Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/15169
Title: A Comparative Study of Land Cover Classification Techniques for "Farmscapes" Using Very High Resolution Remotely Sensed Data
Contributor(s): Verma, Niva (author); Lamb, David (author); Reid, Nick (author)orcid ; Wilson, Brian (author)orcid 
Publication Date: 2014
DOI: 10.14358/PERS.80.5.461
Handle Link: https://hdl.handle.net/1959.11/15169
Abstract: High spatial resolution images (~10 cm) are routinely available from airborne platforms. Few studies have examined the applicability of using such data to characterize land cover in "farmscapes" comprising open pasture and remnant vegetation communities of varying density. Very high spatial resolution remotely sensed imagery has been used to classify land cover classes on a ~5000 ha extensive grazing farm in Australia. This "farmscape" consisted of open pasture fields, scattered trees, and remnant vegetation (woodlands). The relative performances of object-based and pixel-based approaches to classification were tested for accuracy and applicability. Maximum likelihood classification (MLC) was used for pixel-based classification while the k-nearest neighbor (k-NN) technique was used for object-based classification. A range of image sampling scales was tested for image segmentation. At an optimal sampling scale, the pixel-based classification resulted in an overall accuracy of 77 percent, while the object-based classification achieved an overall accuracy of 86 percent. While both the object- and pixel-based classification techniques yielded higher quantitative accuracies, a "more realistic" land cover classification, with few errors due to intermixing of similar classes, was achieved using the object-based method.
Publication Type: Journal Article
Source of Publication: Photogrammetric Engineering and Remote Sensing, 80(5), p. 461-470
Publisher: American Society for Photogrammetry and Remote Sensing
Place of Publication: United States of America
ISSN: 0099-1112
Field of Research (FOR): 070104 Agricultural Spatial Analysis and Modelling
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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School of Environmental and Rural Science

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