Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density
In precision agriculture detailed geoinformation on plant and soil properties plays an important role. Laser scanning already has been used to describe in-field variations of plant growth in 3D and over time and can serve as valuable complementary topographic data set for remote sensing, such as d...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2013-10-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5-W2/133/2013/isprsannals-II-5-W2-133-2013.pdf |
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| author | K. Koenig B. Höfle L. Müller M. Hämmerle T. Jarmer B. Siegmann H. Lilienthal |
| author_facet | K. Koenig B. Höfle L. Müller M. Hämmerle T. Jarmer B. Siegmann H. Lilienthal |
| author_sort | K. Koenig |
| collection | DOAJ |
| description | In precision agriculture detailed geoinformation on plant and soil properties plays an important role. Laser scanning already has been
used to describe in-field variations of plant growth in 3D and over time and can serve as valuable complementary topographic data
set for remote sensing, such as deriving soil properties from hyperspectral sensors. In this study full-waveform laser scanning data
acquired with a Riegl VZ-400 instrument is used to classify 3D point clouds into post-harvest straw residues and bare soil. A
workflow for point cloud based classification is presented using radiometric and geometric point features. A radiometric correction is
performed by using a range-correction function <i>f(r)</i>, which is derived from lab experiments with a reference target of known
reflectance. Thereafter, the corrected signal amplitude and local height features are explored with respect to the target classes. The
following procedure includes feature calculation, decision tree analysis, point cloud classification and finally result validation using
detailed classified reference RGB images. The classification tree separates the classes of harvest residues and bare soil with an
accuracy of 96% by using geometric and radiometric features. The LiDAR-derived harvest residue coverage value of 75% lies in
accordance with the image-based reference (coverage of 68%). The results indicate the high potential of radiometric features for
natural surface classification, particularly in combination with geometric features. |
| first_indexed | 2024-12-11T20:18:59Z |
| format | Article |
| id | doaj.art-ca897cafcb554ba59dbb00c2efd182e2 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-11T20:18:59Z |
| publishDate | 2013-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-ca897cafcb554ba59dbb00c2efd182e22022-12-22T00:52:07ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502013-10-01II-5-W213313810.5194/isprsannals-II-5-W2-133-2013Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues DensityK. Koenig0B. Höfle1L. Müller2M. Hämmerle3T. Jarmer4B. Siegmann5H. Lilienthal6Institute of Geography, University of Heidelberg, GermanyInstitute of Geography, University of Heidelberg, GermanyInstitute of Geography, University of Heidelberg, GermanyInstitute of Geography, University of Heidelberg, GermanyInstitute for Geoinformatics and Remote Sensing, University of Osnabrueck, GermanyInstitute for Geoinformatics and Remote Sensing, University of Osnabrueck, GermanyJulius Kühn-Institut, Institute for Crop and Soil Science, GermanyIn precision agriculture detailed geoinformation on plant and soil properties plays an important role. Laser scanning already has been used to describe in-field variations of plant growth in 3D and over time and can serve as valuable complementary topographic data set for remote sensing, such as deriving soil properties from hyperspectral sensors. In this study full-waveform laser scanning data acquired with a Riegl VZ-400 instrument is used to classify 3D point clouds into post-harvest straw residues and bare soil. A workflow for point cloud based classification is presented using radiometric and geometric point features. A radiometric correction is performed by using a range-correction function <i>f(r)</i>, which is derived from lab experiments with a reference target of known reflectance. Thereafter, the corrected signal amplitude and local height features are explored with respect to the target classes. The following procedure includes feature calculation, decision tree analysis, point cloud classification and finally result validation using detailed classified reference RGB images. The classification tree separates the classes of harvest residues and bare soil with an accuracy of 96% by using geometric and radiometric features. The LiDAR-derived harvest residue coverage value of 75% lies in accordance with the image-based reference (coverage of 68%). The results indicate the high potential of radiometric features for natural surface classification, particularly in combination with geometric features.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5-W2/133/2013/isprsannals-II-5-W2-133-2013.pdf |
| spellingShingle | K. Koenig B. Höfle L. Müller M. Hämmerle T. Jarmer B. Siegmann H. Lilienthal Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density |
| title_full | Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density |
| title_fullStr | Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density |
| title_full_unstemmed | Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density |
| title_short | Radiometric Correction of Terrestrial LiDAR Data for Mapping of Harvest Residues Density |
| title_sort | radiometric correction of terrestrial lidar data for mapping of harvest residues density |
| url | https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-5-W2/133/2013/isprsannals-II-5-W2-133-2013.pdf |
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