Evaluating Error of Lidar Derived Dem Interpolation for Vegetation Area

Ismail, Zamri; Khanan, M. F. Abdul; Omar, F. Z.; Rahman, Muhammad Zulkarnain Abdul; Salleh, M. R. Mohd

doi:10.5194/isprs-archives-xlii-4-w1-141-2016

Cited by 12 publications

(17 citation statements)

References 22 publications

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“…Existing algorithms running on the HPC to generate DEM from LiDAR points have been designed in multiple processes mode. As we all known, the core computation of this procedure is spatial interpolation [29]: when a grid cell near the boundary of the data block is interpolated, points fall in the search radius of this cell, but are read by other processes that cannot be accessed directly, forming the boundary problem (Figure 1c). Usually, there are two methods to solve this problem: (1) sending the values of the point to processes that need them [15]; and (2) adding a data buffer on each data block [13].…”

Section: Our Ideamentioning

confidence: 99%

A Hybrid Process/Thread Parallel Algorithm for Generating DEM from LiDAR Points

Ren

Chen

et al. 2017

IJGI

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Airborne Light Detection and Ranging (LiDAR) is widely used in digital elevation model (DEM) generation. However, the very large volume of LiDAR datasets brings a great challenge for the traditional serial algorithm. Using parallel computing to accelerate the efficiency of DEM generation from LiDAR points has been a hot topic in parallel geo-computing. Generally, most of the existing parallel algorithms running on high-performance clusters (HPC) were in process-paralleling mode, with a static scheduling strategy. The static strategy would not respond dynamically according to the computation progress, leading to load unbalancing. Additionally, because each process has independent memory space, the cost of dealing with boundary problems increases obviously with the increase in the number of processes. Actually, these two problems can have a significant influence on the efficiency of DEM generation for larger datasets, especially for those of irregular shapes. Thus, to solve these problems, we combined the advantages of process-paralleling with the advantages of thread-paralleling, forming a new idea: using process-paralleling to achieve a flexible schedule and scalable computation, using thread-paralleling inside the process to reduce boundary problems. Therefore, we proposed a hybrid process/thread parallel algorithm for generating DEM from LiDAR points. Firstly, at the process level, we designed a parallel method (PPDB) to accelerate the partitioning of LiDAR points. We also proposed a new dynamic scheduling strategy to achieve better load balancing. Secondly, at the thread level, we designed an asynchronous parallel strategy to hide the cost of LiDAR points' reading. Lastly, we tested our algorithm with three LiDAR datasets. Experiments showed that our parallel algorithm had no influence on the accuracy of the resultant DEM. At the same time, our algorithm reduced the conversion time from 112,486 s to 2342 s when we used the largest dataset (150 GB). The PPDB was parallelizable and the new dynamic scheduling strategy achieved a better load balancing. Furthermore, the asynchronous parallel strategy reduced the impact of LiDAR points reading. When compared with the traditional process-paralleling algorithm, the hybrid process/thread parallel algorithm improved the conversion efficiency by 30%.

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Section: Our Ideamentioning

confidence: 99%

A Hybrid Process/Thread Parallel Algorithm for Generating DEM from LiDAR Points

Ren

Chen

et al. 2017

IJGI

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“…Previous research has not established a definitive consensus regarding the performance of interpolation methods. For example, Inverse Distance Weighted (IDW), possibly the most widely used method, is found to have a relatively low accuracy in some studies [38,[41][42][43][44], while in others it provides the most accurate ground surface [45,46]. However, when comparing previous research, one has to take into account the source of altimetry data.…”

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“…The accuracy of interpolation can be assessed using synthetic [41,47] or real data. The main sources of elevation data are: digital stereo-matching [42], topographical maps with isolines [46] and ground surveys carried out using topographical equipment [43,44], GNSS receivers [48] or LiDAR sensors [38,45,49].Regarding the influence of external factors on DTM quality, the consensus is relatively better. Among the main factors that are usually taken into account when analysing DTM quality, the following are typically found to have some degree of influence on accuracy: morphological complexity of the ground surface [41][42][43]45,49], density of input data [42,43,50,51], model resolution [38,46] and the presence or characteristics of vegetation [44,45,49].In this paper, we propose a quantitative analysis of the relative performance of nine interpolation algorithms, with the aim of testing the accuracy of DTM generation from LiDAR point cloud data in difficult conditions (mountainous terrain with a high degree of surface roughness and covered by dense forest vegetation).…”

mentioning

confidence: 99%

mentioning

confidence: 99%

“…Regarding the influence of external factors on DTM quality, the consensus is relatively better. Among the main factors that are usually taken into account when analysing DTM quality, the following are typically found to have some degree of influence on accuracy: morphological complexity of the ground surface [41][42][43]45,49], density of input data [42,43,50,51], model resolution [38,46] and the presence or characteristics of vegetation [44,45,49].…”

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Accuracy of Ground Surface Interpolation from Airborne Laser Scanning (ALS) Data in Dense Forest Cover

Cățeanu

Ciubotaru

2020

IJGI

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A digital model of the ground surface has many potential applications in forestry. Nowadays, Light Detection and Ranging (LiDAR) is one of the main sources for collecting morphological data. Point clouds obtained via laser scanning are used for modelling the ground surface by interpolation, a process which is affected by various errors. Using LiDAR data to collect ground surface data for forestry applications is a challenging scenario because the presence of forest vegetation will hinder the ability of laser pulses to reach the ground. The density of ground observations will be therefore reduced and not homogenous (as it is affected by the variations in canopy density). Furthermore, forest areas are generally present in mountainous areas, in which case the interpolation of the ground surface is more challenging. In this paper, we present a comparative analysis of interpolation accuracy for nine algorithms, which are used for generating Digital Terrain Models from Airborne Laser Scanning (ALS) data, in mountainous terrain covered by dense forest vegetation. For most of the algorithms we find a similar performance in terms of general accuracy, with RMSE values between 0.11 and 0.28 m (when model resolution is set to 0.5 m). Five of the algorithms (Natural Neighbour, Delauney Triangulation, Multilevel B-Spline, Thin-Plate Spline and Thin-Plate Spline by TIN) have vertical errors of less than 0.20 m for over 90 percent of validation points. Meanwhile, for most algorithms, major vertical errors (of over 1 m) are associated with less than 0.05 percent of validation points. Digital Terrain Model (DTM) resolution, ground slope and point cloud density influence the quality of the ground surface model, while for canopy density we find a less significant link with the quality of the interpolated DTMs.data can be used for tree inventories [17][18][19][20], monitoring vegetation health [21][22][23][24] or generating Canopy Height Models (CHMs) [16,25].The main advantage of LiDAR, with regard to forestry, is the fact that laser pulses can penetrate the canopy cover, with some of the pulses reaching the ground level. Therefore, geomorphological data is collected even if the ground is covered by forest vegetation, allowing the generation of a detailed and accurate ground surface model.In general, a data structure that models the elevation over an area is called a Digital Elevation Model (DEM). In short, a DEM is simply a surface that represents the elevation of a certain area in reference to a common vertical datum. When this type of model is used for a representation of the bare-earth surface, it can also be termed as Digital Terrain Model (DTM). In this paper, we opted for the term DTM, in order to emphasize the fact that the models we analyse, while generally speaking classify as DEMs, are meant specifically to represent the surface of the bare-earth.To obtain such a representation of the ground surface, a LiDAR point cloud must initially be filtered-a process which involves the separation of ground-level points from the orig...

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