FEASIBILITY ANALYSIS OF DEM DIFFERENTIAL METHOD ON TREE HEIGHT ASSESSMENT WITH TERRA-SAR/TANDEM-X DATA

摘要

DEM Differential Method is an effective and efficient way for forest tree height assessment with Polarimetric and interferometric technology, however, the assessment accuracy of it is based on the accuracy of interferometric results and DEM. Terra-SAR/TanDEM-X, which established the first spaceborne bistatic interferometer, can provide highly accurate cross-track interferometric images in the whole global without inherent accuracy limitations like temporal decorrelation and atmospheric disturbance. These characters of Terra-SAR/TandDEM-X give great potential for global or regional tree height assessment, which have been constraint by the temporal decorrelation in traditional repeat-pass interferometry. Currently, in China, it will be costly to collect high accurate DEM with Lidar. At the same time, it is also difficult to get truly representative ground survey samples to test and verify the assessment results. In this paper, we analyzed the feasibility of using TerraSAR/TanDEM-X data to assess forest tree height with current free DEM data like ASTER-GDEM and archived ground in-suit data like forest management inventory data (FMI). At first, the accuracy and of ASTER-GDEM and forest management inventory data had been assessment according to the DEM and canopy height model (CHM) extracted from Lidar data. The results show the average elevation RMSE between ASTER-GEDM and Lidar-DEM is about 13 meters, but they have high correlation with the correlation coefficient of 0.96. With a linear regression model, we can compensate ASTER-GDEM and improve its accuracy nearly to the Lidar-DEM with same scale. The correlation coefficient between FMI and CHM is 0.40. its accuracy is able to be improved by a linear regression model within confidence intervals of 95%. After compensation of ASTER-GDEM and FMI, we calculated the tree height in Mengla test site with DEM Differential Method. The results showed that the corrected ASTER-GDEM can effectively improve the assessment accuracy. The average assessment accuracy before and after corrected is 0.73 and 0.76, the RMSE is 5.5 and 4.4, respectively.