The Experts below are selected from a list of 6624 Experts worldwide ranked by ideXlab platform
Stefan Schindler - One of the best experts on this subject based on the ideXlab platform.
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performance of methods to select Landscape Metrics for modelling species richness
Ecological Modelling, 2015Co-Authors: Stefan Schindler, Henrik Von Wehrden, Kostas Poirazidis, Wesley M Hochachka, Thomas Wrbka, Vassiliki KatiAbstract:Abstract Landscape Metrics are commonly used indicators of ecological pattern and processes in ecological modelling. Numerous Landscape Metrics are available, making the selection of appropriate Metrics a common challenge in model development. In this paper, we tested the performance of methods for preselecting sets of three Landscape Metrics for use in modelling species richness of six groups of organisms (woody plants, orchids, orthopterans, amphibians, reptiles, and small terrestrial birds) and overall species richness in a Mediterranean forest Landscape. The tested methods included expert knowledge, decision tree analysis, principal component analysis, and principal component regression. They were compared with random choice and optimal sets, which were evaluated by testing all possible combinations of Metrics. All pre-selection methods performed significantly worse than the optimal sets. The statistical approaches performed slightly better than random choice that in turn performed slightly better than sets derived by expert knowledge. We concluded that the process of selecting the most appropriate Landscape Metrics for modelling biodiversity is not trivial and that shortcuts to systematic evaluation of Metrics should not be expected to identify appropriate indicators.
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multiscale performance of Landscape Metrics as indicators of species richness of plants insects and vertebrates
Ecological Indicators, 2013Co-Authors: Stefan Schindler, Henrik Von Wehrden, Kostas Poirazidis, Thomas Wrbka, Vassiliki KatiAbstract:Abstract Landscape Metrics are widely used to investigate the spatial structure of Landscapes. Numerous Metrics are currently available, yet only little empirical research has comparatively examined their indicator value for species richness for several taxa at several scales. Taking a Mediterranean forest Landscape – Dadia National Park (Greece) – as a case study area, we explored the performance of 52 Landscape level Landscape Metrics as indicators of species richness for six taxa (woody plants, orchids, orthopterans, amphibians, reptiles, and small terrestrial birds) and for overall species richness. We computed the Landscape Metrics for circular areas of five different extents around each of 30 sampling plots. We applied linear mixed models to evaluate significant relations between Metrics and species richness and to assess the effects of the extent of the considered Landscape on the performance of the Metrics. Our results showed that Landscape Metrics were good indicators for overall species richness, woody plants, orthopterans and reptiles. Metrics quantifying patch shape, proximity, texture and Landscape diversity resulted often in well-fitted models, while those describing patch area, similarity and edge contrast rarely contributed to significant models. Spatial scale affected the performance of the Metrics, since woody plants, orthopterans and small terrestrial birds were usually better predicted at smaller extents of surrounding Landscape, and reptiles frequently at larger ones. The revealed pattern of relations and performances will be useful to understand Landscape structure as a driver and indicator of biodiversity, and to improve forest and Landscape management decisions in Mediterranean and other forest mosaics.
Marti Bosch - One of the best experts on this subject based on the ideXlab platform.
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pylandstats an open source pythonic library to compute Landscape Metrics
bioRxiv, 2019Co-Authors: Marti BoschAbstract:Quantifying the spatial pattern of Landscapes has become a common task of many studies in Landscape ecology. Most of the existing software to compute Landscape Metrics is not well suited to be used in interactive environments such as Jupyter notebooks nor to be included as part of automated computational workflows. This article presents PyLandStats, an open-source Python library to compute Landscape Metrics within the scientific Python stack. The PyLandStats package provides a set of methods to undertake recurrent approaches to quantify Landscape patterns, such as the analysis of the spatiotemporal patterns of land use/land cover change or gradient analysis. The implementation is based on the prevailing Python libraries for geospatial data analysis in a way that they can be forthwith integrated into complex computational workflows. Notably, the provided methods offer a large variety of options so that users can employ PyLandStats in the way that best supports their needs. The source code is publicly available, and is organized in a modular object-oriented structure that enhances its maintainability and extensibility.
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pylandstats an open source pythonic library to compute Landscape Metrics
PLOS ONE, 2019Co-Authors: Marti BoschAbstract:Quantifying the spatial pattern of Landscapes has become a common task of many studies in Landscape ecology. Most of the existing software to compute Landscape Metrics is not well suited to be used in interactive environments such as Jupyter notebooks nor to be included as part of automated computational workflows. This article presents PyLandStats, an open-source Pythonic library to compute Landscape Metrics within the scientific Python stack. The PyLandStats package provides a set of methods to quantify Landscape patterns, such as the analysis of the spatiotemporal patterns of land use/land cover change or zonal analysis. The implementation is based on the prevailing Python libraries for geospatial data analysis in a way that they can be forthwith integrated into complex computational workflows. Notably, the provided methods offer a large variety of options so that users can employ PyLandStats in the way that best supports their needs. The source code is publicly available, and is organized in a modular object-oriented structure that enhances its maintainability and extensibility.
Susanne Frank - One of the best experts on this subject based on the ideXlab platform.
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Assessment of Landscape aesthetics—Validation of a Landscape Metrics-based assessment by visual estimation of the scenic beauty
Ecological Indicators, 2013Co-Authors: Susanne Frank, Christine Fürst, Lars Koschke, Anke Witt, Franz MakeschinAbstract:Abstract The assessment of cultural ecosystem services, in our case Landscape aesthetics, is the most commonly investigated but least formalized issue in the scope of the ecosystem services concept. In contrast to ecological or economic aspects, the assessment of aesthetics cannot easily be based on quantitative information. Therefore, two different methodological approaches that assess Landscape aesthetics either from an objective or a subjective point of view have been established in the past. This article presents in its first part an objective, Landscape Metrics-based assessment approach. We defined naturalness and Landscape diversity as assessment criteria and selected Shannon's Diversity Index (SHDI), Shape Index (SHAPE) and Patch Density (PD) as indicators. We tested our approach for a set of nine different Landscape types in a model region in Saxony, Germany. For validating the developed methodology, we carried out a survey with 153 participants in order to investigate their subjective preferences for the different Landscape types. These preferences had to be expressed by rating the Landscape types on a scale from 1 (very ugly) to 5 (very beautiful). The study was based on three different data sets, namely photographs of the Landscape types, satellite images, and land cover maps. Statistical tests were applied (a) to investigate the impact of personal factors on the ratings, (b) to detect whether abstraction levels are suitable for preference studies, and (c) to compare the results of the objective approach (Landscape Metrics) and the subjective approach (visual assessment). Personal factors did not influence the visual assessment results significantly. We found the highest correlation of the Landscape Metrics-based assessment with the visual assessment results of the photographs. We conclude that the three Landscape Metrics might be applied to the monitoring of Landscape aesthetics. An extended study with more participants might be useful to further investigate the reliability of our findings.
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assessment of Landscape aesthetics validation of a Landscape Metrics based assessment by visual estimation of the scenic beauty
Ecological Indicators, 2013Co-Authors: Susanne Frank, Christine Fürst, Lars Koschke, Anke Witt, Franz MakeschinAbstract:Abstract The assessment of cultural ecosystem services, in our case Landscape aesthetics, is the most commonly investigated but least formalized issue in the scope of the ecosystem services concept. In contrast to ecological or economic aspects, the assessment of aesthetics cannot easily be based on quantitative information. Therefore, two different methodological approaches that assess Landscape aesthetics either from an objective or a subjective point of view have been established in the past. This article presents in its first part an objective, Landscape Metrics-based assessment approach. We defined naturalness and Landscape diversity as assessment criteria and selected Shannon's Diversity Index (SHDI), Shape Index (SHAPE) and Patch Density (PD) as indicators. We tested our approach for a set of nine different Landscape types in a model region in Saxony, Germany. For validating the developed methodology, we carried out a survey with 153 participants in order to investigate their subjective preferences for the different Landscape types. These preferences had to be expressed by rating the Landscape types on a scale from 1 (very ugly) to 5 (very beautiful). The study was based on three different data sets, namely photographs of the Landscape types, satellite images, and land cover maps. Statistical tests were applied (a) to investigate the impact of personal factors on the ratings, (b) to detect whether abstraction levels are suitable for preference studies, and (c) to compare the results of the objective approach (Landscape Metrics) and the subjective approach (visual assessment). Personal factors did not influence the visual assessment results significantly. We found the highest correlation of the Landscape Metrics-based assessment with the visual assessment results of the photographs. We conclude that the three Landscape Metrics might be applied to the monitoring of Landscape aesthetics. An extended study with more participants might be useful to further investigate the reliability of our findings.
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Chances and limits of using Landscape Metrics within the interactive planning tool Pimp Your Landscape
2010Co-Authors: Susanne Frank, Christine Fürst, Carsten Lorz, Lars Koschke, M. AbiyAbstract:Landscape Metrics provide valuable information for the interpretation of Landscape patterns. With regard to the implementation of structural Landscape parameters into the evaluation system of the planning tool Pimp Your Landscape, we studied values of Landscape Metrics at different spatial resolutions. A literature review and a case study led to a choice of Metrics that might be suitable for the assessment of several Landscape functions and services. We tested this set of Landscape Metrics for a test area in Saxony, Germany. Except for diversity indices which gave consistent responses, Landscape Metrics varied significantly with changing resolution. Considering these results, we selected few easily applicable, unambiguous Landscape Metrics. With respect to the spatial resolution, these indices might be useful to quantify processes, functions and services on Landscape level within Pimp Your Landscape. Using class-level indices, a specific aggregation of land use types might be helpful for an exact description of land use pattern changes caused e.g. by Landscape
Vassiliki Kati - One of the best experts on this subject based on the ideXlab platform.
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performance of methods to select Landscape Metrics for modelling species richness
Ecological Modelling, 2015Co-Authors: Stefan Schindler, Henrik Von Wehrden, Kostas Poirazidis, Wesley M Hochachka, Thomas Wrbka, Vassiliki KatiAbstract:Abstract Landscape Metrics are commonly used indicators of ecological pattern and processes in ecological modelling. Numerous Landscape Metrics are available, making the selection of appropriate Metrics a common challenge in model development. In this paper, we tested the performance of methods for preselecting sets of three Landscape Metrics for use in modelling species richness of six groups of organisms (woody plants, orchids, orthopterans, amphibians, reptiles, and small terrestrial birds) and overall species richness in a Mediterranean forest Landscape. The tested methods included expert knowledge, decision tree analysis, principal component analysis, and principal component regression. They were compared with random choice and optimal sets, which were evaluated by testing all possible combinations of Metrics. All pre-selection methods performed significantly worse than the optimal sets. The statistical approaches performed slightly better than random choice that in turn performed slightly better than sets derived by expert knowledge. We concluded that the process of selecting the most appropriate Landscape Metrics for modelling biodiversity is not trivial and that shortcuts to systematic evaluation of Metrics should not be expected to identify appropriate indicators.
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multiscale performance of Landscape Metrics as indicators of species richness of plants insects and vertebrates
Ecological Indicators, 2013Co-Authors: Stefan Schindler, Henrik Von Wehrden, Kostas Poirazidis, Thomas Wrbka, Vassiliki KatiAbstract:Abstract Landscape Metrics are widely used to investigate the spatial structure of Landscapes. Numerous Metrics are currently available, yet only little empirical research has comparatively examined their indicator value for species richness for several taxa at several scales. Taking a Mediterranean forest Landscape – Dadia National Park (Greece) – as a case study area, we explored the performance of 52 Landscape level Landscape Metrics as indicators of species richness for six taxa (woody plants, orchids, orthopterans, amphibians, reptiles, and small terrestrial birds) and for overall species richness. We computed the Landscape Metrics for circular areas of five different extents around each of 30 sampling plots. We applied linear mixed models to evaluate significant relations between Metrics and species richness and to assess the effects of the extent of the considered Landscape on the performance of the Metrics. Our results showed that Landscape Metrics were good indicators for overall species richness, woody plants, orthopterans and reptiles. Metrics quantifying patch shape, proximity, texture and Landscape diversity resulted often in well-fitted models, while those describing patch area, similarity and edge contrast rarely contributed to significant models. Spatial scale affected the performance of the Metrics, since woody plants, orthopterans and small terrestrial birds were usually better predicted at smaller extents of surrounding Landscape, and reptiles frequently at larger ones. The revealed pattern of relations and performances will be useful to understand Landscape structure as a driver and indicator of biodiversity, and to improve forest and Landscape management decisions in Mediterranean and other forest mosaics.
Franz Makeschin - One of the best experts on this subject based on the ideXlab platform.
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assessment of Landscape aesthetics validation of a Landscape Metrics based assessment by visual estimation of the scenic beauty
Ecological Indicators, 2013Co-Authors: Susanne Frank, Christine Fürst, Lars Koschke, Anke Witt, Franz MakeschinAbstract:Abstract The assessment of cultural ecosystem services, in our case Landscape aesthetics, is the most commonly investigated but least formalized issue in the scope of the ecosystem services concept. In contrast to ecological or economic aspects, the assessment of aesthetics cannot easily be based on quantitative information. Therefore, two different methodological approaches that assess Landscape aesthetics either from an objective or a subjective point of view have been established in the past. This article presents in its first part an objective, Landscape Metrics-based assessment approach. We defined naturalness and Landscape diversity as assessment criteria and selected Shannon's Diversity Index (SHDI), Shape Index (SHAPE) and Patch Density (PD) as indicators. We tested our approach for a set of nine different Landscape types in a model region in Saxony, Germany. For validating the developed methodology, we carried out a survey with 153 participants in order to investigate their subjective preferences for the different Landscape types. These preferences had to be expressed by rating the Landscape types on a scale from 1 (very ugly) to 5 (very beautiful). The study was based on three different data sets, namely photographs of the Landscape types, satellite images, and land cover maps. Statistical tests were applied (a) to investigate the impact of personal factors on the ratings, (b) to detect whether abstraction levels are suitable for preference studies, and (c) to compare the results of the objective approach (Landscape Metrics) and the subjective approach (visual assessment). Personal factors did not influence the visual assessment results significantly. We found the highest correlation of the Landscape Metrics-based assessment with the visual assessment results of the photographs. We conclude that the three Landscape Metrics might be applied to the monitoring of Landscape aesthetics. An extended study with more participants might be useful to further investigate the reliability of our findings.
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Assessment of Landscape aesthetics—Validation of a Landscape Metrics-based assessment by visual estimation of the scenic beauty
Ecological Indicators, 2013Co-Authors: Susanne Frank, Christine Fürst, Lars Koschke, Anke Witt, Franz MakeschinAbstract:Abstract The assessment of cultural ecosystem services, in our case Landscape aesthetics, is the most commonly investigated but least formalized issue in the scope of the ecosystem services concept. In contrast to ecological or economic aspects, the assessment of aesthetics cannot easily be based on quantitative information. Therefore, two different methodological approaches that assess Landscape aesthetics either from an objective or a subjective point of view have been established in the past. This article presents in its first part an objective, Landscape Metrics-based assessment approach. We defined naturalness and Landscape diversity as assessment criteria and selected Shannon's Diversity Index (SHDI), Shape Index (SHAPE) and Patch Density (PD) as indicators. We tested our approach for a set of nine different Landscape types in a model region in Saxony, Germany. For validating the developed methodology, we carried out a survey with 153 participants in order to investigate their subjective preferences for the different Landscape types. These preferences had to be expressed by rating the Landscape types on a scale from 1 (very ugly) to 5 (very beautiful). The study was based on three different data sets, namely photographs of the Landscape types, satellite images, and land cover maps. Statistical tests were applied (a) to investigate the impact of personal factors on the ratings, (b) to detect whether abstraction levels are suitable for preference studies, and (c) to compare the results of the objective approach (Landscape Metrics) and the subjective approach (visual assessment). Personal factors did not influence the visual assessment results significantly. We found the highest correlation of the Landscape Metrics-based assessment with the visual assessment results of the photographs. We conclude that the three Landscape Metrics might be applied to the monitoring of Landscape aesthetics. An extended study with more participants might be useful to further investigate the reliability of our findings.
