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Deriving DSMs from LiDAR data with kriging

Deriving DSMs from LiDAR data with kriging
Deriving DSMs from LiDAR data with kriging
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
0143-1161
2519-2524
Lloyd, C.D.
2d3bd538-2045-4fbb-900c-9f77c386bbc9
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Lloyd, C.D.
2d3bd538-2045-4fbb-900c-9f77c386bbc9
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b

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

Record type: Article

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

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Published date: 2002

Identifiers

Local EPrints ID: 14953
URI: http://eprints.soton.ac.uk/id/eprint/14953
ISSN: 0143-1161
PURE UUID: 439af97e-33ad-4f5b-8ded-33a65e47319d
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880

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Date deposited: 10 Mar 2005
Last modified: 16 Mar 2024 02:46

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Contributors

Author: C.D. Lloyd
Author: P.M. Atkinson ORCID iD

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