Height measurement of Cunninghamia lanceolata plantations based on UAV remote sensing

Abstract

Tree height, an important parameter in a forest resource survey, has been problematic in traditional methods of a forest survey making it difficult and inefficient to conduct further investigations. To utilize the rapid development, in recent years, of unmanned aerial vehicle (UAV) technology as a means for quickly estimating the height of forest trees, tree height data were obtained from Cunninghamia lanceolata plantations in Minqing County, Fujian Province. Remote sensing imagery in the study area was obtained through the Eco Drone-UA drone remote sensing system, setting the flight altitude to 120 m and the flight belt overlap to 50%. Pix4D Mapper software was used to preprocess aerial multispectral images and build a DSM (Digital Surface Model) using kriging interpolation to obtain a DEM (Digital Elevation Model). Based on the estimated idea of the canopy height model (CHM)=digital surface model (DSM)-digital elevation model (DEM), the tree height of C. lanceolata was extracted. Results showed that combining the vegetation index, multispectral bands, and random forest algorithm were effective in identifying the true crown vertex, and it was feasible to use high resolution UAV imagery to extract tree height. The minimum relative error for tree height was 0.81%, the maximum was 23.48%, the estimation accuracy was 90.8%, and the standard error was 1.48 m. At the same time, the measurement of tree height was affected by the DEM with the R2 and root mean squared error (RMSE) value for the least DEM being R2=0.781, RMSE=1.99 m, for the next one was R2=0.84, RMSE=1.17 m, and for the largest it was R2=0.966, RMSE=0.67 m. The accuracy of the measured height of the larger DEM was higher than that of the smaller DEM. Therefore, This approach integrates UVA with random forest, which makes up of the shortcomings of each. In addition, the results also provide a reference guidelines for the tree height.