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Title: Tree Cover Extraction from 50 cm Worldview2 Imagery: A Comparison of Image Processing Techniques
Contributor(s): Verma, Niva (author); Lamb, David (author); Reid, Nick (author); Wilson, Brian (author)orcid 
Publication Date: 2013
DOI: 10.1109/IGARSS.2013.6721124
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Abstract: High resolution remote sensing is a valuable tool for quantifying the distribution and density of trees with applications ranging from forest inventory, mapping urban parklands to understanding impacts on soil nutrient and carbon dynamics in farming land. The present study aims to compare the accuracy of different remote sensing techniques for delineating the tree cover in 50 cm resolution WorldView2 imagery of farmland. An image of farmland comprising pastures, remnant vegetation and woodland was initially classified into six classes, namely tree cover, bare soil, rock outcrop, natural pasture, degraded pasture and water body using different techniques. Pixel based classification based on all four available wavebands, were tested and an overall classification accuracy of 96.8% and 72.9 % were achieved for supervised and unsupervised techniques. Object based segmentation and subsequent classification yielded an improved overall classification accuracy of 98.3%. Addition of a fifth NDVI layer to the available wavebands did improve the accuracy but not significantly (98.1%, approx 1.3%). In addition to the improvements in overall classification accuracy, a visual inspections of results from the different methods indicated the object based method to yield a more 'realistic' result, avoiding the 'salt and pepper' effects apparent in the pixel-based methods. Overall, object based classification hence is considered more suitable for tree cover extraction from high resolution images.
Publication Type: Conference Publication
Conference Name: IGARSS 2013: IEEE International Geoscience and Remote Sensing Symposium - Building a Sustainable Earth through Remote Sensing, Melbourne, Australia, 21st - 26th July, 2013
Source of Publication: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), p. 192-195
Publisher: IEEE: Institute of Electrical and Electronics Engineers
Place of Publication: Piscataway, United States of America
ISSN: 2153-6996
Field of Research (FOR): 070101 Agricultural Land Management
080606 Global Information Systems
050206 Environmental Monitoring
Peer Reviewed: Yes
HERDC Category Description: E1 Refereed Scholarly Conference Publication
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Appears in Collections:Conference Publication
School of Environmental and Rural Science

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