Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data

Silva, Carlos Alberto; Klauberg, Carine; Hentz, Ângela Maria Klein; Corte, Ana Paula Dalla; Ribeiro, Uelison; Liesenberg, Veraldo

doi:10.1590/2179-8087.015016

Cited by 18 publications

(22 citation statements)

References 11 publications

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“…The performance of the above-mentioned methods has been tested in forest areas where the results point to a higher discrepancy among filters as the terrain becomes steep and as the undergrowth increases [17][18][19]. This fact is common in other benchmarks, which shows the higher difference among the filter efficiencies as the terrain complexity increases [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 98%

“…Using and calibrating a filtering algorithm can involve several parameters that may require considerable knowledge from forest practitioners along a tedious and time-consuming process. Many benchmark studies applied parameters calibration to compare filters [18,26], but the practical effects of the calibration on the accuracy of the DTM and the forest attribute estimation are still unknown. This understanding would be valuable for ALS users by supporting them during the data processing to produce the DTM, especially when the forest characterization is the goal.…”

Section: Introductionmentioning

confidence: 99%

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Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

et al. 2020

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Ground point filtering of the airborne laser scanning (ALS) returns is crucial to derive digital terrain models (DTMs) and to perform ALS-based forest inventories. However, the filtering calibration requires considerable knowledge from users, who normally perform it by trial and error without knowing the impacts of the calibration on the produced DTM and the forest attribute estimation. Therefore, this work aims at calibrating four popular filtering algorithms and assessing their impact on the quality of the DTM and the estimation of forest attributes through the area-based approach. The analyzed filters were the progressive triangulated irregular network (PTIN), weighted linear least-squares interpolation (WLS) multiscale curvature classification (MCC), and the progressive morphological filter (PMF). The calibration was established by the vertical DTM accuracy, the root mean squared error (RMSE) using 3240 high-accuracy ground control points. The calibrated parameter sets were compared to the default ones regarding the quality of the estimation of the plot growing stock volume and the dominant height through multiple linear regression. The calibrated parameters allowed for producing DTM with RMSE varying from 0.25 to 0.26 m, against a variation from 0.26 to 0.30 m for the default parameters. The PTIN was the least affected by the calibration, while the WLS was the most affected. Compared to the default parameter sets, the calibrated sets resulted in dominant height equations with comparable accuracies for the PTIN, while WLS, MCC, and PFM reduced the models’ RMSE by 6.5% to 10.6%. The calibration of PTIN and MCC did not affect the volume estimation accuracy, whereas calibrated WLS and PMF reduced the RMSE by 3.4% to 7.9%. The filter calibration improved the DTM quality for all filters and, excepting PTIN, the filters increased the quality of forest attribute estimation, especially in the case of dominant height.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

et al. 2020

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“…Some filters are designed to process only the forested areas [3], [29], [30], and others to filter the urban landscapes containing large buildings [2], [31]. Silva et al [32] compared the performance of some airborne LiDAR data filtering algorithms in forested areas. The algorithms are weighted linear least-squares, multiscale curvature classification, progressive morphological filter, and progressive triangulated irregular network.…”

Section: Introductionmentioning

confidence: 99%

Automatic Weighted Splines Filter (AWSF): A New Algorithm for Extracting Terrain Measurements From Raw LiDAR Point Clouds

Abdeldayem

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Airborne raw light detection and ranging (LiDAR) measurements are georeferenced three-dimensional coordinates of ground surface, including all natural and man-made features. Extracting terrain surface measurements from raw LiDAR data is referred to as "filtering." Many filtering algorithms have been published, indicating the difficulty of the task; however, none performs equally well on all kinds of landscapes. This article presents a new algorithm, automatic weighted splines filter (AWSF), to extract the terrain points from raw LiDAR measurements. The mathematical model of the AWSF algorithm utilizes both the cubic smoothing splines (to interpolate and fit raw LiDAR data) and z-shaped function (to estimate the weight value of each point). The AWSF algorithm performance is compared against 14 filtering algorithms published between 1998 and 2019, as well as one unpublished (proprietary) algorithm designed by the world-leading company in processing remote sensing data, Harris Geospatial Solutions. Diverse landscape scenarios are used, ranging from open fields, rural land, and urban areas to dense forests on mountains where most of the filtering algorithms struggle as these areas represent the most difficult and challenging landscapes because the canopy prevents LiDAR pulses from reaching the ground surface. A total of 19 samples were tested; the results clearly show that the filtered terrain measurements are accurate and that the performance of the AWSF algorithm is stable for all the LiDAR data samples in comparison with the other filtering approaches.

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“…These important applications and many others require the creation of an accurate DTM. 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 [1] [2] [3].…”

Section: Introductionmentioning

confidence: 99%

“…However, airborne LiDAR data usually contain measurements for non-ground objects such as ve-F. F. F. Asal DOI: 10.4236/ars.2019.82004 53 Advances in Remote Sensing getation cover, buildings, etc., that need to be efficiently removed for creation of a reliable DTM representing the actual ground surface as if it is a bare earth surface [9]. The Extracted DTMs from airborne LiDAR measurements through stripping off non-ground objects can be employed in many applications such as mapping of the earth's surface and hydrodynamic modeling for flood risk assessments [1]. Thus, since the availability of airborne LiDAR measurements extraction of bare earth DTMs have become attainable with high accuracy through filtering of non-ground point from the high-density airborne LiDAR data termed as point cloud data [10].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of the Digital Terrain Models Extracted from Airborne LiDAR Point Clouds Using Different Filtering Approaches in Residential Landscapes

Asal¹

2019

ARS

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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.

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Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data

Cited by 18 publications

References 11 publications

Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

Impact of Calibrating Filtering Algorithms on the Quality of LiDAR-Derived DTM and on Forest Attribute Estimation through Area-Based Approach

Automatic Weighted Splines Filter (AWSF): A New Algorithm for Extracting Terrain Measurements From Raw LiDAR Point Clouds

Comparative Analysis of the Digital Terrain Models Extracted from Airborne LiDAR Point Clouds Using Different Filtering Approaches in Residential Landscapes

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