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}
}
2020
Wellmann, Thilo; Schug, Franz; Haase, Dagmar; Pflugmacher, Dirk; Linden, Sebastian
Green growth? On the relation between population density, land use and vegetation cover fractions in a city using a 30-years Landsat time series Journal Article
In: Landscape and Urban Planning, 2020, ISSN: 0169-2046.
Abstract | Links | BibTeX | Tags: Berlin, Compact vs. Dispersed developments, Earth observation, Greening City, Landsat, Machine learning, Unmixing, Urban planning
@article{Thilo_Wellmann_75416790,
title = {Green growth? On the relation between population density, land use and vegetation cover fractions in a city using a 30-years Landsat time series},
author = {Thilo Wellmann and Franz Schug and Dagmar Haase and Dirk Pflugmacher and Sebastian Linden},
url = {https://thilowellmann.de/wp/wp-content/uploads/2020/06/WellmannEtAl_GreenGrowth_AcceptedManuscript.pdf},
doi = {10.1016/j.landurbplan.2020.103857},
issn = {0169-2046},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Landscape and Urban Planning},
abstract = {Both compact and dispersed green cities are considered sustainable urban forms, yet some developments accompanied with these planning paradigms seem problematic in times of urban growth. A compact city might lose urban green spaces due to infill and a dispersed-green city might lose green in its outskirts through suburbanisation. To study these storylines, we introduce an operationalised concept of contrasting changes in population density (shrinkage or growth) with vegetation density (sealing or greening) over time. These trends are ascribed to different land use classes and single urban development projects, to quantify threads and pathways for urban green in a densifying city. We mapped the development in vegetation density over 30 years as subpixel fractions based on a Landsat remote sensing time series (for 2015: MAE 0.12). The case study city Berlin, Germany, developed into a city that is both gaining in vegetation–greening–and population–growing–in recent years but featured highly diverse trends for both compact and green city districts before that. Pathways to achieve a greening-growing scenario in a compact city include green roofs, brownfield and industrial revitalisation, and bioswales in predominantly green city districts. A threat for compact cities pose infill developments without greening measures. A threat for dispersed-green cities is microsealing in private residential gardens–gravel gardens–or car parking infrastructure. We conclude that neither a compact nor a dispersed-green city form concept logically leads to a development towards more environmental quality–here vegetation density–in times of densification but rather context specific urban planning.},
keywords = {Berlin, Compact vs. Dispersed developments, Earth observation, Greening City, Landsat, Machine learning, Unmixing, Urban planning},
pubstate = {published},
tppubtype = {article}
}
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}
}
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.