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Open AccessArticle

Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)

1
Faculty of Agriculture and Natural Resources, Lorestan University, Khorramabad, Lorestan 68151-44316, Iran
2
Department of Social Informatics, Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan
3
Center for Agricultural Research and Ecological Studies (CARES), Vietnam National University of Agriculture (VNUA), Trau Quy, Gia Lam, Hanoi 10000, Vietnam
4
Geographic Information System Group, Department of Business and IT, University College of Southeast Norway, Gullbringvegen 36, N-3800 Bø i Telemark, Norway
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 172; https://doi.org/10.3390/rs10020172
Received: 28 October 2017 / Revised: 13 January 2018 / Accepted: 23 January 2018 / Published: 25 January 2018
(This article belongs to the Special Issue Biomass Remote Sensing in Forest Landscapes)
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. View Full-Text
Keywords: aboveground biomass; ALOS-2 PALSAR-2; Sentinel-2A; random forest; support vector regression; gaussian processes; neural networks; Hyrcanian forest; Iran aboveground biomass; ALOS-2 PALSAR-2; Sentinel-2A; random forest; support vector regression; gaussian processes; neural networks; Hyrcanian forest; Iran
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Vafaei, S.; Soosani, J.; Adeli, K.; Fadaei, H.; Naghavi, H.; Pham, T.D.; Tien Bui, D. Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran). Remote Sens. 2018, 10, 172.

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