Zhang, Zhenyu and Liu, Xiaoye and Wright, Wendy (2014) Feature assessment in object-based forest classification using airborne LiDAR data and high spatial resolution satellite imagery. In: 3rd International Workshop on Earth Observation and Remote Sensing Applications (EORSA 2014): Global Change and Sustainable Development: A Remote Sensing Perspective, 11-14 Jun 2014, 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.
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| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | © 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. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying (1 Jul 2013 - 31 Dec 2021) |
| Date Deposited: | 27 Jan 2015 05:02 |
| Last Modified: | 12 Jun 2017 05:59 |
| Uncontrolled Keywords: | decision tree; forest classification; LiDAR; object-based image analysis; WorldView-2 |
| Fields of Research (2008): | 09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing 07 Agricultural and Veterinary Sciences > 0705 Forestry Sciences > 070501 Agroforestry 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080106 Image Processing |
| Fields of Research (2020): | 40 ENGINEERING > 4013 Geomatic engineering > 401304 Photogrammetry and remote sensing 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3007 Forestry sciences > 300701 Agroforestry 46 INFORMATION AND COMPUTING SCIENCES > 4603 Computer vision and multimedia computation > 460306 Image processing |
| Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering |
| Identification Number or DOI: | https://doi.org/10.1109/EORSA.2014.6927920 |
| URI: | http://eprints.usq.edu.au/id/eprint/26641 |
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