The influence of window size on remote sensing-based prediction of forest structural variables

Abstract

Background Determining the appropriate window size is a critical step in the estimation process of 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 was optimal for N and H, the 600 m2 window size was optimal for V and the 1000 m2 window size was 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.

The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture

“…Tis is in agreement with Kayitakire et al [15], who found that a small window size better predicted tree height in a low-diversity spruce forest. In contrast, Ozkan and Demirel [30] detected an inverse pattern between these two variables in monospecifc temperate forest stands. Ultimately, information collected at one scale for a given ecosystem may be totally inappropriate for addressing questions at another scale or a diferent ecosystem [75,76], thus reinforcing the need for multiscalar sampling designs capable of capturing forest community variation on its diferent attributes [77].…”

“…Tis is relevant because it entails that, when setting a study that aims at predicting diferent forest attributes from remote sensing, it is necessary to identify the proper scale both for feld measurements and texture window that best predicts these attributes [72,73]. However, there is no consensus on the plot-window size for each variable, since this variation seems to respond to diferences in the type of forest being studied, the resolution of the images used, and the forest attributes being quantifed [29,30,74]. For instance, our results showed that mean tree height prediction peaks at small plot-window sizes, whereas basal area is best predicted at large plot-window sizes.…”

“…In addition, these results vary signifcantly in the goodnessof-ft obtained by the predictive models (0.2 < R 2 < 0.98). Some studies suggest that the variation present in a particular attribute can help explain the modelling potential using texture metrics [24,28], while others suggest that these variations respond to the type of vegetation and attributes being studied [29,30].…”

“…In spite of these promising results, there is still a third major conclusion derived from these studies that calls for a refection: this procedure appears to be highly sensitive to the scale of analysis, both in image resolution and feld-plot size [21,24,[30][31][32]. Tis situation hinders the possibility of building general models based solely on single-scale studies.…”

“…Attempts to develop such models for GLCM metrics based on a random sampling procedure have yielded satisfactory results [29,30,41]. Other approaches have used multiscale GLCM metrics to increase the performance of these textures for particular classifcation tasks [42,43].…”