Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping
This paper assessed the performance of object-based supervised support vector machine (SVM) and rule-based techniques in classifying tropical vegetated floodplain using 0.6m QuickBird image and LIDAR dataset of 1.4 points per square meter (PPSM). This is particularly significant in hydraulic modelli...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Associazione Italiana di Telerilevamento
2015
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/45638/ http://psasir.upm.edu.my/id/eprint/45638/1/LIDAR.pdf |
| _version_ | 1848850525803511808 |
|---|---|
| author | Yusuf, Badronnisa Mohd Shafri, Helmi Zulhaidi Mohamed, Thamer Ahmed Zahidi, Izni Hamedianfar, Alireza |
| author_facet | Yusuf, Badronnisa Mohd Shafri, Helmi Zulhaidi Mohamed, Thamer Ahmed Zahidi, Izni Hamedianfar, Alireza |
| author_sort | Yusuf, Badronnisa |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | This paper assessed the performance of object-based supervised support vector machine (SVM) and rule-based techniques in classifying tropical vegetated floodplain using 0.6m QuickBird image and LIDAR dataset of 1.4 points per square meter (PPSM). This is particularly significant in hydraulic modelling in which vegetation roughness is a big uncertainty and largely relies on land cover classification. The supervised classification resulted in 79.40% overall accuracy whilst the results improved by 8% with rule-based classification. 40 sample plots of trees and shrubs were measured to be compared to obtain the best classification results. The results showed a linear relationship between tree diameters
and NDVI with a high Pearson correlation of 0.76 and coefficient of determination (r2) of 0.58. The canopy areas of shrubs were found to be representative spatially with an even higher Pearson correlation of 0.98 and r2 of 0.95. The study concluded that the fusion of QuickBird image and low point density LIDAR in rule-based classification together with field data were useful in quantifying tropical trees and shrubs. |
| first_indexed | 2025-11-15T10:07:41Z |
| format | Article |
| id | upm-45638 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:07:41Z |
| publishDate | 2015 |
| publisher | Associazione Italiana di Telerilevamento |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-456382021-01-23T22:42:50Z http://psasir.upm.edu.my/id/eprint/45638/ Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping Yusuf, Badronnisa Mohd Shafri, Helmi Zulhaidi Mohamed, Thamer Ahmed Zahidi, Izni Hamedianfar, Alireza This paper assessed the performance of object-based supervised support vector machine (SVM) and rule-based techniques in classifying tropical vegetated floodplain using 0.6m QuickBird image and LIDAR dataset of 1.4 points per square meter (PPSM). This is particularly significant in hydraulic modelling in which vegetation roughness is a big uncertainty and largely relies on land cover classification. The supervised classification resulted in 79.40% overall accuracy whilst the results improved by 8% with rule-based classification. 40 sample plots of trees and shrubs were measured to be compared to obtain the best classification results. The results showed a linear relationship between tree diameters and NDVI with a high Pearson correlation of 0.76 and coefficient of determination (r2) of 0.58. The canopy areas of shrubs were found to be representative spatially with an even higher Pearson correlation of 0.98 and r2 of 0.95. The study concluded that the fusion of QuickBird image and low point density LIDAR in rule-based classification together with field data were useful in quantifying tropical trees and shrubs. Associazione Italiana di Telerilevamento 2015 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/45638/1/LIDAR.pdf Yusuf, Badronnisa and Mohd Shafri, Helmi Zulhaidi and Mohamed, Thamer Ahmed and Zahidi, Izni and Hamedianfar, Alireza (2015) Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping. European Journal of Remote Sensing, 48. pp. 423-446. ISSN 1129-8596; ESSN: 2279-7254 https://www.researchgate.net/publication/283420893_Object-based_classification_of_QuickBird_image_and_low_point_density_LIDAR_for_tropical_trees_and_shrubs_mapping/link/56379cf308ae30cbeff4d2a3/download 10.5721/EuJRS20154824 |
| spellingShingle | Yusuf, Badronnisa Mohd Shafri, Helmi Zulhaidi Mohamed, Thamer Ahmed Zahidi, Izni Hamedianfar, Alireza Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping |
| title | Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping |
| title_full | Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping |
| title_fullStr | Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping |
| title_full_unstemmed | Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping |
| title_short | Object-based classification of QuickBird image and low point density LIDAR for tropical trees and shrubs mapping |
| title_sort | object-based classification of quickbird image and low point density lidar for tropical trees and shrubs mapping |
| url | http://psasir.upm.edu.my/id/eprint/45638/ http://psasir.upm.edu.my/id/eprint/45638/ http://psasir.upm.edu.my/id/eprint/45638/ http://psasir.upm.edu.my/id/eprint/45638/1/LIDAR.pdf |