The Influence of Widow Size on Remote Sensing-Based Prediction of Forest Structural Variables

Abstract

\n Background: Determining the appropriate window size is a critical step for estimating stand structural variables based on remote sensing data. Because the value of the reference laser and image metrics that affect the quality of the prediction model depends on window size. However, suitable window sizes are usually determined by trial and error. There are a limited number of published studies evaluating appropriate window sizes for different remote sensing data. This research investigated the effect of window size on predicting forest structural variables using airborne LiDAR data, digital aerial image and WorldView-3 satellite image.Results: In the WorldView-3 and digital aerial image, significant differences were observed in the prediction accuracies of the structural variables according to different window sizes. For the estimation based on WorldView-3 in black pine stands, the optimal window sizes for stem number (N), volume (V), basal area (BA) and mean height (H) were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. In oak stands, the R2 values of each moving window size were almost identical for N and BA. The optimal window size was 400 m2 for V and 600 m2 for H. For the estimation based on aerial image in black pine stands, the 800 m2 window size is optimal for N and H, the 600 m2 window size is optimal for V and the 1000 m2 window size is optimal for BA. In the oak stands, the optimal window sizes for N, V, BA and H were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. The optimal window sizes may need to be scaled up or down to match the stand canopy components. In the LiDAR data, the R2 values of each window size were almost identical for all variables of the black pine and the oak stands.Conclusion: This study illustrated that the window size has an effect on the prediction accuracy in estimating forest structural variables based on remote sensing data. Moreover, the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.