Area-based quality control of airborne laser scanning 3D models for different land classes using terrestrial laser scanning: sample survey in Houston, USA

Airborne laser scanning (ALS) is a remote-sensing technique that provides scale-accurate 3D models consisting of dense point clouds with x, y planimetric coordinates and altitude z. Using ALS, very high-resolution (VHR) digital surface models (DSMs) have been widely used for commercial and scientific applications since the early 1990s. Although there is widespread usage, there has been little comprehensive investigation of quality control for ALS DSMs in the literature, as most studies have been limited to assessing point-based vertical accuracy. This article is dedicated to investigating the quality of ALS DSMs for different land classes using statistical and visual approaches based on absolute and relative vertical accuracy metrics. Rather than a limited number of ground control points (GCP), the model-to-model-based approach is applied and DSMs derived from terrestrial laser scanning (TLS) point clouds that have around 5 mm absolute and 3 mm relative geolocation accuracy were used as the reference data for comparison. The results demonstrate that in open, grass, and building land classes, the ALS DSMs reached both standard deviation (?) and normalized median absolute deviation (NMAD) of 3–5 cm after the elimination of any systematic biases. This result sufficiently satisfies the vertical accuracy requirements for 1/1000-scale topographic maps determined by National Digital Elevation Program (NDEP) specifications. In tall vegetation, a higher number of discrepancies larger than 0.5 m exist, reversing the relation between ? and NMAD. These vegetation errors also do not appear to be normally distributed. As an additional investigation, the performance of ALS DEMs under dense high-vegetation areas was assessed. These under-canopy ALS DEMs, created using only classified ground returns, offer both ? and NMAD of 12–14 cm, a performance level that is difficult to achieve under-canopy using photogrammetric techniques. © 2015 Taylor & Francis.

Yazar Sefercik U.G.
Glennie C.
Singhania A.
Hauser D.
Yayın Türü Article
Tek Biçim Adres https://hdl.handle.net/20.500.12628/4346
Tek Biçim Adres 10.1080/01431161.2015.1110260
Koleksiyonlar Araştırma Çıktıları | WoS | Scopus | TR-Dizin | PubMed | SOBİAD
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Dergi Adı International Journal of Remote Sensing
Dergi Cilt Bilgisi 36
Dergi Sayısı 23
Sayfalar 5916 - 5934
Yayın Yılı 2015
Eser Adı
[dc.title]
Area-based quality control of airborne laser scanning 3D models for different land classes using terrestrial laser scanning: sample survey in Houston, USA
Yazar
[dc.contributor.author]
Sefercik U.G.
Yazar
[dc.contributor.author]
Glennie C.
Yazar
[dc.contributor.author]
Singhania A.
Yazar
[dc.contributor.author]
Hauser D.
Yayın Yılı
[dc.date.issued]
2015
Yayıncı
[dc.publisher]
Taylor and Francis Ltd.
Yayın Türü
[dc.type]
article
Özet
[dc.description.abstract]
Airborne laser scanning (ALS) is a remote-sensing technique that provides scale-accurate 3D models consisting of dense point clouds with x, y planimetric coordinates and altitude z. Using ALS, very high-resolution (VHR) digital surface models (DSMs) have been widely used for commercial and scientific applications since the early 1990s. Although there is widespread usage, there has been little comprehensive investigation of quality control for ALS DSMs in the literature, as most studies have been limited to assessing point-based vertical accuracy. This article is dedicated to investigating the quality of ALS DSMs for different land classes using statistical and visual approaches based on absolute and relative vertical accuracy metrics. Rather than a limited number of ground control points (GCP), the model-to-model-based approach is applied and DSMs derived from terrestrial laser scanning (TLS) point clouds that have around 5 mm absolute and 3 mm relative geolocation accuracy were used as the reference data for comparison. The results demonstrate that in open, grass, and building land classes, the ALS DSMs reached both standard deviation (?) and normalized median absolute deviation (NMAD) of 3–5 cm after the elimination of any systematic biases. This result sufficiently satisfies the vertical accuracy requirements for 1/1000-scale topographic maps determined by National Digital Elevation Program (NDEP) specifications. In tall vegetation, a higher number of discrepancies larger than 0.5 m exist, reversing the relation between ? and NMAD. These vegetation errors also do not appear to be normally distributed. As an additional investigation, the performance of ALS DEMs under dense high-vegetation areas was assessed. These under-canopy ALS DEMs, created using only classified ground returns, offer both ? and NMAD of 12–14 cm, a performance level that is difficult to achieve under-canopy using photogrammetric techniques. © 2015 Taylor & Francis.
Kayıt Giriş Tarihi
[dc.date.accessioned]
2019-12-23
Açık Erişim Tarihi
[dc.date.available]
2019-12-23
Yayın Dili
[dc.language.iso]
eng
Haklar
[dc.rights]
info:eu-repo/semantics/closedAccess
ISSN
[dc.identifier.issn]
0143-1161
İlk Sayfa Sayısı
[dc.identifier.startpage]
5916
Son Sayfa Sayısı
[dc.identifier.endpage]
5934
Dergi Adı
[dc.relation.journal]
International Journal of Remote Sensing
Dergi Sayısı
[dc.identifier.issue]
23
Dergi Cilt Bilgisi
[dc.identifier.volume]
36
Tek Biçim Adres
[dc.identifier.uri]
https://dx.doi.org/10.1080/01431161.2015.1110260
Tek Biçim Adres
[dc.identifier.uri]
https://hdl.handle.net/20.500.12628/4346
Görüntülenme Sayısı ( Şehir )
Görüntülenme Sayısı ( Ülke )
Görüntülenme Sayısı ( Zaman Dağılımı )
Görüntülenme
8
09.12.2022 tarihinden bu yana
İndirme
1
09.12.2022 tarihinden bu yana
Son Erişim Tarihi
18 Ocak 2024 17:44
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accuracy absolute vertical number performance ground vegetation relative investigation quality control limited deviation classes models clouds under-canopy scanning discrepancies higher larger median (NMAD) 3–5 cm elimination Digital systematic Elevation Program determined (NDEP) specifications topographic National 1/1000-scale
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