Tree Cover Extraction from 50 cm Worldview2 Imagery: A Comparison of Image Processing Techniques

Title
Tree Cover Extraction from 50 cm Worldview2 Imagery: A Comparison of Image Processing Techniques
Publication Date
2013
Author(s)
Verma, Niva
Lamb, David
Reid, Nick
( author )
OrcID: https://orcid.org/0000-0002-4377-9734
Email: nrei3@une.edu.au
UNE Id une-id:nrei3
Wilson, Brian
( author )
OrcID: https://orcid.org/0000-0002-7983-0909
Email: bwilson7@une.edu.au
UNE Id une-id:bwilson7
Type of document
Conference Publication
Language
en
Entity Type
Publication
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Place of publication
Los Alamitos, United States of America
DOI
10.1109/IGARSS.2013.6721124
UNE publication id
une:14426
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.
Link
Citation
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), p. 192-195
ISSN
2153-7003
2153-6996
ISBN
9781479911141
Start page
192
End page
195

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