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}
}
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}
}
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.