scholarly journals itmlogic: The Irregular Terrain Model by Longley and Rice

2020 ◽  
Vol 5 (51) ◽  
pp. 2266
Author(s):  
Edward Oughton ◽  
Tom Russell ◽  
Joel Johnson ◽  
Caglar Yardim ◽  
Julius Kusuma
Keyword(s):  
Irregular Terrain ◽  
Terrain Model

scholarly journals The Quality of the Propagation Prediction with Radio Mobile for Tetra Communication Systems

2016 ◽  
Vol 22 (3) ◽  
pp. 510-514
Author(s):  
Iulian Bouleanu ◽  
Robert Helbet ◽  
Neculai Craiu
Keyword(s):  
Communication Systems ◽  
Propagation Model ◽  
Irregular Terrain ◽  
Terrain Model ◽  
Radio Propagation Model ◽  
Point To Point ◽  
Propagation Prediction ◽  
Prediction Capability ◽  
Made In

Abstract We present a short analysis of the prediction quality offered by Radio Mobile application when used for frequency band of TETRA systems in Romania. The simulation results provided by Radio Mobile were compared against own measurements made in multiple locations in Sibiu city. The correlation coefficient between simulated and measured values of signal power was of 0.907 while the maximum variation between data in the strings was 9.6 dB and the average standard deviation was 4.4 dB. These results indicate that the radio propagation model ‘Two-ray Irregular Terrain Model Point to Point’ (ITM) provides a prediction capability that offers a reliable planning of the radio coverage of TETRA systems.

A renovated model for spatial analysis of pollutant runoff loads in agricultural watershed

1998 ◽  
Vol 38 (10) ◽  
pp. 207-214 ◽  
Author(s):  
Sung Ryong Ha ◽  
Dhong Il Jung ◽  
Cho Hee Yoon
Keyword(s):  
Stream Water ◽  
Digital Terrain Model ◽  
Pollution Sources ◽  
Agricultural Watershed ◽  
Water Model ◽  
Digital Information ◽  
Gis Technology ◽  
Terrain Model ◽  
Unit Loading ◽  
Pollutant Discharge

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.

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.

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