# Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images
## 5 Conclusions  
This paper takes forest biomass inversion in Liaoning Province as the research object, extracts remote sensing factors through remote sensing data processing, combines topographic data (elevation, slope) and forest sample plot data, discusses the influencing factors of biomass in Liaoning Province and compares three different models to construct an optimal spatial distribution map of biomass in Liaoning Province. The main conclusions are as follows:  
1. The correlation between altitude and biomass was the highest, and the correlation coefficient was 0.404. The B2 band of Landsat 8 and the NDPI characteristic quantity of the vegetation index have important correlations with forest biomass inversion, and the correlation coefficients are \(-\)0.342 and 0.323, respectively. Among the characteristic factors extracted from Sentinel-1A radar images, seven factors were correlated with biomass (\(p<0.05\)), and five factors were negatively correlated, except entropy.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **R\({}^{2}\)** & **Time** \\ \hline Stepwise regression model & 0.5468 & 0.0862 s \\ \hline BP neural network model & 0.78226 & 0.13 s \\ \hline POS improved neural network model & 0.83204 & 235.16 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of prediction accuracy and training time of different models.  
2. Comparing the inversion accuracy and training speed of the three models, the model based on the linear stepwise regression method has the fastest training speed, but the lowest model accuracy. The BP neural network has a strong fitting ability with complex data, a short training time and high model accuracy. Although the training time of the PSO improved neural network model is longer, the coefficient of determination between the predicted value and the measured value is the highest. The results show that there is a nonlinear relationship between the biomass and the strong correlation factors. The neural network model based on the particle swarm optimization algorithm is the best model for forest biomass inversion in the Liaoning region.
3. According to the spatial distribution map of biomass, the areas with high forest biomass in Liaoning Province are mainly distributed in areas with high altitudes and steep slopes in the east and southwest, while the areas with low biomass are mainly concentrated in the plain areas with low altitudes and gentle slopes.  
Writing--original draft, H.N.; Writing--review & editing, D.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [Scientific Research Project of the Department of Education of Liaoning Province, \"Research on Key Issues of Building 3D Reconstruction Based on Open Space Multi-Source Data Fusion\"] grant number [Injc202015]. And The APC was funded by [School of Transportation and Surveying Engineering, Shenyang Jianzhu University].  
Not applicable.  
Because I signed a confidentiality agreement with the National Center for Ecological Sciences, I cannot share the forest information. Sources of remote sensing data have been described above.  
The authors declare no conflict of interest.