Terrain surfaces and 3-D landcover classification from small footprint full-waveform lidar data: application to badlands

Abstract

Abstract. This article presents the use of new remote sensing data acquired from airborne full-waveform lidar systems for hydrological applications. Indeed, the knowledge of an accurate topography and a landcover classification is a prior knowledge for any hydrological and erosion model. Badlands tend to be the most significant areas of erosion in the world with the highest erosion rate values. Monitoring and predicting erosion within badland mountainous catchments is highly strategic due to the arising downstream consequences and the need for natural hazard mitigation engineering.Additionally, beyond the elevation information, fullwaveform lidar data are processed to extract the amplitude and the width of echoes. They are related to the target reflectance and geometry. We will investigate the relevancy of using lidar-derived Digital Terrain Models (DTMs) and the potentiality of the amplitude and the width information for 3-D landcover classification. Considering the novelty and the complexity of such data, they are presented in details as well as guidelines to process them. The morphological validation of DTMs is then performed via the computation of hydrological indexes and photo-interpretation. Finally, a 3-D landcover classification is performed using a Support Vector Machine classifier. The use of an ortho-rectified optical image in the classification process as well as full-waveform lidar data for hydrological purposes is finally discussed.

Extraction of thalweg networks from DTMs: application to badlands

“…Since the plan curvature and the tangential curvature have the same spatial distribution of convex and concave areas (Molly and Stepinski, 2007), these two parameters have the same impact in this work. The CI showed great potential to reveal the gully floor features in a previous work (Bretar et al, 2009). …”

“…The Gaussian distribution of the CI values allows setting the CI T as the mean value minus twice the standard deviation (σ ). We accepted that 2.5 percent of CI values due to hazard on the DTM noise can be classified as significant (Bretar et al, 2009). Following this, a binary image attributed a value of one to significant convergent cells (i.e., for CI>CI T and 0 to the non-convergent cells).…”

“…Three DTMs of the Moulin catchment were used: a 50-cmresolution, a one-meter-resolution and a 2-meter-resolution. The three DTMs come from the same LiDAR survey (detailed in Bretar et al, 2009). They were derived from the point cloud resulting from the last echo.…”

“…3). Since spatially correlated errors were found and modelled on the DTM, the noise simulation process was based on geostatistical Gaussian field simulation, with the spatial covariance function and parameters inferred in the same manner as those used in Bretar et al (2009). The computation of CI from the simulated DTM provided a threshold CI T , beyond which CI values can be considered significantly different from a plane landform.…”

Use of the 3-D scanner in mapping and monitoring the dynamic degradation of soils: case study of the Cucuteni-Baiceni Gully on the Moldavian Plateau (Romania)

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