | ||
Aspect [median] | 10 | ||
CHM [mean, median] | 0.5 | ||
SENTINEL-2 DATA | NDVI, NDII, GNDVI, EVI | 2019–2021 June, July, August [median dataset] | 10 |
Attribute | LOO | FMU | ||||
---|---|---|---|---|---|---|
Attribute | R2 | RMSE | RMSE% | R2 | RMSE | RMSE% |
GSV | 0.67 | 96 | 40 | 0.84 | 118 | 64 |
CAI | 0.46 | 3 | 57 | 0.83 | 2 | 14 |
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Cadez, L.; Tomao, A.; Giannetti, F.; Chirici, G.; Alberti, G. Mapping Forest Growing Stock and Its Current Annual Increment Using Random Forest and Remote Sensing Data in Northeast Italy. Forests 2024, 15, 1356. https://doi.org/10.3390/f15081356
Cadez L, Tomao A, Giannetti F, Chirici G, Alberti G. Mapping Forest Growing Stock and Its Current Annual Increment Using Random Forest and Remote Sensing Data in Northeast Italy. Forests. 2024; 15(8):1356. https://doi.org/10.3390/f15081356
Chicago/Turabian StyleCadez, Luca, Antonio Tomao, Francesca Giannetti, Gherardo Chirici, and Giorgio Alberti. 2024. "Mapping Forest Growing Stock and Its Current Annual Increment Using Random Forest and Remote Sensing Data in Northeast Italy" Forests 15, no. 8: 1356. https://doi.org/10.3390/f15081356