Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis
The aim of this study is to present an automatic approach for olive tree dendrometric parameter estimation from airborne laser scanning (ALS) data. The proposed method is based on a unique combination of the alpha-shape algorithm applied to normalized point cloud and principal component analysis. A...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2017-11-01
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| Series: | GIScience & Remote Sensing |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/15481603.2017.1351148 |
| _version_ | 1797679071941885952 |
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| author | Edyta Hadas Andrzej Borkowski Javier Estornell Przemyslaw Tymkow |
| author_facet | Edyta Hadas Andrzej Borkowski Javier Estornell Przemyslaw Tymkow |
| author_sort | Edyta Hadas |
| collection | DOAJ |
| description | The aim of this study is to present an automatic approach for olive tree dendrometric parameter estimation from airborne laser scanning (ALS) data. The proposed method is based on a unique combination of the alpha-shape algorithm applied to normalized point cloud and principal component analysis. A key issue of the alpha-shape algorithm is to define the α parameter, as it directly affects the crown delineation results. We propose to adjust this parameter based on a group of representative trees in an orchard for which the classical field measurements were performed. The best value of the α parameter is one whose correlation coefficient of dendrometric parameters between field measurements and estimated values is the highest. We determined crown diameters as principal components of ALS points representing a delineated crown. The method was applied to a test area of an olive orchard in Spain. The tree dendrometric parameters estimated from ALS data were compared with field measurements to assess the quality of the developed approach. We found the method to be equally good or even superior to previously investigated semi-automatic methods. The average error is 19% for tree height, 53% for crown base height, and 13% and 9% for the length of the longer diameter and perpendicular diameter, respectively. |
| first_indexed | 2024-03-11T23:09:06Z |
| format | Article |
| id | doaj.art-1afa9b2fd3f0412185930017a5f6f702 |
| institution | Directory Open Access Journal |
| issn | 1548-1603 1943-7226 |
| language | English |
| last_indexed | 2024-03-11T23:09:06Z |
| publishDate | 2017-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj.art-1afa9b2fd3f0412185930017a5f6f7022023-09-21T12:34:14ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262017-11-0154689891710.1080/15481603.2017.13511481351148Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysisEdyta Hadas0Andrzej Borkowski1Javier Estornell2Przemyslaw Tymkow3Wrocław University of Environmental and Life SciencesWrocław University of Environmental and Life SciencesUniversitat Politècnica de ValènciaWrocław University of Environmental and Life SciencesThe aim of this study is to present an automatic approach for olive tree dendrometric parameter estimation from airborne laser scanning (ALS) data. The proposed method is based on a unique combination of the alpha-shape algorithm applied to normalized point cloud and principal component analysis. A key issue of the alpha-shape algorithm is to define the α parameter, as it directly affects the crown delineation results. We propose to adjust this parameter based on a group of representative trees in an orchard for which the classical field measurements were performed. The best value of the α parameter is one whose correlation coefficient of dendrometric parameters between field measurements and estimated values is the highest. We determined crown diameters as principal components of ALS points representing a delineated crown. The method was applied to a test area of an olive orchard in Spain. The tree dendrometric parameters estimated from ALS data were compared with field measurements to assess the quality of the developed approach. We found the method to be equally good or even superior to previously investigated semi-automatic methods. The average error is 19% for tree height, 53% for crown base height, and 13% and 9% for the length of the longer diameter and perpendicular diameter, respectively.http://dx.doi.org/10.1080/15481603.2017.1351148remote sensingairborne laser scanningalpha-shapeprincipal component analysisagriculturetrees |
| spellingShingle | Edyta Hadas Andrzej Borkowski Javier Estornell Przemyslaw Tymkow Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis GIScience & Remote Sensing remote sensing airborne laser scanning alpha-shape principal component analysis agriculture trees |
| title | Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis |
| title_full | Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis |
| title_fullStr | Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis |
| title_full_unstemmed | Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis |
| title_short | Automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha-shape and principal component analysis |
| title_sort | automatic estimation of olive tree dendrometric parameters based on airborne laser scanning data using alpha shape and principal component analysis |
| topic | remote sensing airborne laser scanning alpha-shape principal component analysis agriculture trees |
| url | http://dx.doi.org/10.1080/15481603.2017.1351148 |
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