A new data aggregation technique to improve landscape metric downscaling

Landscape Metrics: Past Progress and Future Directions

“…These gradient datasets capture and represent more of the heterogeneity present in real-world landscapes compared to patch-mosaic models [35,86] and can be derived from categorical maps by statistically combining data (i.e., through moving windows). They can also be created directly from remote sensing imagery through spectral unmixing [18,87] or computation of vegetation indices such as the normalized difference vegetation index [29,88].…”

“…Successful extrapolation would enable spatial pattern metrics to be generated at scales matching the intrinsic scale of the underlying ecological process even if data sources are not directly available at that scale [7,106]. Researchers have tested the power of conventional landscape metric scaling functions to predict metrics at finer grain sizes through a variety of downscaling methods [17,18,[106][107][108][109]. However, the general consensus has been that prediction power is poor, with the loss of heterogeneity during grain size transformation (e.g., majority rules aggregation) cited as a major factor inhibiting progress [18,91].…”

“…Researchers have tested the power of conventional landscape metric scaling functions to predict metrics at finer grain sizes through a variety of downscaling methods [17,18,[106][107][108][109]. However, the general consensus has been that prediction power is poor, with the loss of heterogeneity during grain size transformation (e.g., majority rules aggregation) cited as a major factor inhibiting progress [18,91]. As such, researchers have struggled to operationalize the information in scaling functions for meaningful exploration of cross-scale issues.…”

“…Progress continues today with development of new and improved indices for quantifying composition and configuration [13], ongoing research into the parsimonious selection of metrics for ecological analyses [14], and renewed investigations into enduring issues of uncertainty and error propagation [15,16]. Alongside these lasting topics, new research themes are emerging to create new insights into the prediction power of landscape metrics through scaling functions [17,18] and, most recently, into the innovative application of entropy and thermodynamics [19]. Landscape ecology's vibrant development and diverse disciplinary perspectives continue to infiltrate diverse fields such as urban and regional studies [20,21], economics [22], and social and environmental perception [23][24][25] through metric analyses.…”

Assessment of temporal and spatial landscape and avifauna changes in the Yellow River wetland natural reserves in 1990–2013, China

“…Combined with indexes sensitivity coefficients (He and Zhang, 2009), we choose 11 ecologically representative indexes, which can be efficiently utilized in solving the problem of metrics redundancy during landscape classification and evaluation (Li et al, 2004;He and Zhang, 2009). And these 11 indexes were commonly accepted as effective indicators of landscape change, which are widely used in relative studies (Lausch and Herzog, 2002;Zhang and Li, 2013;Frazier, 2014).…”