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

Title
Rank-Based Methods for Selection of Landscape Metrics for Land Cover Pattern Change Detection
Publication Date
2016
Author(s)
Sinha, Priyakant
( author )
OrcID: https://orcid.org/0000-0002-0278-6866
Email: psinha2@une.edu.au
UNE Id une-id:psinha2
Kumar, Lalit
( author )
OrcID: https://orcid.org/0000-0002-9205-756X
Email: lkumar@une.edu.au
UNE Id une-id:lkumar
Reid, Nick
( author )
OrcID: https://orcid.org/0000-0002-4377-9734
Email: nrei3@une.edu.au
UNE Id une-id:nrei3
Type of document
Journal Article
Language
en
Entity Type
Publication
Publisher
MDPI AG
Place of publication
Switzerland
DOI
10.3390/rs8020107
UNE publication id
une:18816
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, ε, 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.
Link
Citation
Remote Sensing, 8(2), p. 1-19
ISSN
2072-4292
Start page
1
End page
19

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