The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4, 2018 ISPRS TC IV Mid-term Symposium “3D Spatial Information Science – The Engine of Change”, 1–5 October 2018, Delft, The Netherlands THALWEG DETECTION FOR RIVER NETWORK CARTOGRAPHY IN FOREST FROM HIGH-RESOLUTION LIDAR DATA Eric Guilbert1,∗, Sylvain Jutras2 and Thierry Badard1 1 Dept. of Geomatics Sciences, Laval University, Quebec,´ G1V 0A6 (QC) Canada - [email protected] 2 Dept. of Forestry, Laval University, Quebec,´ G1V 0A6 (QC) Canada Commission IV, WG IV/1 KEY WORDS: LiDAR, Digital terrain model, River network, Surface network ABSTRACT: This paper addresses the problem of extracting the drainage network in forested areas. A precise description of the drainage network including intermittent streams is required for the planning of logging operations and environmental conservation. LiDARprovides now high-resolution point clouds from which the terrain is modelled and the drainage extracted but it also brings some challenges for traditional approaches. First, the raster DTM is interpolated from LiDAR ground points and has to be split in tiles for processing, adding approximations. Second, drainage enforcement techniques alter the terrain and rely on parameters difficult to fix and limiting the optimisation of the process. In this context, we discuss a new approach aiming at: (1) Designing a data structure to model the terrain with a Triangulated Irregular Network in order to avoid interpolation. This data structure must enable the distribution of data and processes across several nodes in Big data architectures and eventually, the processing of complete watersheds with no tiling. (2) Modelling the river network through thalwegs and avoiding the filling and breaching operations. Thalweg detection is more robust, removing the need for filling and breaching. However, it yields a very dense network requiring a simplification step. Combining this model and the architecture will enable the design and modelling of a new tool for river network computation directly from LiDAR ground points. In this paper, we mainly discuss the second point and propose to model the drainage by a network of thalwegs computed from the terrain. Thalwegs are extracted from the surface network, a topological structure formed of peaks, pits and saddles as vertices and ridges and thalwegs as vertices. We present preliminary results comparing the thalweg network and the drainage network. 1. INTRODUCTION techniques enforcing the drainage by altering the terrain. Their parameters may depend on more or less arbitrary factors and re- In forest management, knowledge of the hydrology is required for quire validation by an expert. planning logging operations and designing haul routes. Tradition- In order to move towards a fully automated processing of LiDAR ally, hydrological maps were drawn from photointerpretation on point clouds, we need to limit manual operations and avoid arbi- aerial photographies and from digital terrain models. However, trary parameters which affect the reliability of the result. We also the resolution and the quality of these maps were low because need to develop data structures that optimise data storage and ac- most of the details is hidden by the canopy. Hence, a survey of cess and to preserve the quality of original measurements. The the logging area is performed before the operations start to locate objective is to be able to work on complete watersheds without streams and wetlands. However, such surveys are long, fastidious tiling and to avoid or linit interpolation and drainage enforcement and, depending on the time of year and weather conditions, may operations that modify the terrain model. overlook some ephemeral or intermittent streams or some humid areas. In this paper, we discuss the need for a change of paradigm and propose (1) to model the terrain with a TIN directly from Li- Now, LiDAR provides datasets at a much higher resolution and DAR points and (2) to identify rivers from thalwegs in order to can see the floor under the canopy. As a consequence, many avoid limitations of flow accumulation approaches. The next sec- mapping agencies or governmental organisations have launched tion presents the current approaches with its limitations. Section programmes to collect high-density point clouds and build high- 3 more specifically addresses thalweg computation and presents resolution DTM from which the hydrology is computed. The preliminary results comparing thalweg network and drainage net- principle of the approach consists in interpolating a grid DTM- work. The last section concludes and presents on-going work and from the points on the ground, usually at a resolution around one future developments. metre. The river network is then extracted by traditional flow accumulation techniques. 2. LIMITATIONS OF EXISTING APPROACHES However, LiDAR point clouds are massive, and a single water- shed amounts to several terabytes of data. Current software and 2.1 The processing chain approaches are not designed to handle such volumes at once. The Current approaches rely on the extraction of the river network terrain is split into tiles but a watershed may be spread over sev- from a raster DTM. Since LiDAR points are irregularly dis- eral tiles. The DTM is interpolated from scattered ground points tributed, the DTM is computed by interpolation. However, the but their density varies according to the type of terrain, the thick- amount of points is too large for current software to process them ness of the canopy and the quality of the classification. Further- all at once and they have to be tiled. LiDAR point classifica- more, network computation methods often rely on preprocessing tion and DTM interpolation are then performed in each tiles sep- ∗Corresponding author arately. This contribution has been peer-reviewed. https://doi.org/10.5194/isprs-archives-XLII-4-241-2018 | © Authors 2018. CC BY 4.0 License. 241 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4, 2018 ISPRS TC IV Mid-term Symposium “3D Spatial Information Science – The Engine of Change”, 1–5 October 2018, Delft, The Netherlands Aerial LiDAR mesures returns from the ground or objects on the LiDAR point Ground point Tiling ground. Logically, in open areas, all points reach the ground cloud selection while, if the canopy is thick, most points hit the canopy and few hit the ground. It is also more difficult in steep slopes to classify ground points, leading to more unclassified points. In previous Watershed works, (Sherba et al., 2014) obtained a ground point spacing of Raster DTM Interpolation extraction 0.91 metre in average with a LiDAR emitting 6 pulses per square metre. However, (White et al., 2010) conducted a survey at high- density (12 pts/m2) in forest with steep slope and a thick canopy and observed that 94% of the points did not reach the ground. DTM Drainage Flow Hence, the density of ground points varies considerably on a ter- filtering enforcement calculation rain. (Montealegre et al., 2015) showed that in such areas, the density is below 0.3 pts/m2. Hence, the raster DTM homoge- neous resolution does not reflect the ground point distribution. In River places where few LiDAR points hit the ground, the raster gives a false impression of precision since the DTM at 1 m was built network in some areas from samples of points distant of several metres, sometimes more than 10 metres apart. The only piece of infor- Figure 1: River network extraction through DTM interpolation mation about the quality given to the user is the point density over and flow accumulation the whole terrain. To a lesser extent, the DTM also depends on the interpolation Computation of the river network is done in a separate stage for method computing the raster from the ground points. There is each watershed. In the case a watershed spreads over several tiles, no best method here and the quality depends also on the point these tiles have to be assembled, sometimes requiring some ad- density and the terrain. Common methods are inverse distance justment on the overlapping areas between tiles. If the watershed weight, kriging and triangulation followed by a linear interpola- is too big to be processed at once, tiles are processed separately tion. (Montealegre et al., 2015) obtained best results with the and sub-networks are connected afterward. triangulation. (Sterenczak´ et al., 2016) also found that triangu- Flow accumulation methods simulate a water flow over a terrain. lation yields smaller errors but all methods can achieve similar Rivers are defined by pixels whose flow accumulation is above errors. However, bias and RMSE depend on the slope and the a given threshold. While the principle is simple, the method re- undergrowth height, meaning that the quality of the DTM is not quires some preprocessing to enforce the drainage. The process- homogeneous. ing chain is summarised in Figure 1. Errors and approximations As mentioned above, interpolation is conducted separately on mainly come from the steps in red on the figure where the terrain each tile. When computing the drainage network of a given wa- model is modified. The quality of the terrain model also depends tershed, only the part of the terrain corresponding to this water- on the quality of the ground point classification algorithm (in yel- shed is processed. If the watershed overlaps two or more tiles, low). This step is still an open issue, especially in forest (Meng Acquisition Sélection des Identificationthe DTM is built by mergingExtraction the tiles. Sincedes elevations on over- et al., 2010), but will not be addressed in thisTriangulation paper. lidar points au sol lappingdes chemins areas do not matchcours exactlyd’eau (they were computed in two different interpolation processes), a resampling may be done on 2.2 DTM construction pas de perte MNT non modifié these areas. In the case the watershed covers too many tiles to be processed at once, the watershed must be split in several pieces Traditionally, terrain analysis is conducted on raster DTM since which will be treated separately.