Comparison of Pleiades and LiDAR Digital Elevation Models for Terraces Detection in Farmlands

Among the most evident anthropogenic modifications of the landscape, terraces related to agricultural activities are ubiquitous structures that constitute important investments worldwide, and they recently acquired a new relevance to modern concerns about land-use management and erosion control. Conservation agriculture and terraces management are an application with great potentialities for Satellite Earth observation and the derived high-resolution topography. Due to its high agility, the Pleiades satellite constellation provides new, high-resolution digital elevation models (DEMs) with a submetric resolution that could be potentially useful for this task, and their application in a farmland context is nowadays an open research line. This work provides a first analysis, performing an automatic terrace mapping from DEMs obtained from Pleiades images, as compared to LiDAR DEMs. Two existing methods are considered: 1) the fast line segment detector (LSD) algorithm and 2) a geomorphometric method based on surface curvature. Despite the lower performances of Pleiades DEMs with respect to that of the LiDAR models, the results indicate that the Pleiades models can be used to automatically detect terrace slopes greater than 2 m with a detection rate of more than 80% of the total length of the terraces. In addition, the results showed that when using noisy DEMs, the geomorphometric method is more robust, and it slightly outperforms the LSD algorithm. These results provide a first analysis on how effective Pleiades DEMs can be as an alternative to LiDAR DEMs, also highlighting the future challenges for monitoring large extents in a farmland context.