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Title: Rank-Based Methods for Selection of Landscape Metrics for Land Cover Pattern Change Detection
Contributor(s): Sinha, Priyakant  (author)orcid ; Kumar, Lalit  (author)orcid ; Reid, Nick  (author)orcid 
Publication Date: 2016
Open Access: Yes
DOI: 10.3390/rs8020107Open Access Link
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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, 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.
Publication Type: Journal Article
Source of Publication: Remote Sensing, 8(2), p. 1-19
Publisher: MDPI AG
Place of Publication: Switzerland
ISSN: 2072-4292
Fields of Research (FoR) 2008: 050104 Landscape Ecology
090905 Photogrammetry and Remote Sensing
090903 Geospatial Information Systems
Fields of Research (FoR) 2020: 401304 Photogrammetry and remote sensing
410206 Landscape ecology
401302 Geospatial information systems and geospatial data modelling
Socio-Economic Objective (SEO) 2008: 960604 Environmental Management Systems
960805 Flora, Fauna and Biodiversity at Regional or Larger Scales
960501 Ecosystem Assessment and Management at Regional or Larger Scales
Socio-Economic Objective (SEO) 2020: 190203 Environmental education and awareness
180403 Assessment and management of Antarctic and Southern Ocean ecosystems
189999 Other environmental management not elsewhere classified
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Environmental and Rural Science
School of Science and Technology

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