The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for improving the accuracy of the Aboveground Biomass (AGB) measurement. According to the current literature, this kind of investigation has rarely been conducted. The Hyrcanian forest area (Iran) is selected as the case study. For this purpose, a total of 149 sample plots for the study area were documented through fieldwork. Using the imagery, three datasets were generated including the Sentinel-2A dataset, the ALOS-2 PALSAR-2 dataset, and the combination of the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset (Sentinel-ALOS). Because the accuracy of the AGB estimation is dependent on the method used, in this research, four machine learning techniques were selected and compared, namely Random Forests (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MPL Neural Nets), and Gaussian Processes (GP). The performance of these AGB models was assessed using the coefficient of determination (R²), the root-mean-square error (RMSE), and the mean absolute error (MAE). The results showed that the AGB models derived from the combination of the Sentinel-2A and the ALOS-2 PALSAR-2 data had the highest accuracy, followed by models using the Sentinel-2A dataset and the ALOS-2 PALSAR-2 dataset. Among the four machine learning models, the SVR model (R² = 0.73, RMSE = 38.68, and MAE = 32.28) had the highest prediction accuracy, followed by the GP model (R² = 0.69, RMSE = 40.11, and MAE = 33.69), the RF model (R² = 0.62, RMSE = 43.13, and MAE = 35.83), and the MPL Neural Nets model (R² = 0.44, RMSE = 64.33, and MAE = 53.74). Overall, the Sentinel-2A imagery provides a reasonable result while the ALOS-2 PALSAR-2 imagery provides a poor result of the forest AGB estimation. The combination of the Sentinel-2A imagery and the ALOS-2 PALSAR-2 imagery improved the estimation accuracy of AGB compared to that of the Sentinel-2A imagery only.

Plantations established in highly-pollutant industrial areas have a crucial role to absorb greenhouse gases, particularly CO2. A thorough monitoring of their aboveground biomass and carbon balance is essential to ensure their beneficial effects. This can be operationally supported by using a combination of field and multispectral stereo remote sensing data to provide surface height information with high resolution and wide coverage. We estimated the fresh and dry aboveground biomass and the carbon sequestration from pairs of Pléiades satellite imagery of 25-year-old monoculture plantations of Pinus eldarica Medw., Cupressus arizonica Greene, Morus alba L. and Robinia pseudoacacia L., around the Mobarakeh Steel Complex near the megacity Isfahan. This complex is the largest-scale of its kind in semi-arid Iran. Tree heights were derived from a Canopy height model (CHM) at plantation management unit level. Parsimonious regression models were developed, and the accuracy was assessed by the coefficient of determination, bias and root mean square errors (RMSEs) at plot level. This resulted in R2 of total biomass, dry biomass, carbon sequestration, tree height and tree count of 0.90, 0.90, 0.91, 0.89, and 0.88, respectively. Moreover, mixed bias (with lowest value of -0.12 m for tree height) and NRMSE% (with lowest value of 5.93 % for tree carbon sequestration) values were obtained. The results demonstrated that pairs of stereo imageries can be effectively used for predicting forest biomass and carbon sequestration across semi-arid plantations, hence enabling a continuous monitoring of vegetation established around pollutant industrial areas.

The semi-Mediterranean Zagros forests in western Iran are a crucial source of environmental services, but are severely threatened by climatic and anthropological constraints. Thus, an adequate inventory of existing tree cover is essential for conservation purposes. We combined ground samples and Quickbird imagery for mapping the canopy cover in a portion of unmanaged Quercus brantii stands. Orthorectified Quickbird imagery was preprocessed to derive a set of features to enhance the vegetation signal by minimizing solar irradiance effects. A recursive feature elimination was conducted to screen the predictor feature space. The random forest (RF) and support vector machines (SVMs) were applied for modeling. The input datasets were composed of four sets of predictors including the full set of predictors, the four original Quickbird bands, selected vegetation indices, and the soil line-based vegetation indices. The highest $r2$ and lowest relative root mean square error (RMSE) were observed in modeling with total indices and the full data set in both modeling methods. Regardless of the input dataset used, the RF models outperformed the SVM by returning higher $r2$ and lower relative RMSEs. It can be concluded that applying these methods and vegetation indices can provide useful information for the retrieval of canopy cover in mountainous, semiarid stands which is crucial for conservation practices in such areas.