2021
Scheuer, Sebastian; Haase, Dagmar; Haase, Annegret; Wolff, Manuel; Wellmann, Thilo
In: Natural Hazards and Earth System Sciences, vol. 21, no. 1, pp. 203–217, 2021.
Abstract | Links | BibTeX | Tags: Climate Change, Climate Change Adaptation, Leipzig, Machine learning, Natural hazards, Random forest, Risk assessment
@article{Scheuer_2021,
title = {A glimpse into the future of exposure and vulnerabilities in cities? Modelling of residential location choice of urban population with random forest},
author = {Sebastian Scheuer and Dagmar Haase and Annegret Haase and Manuel Wolff and Thilo Wellmann},
url = {https://doi.org/10.5194%2Fnhess-21-203-2021},
doi = {10.5194/nhess-21-203-2021},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Natural Hazards and Earth System Sciences},
volume = {21},
number = {1},
pages = {203--217},
publisher = {Copernicus GmbH},
abstract = {The most common approach to assessing natural hazard risk is investigating the willingness to pay in the presence or absence of such risk. In this work, we propose a new, machine-learning-based, indirect approach to the problem, i.e. through residential-choice modelling. Especially in urban environments, exposure and vulnerability are highly dynamic risk components, both being shaped by a complex and continuous reorganization and redistribution of assets within the urban space, including the (re-)location of urban dwellers. By modelling residential-choice behaviour in the city of Leipzig, Germany, we seek to examine how exposure and vulnerabilities are shaped by the residential-location-choice process. The proposed approach reveals hot spots and cold spots of residential choice for distinct socioeconomic groups exhibiting heterogeneous preferences. We discuss the relationship between observed patterns and disaster risk through the lens of exposure and vulnerability, as well as links to urban planning, and explore how the proposed methodology may contribute to predicting future trends in exposure, vulnerability, and risk through this analytical focus. Avenues for future research include the operational strengthening of these linkages for more effective disaster risk management.},
keywords = {Climate Change, Climate Change Adaptation, Leipzig, Machine learning, Natural hazards, Random forest, Risk assessment},
pubstate = {published},
tppubtype = {article}
}
The most common approach to assessing natural hazard risk is investigating the willingness to pay in the presence or absence of such risk. In this work, we propose a new, machine-learning-based, indirect approach to the problem, i.e. through residential-choice modelling. Especially in urban environments, exposure and vulnerability are highly dynamic risk components, both being shaped by a complex and continuous reorganization and redistribution of assets within the urban space, including the (re-)location of urban dwellers. By modelling residential-choice behaviour in the city of Leipzig, Germany, we seek to examine how exposure and vulnerabilities are shaped by the residential-location-choice process. The proposed approach reveals hot spots and cold spots of residential choice for distinct socioeconomic groups exhibiting heterogeneous preferences. We discuss the relationship between observed patterns and disaster risk through the lens of exposure and vulnerability, as well as links to urban planning, and explore how the proposed methodology may contribute to predicting future trends in exposure, vulnerability, and risk through this analytical focus. Avenues for future research include the operational strengthening of these linkages for more effective disaster risk management.
2020
Andersson, Erik; Haase, Dagmar; Scheuer, Sebastian; Wellmann, Thilo
Neighbourhood character affects the spatial extent and magnitude of the functional footprint of urban green infrastructure Journal Article
In: Landscape Ecology, vol. 35, no. 7, pp. 1605–1618, 2020.
Abstract | Links | BibTeX | Tags: Ecological flows, Land surfacae temperature, Landsat, Leipzig, Neighbouring effects, Rise-and-decay functions, Urban birds, Urban green infrastructure
@article{Andersson_2020,
title = {Neighbourhood character affects the spatial extent and magnitude of the functional footprint of urban green infrastructure},
author = {Erik Andersson and Dagmar Haase and Sebastian Scheuer and Thilo Wellmann},
url = {https://doi.org/10.1007%2Fs10980-020-01039-z},
doi = {10.1007/s10980-020-01039-z},
year = {2020},
date = {2020-06-01},
urldate = {2020-06-01},
journal = {Landscape Ecology},
volume = {35},
number = {7},
pages = {1605--1618},
publisher = {Springer Science and Business Media LLC},
abstract = {Context
Urban densification has been argued to increase the contrast between built up and open green space. This contrast may offer a starting point for assessing the extent and magnitude of the positive influences urban green infrastructure is expected to have on its surroundings.
Objectives
Drawing on insights from landscape ecology and urban geography, this exploratory study investigates how the combined properties of green and grey urban infrastructures determine the influence of urban green infrastructure on the overall quality of the urban landscape.
Methods
This article uses distance rise-or-decay functions to describe how receptive different land uses are to the influence of neighbouring green spaces, and does this based on integrated information on urban morphology, land surface temperature and habitat use by breeding birds.
Results
Our results show how green space has a non-linear and declining cooling influence on adjacent urban land uses, extending up to 300–400 m in densely built up areas and up to 500 m in low density areas. Further, we found a statistically significant declining impact of green space on bird species richness up to 500 m outside its boundaries.
Conclusions
Our focus on land use combinations and interrelations paves the way for a number of new joint landscape level assessments of direct and indirect accessibility to different ecosystem services. Our early results reinforce the challenging need to retain more green space in densely built up part of cities.},
keywords = {Ecological flows, Land surfacae temperature, Landsat, Leipzig, Neighbouring effects, Rise-and-decay functions, Urban birds, Urban green infrastructure},
pubstate = {published},
tppubtype = {article}
}
Context
Urban densification has been argued to increase the contrast between built up and open green space. This contrast may offer a starting point for assessing the extent and magnitude of the positive influences urban green infrastructure is expected to have on its surroundings.
Objectives
Drawing on insights from landscape ecology and urban geography, this exploratory study investigates how the combined properties of green and grey urban infrastructures determine the influence of urban green infrastructure on the overall quality of the urban landscape.
Methods
This article uses distance rise-or-decay functions to describe how receptive different land uses are to the influence of neighbouring green spaces, and does this based on integrated information on urban morphology, land surface temperature and habitat use by breeding birds.
Results
Our results show how green space has a non-linear and declining cooling influence on adjacent urban land uses, extending up to 300–400 m in densely built up areas and up to 500 m in low density areas. Further, we found a statistically significant declining impact of green space on bird species richness up to 500 m outside its boundaries.
Conclusions
Our focus on land use combinations and interrelations paves the way for a number of new joint landscape level assessments of direct and indirect accessibility to different ecosystem services. Our early results reinforce the challenging need to retain more green space in densely built up part of cities.
Urban densification has been argued to increase the contrast between built up and open green space. This contrast may offer a starting point for assessing the extent and magnitude of the positive influences urban green infrastructure is expected to have on its surroundings.
Objectives
Drawing on insights from landscape ecology and urban geography, this exploratory study investigates how the combined properties of green and grey urban infrastructures determine the influence of urban green infrastructure on the overall quality of the urban landscape.
Methods
This article uses distance rise-or-decay functions to describe how receptive different land uses are to the influence of neighbouring green spaces, and does this based on integrated information on urban morphology, land surface temperature and habitat use by breeding birds.
Results
Our results show how green space has a non-linear and declining cooling influence on adjacent urban land uses, extending up to 300–400 m in densely built up areas and up to 500 m in low density areas. Further, we found a statistically significant declining impact of green space on bird species richness up to 500 m outside its boundaries.
Conclusions
Our focus on land use combinations and interrelations paves the way for a number of new joint landscape level assessments of direct and indirect accessibility to different ecosystem services. Our early results reinforce the challenging need to retain more green space in densely built up part of cities.
Wellmann, Thilo; Lausch, Angela; Scheuer, Sebastian; Haase, Dagmar
In: Ecological Indicators, vol. 111, pp. 106029, 2020.
Abstract | Links | BibTeX | Tags: Leipzig, Machine learning, Random forest, RapidEye, Remote Sensing, Species Distribution Models, Spectral trait variations, Spectral traits, Urban birds
@article{wellmann2020earth,
title = {Earth observation based indication for avian species distribution models using the spectral trait concept and machine learning in an urban setting},
author = {Thilo Wellmann and Angela Lausch and Sebastian Scheuer and Dagmar Haase},
url = {https://thilowellmann.de/wp/wp-content/uploads/2020/01/WellmannEtAl_BreedngbirdsEO_SDM_Leipzig_AcceptedManuscript.pdf},
doi = {10.1016/j.ecolind.2019.106029},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Ecological Indicators},
volume = {111},
pages = {106029},
publisher = {Elsevier},
abstract = {Birds respond strongly to vegetation structure and composition, yet typical species distribution models (SDMs) that incorporate Earth observation (EO) data use discrete land-use/cover data to model habitat suitability. Since this neglects factors of internal spatial composition and heterogeneity of EO data, we suggest a novel scheme deriving continuous indicators of vegetation heterogeneity from high-resolution EO data.
The deployed concepts encompass vegetation fractions for determining vegetation density and spectral traits for the quantification of vegetation heterogeneity. Both indicators are derived from RapidEye data, thus featuring a continuous spatial resolution of 6.5 m. Using these indicators as predictors, we model breeding bird habitats using a random forest (RF) classifier for the city of Leipzig, Germany using a single EO image.
SDMs are trained for the breeding sites of 44 urban bird species, featuring medium to very high accuracies (59–90%). Analysing similarities between the models regarding variable importance of single predictors allows species groups to be determined based on their preferences and dependencies regarding the amount of vegetation and its spatial and structural heterogeneity. When combining the SDMs, models of urban bird species richness can be derived.
The combination of high-resolution EO data paired with the RF machine learning technique creates very detailed insights into the ecology of the urban avifauna, opening up opportunities of optimising greenspace management schemes or urban development in densifying cities concerning overall bird species richness or single species under threat of local extinction.},
keywords = {Leipzig, Machine learning, Random forest, RapidEye, Remote Sensing, Species Distribution Models, Spectral trait variations, Spectral traits, Urban birds},
pubstate = {published},
tppubtype = {article}
}
Birds respond strongly to vegetation structure and composition, yet typical species distribution models (SDMs) that incorporate Earth observation (EO) data use discrete land-use/cover data to model habitat suitability. Since this neglects factors of internal spatial composition and heterogeneity of EO data, we suggest a novel scheme deriving continuous indicators of vegetation heterogeneity from high-resolution EO data.
The deployed concepts encompass vegetation fractions for determining vegetation density and spectral traits for the quantification of vegetation heterogeneity. Both indicators are derived from RapidEye data, thus featuring a continuous spatial resolution of 6.5 m. Using these indicators as predictors, we model breeding bird habitats using a random forest (RF) classifier for the city of Leipzig, Germany using a single EO image.
SDMs are trained for the breeding sites of 44 urban bird species, featuring medium to very high accuracies (59–90%). Analysing similarities between the models regarding variable importance of single predictors allows species groups to be determined based on their preferences and dependencies regarding the amount of vegetation and its spatial and structural heterogeneity. When combining the SDMs, models of urban bird species richness can be derived.
The combination of high-resolution EO data paired with the RF machine learning technique creates very detailed insights into the ecology of the urban avifauna, opening up opportunities of optimising greenspace management schemes or urban development in densifying cities concerning overall bird species richness or single species under threat of local extinction.
The deployed concepts encompass vegetation fractions for determining vegetation density and spectral traits for the quantification of vegetation heterogeneity. Both indicators are derived from RapidEye data, thus featuring a continuous spatial resolution of 6.5 m. Using these indicators as predictors, we model breeding bird habitats using a random forest (RF) classifier for the city of Leipzig, Germany using a single EO image.
SDMs are trained for the breeding sites of 44 urban bird species, featuring medium to very high accuracies (59–90%). Analysing similarities between the models regarding variable importance of single predictors allows species groups to be determined based on their preferences and dependencies regarding the amount of vegetation and its spatial and structural heterogeneity. When combining the SDMs, models of urban bird species richness can be derived.
The combination of high-resolution EO data paired with the RF machine learning technique creates very detailed insights into the ecology of the urban avifauna, opening up opportunities of optimising greenspace management schemes or urban development in densifying cities concerning overall bird species richness or single species under threat of local extinction.
2019
Haase, Dagmar; Jänicke, Clemens; Wellmann, Thilo
Front and back yard green analysis with subpixel vegetation fractions from earth observation data in a city Journal Article
In: Landscape and Urban Planning, vol. 182, pp. 44–54, 2019.
Abstract | Links | BibTeX | Tags: Leipzig, Private green, RapidEye, Remote Sensing, Spectral unmixing
@article{haase2019front,
title = {Front and back yard green analysis with subpixel vegetation fractions from earth observation data in a city},
author = {Dagmar Haase and Clemens Jänicke and Thilo Wellmann},
url = {https://thilowellmann.de/wp/wp-content/uploads/2018/11/HaaseJänickeWellmann_FrontBackyardGreen_AcceptedManuscript.pdf},
doi = {10.1016/j.landurbplan.2018.10.010},
year = {2019},
date = {2019-01-01},
urldate = {2019-01-01},
journal = {Landscape and Urban Planning},
volume = {182},
pages = {44--54},
publisher = {Elsevier},
abstract = {This paper introduces a novel approach to green space availability in cities that includes the thus-far mostly neglected urban front and backyard green space around residential buildings on privately owned ground. To quantify the full spatial scope of urban green space, we calculated subpixel vegetation fractions from RapidEye remote-sensing data for the entire city with a spectral unmixing technique that enabled us to model the extent of urban vegetation with a high degree of confidence (MAE 7%, R2 0.92). We then applied a new ‘urban front and back yard green space derivation algorithm’, namely, a masking of the fractional vegetation data using GIS vector data of land cover, in order to delineate the front and backyard greenspace of residential houses in a city with an accuracy of 96%. Combining these two approaches, we can calculate the area of urban front and back yard green space for the entire city (including different residential structure types) and compare this data to the area of public (parks, urban forests) and semi-public (allotment gardens) green spaces that have been used for prevailing per capita green space availability analyses. The new method is exemplified at the city of Leipzig, Germany, which provides different residential structures concerning house types and the surrounding green that are characteristic of many European cities. Key findings include that the total amount of urban front and back yard green space is almost 2000 ha, which is ∼40% of the amount of public green space (4768 ha). In 15 out of the 63 total districts, there is more front and backyard than public green space, which highlights the importance of these urban front and back yard green space for the analysis of urban livelihoods and a tool for detailed ecosystem services-oriented urban planning.},
keywords = {Leipzig, Private green, RapidEye, Remote Sensing, Spectral unmixing},
pubstate = {published},
tppubtype = {article}
}
This paper introduces a novel approach to green space availability in cities that includes the thus-far mostly neglected urban front and backyard green space around residential buildings on privately owned ground. To quantify the full spatial scope of urban green space, we calculated subpixel vegetation fractions from RapidEye remote-sensing data for the entire city with a spectral unmixing technique that enabled us to model the extent of urban vegetation with a high degree of confidence (MAE 7%, R2 0.92). We then applied a new ‘urban front and back yard green space derivation algorithm’, namely, a masking of the fractional vegetation data using GIS vector data of land cover, in order to delineate the front and backyard greenspace of residential houses in a city with an accuracy of 96%. Combining these two approaches, we can calculate the area of urban front and back yard green space for the entire city (including different residential structure types) and compare this data to the area of public (parks, urban forests) and semi-public (allotment gardens) green spaces that have been used for prevailing per capita green space availability analyses. The new method is exemplified at the city of Leipzig, Germany, which provides different residential structures concerning house types and the surrounding green that are characteristic of many European cities. Key findings include that the total amount of urban front and back yard green space is almost 2000 ha, which is ∼40% of the amount of public green space (4768 ha). In 15 out of the 63 total districts, there is more front and backyard than public green space, which highlights the importance of these urban front and back yard green space for the analysis of urban livelihoods and a tool for detailed ecosystem services-oriented urban planning.