Feature assessment in object-based forest classification using airborne LiDAR data and high spatial resolution satellite imagery

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. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Civil Engineering and Surveying
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 : 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
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: 10.1109/EORSA.2014.6927920
URI: http://eprints.usq.edu.au/id/eprint/26641

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