Surface metrics: an alternative to patch metrics for the quantification of landscape structure

McGarigal, Kevin; Tağil, Şermin; Cushman, Samuel A.

doi:10.1007/s10980-009-9327-y

Cited by 321 publications

(248 citation statements)

References 31 publications

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“…Numerous metrics (Baker and Cai, 1992;McGarigal and Marks, 1994;McGarigal et al, 2002McGarigal et al, , 2009O'neill et al, 1988) have been proposed and used to describe landscape patterns, and landscape composition and configuration have been addressed. These metrics are not only used in UHI-landscape pattern analysis, but also in many other fields such as water quality and biodiversity (Schindler et al, 2013;Uuemaa et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Application of a new integrated landscape index to predict potential urban heat islands

Chen

Zhao

Yao

et al. 2016

Ecological Indicators

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a b s t r a c tStudies on the interactions between urban landscape patterns and land surface temperatures are the key to urban heat island (UHI) mitigation. However, the landscape pattern indices used in previous studies were inconsistent in type and number. Furthermore, few studies combined the composition and configuration indices into one integrated indicator. The description of landscape pattern is thus a great yet rewarding challenge. In this study, we used the integrated location weighted landscape index (LWLI) proposed by Chen et al. (2009) and revised it to indicate potential UHIs. Fifty-six circular landscape samples along four transects were created and the LWLI was derived based on the theory of the GINI index. The LWLI considers the type, composition and configuration of different land covers inside a landscape, where configuration is treated as location weights. The potential UHI was represented by the land surface temperature (LST) of each circle landscape center. The correlation analysis results showed that the LWLI was significantly positively correlated with summer potential UHIs, with a Pearson R equaling 0.736, and barely correlated with winter potential UHI. Moreover, the LWLI was slightly more strongly correlated with potential UHI than composition alone. These correlations weakened as the landscapes grew larger. The linear regression results further revealed that the LWLI explained about 53% of the summer potential UHI, which was slightly better than composition alone (49%). This indicated that the LWLI was as effective in predicting the potential UHI as the combination of several pattern indicators, echoing Tobler's first law of geography, which states that "all things are related, but nearby things are more related than distant things." This study also leaves room for improvement of the index by integrating more environmental/ecological parameters as weights, and for further application of the index in other fields.

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Section: Introductionmentioning

confidence: 99%

Application of a new integrated landscape index to predict potential urban heat islands

Chen

Zhao

Yao

et al. 2016

Ecological Indicators

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“…In this day and age, there are many researchers advancing the needs for new landscape metrics (McGarigal et al, 2009) or new methodologies (Rempel and Kaufman, 2008;Saura and Torné, 2009), and this will result in the development of new software. It is essential to be planners at this moment to design the best possible tools, avoiding the inconveniences of the past.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, habitat is viewed as a continuous gradient instead of discrete patches within a homogeneous matrix. Moreover, heterogeneity is viewed as a three-dimensional surface and can represent any ecological attribute of interest (Hoechestter and Walz, 2009;McGarigal et al, 2009). This new approach provides greater accuracy than the previous -2D‖ metrics.…”

Section: Landscape Metrics Applicationsmentioning

confidence: 99%

A free and open source programming library for landscape metrics calculations

Zaragozí

Belda

Linares

et al. 2012

Environmental Modelling & Software

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Landscape metrics are used in a wide range of environmental studies such as land use change and land degradation studies, soil erosion and runoff predictions, management of hunting communities, and strategic planning for environmental management, to name but a few. Due to their utility for a variety of applications, there are many indices and software packages that have been designed to provide calculations and analysis of landscape structure patterns in categorical maps. With the purpose of making a profound comparison between the most used tools (Fragstats, V-Late, PA4…), we examined their advantages and disadvantages in order to create a list of common features that need to be incorporated into this type of software. We believe that an API without limitations on data input is necessary, capable of calculating vector or raster metrics and very extensible. This API should make it possible not only to build third party applications in easily, but would also make it possible to add new metrics and research into new paradigms related to traditional landscape metrics. We have started to develop a proposal based on open standards, which is FOSS. We have called this API Land-metrics DIY (Do It Yourself). It can calculate almost 40 landscape metrics from geometry provided by an ESRI Shapefile, but we are working to complete its contents as we explain in this article.

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“…Anthropogenic activities can disrupt the structural integrity of landscapes and are expected to impede or facilitate, organism's movement across the landscape (Gardner et al 1993). Therefore, much emphasis has been given on developing methods to quantify landscape patterns, which is considered as prerequisite to the study of patternprocess relationships (O'Neill et al 1988;McGarigal et al 2009). …”

Section: Methodsmentioning

confidence: 99%

Habitat suitability modelling for Gaur (Bos gaurus) using multiple logistic regression, remote sensing and GIS

Imam

Kushwaha

2013

Journal of Applied Animal Research

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The aim of this study is to produce georeferenced ecological information about the suitable habitats available for gaur Bos gaurus in Chandoli tiger reserve, India (178 04? 00ƒ N to 178 19? 54ƒ N and 738 40? 43ƒ E to 738 53? 09ƒ E). Habitat suitability index (H.S.I.) was developed using multiple logistic regression (MLR) integrated with remote sensing (RS) and geographic information system (GIS). Satellite imageries of study area, acquired from Indian remote sensing satellite-P6, linear imaging self-scanning satellite-III of 2005, was digitally processed, whereas topographic maps were used for generating the collateral data in a GIS framework. Various layers of different variables such as forest density, Landuse land cover, measures of proximity to disturbances and water resources and a digital terrain model were created from satellite and topographic data. These layers along with global positioning system location of gaur presence/absence and MLR technique were integrated in a GIS environment for modelling the H.S.

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Surface metrics: an alternative to patch metrics for the quantification of landscape structure

Cited by 321 publications

References 31 publications

Application of a new integrated landscape index to predict potential urban heat islands

Application of a new integrated landscape index to predict potential urban heat islands

A free and open source programming library for landscape metrics calculations

Habitat suitability modelling for Gaur (Bos gaurus) using multiple logistic regression, remote sensing and GIS

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