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Remote Sens. 2014, 6(9), 7878-7910; doi:10.3390/rs6097878

Estimating Forest Aboveground Biomass by Combining ALOS PALSAR and WorldView-2 Data: A Case Study at Purple Mountain National Park, Nanjing, China

1
Forest Measurement and Planning Laboratory, Agriculture Faculty, Shinshu University, 8304, Minamiminowa-Vill., Kamiina-Dtrct., Nagano Pref. 399-4598, Japan
2
Forest Resources and Environment Faculty, Nanjing Forestry University, Nanjing 210037, China
3
Forest Environment and Ecology Laboratory, Agriculture Faculty, Shinshu University, 8304, Minamiminowa-Vill., Kamiina-Dtrct., Nagano Pref. 399-4598, Japan
*
Author to whom correspondence should be addressed.
Received: 14 May 2014 / Revised: 9 August 2014 / Accepted: 12 August 2014 / Published: 25 August 2014
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Abstract

Enhanced methods are required for mapping the forest aboveground biomass (AGB) over a large area in Chinese forests. This study attempted to develop an improved approach to retrieving biomass by combining PALSAR (Phased Array type L-band Synthetic Aperture Radar) and WorldView-2 data. A total of 33 variables with potential correlations with forest biomass were extracted from the above data. However, these parameters had poor fits to the observed biomass. Accordingly, the synergies of several variables were explored to identify improved relationships with the AGB. Using principal component analysis and multivariate linear regression (MLR), the accuracies of the biomass estimates obtained using PALSAR and WorldView-2 data were improved to approximately 65% to 71%. In addition, using the additional dataset developed from the fusion of FBD (fine beam dual-polarization) and WorldView-2 data improved the performance to 79% with an RMSE (root mean square error) of 35.13 Mg/ha when using the MLR method. Moreover, a further improvement (R2 = 0.89, relative RMSE = 17.08%) was obtained by combining all the variables mentioned above. For the purpose of comparison with MLR, a neural network approach was also used to estimate the biomass. However, this approach did not produce significant improvements in the AGB estimates. Consequently, the final MLR model was recommended to map the AGB of the study area. Finally, analyses of estimated error in distinguishing forest types and vertical structures suggested that the RMSE decreases gradually from broad-leaved to coniferous to mixed forest. In terms of different vertical structures (VS), VS3 has a high error because the forest lacks undergrowth trees, while VS4 forest, which has approximately the same amounts of stems in each of the three DBH (diameter at breast height) classes (DBH > 20, 10 ≤ DBH ≤ 20, and DBH < 10 cm), has the lowest RMSE. This study demonstrates that the combination of PALSAR and WorldView-2 data is a promising approach to improve biomass estimation. View Full-Text
Keywords: forest biomass estimation; PALSAR; WorldView-2 data; synergy; stepwise regression; neural network model forest biomass estimation; PALSAR; WorldView-2 data; synergy; stepwise regression; neural network model
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Deng, S.; Katoh, M.; Guan, Q.; Yin, N.; Li, M. Estimating Forest Aboveground Biomass by Combining ALOS PALSAR and WorldView-2 Data: A Case Study at Purple Mountain National Park, Nanjing, China. Remote Sens. 2014, 6, 7878-7910.

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