Sensitivity of landscape pattern indices to input data characteristics on real landscapes: implications for their use in natural disturbance emulation

Baldwin, David J. B.; Weaver, Kevin; Schnekenburger, Frank; Perera, Ajith H.

doi:10.1023/b:land.0000030442.96122.ef

Cited by 97 publications

(55 citation statements)

References 51 publications

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“…The thematic and geometric resolution influences the results of analyses using landscape metrics (Baldwin et al, 2004;Castilla et al, 2009;Mas et al, 2010). It is crucial that the scale of investigation (Wiens, 1989, 386) and the spatial resolution of the data correspond to one another (Corry, 2005, 606).…”

Section: Living Reviews In Landscape Researchmentioning

confidence: 99%

Landscape Structure, Landscape Metrics and Biodiversity

Walz¹

2011

Living Rev. Landscape Res.

151

113

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This paper deals with the question of the role landscape metrics can play in the investigation, evaluation and monitoring of landscape structure, and which linkages between landscape structure and biodiversity are known. In the first part, the scientific state of the art is presented; in the second part, the meaning of landscape metrics for nature protection, landscape management and biodiversity monitoring is discussed. A number of studies indicate that such metrics on an aggregated, overall landscape level are quite appropriate to describe the state of biodiversity. On the other hand, gaps in the knowledge become apparent, and the results of such studies are strongly dependent on the scale of investigation and the underlying database. Nevertheless, the landscape structure approach seems to be expedient for management and planning at the landscape level.

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Section: Living Reviews In Landscape Researchmentioning

confidence: 99%

Landscape Structure, Landscape Metrics and Biodiversity

Walz¹

2011

Living Rev. Landscape Res.

151

113

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“…SHDI is more sensitive to richness than evenness [91]. In order to calculate the composition in urban spaces and natural spaces, like mitigation wetlands, SHDI is selected by focusing on species richness [93,[104][105][106][107].…”

Section: Evaluation Of Landscape Structure At the Landscape Levelmentioning

confidence: 99%

“…Area-Weighted Mean Patch Size (AREA_AM) is commonly used to compare alternative sites [53,113] based on the theory that bigger habitats are more valuable than smaller ones [93]. Landscape Shape Index (LSI), Total Edge (TE) and Edge Density (ED) are related to the edge effect in the field of landscape ecology [104,105,107,[113][114][115]. Total edge in class-level metrics is the most important index used in the study of fragmentation, and many of the class indices reflect the amount of class edge [91].…”

Section: Analysis Of Landscape Structure At the Class Levelmentioning

confidence: 99%

“…Interspersion and Juxtaposition Pattern (IJI) is an index for measuring the degree of isolation of the landscape structure. This index is typically used to devise landscape resource management strategies based on isolated patch distribution or to evaluate the isolation of wildlife habitat caused by urbanization [84,93,104,106,114].…”

Section: Analysis Of Landscape Structure At the Class Levelmentioning

confidence: 99%

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An Integrated Approach to Mitigation Wetland Site Selection: A Case Study in Gwacheon, Korea

et al. 2015

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This paper presents an integrated approach to mitigation wetland site selection using functional landscape connectivity and landscape structure. This approach enables landscape designers to evaluate the relative priorities of mitigation wetland areas based on functional landscape connectivity and wildlife mobility, as well as landscape structure, composition, and configuration. The least-cost path method is used to evaluate candidate sites for mitigation wetlands with regard to wildlife movement. A set of assessments for landscape indices using FRAGSTATS was applied to identify suitable mitigation wetland areas on the basis of landscape connectivity, composition, and configuration. The study was conducted in Gwacheon, Korea, where there are plans for regional development that will change the landscape. In the first step, a group of 14 candidate sites is identified via analysis of functional landscape connectivity using the least-cost path method. In the second step, candidate mitigation wetland areas are ranked according to landscape connectivity and composition. The five mitigation wetland areas that were found to be suitable were analyzed based on landscape configuration at the class level. This study demonstrates OPEN ACCESSSustainability 2015, 7 3387 that functional landscape connectivity and landscape structure are important aspects to consider when identifying suitable sites for mitigation wetland planning and restoration.

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“…Si bien, su utilización y apli caciones son amplias, se ha encontrado que los índices de paisaje son muy sensibles a algunos aspectos relacionados con el análisis de imágenes de sensores remotos. Dentro de estos destacan la resolución espacial de las imágenes (Wickham y Riitters 1995, Baldwin et al 2004, la exten sión del área de estudio (Saura 2001, Baldwin et al 2004, el método de clasificación (Mas et al 2010), el número de clases utilizadas para la clasificación (Buyantuyev y Wu 2006), entre otros. Esto implica que las fuentes de incerti dumbre en el análisis de la estructura espacial del paisaje, mediante índices de paisaje, pueden provenir de distintas fuentes en el proceso de análisis de imágenes satelitales, lo cual puede hacer muy variable los resultados obtenidos mediante este método.…”

Section: Introductionunclassified

Incertidumbre de los índices de paisaje en el análisis de la estructura espacial

2012

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SUMMARYLandscape indices or metrics have been widely used to relate spatial patterns to ecological processes or ecological functions. Landscape indices have several uses and applications. However, there is evidence that landscape indices are very sensitive to remote sensing imagery analysis. The aim of this study was to assess the effect of accuracy of satellite imagery classification on landscape indices for the analysis of spatial structure. We used the supervised classification method with different classification strategies based on spectral and ancillary information. Eight images classified using different strategies were produced from a single satellite image. Landscape indices at landscape and class level were computed. Overall, results showed that images with higher classification accuracy have lower coefficient of variation of landscape indices. Some landscape indices as Shannon and Simpson diversity, percentage of like adjacencies, and aggregation index showed lower sensitivity to the increase of classification accuracy. However, other indices as patch density and core area were more sensitive. Sources of error in the satellite imagery analysis are very diverse. Hence, caution is advised in the analysis of spatial structure using landscape indices.Key words: native forest, supervised classification, landscape metrics, remote sensing. RESUMENLos índices o métricas de paisaje han sido ampliamente utilizados para relacionar patrones espaciales con procesos o funciones ecológicas. Si bien, su utilización y aplicaciones son amplias, se ha encontrado que los índices de paisaje son muy sensibles a algunos aspectos relacionados con el análisis de imágenes de sensores remotos. El objetivo de este estudio fue evaluar el efecto de la exactitud de clasificación de imágenes satelitales en los índices de paisaje para el análisis de la estructura espacial. Se utilizó el método de clasificación supervisada con distintas estrategias basadas en información espectral y auxiliar. A partir de una imagen satelital se generaron un total de ocho imágenes clasificadas con las distintas estrategias. Se calcularon índices de paisaje a nivel de paisaje y clase derivados de cada una de las imágenes clasificadas. Los resultados muestran que, en general, las imágenes con mayor exactitud presentan menores valores de coeficiente de variación en los índices de paisaje. Existen índices que muestran una menor sensibilidad frente al incremento en la exactitud de la clasificación, como los índices de diversidad de Shannon y Simpson, el porcentaje de adyacencias comunes y el índice de agregación. Sin embargo, otros índices muestran mayor sensibilidad, como la densidad de parches y el área núcleo. Las fuentes de error en el análisis de imágenes de sensores remotos son muy variadas. Debido a esto, se debe tener especial precaución en estos aspectos cuando se realice un análisis de la estructura espacial mediante índices de paisaje.Palabras clave: bosque nativo, clasificación supervisada, métricas de paisaje, sensores remotos. INTRODUCCIÓNLa e...

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Sensitivity of landscape pattern indices to input data characteristics on real landscapes: implications for their use in natural disturbance emulation

Cited by 97 publications

References 51 publications

Landscape Structure, Landscape Metrics and Biodiversity

Landscape Structure, Landscape Metrics and Biodiversity

An Integrated Approach to Mitigation Wetland Site Selection: A Case Study in Gwacheon, Korea

Incertidumbre de los índices de paisaje en el análisis de la estructura espacial

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