Feature assessment in object-based forest classification using airborne LiDAR data and high spatial resolution satellite imagery
Paper
Zhang, Zhenyu, Liu, Xiaoye and Wright, Wendy. 2014. "Feature assessment in object-based forest classification using airborne LiDAR data and high spatial resolution satellite imagery." Gamba, Paolo, Xian, George, Wang, Guangxing, Zhu, Jianjun and Weng, Qihao (ed.) 3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA 2014): Global Change and Sustainable Development: A Remote Sensing Perspective. Changsha, China 11 - 14 Jun 2014 Piscataway, NJ. United States. https://doi.org/10.1109/EORSA.2014.6927920
| Paper/Presentation Title | Feature assessment in object-based forest classification using airborne LiDAR data and high spatial resolution satellite imagery |
|---|---|
| Presentation Type | Paper |
| Authors | Zhang, Zhenyu (Author), Liu, Xiaoye (Author) and Wright, Wendy (Author) |
| Editors | Gamba, Paolo, Xian, George, Wang, Guangxing, Zhu, Jianjun and Weng, Qihao |
| Journal or Proceedings Title | Proceedings of the 3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA 2014) |
| Number of Pages | 5 |
| Year | 2014 |
| Place of Publication | Piscataway, NJ. United States |
| ISBN | 9781479957576 |
| 9781479941841 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/EORSA.2014.6927920 |
| Web Address (URL) of Paper | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6927920 |
| Conference/Event | 3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA 2014): Global Change and Sustainable Development: A Remote Sensing Perspective |
| Event Details | 3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA 2014): Global Change and Sustainable Development: A Remote Sensing Perspective Event Date 11 to end of 14 Jun 2014 Event Location Changsha, China |
| Abstract | The last decade has witnessed an increase in interest in the application of airborne LiDAR data and high spatial resolution satellite imagery for forest structure modelling, tree species identification and classification. The integration of LiDAR data and WorldView-2 satellite imagery produced different combinations of input data layers for image segmentations and a large number of variables derived from these data layers for object-based classifications. Assessment of different features (including the input data layers and subsequently derived variables) for object-based forest classification is important. In this study, five image segmentation schemes were explored to test the effectiveness of the different input data layers, in particular, the new WorldView-2 multispectral bands to object-based forest classification. Object-based variables derived from these data layers were assessed to rank their importance before inputting into decision trees for forest classifications. It demonstrated that, using methods developed in this study, the integration of airborne LiDAR and eight WorldView-2 bands can significantly improve the accuracy of forest classification in our study area. The variable importance was ranked, indicating how important a variable contributes to the classification in a particular decision tree. The results showed that using LiDAR data alone or four conventional bands only, the overall accuracies achieved were 61.39% and 61.42% respectively, but the overall accuracy increased to 82.35% when all eight bands and the LiDAR data were used. |
| Keywords | decision tree; forest classification; LiDAR; object-based image analysis; WorldView-2 |
| ANZSRC Field of Research 2020 | 401304. Photogrammetry and remote sensing |
| 300701. Agroforestry | |
| 460306. Image processing | |
| Public Notes | © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Byline Affiliations | School of Civil Engineering and Surveying |
| Monash University | |
| Institution of Origin | University of Southern Queensland |
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