Abstract:Accurate estimation of forest leaf area index (LAI), which is defined as half the total area of green leaves per unit ground surface area, is the important embodiment of precision forestry. In order to monitor forest LAI faster, more accurate and non-destructively, LAI—2200 plant canopy analyzer was used to acquire LAI data from the forest plots in western Fujian. Totally 12 kinds of vegetation index based on the Pleiades satellite images in the same period were calculated and the correlation between measured LAI and the vegetation index was analyzed. The purpose was to construct LAI estimation model specifically by using random forest algorithm (RF). Additionally for each sample group, the models based on support vector regression model (SVR) and back-propagation neural network model (BP) were employed as comparison models. The estimation accuracy of the three models for each sample group was compared based on determination coefficients (R2), root mean square errors (RMSE), mean relative errors (MAE) and relative percent deviation (RPD). The results indicated that the vegetation indices and LAI values were significantly correlated (P<0.01), and the correlation coefficients were greater than 0.4 for all sample data. The forecast accuracy of RF model in three different sample groups was higher than those of the SVR and BP models in the same period. R2 of LAI estimated and measured values in the three sample groups based on RF model were 0.688, 0.796 and 0.707, respectively. RPD were 1.653, 1.984 and 1.731, respectively. These data were all higher than those of SVR model and BP model, and RF model showed a higher accuracy than the other two models (RMSE of RF model were 0.509, 0.658 and 0.696, respectively;MAE were 0.417, 0.414 and 0.466, respectively). These results would be helpful for improving the forest LAI remote sensing estimation accuracy.
