Evaluating the Differences of Gridding Techniques for Digital Elevation Models Generation and Their Influence on the Modeling of Stony Debris Flows Routing: A Case Study From Rovina di Cancia Basin (North-Eastern Italian Alps)

Debris \ufb02ows are among the most hazardous phenomena in mountain areas. To cope with debris \ufb02ow hazard, it is common to delineate the risk-prone areas through routing models. The most important input to debris \ufb02ow routing models are the topographic data, usually in the form of Digital Elevation Models (DEMs). The quality of DEMs depends on the accuracy, density, and spatial distribution of the sampled points; on the characteristics of the surface; and on the applied gridding methodology. Therefore, the choice of the interpolation method affects the realistic representation of the channel and fan morphology, and thus potentially the debris \ufb02ow routing modeling outcomes. In this paper, we initially investigate the performance of common interpolation methods (i.e., linear triangulation, natural neighbor, nearest neighbor, Inverse Distance to a Power, ANUDEM, Radial Basis Functions, and ordinary kriging) in building DEMs with the complex topography of a debris \ufb02ow channel located in the Venetian Dolomites (North-eastern Italian Alps), by using small footprint full- waveform Light Detection And Ranging (LiDAR) data. The investigation is carried out through a combination of statistical analysis of vertical accuracy, algorithm robustness, and spatial clustering of vertical errors, and multi-criteria shape reliability assessment. After that, we examine the in\ufb02uence of the tested interpolation algorithms on the performance of a Geographic Information System (GIS)-based cell model for simulating stony debris \ufb02ows routing. In detail, we investigate both the correlation between the DEMs heights uncertainty resulting from the gridding procedure and that on the corresponding simulated erosion/deposition depths, both the effect of interpolation algorithms on simulated areas, erosion and deposition volumes, solid-liquid discharges, and channel morphology after the event. The comparison among the tested interpolation methods highlights that the ANUDEM and ordinary kriging algorithms are not suitable for building DEMs with complex topography. Conversely, the linear triangulation, the natural neighbor algorithm, and the thin-plate spline plus tension and completely regularized spline functions ensure the best trade-off among accuracy and shape reliability. Anyway, the evaluation of the effects of gridding techniques on debris \ufb02ow routing modeling reveals that the choice of the interpolation algorithm does not signi\ufb01cantly affect the model outcomes

Reducción del ruido en la generación de curvas de nivel y mapas de pendiente a partir de datos LIDAR de media/alta densidad

[EN] The use of medium/high-density LIDAR (Light Detection And Ranging) data for land modelling and DTM (Digital Terrain Model) is becoming more widespread. This level of detail is difficult to achieve with other means or materials. However, the horizontal and vertical geometric accuracy of the LIDAR points obtained, although high, is not homogeneous. Horizontally you can reach precisions around 30-50 cm, while the vertical precision is rarely greater than 10-15 cm. The result of LIDAR flights, are clouds of points very close to each other (30-60 cm) with significant elevation variations, even if the terrain is flat. And this makes the triangulated models TIN (Triangulated Irregular Network) obtained from such LIDAR data especially chaotic. Since contour lines are generated directly from such triangulated models, their appearance shows excessive noise, with excessively broken and rapidly closed on themselves. Getting smoothed contour liness, without decreasing accuracy, is a challenge for terrain model software. In addition, triangulated models obtained from LIDAR data are the basis for future slope maps of the land. And for the same reason explained in the previous paragraph, these slope maps generated from high or medium density LIDAR point clouds are especially heterogeneous. Achieving uniformity and greater adjustment to reality by reducing the natural noise of LIDAR data is another added challenge. In this paper, the problem of excessive noise from LIDAR data of high (around 8 points/m2) and medium density (around 2 points/m2) in the generation of contour lines and terrain slope maps is raised and solutions are proposed to reduce this noise. All this, in the area of specific software for the management of TIN models and GIS (Geographic Information System) and adapting the alternatives proposed by these programmes.[ES] El uso de datos LIDAR de alta densidad para la modelización del terreno y obtención de MDT (Modelo Digital del Terreno) está cada día más generalizado. El nivel de detalle conseguido es difícil de alcanzar con otros medios o materiales. No obstante, la precisión geométrica horizontal y vertical de los puntos LIDAR obtenidos, aunque es alta, no es homogénea. Horizontalmente se puede llegar a precisiones del orden de los 30-50 cm, mientras la precisión vertical raras veces es mayor de 10-15 cm. El resultado de los vuelos LIDAR, son nubes de puntos muy próximos entre sí (30-60 cm) con variaciones de cota importantes, aunque el terreno sea llano. Y esto hace que los modelos triangulados TIN (Triangulated Irregular Network) obtenidos a partir de dichos datos LIDAR sean especialmente caóticos. Dado que las curvas de nivel se generan directamente a partir de dichos modelos triangulados, su apariencia muestra excesivo ruido, con curvas excesivamente quebradas y rápidamente cerradas sobre sí mismas. Conseguir curvas suavizadas, sin disminuir la precisión, es un reto para los programas de modelización de terrenos. Además, los modelos triangulados obtenidos a partir de datos LIDAR, son la base de los futuros mapas de pendiente de los terrenos. Y por la misma razón explicada en el párrafo anterior, estos mapas de pendiente generados a partir de nubes de puntos LIDAR de alta o media densidad, son especialmente heterogéneos. Conseguir uniformidad y mayor ajuste a la realidad reduciendo el ruido natural de los datos LIDAR es otro reto añadido. En esta comunicación, se plantea la problemática del excesivo ruido de los datos LIDAR de alta (en torno a 8 puntos/m2) y media densidad (en torno a 2 puntos/m2) en la generación de curvas de nivel y mapas de pendiente del terreno y se proponen soluciones para reducir dicho ruido. Todo ello, en el ámbito de programas específicos de gestión de modelos TIN y de los SIG (Sistemas de Información Geográfica) y adaptando las alternativas que dichos programas plantean.Our gratitude to the National Geographic Institute and the Government of La Rioja, for making available to us in a totally disinterested way sheets of the LIDAR PNOA 2016 flight produced within the framework of the National Cartographic System (SCNE).Santamaría-Peña, J.; Palacios-Ruiz, E.; Santamaría-Palacios, T. (2021). Noise reduction in contour lines and slope maps from medium/high-density LIDAR data. En Proceedings 3rd Congress in Geomatics Engineering. Editorial Universitat Politècnica de València. 1-6. https://doi.org/10.4995/CiGeo2021.2021.12743OCS1

Interpolation routines assessment in ALS-derived Digital Elevation Models for forestry applications

Airborne Laser Scanning (ALS) is capable of estimating a variety of forest parameters using different metrics extracted from the normalized heights of the point cloud using a Digital Elevation Model (DEM). In this study, six interpolation routines were tested over a range of land cover and terrain roughness in order to generate a collection of DEMs with spatial resolution of 1 and 2 m. The accuracy of the DEMs was assessed twice, first using a test sample extracted from the ALS point cloud, second using a set of 55 ground control points collected with a high precision Global Positioning System (GPS). The effects of terrain slope, land cover, ground point density and pulse penetration on the interpolation error were examined stratifying the study area with these variables. In addition, a Classification and Regression Tree (CART) analysis allowed the development of a prediction uncertainty map to identify in which areas DEMs and Airborne Light Detection and Ranging (LiDAR) derived products may be of low quality. The Triangulated Irregular Network (TIN) to raster interpolation method produced the best result in the validation process with the training data set while the Inverse Distance Weighted (IDW) routine was the best in the validation with GPS (RMSE of 2.68 cm and RMSE of 37.10 cm, respectively)

Large scale characterization of artificial drainage networks: new opportinities from high resolution topography .

openTES-81

Land-Surface Parameters for Spatial Predictive Mapping and Modeling

Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and the need to define moving window shapes, sizes, and cell weightings further complicate selecting and optimizing the feature space. This review focuses on the calculation and use of DLSM parameters for empirical spatial predictive modeling applications, which rely on training data and explanatory variables to make predictions of landscape features and processes over a defined geographic extent. The target audience for this review is researchers and analysts undertaking predictive modeling tasks that make use of the most widely used terrain variables. To outline best practices and highlight future research needs, we review a range of land-surface parameters relating to steepness, local relief, rugosity, slope orientation, solar insolation, and moisture and characterize their relationship to geomorphic processes. We then discuss important considerations when selecting such parameters for predictive mapping and modeling tasks to assist analysts in answering two critical questions: What landscape conditions or processes does a given measure characterize? How might a particular metric relate to the phenomenon or features being mapped, modeled, or studied? We recommend the use of landscape- and problem-specific pilot studies to answer, to the extent possible, these questions for potential features of interest in a mapping or modeling task. We describe existing techniques to reduce the size of the feature space using feature selection and feature reduction methods, assess the importance or contribution of specific metrics, and parameterize moving windows or characterize the landscape at varying scales using alternative methods while highlighting strengths, drawbacks, and knowledge gaps for specific techniques. Recent developments, such as explainable machine learning and convolutional neural network (CNN)-based deep learning, may guide and/or minimize the need for feature space engineering and ease the use of DLSMs in predictive modeling tasks

Evaluation of Cartosat-1 Multi-Scale Digital Surface Modelling Over France

On 5 May 2005, the Indian Space Research Organization launched Cartosat-1, the eleventh satellite of its constellation, dedicated to the stereo viewing of the Earth's surface for terrain modeling and large-scale mapping, from the Satish Dhawan Space Centre (India). In early 2006, the Indian Space Research Organization started the Cartosat-1 Scientific Assessment Programme, jointly established with the International Society for Photogrammetry and Remote Sensing. Within this framework, this study evaluated the capabilities of digital surface modeling from Cartosat-1 stereo data for the French test sites of Mausanne les Alpilles and Salon de Provence. The investigation pointed out that for hilly territories it is possible to produce high-resolution digital surface models with a root mean square error less than 7.1 m and a linear error at 90% confidence level less than 9.5 m. The accuracy of the generated digital surface models also fulfilled the requirements of the French Reference 3D®, so Cartosat-1 data may be used to produce or update such kinds of products

Enhanced flood hydraulic modelling using topographic remote sensing.

Available from British Library Document Supply Centre-DSC:DXN044421 / BLDSC - British Library Document Supply CentreSIGLEGBUnited Kingdo

LR B-splines to approximate bathymetry datasets: An improved statistical criterion to judge the goodness of fit

The task of representing remotely sensed scattered point clouds with mathematical surfaces is ubiquitous to reduce a high number of observations to a compact description with as few coefficients as possible. To reach that goal, locally refined B-splines provide a simple framework to perform surface approximation by allowing an iterative local refinement. Different setups exist (bidegree of the splines, tolerance, refinement strategies) and the choice is often made heuristically, depending on the applications and observations at hand. In this article, we introduce a statistical information criterion based on the t-distribution to judge the goodness of fit of the surface approximation for remote sensing data with outliers. We use a real bathymetry dataset and illustrate how concepts from model selection can be used to select the most adequate refinement strategy of the LR B-splines.publishedVersio