Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data
ABSTRACT The aim of this study was to evaluate the performance of four ground filtering algorithms to generate digital terrain models (DTMs) from airborne light detection and ranging (LiDAR) data. The study area is a forest environment located in Washington state, USA with distinct classes of land u...
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Universidade Federal Rural do Rio de Janeiro
2018-02-01
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doaj-88084ba8d221414381f3d8fa558281c82020-11-25T00:22:45ZengUniversidade Federal Rural do Rio de JaneiroFloresta e Ambiente2179-80872018-02-0125210.1590/2179-8087.015016S2179-80872018000200105Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR DataCarlos Alberto SilvaCarine KlaubergÂngela Maria Klein HentzAna Paula Dalla CorteUelison RibeiroVeraldo LiesenbergABSTRACT The aim of this study was to evaluate the performance of four ground filtering algorithms to generate digital terrain models (DTMs) from airborne light detection and ranging (LiDAR) data. The study area is a forest environment located in Washington state, USA with distinct classes of land use and land cover (e.g., shrubland, grassland, bare soil, and three forest types according to tree density and silvicultural interventions: closed-canopy forest, intermediate-canopy forest, and open-canopy forest). The following four ground filtering algorithms were assessed: Weighted Linear Least Squares (WLS), Multi-scale Curvature Classification (MCC), Progressive Morphological Filter (PMF), and Progressive Triangulated Irregular Network (PTIN). The four algorithms performed well across the land cover, with the PMF yielding the least number of points classified as ground. Statistical differences between the pairs of DTMs were small, except for the PMF due to the highest errors. Because the forestry sector requires constant updating of topographical maps, open-source ground filtering algorithms, such as WLS and MCC, performed very well on planted forest environments. However, the performance of such filters should also be evaluated over complex native forest environments.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872018000200105&lng=en&tlng=enconiferground filteringdigital terrain modelpoint cloud |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Carlos Alberto Silva Carine Klauberg Ângela Maria Klein Hentz Ana Paula Dalla Corte Uelison Ribeiro Veraldo Liesenberg |
spellingShingle |
Carlos Alberto Silva Carine Klauberg Ângela Maria Klein Hentz Ana Paula Dalla Corte Uelison Ribeiro Veraldo Liesenberg Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data Floresta e Ambiente conifer ground filtering digital terrain model point cloud |
author_facet |
Carlos Alberto Silva Carine Klauberg Ângela Maria Klein Hentz Ana Paula Dalla Corte Uelison Ribeiro Veraldo Liesenberg |
author_sort |
Carlos Alberto Silva |
title |
Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data |
title_short |
Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data |
title_full |
Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data |
title_fullStr |
Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data |
title_full_unstemmed |
Comparing the Performance of Ground Filtering Algorithms for Terrain Modeling in a Forest Environment Using Airborne LiDAR Data |
title_sort |
comparing the performance of ground filtering algorithms for terrain modeling in a forest environment using airborne lidar data |
publisher |
Universidade Federal Rural do Rio de Janeiro |
series |
Floresta e Ambiente |
issn |
2179-8087 |
publishDate |
2018-02-01 |
description |
ABSTRACT The aim of this study was to evaluate the performance of four ground filtering algorithms to generate digital terrain models (DTMs) from airborne light detection and ranging (LiDAR) data. The study area is a forest environment located in Washington state, USA with distinct classes of land use and land cover (e.g., shrubland, grassland, bare soil, and three forest types according to tree density and silvicultural interventions: closed-canopy forest, intermediate-canopy forest, and open-canopy forest). The following four ground filtering algorithms were assessed: Weighted Linear Least Squares (WLS), Multi-scale Curvature Classification (MCC), Progressive Morphological Filter (PMF), and Progressive Triangulated Irregular Network (PTIN). The four algorithms performed well across the land cover, with the PMF yielding the least number of points classified as ground. Statistical differences between the pairs of DTMs were small, except for the PMF due to the highest errors. Because the forestry sector requires constant updating of topographical maps, open-source ground filtering algorithms, such as WLS and MCC, performed very well on planted forest environments. However, the performance of such filters should also be evaluated over complex native forest environments. |
topic |
conifer ground filtering digital terrain model point cloud |
url |
http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2179-80872018000200105&lng=en&tlng=en |
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