Pléiades satellite images for deriving forest metrics in the Alpine region

Abstract

The landscape-human relationships on the Alps, the more populated mountain region globally, depend on tree species diversity, their canopy height and canopy gaps (soil cover). The monitoring of such forest information plays an important role in forest management planning and therefore in the definition of present and future mountain forest services. In order to gain wide scale and high-resolution forest information, very high-resolution (VHR) stereo satellite imagery has the main benefit of covering large areas with short repetition intervals. However, the application of this technology is not fully assessed in terms of accuracy in dynamic year-around forest conditions. In this study, we investigate on four study sites in the Swiss Alps 1) the accuracy of forest metrics in the Alpine forests derived from VHR Pléiades satellite images and 2) the relation of associated errors with shadows, terrain aspect and slope, and forest characteristics. We outline a grid-based approach to derive the main forest metrics (descriptive statistics) from the canopy height models (CHMs) such as the maximum height (Hmax), height percentiles (Hp95, Hp50), the standard deviation of the height values (HStd) and canopy gaps. The Pléiades-based forest metrics are compared with those obtained by aerial image matching, a technology operationally used for deriving this information. For the study site with aerial and satellite images acquired almost at the same time, this comparison shows that the medians of Pléiades forest metrics error are -0.25 m (Hmax), 0.33 m (Hp95), −0.03 m (HStd) and -5.6% for the canopy gaps. The highest correlation (R2 = 0.74) between Pléiades and aerial canopy gaps is found for very bright areas. Conversely, in shadowed forested areas a R2 of only 0.16 is obtained. In forested areas with steep terrain (>50°), Pléiades forest metrics show high variance for all the study areas. Concerning the canopy gaps in these areas, the correlation between Pléiades and the reference data provides a correlation value of R2 = 0.20, whereas R2 increases to 0.66 for gently sloped areas (10-20°). The aspect does not provide a significant correlation with the accuracy of the Pléiades forest metrics. However, the extended shadowed mainly on north/northwest facing slopes caused by trees or terrain shade negatively affect the performance of stereo dense image matching, and hence the forests metrics. The occurrence of strong shadows in the forested areas increases dramatically by ˜40% in the winter season due to the lower sun elevation. Furthermore, due to the leaf-off condition in the winter season dense image matching may fail to derive the canopy heights. Our results show that Pléiades CHMs could be a useful alternative to CHMs based on aerial images matching for monitoring forest metrics and canopy gaps in mountain forests if captured during leaf-on conditions. Our study offers forest research, as well as forest management planning, the benefit of a better understanding of the performance of VHR satellite imagery used for forest inventory in mountainous regions and in similar forest environments.