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Remote Sens. 2016, 8(2), 107; doi:10.3390/rs8020107

Rank-Based Methods for Selection of Landscape Metrics for Land Cover Pattern Change Detection

1,* , 2,†
and
2,†
1
Soil and Agronomy, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
2
Ecosystem Management, School of Environmental and Rural Science, University of New England, Armidale, NSW 2351, Australia
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail
Received: 12 November 2015 / Revised: 10 January 2016 / Accepted: 20 January 2016 / Published: 1 February 2016
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Abstract

Often landscape metrics are not thoroughly evaluated with respect to remote sensing data characteristics, such as their behavior in relation to variation in spatial and temporal resolution, number of land cover classes or dominant land cover categories. In such circumstances, it may be difficult to ascertain whether a change in a metric is due to landscape pattern change or due to the inherent variability in multi-temporal data. This study builds on this important consideration and proposes a rank-based metric selection process through computation of four difference-based indices (β, γ, ξ and θ) using a Max–Min/Max normalization approach. Land cover classification was carried out for two contrasting provinces, the Liverpool Range (LR) and Liverpool Plains (LP), of the Brigalow Belt South Bioregion (BBSB) of NSW, Australia. Landsat images, Multi Spectral Scanner (MSS) of 1972–1973 and TM of 1987–1988, 1993–1994, 1999–2000 and 2009–2010 were classified using object-based image analysis methods. A total of 30 landscape metrics were computed and their sensitivities towards variation in spatial and temporal resolutions, number of land cover classes and dominant land cover categories were evaluated by computing a score based on Max–Min/Max normalization. The landscape metrics selected on the basis of the proposed methods (Diversity index (MSIDI), Area weighted mean patch fractal dimension (SHAPE_AM), Mean core area (CORE_MN), Total edge (TE), No. of patches (NP), Contagion index (CONTAG), Mean nearest neighbor index (ENN_MN) and Mean patch fractal dimension (FRAC_MN)) were successful and effective in identifying changes over five different change periods. Major changes in land cover pattern after 1993 were observed, and though the trends were similar in both cases, the LP region became more fragmented than the LR. The proposed method was straightforward to apply, and can deal with multiple metrics when selection of an appropriate set can become difficult. View Full-Text
Keywords: change detection; image classification; land cover; pattern metric; landscape; spatial resolution; temporal resolution; correlation; Australia change detection; image classification; land cover; pattern metric; landscape; spatial resolution; temporal resolution; correlation; Australia
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Sinha, P.; Kumar, L.; Reid, N. Rank-Based Methods for Selection of Landscape Metrics for Land Cover Pattern Change Detection. Remote Sens. 2016, 8, 107.

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