Impacts of Position Errors on Accuracy of Single Tree Volume Inversion of Cunninghamia lanceolata based on GF-2 Data

摘要

Combing forest inventory sample plot data and remotely sensed images has been rarely applied to single tree volume inversion by spatial interpolation methods such as regression and spatial simulation, With the increase of the resolution of remote sensing image, the inversion of single tree volume is possible. However, various uncertainty factors seriously affect the inversion accuracy, and the position error is the most deadly uncertainty factor in the single-tree volume inversion. In this study, 909 Cunninghamia lanceolata measured in 15 sample plots of middle-aged planted in the experimental area of Huangfengqiao Forest Farm in Hunan Province were selected as the research object. Multivariate Stepwise Regression, Partial Least-Squares Regression and BP Neural Network were used to establish the estimation model of Cunninghamia lanceolata volume based on GF-2 images. The position error is simulated through the location of moving tree. Using a random array, each tree can be randomly divided into 8 directions (east, west, south, north, northeast, northwest, southwest, and southeast), and the moving distance is 1m, 2m, until 10m in sequence. The inversion and accuracy evaluation of Cunninghamia lanceolata volume with three established models for each movement. The results show that the inversion accuracy decreases slowly with the offset distance within 2m. When the offset exceeds the 2m, the inversion accuracy drops sharply, and then subtle fluctuations occur, maintaining a steady state with low accuracy. The three models showed the same trend. More importantly, it was found that there is a spatial autocorrelation between the blades, which can effectively alleviate the effect of the position error on the inversion accuracy in the crown. But when the position error exceeds the crown, the inversion of single tree volume is completely meaningless.