Abstract
Abstract. Accurate Digital Terrain Models (DTM) are inevitable inputs for mapping areas subject to natural hazards. Topographic airborne laser scanning has become an established technique to characterize the Earth surface: lidar provides 3D point clouds allowing a fine reconstruction of the topography. For flood hazard modeling, the key step before terrain modeling is the discrimination of land and water surfaces within the delivered point clouds. Therefore, instantaneous shoreline, river borders, inland waters can be extracted as a basis for more reliable DTM generation. This paper presents an automatic, efficient, and versatile workflow for land/water classification of airborne topographic lidar data. For that purpose, a classification framework based on Support Vector Machines (SVM) is designed. First, a restricted set of features, based only 3D lidar point coordinates and flightline information, is defined. Then, the SVM learning step is performed on small but well-targeted areas thanks to an automatic region growing strategy. Finally, label probabilities given by the SVM are merged during a probabilistic relaxation step in order to remove pixel-wise misclassification. Results show that survey of millions of points are labelled with high accuracy (>95% in most cases for coastal areas, and >89% for rivers) and that small natural and anthropic features of interest are still well classified though we work at low point densities (0.5–4 pts/m2). Our approach is valid for coasts and rivers, and provides a strong basis for further discrimination of land-cover classes and coastal habitats.
Highlights
1.1 Motivation for seashore and river monitoringClimate change and global warming should lead to an increasing number of severe storms, more significant winter rains, and sea level rise in the forthcoming years
We focus on airborne topographic lidar data, that operates on the near-infrared channel (NIR)
Even when the training step focuses on seashore areas or main river beds, the Support Vector Machines (SVM) is able to generalize to inland waters, harbours, other rivers and channels
Summary
1.1 Motivation for seashore and river monitoringClimate change and global warming should lead to an increasing number of severe storms, more significant winter rains, and sea level rise in the forthcoming years. Coastal and river areas are at risk, but their physical characteristics are barely described, especially their accurate topography, yet essential data for forecasting and management purposes. Repetitive and up-to-date measurements are crucial for areas undergoing most changes that are flooding, erosion, accretion or retreating such as beaches, cliffs or unstable slopes (Miller et al, 2008; Addo et al, 2008) For this purpose, the small-footprint airborne lidar technology appears to be attractive because it provides fine-scale Digital Terrain Models (DTM) over large coverage. The small-footprint airborne lidar technology appears to be attractive because it provides fine-scale Digital Terrain Models (DTM) over large coverage It allows to survey hundreds of kilometers of shoreline and rivers with a high spatial resolution within a few days only. The aim of this paper is to propose a workflow for water/land classification in topographic lidar datasets, that is adapted to various landscapes
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