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Deriving DSMs from LiDAR data with kriging

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Deriving DSMs from LiDAR data with kriging. / Lloyd, Christopher D.; Atkinson, Peter M.
In: International Journal of Remote Sensing, Vol. 23, No. 12, 2002, p. 2519-2524.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Lloyd, CD & Atkinson, PM 2002, 'Deriving DSMs from LiDAR data with kriging', International Journal of Remote Sensing, vol. 23, no. 12, pp. 2519-2524. https://doi.org/10.1080/01431160110097998

APA

Lloyd, C. D., & Atkinson, P. M. (2002). Deriving DSMs from LiDAR data with kriging. International Journal of Remote Sensing, 23(12), 2519-2524. https://doi.org/10.1080/01431160110097998

Vancouver

Lloyd CD, Atkinson PM. Deriving DSMs from LiDAR data with kriging. International Journal of Remote Sensing. 2002;23(12):2519-2524. doi: 10.1080/01431160110097998

Author

Lloyd, Christopher D. ; Atkinson, Peter M. / Deriving DSMs from LiDAR data with kriging. In: International Journal of Remote Sensing. 2002 ; Vol. 23, No. 12. pp. 2519-2524.

Bibtex

@article{c13733f755ad48fc964a067b3c22eb4f,
title = "Deriving DSMs from LiDAR data with kriging",
abstract = "Light Detection And Ranging (LiDAR) is becoming a widely used source of digital elevation data. LiDAR data are obtained on a point support and it is necessary to interpolate to a regular grid if a digital surface model (DSM) is required. When the data are numerous, and close together in space, simple linear interpolation algorithms are usually considered sufficient. In this letter, inverse distance weighting (IDW), ordinary kriging (OK) and kriging with a trend model (KT) are assessed for the construction of DSMs from LiDAR data. It is shown that the advantages of KT become more apparent as the number of data points decrease (and the sample spacing increases). It is argued that KT may be advantageous in some instances where the desire is to derive a DSM from LiDAR point data but in many cases a simpler approach, such as IDW, may suffice.",
author = "Lloyd, {Christopher D.} and Atkinson, {Peter M.}",
note = "M1 - 12",
year = "2002",
doi = "10.1080/01431160110097998",
language = "English",
volume = "23",
pages = "2519--2524",
journal = "International Journal of Remote Sensing",
issn = "0143-1161",
publisher = "TAYLOR & FRANCIS LTD",
number = "12",

}

RIS

TY - JOUR

T1 - Deriving DSMs from LiDAR data with kriging

AU - Lloyd, Christopher D.

AU - Atkinson, Peter M.

N1 - M1 - 12

PY - 2002

Y1 - 2002

N2 - Light Detection And Ranging (LiDAR) is becoming a widely used source of digital elevation data. LiDAR data are obtained on a point support and it is necessary to interpolate to a regular grid if a digital surface model (DSM) is required. When the data are numerous, and close together in space, simple linear interpolation algorithms are usually considered sufficient. In this letter, inverse distance weighting (IDW), ordinary kriging (OK) and kriging with a trend model (KT) are assessed for the construction of DSMs from LiDAR data. It is shown that the advantages of KT become more apparent as the number of data points decrease (and the sample spacing increases). It is argued that KT may be advantageous in some instances where the desire is to derive a DSM from LiDAR point data but in many cases a simpler approach, such as IDW, may suffice.

AB - Light Detection And Ranging (LiDAR) is becoming a widely used source of digital elevation data. LiDAR data are obtained on a point support and it is necessary to interpolate to a regular grid if a digital surface model (DSM) is required. When the data are numerous, and close together in space, simple linear interpolation algorithms are usually considered sufficient. In this letter, inverse distance weighting (IDW), ordinary kriging (OK) and kriging with a trend model (KT) are assessed for the construction of DSMs from LiDAR data. It is shown that the advantages of KT become more apparent as the number of data points decrease (and the sample spacing increases). It is argued that KT may be advantageous in some instances where the desire is to derive a DSM from LiDAR point data but in many cases a simpler approach, such as IDW, may suffice.

U2 - 10.1080/01431160110097998

DO - 10.1080/01431160110097998

M3 - Journal article

VL - 23

SP - 2519

EP - 2524

JO - International Journal of Remote Sensing

JF - International Journal of Remote Sensing

SN - 0143-1161

IS - 12

ER -