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

Abstract

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

Impact of farmland fragmentation on rainfed crop allocation in Mediterranean landscapes: A case study of the Lebna watershed in Cap Bon, Tunisia

“…that mark a limit between adjacent fields and permit field protection against possible damage from herds and (3) the visibility from the farmstead. In the near future, thanks to the development of techniques for acquiring and processing very high spatial resolution satellite images (Fauvel et al, 2013;Sofia et al, 2016), the automated acquisition of path networks and other linear elements might be possible and would help define a more precise field context indicator. Visibility from the farmstead could be evaluated with digital surface models (DSM) that incorporate the heights of the elements above the terrain (Floriani and Magillo, 2003).…”

High‐resolution mapping of Manawatu palaeochannels

“…Furthermore, Sofia et al . () compared true positive metrics, omission errors, which are the length of the reference not included in the detected domain; and commission errors, which are the length of the extracted element not included in the reference domain. In this work, the geomorphic approach was also shown to produce a higher number of true positives, and lower number of omissions.…”

“…The final centreline can be more than one pixel wide; therefore, the output produced is further thinned to reduce the centreline to a width of one pixel, and the final centreline is converted into a vector line (Sofia et al . ). Both the reference and extracted features have been buffered as suggested by Heipke et al .…”

“…The skeletonisation approach follows the method proposed in Sofia et al . (), inspired by Pavelsky and Smith (). The algorithm focuses on each extracted region of pixels and processes using the following steps: A distance map is created, representing the Euclidean distance between each feature pixel and the nearest background pixel. The distance map is convolved with a bidirectional Laplacian filter like that of the Marr–Hildreth (Marr & Hildreth ) edge detection algorithm The output of the previous point is thresholded to obtain the centerline.…”

Analysis and Processing of Nadir and Stereo VHR Pleiadés Images for 3D Mapping and Planning the Land of Nineveh, Iraqi Kurdistan

“…Several articles deal with the realization of DEM highlighting the benefits of using Pleiadés sensors as the data source. As a demonstration of that, accuracy evaluations are stated in different works, using as a benchmark SRTM images at 30 m [19], as well as more accurate LiDAR data [20]. The purpose of Pleiadés is to deliver optical images of sub-metric resolution with daily access to any point on the globe (with its two satellites): a panchromatic channel with a 70 cm vertical viewing resolution and a multispectral one composed of four spectral bands (blue, green, red and near-infrared) with a 2.8 m resolution (re-sampled at 0.5 m and 2 m, respectively), image swath of about 20 km in vertical viewing, acquisition capacity, in a single pass, of a 100 × 100 km mosaic of images, virtually instantaneous acquisition capacity for stereoscopic pairs (and even triplets) of 20 km up to 300 km cloud-free image coverage of 2,500,000 km 2 per year.…”