Dimitri Lague

Bio: Dimitri Lague is an academic researcher from University of Rennes. The author has contributed to research in topics: Erosion & Point cloud. The author has an hindex of 26, co-authored 69 publications receiving 4684 citations. Previous affiliations of Dimitri Lague include University of Rennes 1 & Centre national de la recherche scientifique.

Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)

Abstract: Surveying techniques such as terrestrial laser scanner have recently been used to measure surface changes via 3D point cloud (PC) comparison. Two types of approaches have been pursued: 3D tracking of homologous parts of the surface to compute a displacement field, and distance calculation between two point clouds when homologous parts cannot be defined. This study deals with the second approach, typical of natural surfaces altered by erosion, sedimentation or vegetation between surveys. Current comparison methods are based on a closest point distance or require at least one of the PC to be meshed with severe limitations when surfaces present roughness elements at all scales. To solve these issues, we introduce a new algorithm performing a direct comparison of point clouds in 3D. The method has two steps: (1) surface normal estimation and orientation in 3D at a scale consistent with the local surface roughness; (2) measurement of the mean surface change along the normal direction with explicit calculation of a local confidence interval. Comparison with existing methods demonstrates the higher accuracy of our approach, as well as an easier workflow due to the absence of surface meshing or Digital Elevation Model (DEM) generation. Application of the method in a rapidly eroding, meandering bedrock river (Rangitikei River canyon) illustrates its ability to handle 3D differences in complex situations (flat and vertical surfaces on the same scene), to reduce uncertainty related to point cloud roughness by local averaging and to generate 3D maps of uncertainty levels. We also demonstrate that for high precision survey scanners, the total error budget on change detection is dominated by the point clouds registration error and the surface roughness. Combined with mm-range local georeferencing of the point clouds, levels of detection down to 6 mm (defined at 95% confidence) can be routinely attained in situ over ranges of 50 m. We provide evidence for the self-affine behaviour of different surfaces. We show how this impacts the calculation of normal vectors and demonstrate the scaling behaviour of the level of change detection. The algorithm has been implemented in a freely available open source software package. It operates in complex 3D cases and can also be used as a simpler and more robust alternative to DEM differencing for the 2D cases.

Links between erosion, runoff variability and seismicity in the Taiwan orogen

Abstract: The erosion of mountain belts controls their topographic and structural evolution1,2,3 and is the main source of sediment delivered to the oceans4 Mountain erosion rates have been estimated from current relief and precipitation, but a more complete evaluation of the controls on erosion rates requires detailed measurements across a range of timescales Here we report erosion rates in the Taiwan mountains estimated from modern river sediment loads, Holocene river incision and thermochronometry on a million-year scale Estimated erosion rates within the actively deforming mountains are high (3–6 mm yr-1) on all timescales, but the pattern of erosion has changed over time in response to the migration of localized tectonic deformation Modern, decadal-scale erosion rates correlate with historical seismicity and storm-driven runoff variability The highest erosion rates are found where rapid deformation, high storm frequency and weak substrates coincide, despite low topographic relief

3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology

Abstract: 3D point clouds of natural environments relevant to problems in geomorphology (rivers, coastal environments, cliffs, ...) often require classification of the data into elementary relevant classes. A typical example is the separation of riparian vegetation from ground in fluvial environments, the distinction between fresh surfaces and rockfall in cliff environments, or more generally the classification of surfaces according to their morphology (e.g. the presence of bedforms or by grain size). Natural surfaces are heterogeneous and their distinctive properties are seldom defined at a unique scale, prompting the use of multi-scale criteria to achieve a high degree of classification success. We have thus defined a multi-scale measure of the point cloud dimensionality around each point. The dimensionality characterizes the local 3D organization of the point cloud within spheres centered on the measured points and varies from being 1D (points set along a line), 2D (points forming a plane) to the full 3D volume. By varying the diameter of the sphere, we can thus monitor how the local cloud geometry behaves across scales. We present the technique and illustrate its efficiency in separating riparian vegetation from ground and classifying a mountain stream as vegetation, rock, gravel or water surface. In these two cases, separating the vegetation from ground or other classes achieve accuracy larger than 98%. Comparison with a single scale approach shows the superiority of the multi-scale analysis in enhancing class separability and spatial resolution of the classification. Scenes between 10 and one hundred million points can be classified on a common laptop in a reasonable time. The technique is robust to missing data, shadow zones and changes in point density within the scene. The classification is fast and accurate and can account for some degree of intra-class morphological variability such as different vegetation types. A probabilistic confidence in the classification result is given at each point, allowing the user to remove the points for which the classification is uncertain. The process can be both fully automated (minimal user input once, all scenes treated in large computation batches), but also fully customized by the user including a graphical definition of the classifiers if so desired. Working classifiers can be exchanged between users independently of the instrument used to acquire the data avoiding the need to go through full training of the classifier. Although developed for fully 3D data, the method can be readily applied to 2.5D airborne lidar data.

3D Terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: applications in geomorphology

Abstract: 3D point clouds of natural environments relevant to problems in geomorphology often require classification of the data into elementary relevant classes. A typical example is the separation of riparian vegetation from ground in fluvial environments, the distinction between fresh surfaces and rockfall in cliff environments, or more generally the classification of surfaces according to their morphology. Natural surfaces are heterogeneous and their distinctive properties are seldom defined at a unique scale, prompting the use of multi-scale criteria to achieve a high degree of classification success. We have thus defined a multi-scale measure of the point cloud dimensionality around each point, which characterizes the local 3D organization. We can thus monitor how the local cloud geometry behaves across scales. We present the technique and illustrate its efficiency in separating riparian vegetation from ground and classifying a mountain stream as vegetation, rock, gravel or water surface. In these two cases, separating the vegetation from ground or other classes achieve accuracy larger than 98 %. Comparison with a single scale approach shows the superiority of the multi-scale analysis in enhancing class separability and spatial resolution. The technique is robust to missing data, shadow zones and changes in point density within the scene. The classification is fast and accurate and can account for some degree of intra-class morphological variability such as different vegetation types. A probabilistic confidence in the classification result is given at each point, allowing the user to remove the points for which the classification is uncertain. The process can be both fully automated, but also fully customized by the user including a graphical definition of the classifiers. Although developed for fully 3D data, the method can be readily applied to 2.5D airborne lidar data.

The stream power river incision model: evidence, theory and beyond

Abstract: The stream power incision model (SPIM) is a cornerstone of quantitative geomorphology. It states that river incision rate is the product of drainage area and channel slope raised to the power exponents m and n, respectively. It is widely used to predict patterns of deformation from channel long profile inversion or to model knickpoint migration and landscape evolution. Numerous studies have attempted to test its applicability with mixed results prompting the question of its validity. This paper synthesizes these results, highlights the SPIM deficiencies, and offers new insights into the role of incision thresholds and channel width. By reviewing quantitative data on incising rivers, I first propose six sets of field evidence that any long-term incision model should be able to predict. This analysis highlights several inconsistencies of the standard SPIM. Next, I discuss the methods used to construct physics-based long-term incision laws. I demonstrate that all published incising river datasets away from knickpoints or knickzones are in a regime dominated by threshold effects requiring an explicit upscaling of flood stochasticity neglected in the standard SPIM and other incision models. Using threshold-stochastic simulations with dynamic width, I document the existence of composite transient dynamics where knickpoint propagation locally obeys a linear SPIM (n=1) while other part of the river obey a non-linear SPIM (n>1). The threshold-stochastic SPIM resolves some inconsistencies of the standard SPIM and matches steady-state field evidence when width is not sensitive to incision rate. However it fails to predict the scaling of slope with incision rate for cases where width decreases with incision rate. Recent proposed models of dynamic width cannot resolve these deficiencies. An explicit upscaling of sediment flux and threshold-stochastic effects combined with dynamic width should take us beyond the SPIM which is shown here to have a narrow range of validity.

This is How the Discussion Started

Abstract: A copy of Guangbo jiemu bao [Broadcast Program Report] was being passed from hand to hand among a group of young people eager to be the first to read the article introducing the program "What Is Revolutionary Love?" It said: "… Young friends, you are certainly very concerned about this problem'. So, we would like you to meet the young women workers Meng Xiaoyu and Meng Yamei and the older cadre Miss Feng. They are the three leading characters in the short story ‘The Place of Love.’ Through the description of the love lives of these three, the story induces us to think deeply about two questions that merit further examination.

Toward a complete Himalayan hydrological budget: Spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge

Abstract: [1] The hydrological budget of Himalayan rivers is dominated by monsoonal rainfall and snowmelt, but their relative impact is not well established because this remote region lacks a dense gauge network. Here, we use a combination of validated remotely-sensed climate parameters to characterize the spatiotemporal distribution of rainfall, snowfall, and evapotranspiration in order to quantify their relative contribution to mean river discharge. Rainfall amounts are calculated from calibrated, orbital, high-resolution Tropical Rainfall Measurement Mission data, and snow-water equivalents are computed from a snowmelt model based on satellite-derived snow cover, surface temperature, and solar radiation. Our data allow us to identify three key aspects of the spatiotemporal precipitation pattern. First, we observe a strong decoupling between the rainfall on the Himalayan foreland versus that in the mountains: a pronounced sixfold, east-west rainfall gradient in the Ganges plains exists only at elevations <500 m asl. Mountainous regions (500 to 5000 m asl) receive nearly equal rainfall amounts along strike. Second, whereas the Indian summer monsoon is responsible for more than 80% of annual rainfall in the central Himalaya and Tibetan Plateau, the eastern and western syntaxes receive only ∼50% of their annual rainfall during the summer season. Third, snowmelt contributions to discharge differ widely along the range. As a fraction of the total annual discharge, snowmelt constitutes up to 50% in the far western (Indus area) catchments, ∼25% in far eastern (Tsangpo) catchments, and <20% elsewhere. Despite these along-strike variations, snowmelt in the pre- and early-monsoon season (April to June) is significant and important in all catchments, although most pronounced in the western catchments. Thus, changes in the timing or amount of snowmelt due to increasing temperatures or decreasing winter precipitation may have far-reaching societal consequences. These new data on precipitation and runoff set the stage for far more detailed investigations than have previously been possible of climate-erosion interactions in the Himalaya.

Tectonics from topography: Procedures, promise, and pitfalls

Abstract: Empirical observations from fl uvial systems across the globe reveal a consistent power-law scaling between channel slope and contributing drainage area. Theoretical arguments for both detachmentand transport-limited erosion regimes suggest that rock uplift rate should exert fi rst-order control on this scaling. Here we describe in detail a method for exploiting this relationship, in which topographic indices of longitudinal profi le shape and character are derived from digital topographic data. The stream profi le data can then be used to delineate breaks in scaling that may be associated with tectonic boundaries. The description of the method is followed by three case studies from varied tectonic settings. The case studies illustrate the power of stream profi le analysis in delineating spatial patterns of, and in some cases, temporal changes in, rock uplift rate. Owing to an incomplete understanding of river response to rock uplift, the method remains primarily a qualitative tool for neotectonic investigations; we conclude with a discussion of research needs that must be met before we can extract quantitative information about tectonics directly from topography.

Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)

Abstract: Surveying techniques such as terrestrial laser scanner have recently been used to measure surface changes via 3D point cloud (PC) comparison. Two types of approaches have been pursued: 3D tracking of homologous parts of the surface to compute a displacement field, and distance calculation between two point clouds when homologous parts cannot be defined. This study deals with the second approach, typical of natural surfaces altered by erosion, sedimentation or vegetation between surveys. Current comparison methods are based on a closest point distance or require at least one of the PC to be meshed with severe limitations when surfaces present roughness elements at all scales. To solve these issues, we introduce a new algorithm performing a direct comparison of point clouds in 3D. The method has two steps: (1) surface normal estimation and orientation in 3D at a scale consistent with the local surface roughness; (2) measurement of the mean surface change along the normal direction with explicit calculation of a local confidence interval. Comparison with existing methods demonstrates the higher accuracy of our approach, as well as an easier workflow due to the absence of surface meshing or Digital Elevation Model (DEM) generation. Application of the method in a rapidly eroding, meandering bedrock river (Rangitikei River canyon) illustrates its ability to handle 3D differences in complex situations (flat and vertical surfaces on the same scene), to reduce uncertainty related to point cloud roughness by local averaging and to generate 3D maps of uncertainty levels. We also demonstrate that for high precision survey scanners, the total error budget on change detection is dominated by the point clouds registration error and the surface roughness. Combined with mm-range local georeferencing of the point clouds, levels of detection down to 6 mm (defined at 95% confidence) can be routinely attained in situ over ranges of 50 m. We provide evidence for the self-affine behaviour of different surfaces. We show how this impacts the calculation of normal vectors and demonstrate the scaling behaviour of the level of change detection. The algorithm has been implemented in a freely available open source software package. It operates in complex 3D cases and can also be used as a simpler and more robust alternative to DEM differencing for the 2D cases.