Modeling Micro-Terrain Using the Variable Resolution Terrain Model.

Runoff loads of pollutant in agricultural watersheds were spatially analyzed by using geographic information system(GIS) technology. The topological relationship between pollution sources in the watershed was, first of all, identified by using the developed digital map of land use and then the pollutant loads generated from each source was estimated by applying a conventional unit loading factor on the obtained digital information of pollution sources. To evaluate the loads delivered from spatially distributed pollution sources to monitoring stations in down stream via surface of watershed, a renovated empirical model incorporated with the information of pollutant discharge path was developed through introducing a digital terrain model(DTM) technique. In this model, the function of degradation of pollution loads during delivery process was simplified so that each watershed could have a basin-wide self-purification capacity which would be considered to be possessed inherently in each watershed and could retard the discharge of pollutants from sources generated to stream water. Model credibility showed good consistency with comparing the simulated values with observed data. Monte Carlo optimizing technique made it possible to estimate the basin-wide self-purification coefficients.

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A Non-hydrostatic Variable Resolution Atmospheric Model in ACME

10.2172/1509842 ◽

2018 ◽

Author(s):

David Matthew Hall ◽

Mark Taylor ◽

Paul Ullrich ◽

Carol Woodward

Keyword(s):

Atmospheric Model ◽

Variable Resolution

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

Forests ◽

10.3390/f12030265 ◽

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.

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Creating a Terrain Model for Floodplain Mapping

Journal of Hydrologic Engineering ◽

10.1061/(asce)1084-0699(2002)7:2(100) ◽

2002 ◽

Vol 7 (2) ◽

pp. 100-108 ◽

Cited By ~ 56

Author(s):

Eric C. Tate ◽

David R. Maidment ◽

Francisco Olivera ◽

David J. Anderson

Keyword(s):

Terrain Model ◽

Floodplain Mapping

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