Abstract
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.
Highlights
Forests play an important role in the global carbon cycle for reducing carbon dioxide concentrations, which further mitigates the impact of global warming and climate change [1,2,3]
Our results suggest that SVI, NDVI, RVI, and PCA1 generated from Sentinel-2A data play an important role in the forest aboveground biomass (AGB) estimation compared to other vegetation indices in the study area
The results of this study indicate that the combination of the Sentinel-2A data and the ALOS-2 PALSAR-2 data significantly improved the estimation accuracy of the forest AGB
Summary
Forests play an important role in the global carbon cycle for reducing carbon dioxide concentrations, which further mitigates the impact of global warming and climate change [1,2,3]. Accurate measurement of forest carbon stocks and aboveground biomass (AGB) is considered key for understanding the global carbon cycle, for evaluating emissions from deforestation, and for regional, sustainable land-use planning [5,6]. These studies could be grouped into destructive methods and non-destructive methods Destructive approaches such as field measurements are considered to be the most accurate method for estimating AGB, they are time-consuming and costly. They may not be feasible for a large-scale analysis [7], in regions of dense and mixed forests (i.e., in tropical and subtropical mountainous areas). The development of accurate and low-cost models to estimate the forest biomass is still greatly needed to support global climate change mitigation programs such as the United Nations’ Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD+) scheme [8,9]
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