With continuous developments in LiDAR technologies high point cloud densities have been attainable but accompanied by challenges for processing big volumes of data. Reductions in high point cloud densities are expected to lower data acquisition and data processing costs; however this could affect the characteristics of the generated Digital Elevation Models (DEMs). This research aimed to evaluate the effects of reductions in airborne LiDAR point cloud data densities on the visual and statistical characteristics of the generated DEMs. DEMs have been created from a dataset which constitutes last returns of raw LiDAR data that was acquired at bare lands for Gilmer County, USA between March and April 2004, where qualitative and quantitative testing analyses have been performed. Visual analysis has shown that the DEM can withstand a considerable degree of quality with reduced densities down to 0.128pts/m 2 (47% of the data remaining), however degradations in the DEM visual characteristics appeared in coarser tones and rougher textures have occurred with more reductions. Additionally, the statistical analysis has indicated that the standard deviations of the DEM elevations have decreased by only 22% of the total decrease with data density reductions down to 0.101pts/m 2 (37% of the data remaining) while greater rate of decreasing in the standard deviations has occurred with more reductions referring to greater rate of surface smoothing and elevation approximating. Furthermore, the accuracy analysis testing has given that the DEM accuracy has degraded by only 4.83% of the total degradations with data density reductions down to 0.128pts/m 2 , however great deteriorations in the DEM accuracy have occurred with more data reductions. Finally, it is recommended that LiDAR data can withstand point density reductions down to 0.128pts/m 2 (about 50% of the data) without big deteriorations in the visual and statistical characteristics of the generated DEMs.

Light Detection And Ranging (LiDAR) is a well-established active remote sensing technology that can provide accurate digital elevation measurements for the terrain and non-ground objects such as vegetations and buildings, etc. Non-ground objects need to be removed for creation of a Digital Terrain Model (DTM) which is a continuous surface representing only ground surface points. This study aimed at comparative analysis of three main filtering approaches for stripping off non-ground objects namely; Gaussian low pass filter, focal analysis mean filter and DTM slope-based filter of varying window sizes in creation of a reliable DTM from airborne LiDAR point clouds. A sample of LiDAR data provided by the ISPRS WG III/4 captured at Vaihingen in Germany over a pure residential area has been used in the analysis. Visual analysis has indicated that Gaussian low pass filter has given blurred DTMs of attenuated high-frequency objects and emphasized low-frequency objects while it has achieved improved removal of non-ground object at larger window sizes. Focal analysis mean filter has shown better removal of nonground objects compared to Gaussian low pass filter especially at large window sizes where details of non-ground objects almost have diminished in the DTMs from window sizes of 25 × 25 and greater. DTM slope-based filter has created bare earth models that have been full of gabs at the positions of the non-ground objects where the sizes and numbers of that gabs have increased with increasing the window sizes of filter. Those gaps have been closed through exploitation of the spline interpolation method in order to get continuous surface representing bare earth landscape. Comparative analysis has shown that the minimum elevations of the DTMs increase with increasing the filter widow sizes till 21 × 21 and 31 × 31 for the Gaussian low pass filter and How to cite this paper: Asal, F.F.F. (2019) Comparative Analysis of the Digital Terrain Models Extracted from Airborne Li-DAR Point Clouds Using Different Filtering Approaches in Residential Landscapes.

No abstract

ABSTRACT:Digital elevation data obtained from different Engineering Surveying techniques is utilized in generating Digital Elevation Model (DEM), which is employed in many Engineering and Environmental applications. This data is usually in discrete point format making it necessary to utilize an interpolation approach for the creation of DEM. Quality assessment of the DEM is a vital issue controlling its use in different applications; however this assessment relies heavily on statistical methods with neglecting the visual methods. The research applies visual analysis investigation on DEMs generated using IDW interpolator of varying powers in order to examine their potential in the assessment of the effects of the variation of the IDW power on the quality of the DEMs. Real elevation data has been collected from field using total station instrument in a corrugated terrain. DEMs have been generated from the data at a unified cell size using IDW interpolator with power values ranging from one to ten. Visual analysis has been undertaken using 2D and 3D views of the DEM; in addition, statistical analysis has been performed for assessment of the validity of the visual techniques in doing such analysis. Visual analysis has shown that smoothing of the DEM decreases with the increase in the power value till the power of four; however, increasing the power more than four does not leave noticeable changes on 2D and 3D views of the DEM. The statistical analysis has supported these results where the value of the Standard Deviation (SD) of the DEM has increased with increasing the power. More specifically, changing the power from one to two has produced 36% of the total increase (the increase in SD due to changing the power from one to ten) in SD and changing to the powers of three and four has given 60% and 75% respectively. This refers to decrease in DEM smoothing with the increase in the power of the IDW. The study also has shown that applying visual methods supported by statistical analysis has proven good potential in the DEM quality assessment.

In this paper, the effect of integration cap size on geoid determination was studied, when using gravity disturbances and vertical deflection as input data. For this purpose, a series of varying cap radii was used to predict gravimetric geoidal heights at discrete GPS-Benchmarks, using both the Hotine and deflection-geoid techniques. In both cases, the results showed significant dependence of the resulting geoid accuracy on the integration cap size. The two methods showed comparable behavior in the vicinity of the cap radius, which i s consistent wit? the maximal resolution of the reference geopotential model. At larger cap sizes, the perfomance of the deflection data type was significantly better than the gravity disturbances, which in turn showed a dramatic degradation of the geoid accuracy. Therefore, when solving for the geoid without modifying the integration kemel, it is strongly recommended to nse large cap sizes along with the deflection-geoid method. If gravity disturbances are to be used for geoid determination, then it is recommended to use as small integration cap as possible.