# Inversion of Forest Biomass Based on Multi-Source Remote Sensing Images
## 3 Results
### Linear Regression Model Processing Results  
The results of the regression estimation model are shown in the table below (Table 2):  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline Factor & B2 & B3 & B4 & B5 & B6 & B7 \\ \hline Correlation coefficient & \(-0.342\) \({}^{\ast\ast}\) & \(-0.290\) \({}^{\ast\ast}\) & \(-0.302\) \({}^{\ast\ast}\) & \(-0.157\) \({}^{\ast}\) & \(-0.337\) \({}^{\ast\ast}\) & \(-0.307\) \({}^{\ast\ast}\) \\ \hline Factor & ARVI & DVI & EVI & NDPI & NDVI & RVI \\ \hline Correlation coefficient & \(0.250\) \({}^{\ast\ast}\) & \(0.249\) \({}^{\ast\ast}\) & \(-0.228\) \({}^{\ast\ast}\) & \(0.323\) \({}^{\ast\ast}\) & \(0.313\) \({}^{\ast\ast}\) & \(0.322\) \({}^{\ast\ast}\) \\ \hline Factor & SVAI & VH & VV & altitude & slope & canopy closure \\ \hline Correlation coefficient & \(0.310\) \({}^{\ast\ast}\) & \(-0.070\) & \(-0.004\) & \(0.404\) \({}^{\ast\ast}\) & \(-0.015\) & \(0.328\) \({}^{\ast\ast}\) \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & Mean & Variance & Homogeneity & Contrast & Dissimilarity & Entropy & Second & Correlation & ASM & MAX & Energy \\ \hline B2 & \(-0.310\) \({}^{\ast\ast}\) & \(-0.056\) & \(0.069\) & \(-0.036\) & \(-0.063\) & \(-0.087\) & \(0.083\) & \(0.107\) & & \\ \hline B3 & \(-0.271\) \({}^{\ast\ast}\) & \(-0.092  
The \(t\)-test significance levels of B6 Mean, VVEntropy and constant in the model were greater than 0.05; the significance levels of other variables' \(t\)-tests were less than 0.05; and the F-test significance level of the model was less than 0.001 for the whole model, indicating that the regression model passed the test. By comparing the absolute value of the standard coefficient, it can be found that RVI has the greatest impact on biomass, but the absolute value of the Pearson correlation test coefficient of RVI on biomass is not the largest, and the Pearson correlation test does not match the standard coefficient. On the one hand, this shows that the data distribution does not satisfy the normality assumption, and on the other hand, it shows that there is a linear correlation between the regression coefficients of the model. Therefore, regression model results use unstandardized coefficients.  
\[\begin{array}{l}\text{\it Biomass}=-243.422+0.09*\text{\it Altitude}+125.943* \text{\it Canopy\ closure}-156.917*\text{\it ARVI}+73.454*\text{\it EVI}\\ +148.34*\text{\it RVI}+34.447*\text{\it VVEntropy}+24.306*\text{\it B6 Mean}-24 5.049*\text{\it B6}\end{array} \tag{10}\]  
Through linear model prediction, the predicted value of forest biomass was compared with the real value, as shown in Figure 3.  
The linear model was used to measure the biomass of the sample plot, and the measured data were used as the real value of the sample plot to test the model. The comparison between the predicted value and the actual value of forest biomass in the test set is shown in Figure 3, and the coefficient of determination is 0.3625. The maximum relative error of linear simulation is 209.61 and the minimum relative error is \(-37.35\)  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Unstandardized Coefficient**} & \multirow{2}{*}{**Standardized Coefficient**} & \multirow{2}{*}{**t**} & \multirow{2}{*}{**Sig.**} \\ \cline{2-2} \cline{5-6}  & **B** & **Standard Error** & & & \\ \hline ARVI & \(-156.917\) & \(64.266\) & \(-0.472\) & \(-2.442\) & 0.016 \\ \hline EVI & 73.454 & 35.773 & 0.236 & 2.053 & 0.042 \\ \hline RVI & 148.340 & 43.052 & 0.676 & 3.446 & 0.001 \\ \hline VVEntropy & 34.447 & 18.391 & 0.127 & 1.873 & 0.063 \\ \hline B6 Mean & 24.306 & 13.932 & 0.417 & 1.745 & 0.083 \\ \hline B6 & \(-245.049\) & 93.419 & \(-0.656\) & \(-2.623\) & 0.010 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: A graph comparing the results of the linear model test set with the true values.  
After disposing the values with relative errors greater than 100% as outliers, the average deviation of the predicted values is calculated to be 32%.