Textural measures for estimating oil palm age
In oil palm management, age is one of the yield determinant factors. The conventional field investigations are often exhaustive and costly methods when implemented on a large scale. Despite much attention to classify individual oil palm ages by using various remote sensing images, none of the studie...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis
2018
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| Online Access: | http://psasir.upm.edu.my/id/eprint/82159/ http://psasir.upm.edu.my/id/eprint/82159/1/Textural%20measures%20.pdf |
| _version_ | 1848859248731095040 |
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| author | Hamsa, Camalia Saini Kanniah, Kasturi Devi Muharam, Farrah Melissa Idris, Nurul Hazrina Abdullah, Zainuriah Mohamed, Luqman |
| author_facet | Hamsa, Camalia Saini Kanniah, Kasturi Devi Muharam, Farrah Melissa Idris, Nurul Hazrina Abdullah, Zainuriah Mohamed, Luqman |
| author_sort | Hamsa, Camalia Saini |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | In oil palm management, age is one of the yield determinant factors. The conventional field investigations are often exhaustive and costly methods when implemented on a large scale. Despite much attention to classify individual oil palm ages by using various remote sensing images, none of the studies depicted satisfying overall accuracies. The overall aim of this study was to optimize window size and number of texture measurements for oil palm ages classification. The study was conducted in a commercial oil palm plantation comprised of palms of multiple ages planted from 1991 to 2008. Three Satellite Pour l’Observation de la Terre (SPOT)-5 multispectral images, acquired on 12 April 2012, 4 April 2013, and 14 April 2014, were evaluated. The individual ages were successfully classified with accuracy ranging from 59% to 97%, with an average overall accuracy of 84%. The results illustrated that the largest window size related to the smallest oil palm planting block in the study area, 390 m × 390 m on-the-ground window size, and seven combination of texture measurements, resulted in the highest classification overall accuracy. The utilization of texture measurements produced synergistic effects able to discriminate the oil palm age, with mean, entropy, homogeneity, and angular second moment as among the significant textures. |
| first_indexed | 2025-11-15T12:26:20Z |
| format | Article |
| id | upm-82159 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T12:26:20Z |
| publishDate | 2018 |
| publisher | Taylor & Francis |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-821592020-12-16T14:52:14Z http://psasir.upm.edu.my/id/eprint/82159/ Textural measures for estimating oil palm age Hamsa, Camalia Saini Kanniah, Kasturi Devi Muharam, Farrah Melissa Idris, Nurul Hazrina Abdullah, Zainuriah Mohamed, Luqman In oil palm management, age is one of the yield determinant factors. The conventional field investigations are often exhaustive and costly methods when implemented on a large scale. Despite much attention to classify individual oil palm ages by using various remote sensing images, none of the studies depicted satisfying overall accuracies. The overall aim of this study was to optimize window size and number of texture measurements for oil palm ages classification. The study was conducted in a commercial oil palm plantation comprised of palms of multiple ages planted from 1991 to 2008. Three Satellite Pour l’Observation de la Terre (SPOT)-5 multispectral images, acquired on 12 April 2012, 4 April 2013, and 14 April 2014, were evaluated. The individual ages were successfully classified with accuracy ranging from 59% to 97%, with an average overall accuracy of 84%. The results illustrated that the largest window size related to the smallest oil palm planting block in the study area, 390 m × 390 m on-the-ground window size, and seven combination of texture measurements, resulted in the highest classification overall accuracy. The utilization of texture measurements produced synergistic effects able to discriminate the oil palm age, with mean, entropy, homogeneity, and angular second moment as among the significant textures. Taylor & Francis 2018 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/82159/1/Textural%20measures%20.pdf Hamsa, Camalia Saini and Kanniah, Kasturi Devi and Muharam, Farrah Melissa and Idris, Nurul Hazrina and Abdullah, Zainuriah and Mohamed, Luqman (2018) Textural measures for estimating oil palm age. International Journal of Remote Sensing, 40 (4). pp. 1-22. ISSN 0143-1161; ESSN: 1366-5901 10.1080/01431161.2018.1530813 |
| spellingShingle | Hamsa, Camalia Saini Kanniah, Kasturi Devi Muharam, Farrah Melissa Idris, Nurul Hazrina Abdullah, Zainuriah Mohamed, Luqman Textural measures for estimating oil palm age |
| title | Textural measures for estimating oil palm age |
| title_full | Textural measures for estimating oil palm age |
| title_fullStr | Textural measures for estimating oil palm age |
| title_full_unstemmed | Textural measures for estimating oil palm age |
| title_short | Textural measures for estimating oil palm age |
| title_sort | textural measures for estimating oil palm age |
| url | http://psasir.upm.edu.my/id/eprint/82159/ http://psasir.upm.edu.my/id/eprint/82159/ http://psasir.upm.edu.my/id/eprint/82159/1/Textural%20measures%20.pdf |