Surface metrics for landscape ecology: a comparison of landscape models across ecoregions and scales

Kedron, Peter; Frazier, Amy E.; Ovando-Montejo, Gustavo A.; Wang, Jing

doi:10.1007/s10980-018-0685-1

Cited by 41 publications

(30 citation statements)

References 36 publications

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“…GRASS [https://grass.osgeo.org/ home/history), and the earliest implementations of landscape pattern metrics relied heavily on the patch mosaic model and metrics derived from categorical maps (Baker and Cai 1992;McGarigal and Marks 1995). Over time, supported by advances in remote sensing, GIS, and computation, it became more feasible to analyze large data sets representing more aspects of landscape pattern, and other conceptual approaches such as surface metrics from microscopy and molecular physics, connectivity metrics from circuit theory, and pattern recognition from mathematical morphology have emerged recently as promising frameworks for measuring landscape patterns from those maps (Vogt et al 2007(Vogt et al , 2009McRae 2008;McGarigal et al 2009;Cushman and Huettmann 2010;Kedron et al 2018).…”

Section: Introductionmentioning

confidence: 99%

Describing and analyzing landscape patterns: where are we now, and where are we going?

et al. 2019

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Section: Introductionmentioning

confidence: 99%

Describing and analyzing landscape patterns: where are we now, and where are we going?

et al. 2019

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“…A landscape pattern is the concrete manifestation of landscape heterogeneity, and is the result of various ecological processes acting on different scales [35,36]. The landscape pattern indices are vital for quantifying and analyzing landscape patterns, dynamic changes in landscape patterns, and the factors driving land cover composition [37][38][39][40].…”

Section: Selection Of Landscape Pattern Indicesmentioning

confidence: 99%

Spatial Pattern Consistency among Different Remote-Sensing Land Cover Datasets: A Case Study in Northern Laos

Sui

Yang

Wang

et al. 2019

IJGI

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Comparisons of the accuracy and consistency of different remote-sensing land cover datasets are important for the rational application of multi-source land cover datasets to regional development, or to studies of global or local environmental change. Existing comparisons of accuracy or spatial consistency among land cover datasets primarily use confusion or transfer matrices and focus on the type and area consistency of land cover. However, less attention has been paid to the consistency of spatial patterns, and quantitative analyses of spatial pattern consistency are rare. However, when proportions of land cover types are similar, spatial patterns are essential for studies of the ecological functions of a landscape system. In this study, we used classical landscape indices that quantifies spatial patterns to analyze the spatial pattern consistency among different land cover datasets, and chose three datasets (GlobeLand30-2010, FROM-GLC2010, and SERVIR MEKONG2010) in northern Laos as a case study. We also analyzed spatial pattern consistency at different scales after comparing the landscape indices method with the confusion matrix method. We found that the degree of consistency between GlobeLand30-2010 and SERVIR MEKONG2010 was higher than that of GlobeLand30-2010 and FROM-GLC2010, FROM-GLC2010, and SERVIR MEKONG2010 based on the confusion matrix, mainly because of the best forest consistency and then water. However, the spatial consistency results of the landscape indices analysis show that the three datasets have large differences in the number of patches (NP), patch density (PD), and landscape shape index (LSI) at the original scale of 30 m, and decrease with the increase of the scale. Meanwhile, the aggregation index (AI) shows different changes, such as the changing trend of the forest aggregation index increasing with the scale. Our results suggested that, when using or producing land cover datasets, it is necessary not only to ensure the consistency of landscape types and areas, but also to ensure that differences among spatial patterns are minimized, especially those exacerbated by scale. Attention to these factors will avoid larger deviations and even erroneous conclusions from these data products.

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“…Moreover, this form of entropy is based on statistical theory and thus avoids the need for a series of numerical calculation processes. This entropy has the potential to be useful in many applications, such as landscape pattern quantification [53,54] and image quality assessment [55,56]. Due to the large amount of numerical calculation required to measure this entropy, hand calculation is impractical, and accordingly, this form of entropy has very limited applicability to date.…”

Section: Introductionmentioning

confidence: 99%

Calculating the Wasserstein Metric-Based Boltzmann Entropy of a Landscape Mosaic

Zhang

Lan

et al. 2020

Entropy

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Shannon entropy is currently the most popular method for quantifying the disorder or information of a spatial data set such as a landscape pattern and a cartographic map. However, its drawback when applied to spatial data is also well documented; it is incapable of capturing configurational disorder. In addition, it has been recently criticized to be thermodynamically irrelevant. Therefore, Boltzmann entropy was revisited, and methods have been developed for its calculation with landscape patterns. The latest method was developed based on the Wasserstein metric. This method incorporates spatial repetitiveness, leading to a Wasserstein metric-based Boltzmann entropy that is capable of capturing the configurational disorder of a landscape mosaic. However, the numerical work required to calculate this entropy is beyond what can be practically achieved through hand calculation. This study developed a new software tool for conveniently calculating the Wasserstein metric-based Boltzmann entropy. The tool provides a user-friendly human–computer interface and many functions. These functions include multi-format data file import function, calculation function, and data clear or copy function. This study outlines several essential technical implementations of the tool and reports the evaluation of the software tool and a case study. Experimental results demonstrate that the software tool is both efficient and convenient.

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Surface metrics for landscape ecology: a comparison of landscape models across ecoregions and scales

Cited by 41 publications

References 36 publications

Describing and analyzing landscape patterns: where are we now, and where are we going?

Describing and analyzing landscape patterns: where are we now, and where are we going?

Spatial Pattern Consistency among Different Remote-Sensing Land Cover Datasets: A Case Study in Northern Laos

Calculating the Wasserstein Metric-Based Boltzmann Entropy of a Landscape Mosaic

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