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.

For successful characterization of ecological processes and prioritization of habitat networks it is necessary to describe and quantify landscape structure and connectivity. However, at landscape scale, it is highly impractical to measure and map all elements of biodiversity, and therefore, biodiversity surrogates are commonly used to represent biodiversity values. Land cover and vegetation are most often used as a biodiversity surrogate. The study investigated how land use change affects the status of the biodiversity surrogates in terms of the loss or gain of habitat (areal extent), loss of habitat condition (degradation) and habitat fragmentation. Effective habitat area (EHA) and raster based cost-benefit analysis (CBA) modeling techniques were used for the assessment of the impact of land use change scenarios on wildlife habitat as biodiversity surrogates. The modeling was carried out on time-series land cover data from 1972 to 2009 for the Liverpool Range of New South Wales (NSW). The model estimated the future condition of vegetation in each and every grid-cell in the region as a function of current condition, existing land cover, and the threatening processes. The results indicated a continuous pattern of clearing in the region, while the habitat conditions were mostly static throughout the study period. There was a decline in EHA after 1993, by 3%. Clearing was identified as the main cause of such decline during the change period.