# A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
## 4 Applications of Ensemble Learning Algorithms
### Estimation of Forest Structure Parameters and Biomass  
Forest structure parameters (e.g., forest height) and forest biomass are critical indicators of carbon stocks and are increasingly important in fields related to the carbon cycle and climate change [100]. [PERSON] et al. [76] compared a multiple linear regression (MLR) model, a neural network, SVR, kNN, and RF in estimating forest parameters in the province of Lugo (Galizia, Spain) from lidar data and found that MLR was outperformed by ML algorithms and that SVR with Gaussian kernels outperformed the other algorithms. [PERSON] et al. [69] used SVR, ANN, RF, and XGBoost to estimate tree dendrometry parameters such as volume, height and diameter at breast height from UAV-lidar point clouds. Their results showed that all models were robust, with relative RMSE <29% for volume, <9% for height and <15% for diameter at breast height; SVR performed the best in terms of minimal error rates. SVR outperformed the ensemble algorithms in both studies and gave the most accurate predictions. Various ensemble algorithms have been developed to estimate forest parameters. For example, [PERSON] et al. [67] used a two-stage model to derive forest canopy height and growing stock volume (GSV) in Chile from a combination of airborne laser scanner (ALS), PALSAR and Landsat data. They developed an RF model of canopy height and GSV using ALS-derived values that were validated with in situ measurements and then used a second RF model that used multitemporal PALSAR and Landsat data as predictor variables and ALS-based estimates as response variables. At three test sites, the retrieval of canopy height and GSV reached good accuracies with the R\({}^{2}\) of 0.70-0.85. [PERSON] et al. [96] developed a two-stage ensemble approach to increase the accuracy of forest GSV estimation. They selected variables using a collinearity test and ran four base learners (CART, kNN, SVR, and ANN) and combined them first using bagging and then using AdaBoost to generate eight ensemble models. The eight ensemble models were then aggregated using a weighted average method in which the weights were determined by the validated relative RMSE values of the eight ensemble models. The experimental results showed that the combined ensemble approach significantly reduced the uncertainty of GSV mapping from the Sentinel-1A and Sentinel-2A data, with relative RMSE values in the range 18.89-21.34%. [PERSON] et al. [73] used stochastic gradient boosting (SGB) to estimate the stand volume of eucalyptus plantations in South Africa from multisource data and found that SGB accurately predicted stand volume, with R\({}^{2}\) = 0.78 and RMSE = 33.16 m\({}^{3}\)/ha. These results were more accurate than the results given by RF or stepwise regression. [PERSON] et al. [28] comprehensively assessed the performance of eight algorithms (SVR, MARS, MLP, RF, ERT, SGB, GBRT, and CatBoost) in predicting forest biomass using several remote sensing datasets. Their results indicated that five ensemble algorithms (RF, ERT, SGB, GBRT and CatBoost) produced more accurate predictions than the other three individual algorithms and that CatBoost obtained slightly more accurate results than the other four ensemble algorithms, with R\({}^{2}\) = 0.72 and RMSE = 45.63 t/ha. In a subsequent study, they developed a stacking model to combine several accurate base learners to further increase the accuracy of biomass prediction, and the results indicated that the stacking ensemble increased prediction accuracy; in particular, it decreased the biases [58]. [PERSON] et al. [77] used a stacked set of ensemble algorithms (RF, GBM, and XGBoost) to predict the aboveground biomass of Indian mangroves from Sentinel-1 and Sentinel-2 time series. The results indicated that stacking increased AGB prediction accuracy with RMSE = 72.864 t/ha and relative RMSE = 11.38%. [PERSON] et al.