Estimation of forest structural parameters based on stand structure response and PALSAR data

To improve the estimation accuracy of forest structural parameters with ALOS PALSAR data, we introduced adjusted entropy (ENTadj) which represents the complexity of stand structure for the estimation. Thus, the interference of radar backscattering coefficient caused by stand structure could be eliminated. Firstly, the ENTadj of stand was defined by the measured tree heights in sample plots of the field. And then, the ENTadj based on pixels was calculated by linear regression model established with the integration of the ENTadj of stand and Landsat 8 OLI band 6. Commonly, the relationship between stand structural parameters and ALOS PALSAR backscattering coefficient could be simulated by a logarithm regression model. In this research, ENTadj based on pixel was introduced as a new independent variable to improve the original logarithm model. Three types of improved models were established for stand mean tree height, stand mean DBH and stand stock volume respectively. The original model and three improved models were used to estimate the above stand structural parameters for Cunninghamia lanceolata stand, Pinus massoniana stand, broadleaf stand and mixed stand. Ultimately, optimal estimating models for each stand structural parameter in the four types of stands were selected by comparing R2 (coefficient of determination) with totally 12 results. The results showed that the R2 of models in radar estimating stand structural parameters increased after considering the influence of stand structure, and the R2 of entire optimal stand models for P. massoniana stand increased the most. The results of accuracy examination revealed that there were desired precisions for estimating tree height (RMSE: 0.74-2.51 m), DBH (RMSE: 2.61-5.61 cm) and stock volume (RMSE: 21.71-30.92 m3/ha). This study explored the potential of applying stand structure information in forest structural parameters, and increased the ability to estimate the forest structural parameters by combining the optical and radar remote sensing data.