DIGITAL TERRAIN MODEL AND LANDFORMS CLASSIFICATION

2013 ◽  
Author(s):  
Katerina Ruzickova
Keyword(s):  
Digital Terrain Model ◽  
Terrain Model ◽  
Digital Terrain

scholarly journals The Effect of LiDAR Sampling Density on DTM Accuracy for Areas with Heavy Forest Cover

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 265
Author(s):  
Mihnea Cățeanu ◽  
Arcadie Ciubotaru
Keyword(s):  
Forest Cover ◽  
Initial Point ◽  
Ground Surface ◽  
Digital Terrain Model ◽  
Morphological Data ◽  
Sampling Density ◽  
Terrain Model ◽  
Digital Terrain ◽  
Point Density ◽  
The Impact

Laser scanning via LiDAR is a powerful technique for collecting data necessary for Digital Terrain Model (DTM) generation, even in densely forested areas. LiDAR observations located at the ground level can be separated from the initial point cloud and used as input for the generation of a Digital Terrain Model (DTM) via interpolation. This paper proposes a quantitative analysis of the accuracy of DTMs (and derived slope maps) obtained from LiDAR data and is focused on conditions common to most forestry activities (rough, steep terrain with forest cover). Three interpolation algorithms were tested: Inverse Distance Weighted (IDW), Natural Neighbour (NN) and Thin-Plate Spline (TPS). Research was mainly focused on the issue of point data density. To analyze its impact on the quality of ground surface modelling, the density of the filtered data set was artificially lowered (from 0.89 to 0.09 points/m2) by randomly removing point observations in 10% increments. This provides a comprehensive method of evaluating the impact of LiDAR ground point density on DTM accuracy. While the reduction of point density leads to a less accurate DTM in all cases (as expected), the exact pattern varies by algorithm. The accuracy of the LiDAR-derived DTMs is relatively good even when LiDAR sampling density is reduced to 0.40–0.50 points/m2 (50–60 % of the initial point density), as long as a suitable interpolation algorithm is used (as IDW proved to be less resilient to density reductions below approximately 0.60 points/m2). In the case of slope estimation, the pattern is relatively similar, except the difference in accuracy between IDW and the other two algorithms is even more pronounced than in the case of DTM accuracy. Based on this research, we conclude that LiDAR is an adequate method for collecting morphological data necessary for modelling the ground surface, even when the sampling density is significantly reduced.

scholarly journals DTM-based analysis of the spatial distribution of topolineaments

2020 ◽  
Vol 12 (1) ◽  
pp. 1185-1199
Author(s):  
Mirosław Kamiński
Keyword(s):  
Laser Scanning ◽  
Digital Terrain Model ◽  
Research Area ◽  
Tectonic Structures ◽  
Empirical Bayesian ◽  
Scanning Method ◽  
Empirical Bayesian Kriging ◽  
Terrain Model ◽  
Digital Terrain ◽  
Airborne Laser

AbstractThe research area is located on the boundary between two Paleozoic structural units: the Radom–Kraśnik Block and the Mazovian–Lublin Basin in the southeastern Poland. The tectonic structures are separated by the Ursynów–Kazimierz Dolny fault zone. The digital terrain model obtained by the ALS (Airborne Laser Scanning) method was used. Classification and filtration of an elevation point cloud were performed. Then, from the elevation points representing only surfaces, a digital terrain model was generated. The model was used to visually interpret the course of topolineaments and their automatic extraction from DTM. Two topolineament systems, trending NE–SW and NW–SE, were interpreted. Using the kernel density algorithm, topolineament density models were generated. Using the Empirical Bayesian Kriging, a thickness model of quaternary deposits was generated. A relationship was observed between the course of topolineaments and the distribution and thickness of Quaternary formations. The topolineaments were compared with fault directions marked on tectonic maps of the Paleozoic and Mesozoic. Data validation showed consistency between topolineaments and tectonic faults. The obtained results are encouraging for further research.

scholarly journals Production, Validation and Morphometric Analysis of a Digital Terrain Model for Lake Trichonis Using Geospatial Technologies and Hydroacoustics

2021 ◽  
Vol 10 (2) ◽  
pp. 91
Author(s):  
Triantafyllia-Maria Perivolioti ◽  
Antonios Mouratidis ◽  
Dimitrios Terzopoulos ◽  
Panagiotis Kalaitzis ◽  
Dimitrios Ampatzidis ◽  
...  
Keyword(s):  
Morphometric Analysis ◽  
Ad Hoc ◽  
Regional Scale ◽  
Digital Terrain Model ◽  
Relevant Information ◽  
Relative Difference ◽  
Systematic Observation ◽  
Qualitative And Quantitative ◽  
Terrain Model ◽  
Digital Terrain

Covering an area of approximately 97 km2 and with a maximum depth of 58 m, Lake Trichonis is the largest and one of the deepest natural lakes in Greece. As such, it constitutes an important ecosystem and freshwater reserve at the regional scale, whose qualitative and quantitative properties ought to be monitored. Depth is a crucial parameter, as it is involved in both qualitative and quantitative monitoring aspects. Thus, the availability of a bathymetric model and a reliable DTM (Digital Terrain Model) of such an inland water body is imperative for almost any systematic observation scenario or ad hoc measurement endeavor. In this context, the purpose of this study is to produce a DTM from the only official cartographic source of relevant information available (dating back approximately 70 years) and evaluate its performance against new, independent, high-accuracy hydroacoustic recordings. The validation procedure involves the use of echosoundings coupled with GPS, and is followed by the production of a bathymetric model for the assessment of the discrepancies between the DTM and the measurements, along with the relevant morphometric analysis. Both the production and validation of the DTM are conducted in a GIS environment. The results indicate substantial discrepancies between the old DTM and contemporary acoustic data. A significant overall deviation of 3.39 ± 5.26 m in absolute bottom elevation differences and 0.00 ± 7.26 m in relative difference residuals (0.00 ± 2.11 m after 2nd polynomial model corrector surface fit) of the 2019 bathymetric dataset with respect to the ~1950 lake DTM and overall morphometry appear to be associated with a combination of tectonics, subsidence and karstic phenomena in the area. These observations could prove useful for the tectonics, geodynamics and seismicity with respect to the broader Corinth Rift region, as well as for environmental management and technical interventions in and around the lake. This dictates the necessity for new, extensive bathymetric measurements in order to produce an updated DTM of Lake Trichonis, reflecting current conditions and tailored to contemporary accuracy standards and state-of-the-art research in various disciplines in and around the lake.

scholarly journals Modeling Streamflow and Sediment Loads with a Photogrammetrically Derived UAS Digital Terrain Model: Empirical Evaluation from a Fluvial Aggregate Excavation Operation

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 20
Author(s):  
Joseph P. Hupy ◽  
Cyril O. Wilson
Keyword(s):  
Soil Erosion ◽  
Point Cloud ◽  
Empirical Evaluation ◽  
Digital Terrain Model ◽  
Surface Flow ◽  
Sediment Loading ◽  
Terrain Model ◽  
Digital Terrain ◽  
Statistical Measures ◽  
Geospatial Datasets

Soil erosion monitoring is a pivotal exercise at macro through micro landscape levels, which directly informs environmental management at diverse spatial and temporal scales. The monitoring of soil erosion can be an arduous task when completed through ground-based surveys and there are uncertainties associated with the use of large-scale medium resolution image-based digital elevation models for estimating erosion rates. LiDAR derived elevation models have proven effective in modeling erosion, but such data proves costly to obtain, process, and analyze. The proliferation of images and other geospatial datasets generated by unmanned aerial systems (UAS) is increasingly able to reveal additional nuances that traditional geospatial datasets were not able to obtain due to the former’s higher spatial resolution. This study evaluated the efficacy of a UAS derived digital terrain model (DTM) to estimate surface flow and sediment loading in a fluvial aggregate excavation operation in Waukesha County, Wisconsin. A nested scale distributed hydrologic flow and sediment loading model was constructed for the UAS point cloud derived DTM. To evaluate the effectiveness of flow and sediment loading generated by the UAS point cloud derived DTM, a LiDAR derived DTM was used for comparison in consonance with several statistical measures of model efficiency. Results demonstrate that the UAS derived DTM can be used in modeling flow and sediment erosion estimation across space in the absence of a LiDAR-based derived DTM.

scholarly journals A Physical Process Driven Digital Terrain Model Generating Method Based on D-NURBS

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 3115-3122
Author(s):  
Danlei Ye ◽  
Xin Jiang ◽  
Guanying Huo ◽  
Cheng Su ◽  
Zehong Lu ◽  
...  
Keyword(s):  
Physical Process ◽  
Digital Terrain Model ◽  
Terrain Model ◽  
Digital Terrain ◽  
Generating Method

scholarly journals Neotectonic evolution and sediment budget of the Meuse catchment in the Ardennes and the Roer Valley Rift System

2002 ◽  
Vol 81 (2) ◽  
pp. 211-215 ◽  
Author(s):  
R.T. Van Balen ◽  
R.F. Houtgast ◽  
F.M. Van der Wateren ◽  
J. Vandenberghe
Keyword(s):  
Digital Terrain Model ◽  
River System ◽  
Sediment Budget ◽  
Middle Pleistocene ◽  
Rift System ◽  
Terrain Model ◽  
Fluvial Terraces ◽  
Digital Terrain ◽  
History Of ◽  
Planation Surfaces

AbstractUsing marine planation surfaces, fluvial terraces and a digital terrain model, the amount of eroded rock volume versus time for the Meuse catchment has been computed. A comparison of the amount of eroded volume with the volume of sediment preserved in the Roer Valley Rift System shows that 12% of the eroded volume is trapped in this rift. The neotectonic uplift evolution of the Ardennes is inferred from the incision history of the Meuse River system and compared to the subsidence characteristics of the Roer Valley Rift System. Both areas are characterized by an early Middle Pleistocene uplift event.

scholarly journals Haziness Degree Evaluator: A Knowledge-Driven Approach for Haze Density Estimation

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3896
Author(s):  
Dat Ngo ◽  
Gi-Dong Lee ◽  
Bongsoon Kang
Keyword(s):  
Objective Function ◽  
Density Estimation ◽  
Digital Terrain Model ◽  
Single Image ◽  
Image Dehazing ◽  
Terrain Model ◽  
Digital Terrain ◽  
Proposed Model ◽  
Synthetic Datasets ◽  
Computation Analysis

Haze is a term that is widely used in image processing to refer to natural and human-activity-emitted aerosols. It causes light scattering and absorption, which reduce the visibility of captured images. This reduction hinders the proper operation of many photographic and computer-vision applications, such as object recognition/localization. Accordingly, haze removal, which is also known as image dehazing or defogging, is an apposite solution. However, existing dehazing algorithms unconditionally remove haze, even when haze occurs occasionally. Therefore, an approach for haze density estimation is highly demanded. This paper then proposes a model that is known as the haziness degree evaluator to predict haze density from a single image without reference to a corresponding haze-free image, an existing georeferenced digital terrain model, or training on a significant amount of data. The proposed model quantifies haze density by optimizing an objective function comprising three haze-relevant features that result from correlation and computation analysis. This objective function is formulated to maximize the image’s saturation, brightness, and sharpness while minimizing the dark channel. Additionally, this study describes three applications of the proposed model in hazy/haze-free image classification, dehazing performance assessment, and single image dehazing. Extensive experiments on both real and synthetic datasets demonstrate its efficacy in these applications.

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