Application of High-Resolution Remote Sensing Image for Individual Tree Identification of Pinus sylvestris and Pinus tabulaeformis

Abstract

Aiming at applying unmanned aerial vehicle (UAV) remote sensing technology in extracting individual standing tree information, a new automatic single-tree information extraction method is proposed in this paper. The spectral information enhancement processing was performed on the original UAV image to highlight detailed local features; by importing DBI index, the optimal cluster number of the K -means clustering was automatically determined, and image pixels were then marked; Gauss Markov random field (GMRF) model was employed to segment the image further; by mathematical morphology, operators to postprocess the segmentation results to obtain the individual standing tree crown information, and individual standing tree position was calculated through image geometric moment as the basis for its identification. The results show that with the proposed extraction method, the overall accuracy of standing tree identification for the Pinus sylvestris and Pinus tabulaeformis forest areas are 95.65% and 89.52%; the single-tree crown extraction accuracy is 95.65% and 81.90%, respectively. This method exhibits good applicability while it does not require a large amount of manual intervention and prior knowledge, which significantly improves the automation of information extraction.