Understanding and quantifying landscape structure – A review on relevant process characteristics, data models and landscape metrics

Lausch, Angela; Blaschke, Thomas; Haase, Dagmar; Herzog, Félix; Syrbe, Ralf Uwe; Tischendorf, Lutz; Walz, Ulrich

doi:10.1016/j.ecolmodel.2014.08.018

Cited by 324 publications

(182 citation statements)

References 50 publications

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“…The GM represents landscape structure as continuous data, which usually originated from remote sensing, and using GM landscape models should help to improve our understanding of specieslandscape interactions (Cushman et al, 2010). GM-based models, however, usually evaluate only one variable of interest in the landscape -such as elevation or habitat quality for single species or green vegetation density -but this corresponds only to one land-cover type or category in the PMM (Lausch et al, 2015).…”

Section: Discussionmentioning

confidence: 99%

Analysis of the development of land use in the Morava River floodplain, with special emphasis on the landscape matrix

Kilianová

Pechanec

Brus

et al. 2017

Moravian Geographical Reports

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Section: Discussionmentioning

confidence: 99%

Analysis of the development of land use in the Morava River floodplain, with special emphasis on the landscape matrix

Kilianová

Pechanec

Brus

et al. 2017

Moravian Geographical Reports

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“…Remote sensing-based predictors of habitat characteristics may contribute to improve the performances of species distribution models (SDM) [7]. However, the question of the most appropriate data and representation (discrete vs. continuous-based metrics) in SDM still remains [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France

Bonthoux

Lefèvre²,

Herrault

et al. 2018

Remote Sensing

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Abstract:Continuous-based predictors of habitat characteristics derived from satellite imagery are increasingly used in species distribution models (SDM). This is especially the case of Normalized Difference Vegetation Index (NDVI) which provides estimates of vegetation productivity and heterogeneity. However, when NDVI predictors are incorporated into SDM, synchrony between biological observations and image acquisition must be questionned. Due to seasonal variations of NDVI during the year, landscape patterns of habitats are revealed differently from one date to another leading to variations in models' performance. In this paper, we investigated the influence of acquisition time period of NDVI to explain and predict bird community patterns over France. We examined if the NDVI acquisition period that best fit the bird data depends on the dominant land cover context. We also compared models based on single time period of NDVI with one model built from the Dynamic Habitat Index (DHI) components which summarize variations in vegetation phenology throughout the year from the fraction of radiation absorbed by the canopy (fPAR). Bird species richness was calculated as response variable for 759 plots of 4 km 2 from the French Breeding Bird Survey. Bird specialists and generalists to habitat were considered. NDVI and DHI predictors were both derived from MODIS products. For NDVI, five time periods in 2010 were compared, from late winter to begin of autumn. A climate predictor was also used and Generalized Additive Models were fitted to explain and predict bird species richness. Results showed that NDVI-based proxies of dominant habitat identity and spatial heterogeneity explain more bird community patterns than DHI-based proxies of annual productivity and seasonnality. We also found that models' performance was both time and context-dependent, varying according to the bird groups. In general, best time period of NDVI did not match with the acquisition period of bird data because in case of synchrony, differences in habitats are less pronounced. These findings suggest that the most powerful approach to estimate bird community patterns is the simplest one. It only requires NDVI predictors from a single appropriate time period, in addition to climate, which makes the approach very operational.

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“…Correspondingly two types of model have been used to represent landscape structure: Gradient Model (GM) and Patch Corridor Matrix Model (PCMM). Lausch et al (2015) have concluded that the characteristics of research area and research objective are the decisive factors for choosing the appropriate model representing the landscape pattern. Landscapes under low human pressure are recommended for using the GM approach; anthropogenic-dominated landscapes should preferably be represent-ed with the PCMM model.…”

Section: Discussionmentioning

confidence: 99%

“…The both methods consider the landscape as a continuous surface instead of the patch mosaic model. The surface metrics are derived from a raster based data in which the only discrete unit is a pixel or grid cell (Lausch et al 2015). In this paper, the landscape is regarded as a mosaic with discrete patches and permeable boundaries between them (intermediate edge contrast).…”

Section: Introductionmentioning

confidence: 99%

An integrated approach for landscape contrast analysis with particular consideration of small habitats and ecotones

Hou¹,

Walz²

2016

Self Cite

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Citation: Hou W, Walz U (2016) An integrated approach for landscape contrast analysis with particular consideration of small habitats and ecotones. Nature Conservation 14: 25-39. doi: 10.3897/natureconservation.14.7010 AbstractHabitat diversity is considered as an expression of biodiversity at landscape level in addition to genetic and species diversity. Thus, effective methods for measuring habitat pattern at landscape level are crucial for understanding the ecological processes. In this paper we propose to extend the commonly used model Patch Corridor Matrix Model (PCMM) for spatial pattern analysis. Originally, this model assumes discrete structures within the landscape without explicit consideration of "gradients" between patches. The gradients, often called "ecotones", can be considered as "soft edges" which have a profound influence on adjacent ecosystems. Another part of information that has often been ignored are "small habitats" inside patches (e.g. hedgerows, tree rows, copse, and scattered trees), which leads to within-patch heterogeneity being underestimated. In this paper, the concept of landscape contrast is used to integrate the discrete and gradient landscape representations by incorporating small habitats and ecotones in methods to measure landscape heterogeneity. A height gradient is used to define the ecotones between forest and field. Then, patch contrast (i.e. Edge Contrast Index (ECON)) is calculated based on the height difference between adjacent vegetation patches. Artificial elements (e.g. traffic roads) are considered as barriers which are assigned with the highest edge contrast value. At the landscape level, a metric called Area-Weighted Edge Contrast (AWEC) is introduced to describe the landscape structure. The edge effects of ecotones, small habitats, and traffic roads are incorporated in the calculation of AWEC. Our test examples show that incorporation of ecotones and small habitats can smooth "edge effects" among patches and result in a more realistic quantification of habitat contrast. The contrast concept is especially useful in a vegetated landscape with less human impact. It could be understood as an additional interpretation to fragmentation of habitats with permeable edges among them. Consequently, this presented approach may enhance the understanding of the relationship between landscape pattern and process.

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Understanding and quantifying landscape structure – A review on relevant process characteristics, data models and landscape metrics

Cited by 324 publications

References 50 publications

Analysis of the development of land use in the Morava River floodplain, with special emphasis on the landscape matrix

Analysis of the development of land use in the Morava River floodplain, with special emphasis on the landscape matrix

Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France

An integrated approach for landscape contrast analysis with particular consideration of small habitats and ecotones

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