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.
Highlights
Precise quantification of forest aboveground biomass (AGB) on a regional to global scale is of increasing importance in the context of reducing emissions from deforestation and forest degradation in developing countries (REDD+) and compliance with the Kyoto Protocol [1,2,3]
In our study, using the single variables derived from the PALSAR/ALOS and WorldView-2 data explained only approximately 20% to 50% of the variance in the plot-level measurements for the forest biomass (Table 2)
The results from this study suggest that the standard normalized difference vegetation index (NDVI) calculated from band 7 (NIR1, 0.77–0.90 μm) and band 5 (Red, 0.63–0.69 μm) of the WorldView-2 data had the lowest correlation with the surveyed AGB in the 4 NDVIs
Summary
Precise quantification of forest aboveground biomass (AGB) on a regional to global scale is of increasing importance in the context of reducing emissions from deforestation and forest degradation in developing countries (REDD+) and compliance with the Kyoto Protocol [1,2,3]. In a forest inventory, the sample plotting method provides very accurate AGB values at the plot level [4]. Due to the high cost of this traditional plot-based investigation for AGB and the difficulties of its implementation in remote areas, interest in the use of remotely sensed data acquired from spaceborne or airborne sensors to estimate forest AGB has increased in recent decades. Remote sensing provides a key source of data for updated, consistent, and spatially explicit assessment of forest biomass and its dynamics, in large countries with limited accessibility [5,6]. A major limitation of vegetation indices is that these indices reach a saturation level during the estimation of high-density biomass [13,14,15]. The saturation point varies greatly depending on the source data and the vegetation type and ranges from 15 to
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