2023
Xie, Chenghan; Wang, Jingxia; Haase, Dagmar; Wellmann, Thilo; Lausch, Angela
In: Science of The Total Environment, vol. 855, pp. 158608, 2023, ISSN: 0048-9697.
Abstract | Links | BibTeX | Tags: Internal orderliness, RapidEye data, Spatial heterogeneity, Urban green space, Urban planning, Vegetation management
@article{XIE2023158608,
title = {Measuring spatio-temporal heterogeneity and interior characteristics of green spaces in urban neighborhoods: A new approach using gray level co-occurrence matrix},
author = {Chenghan Xie and Jingxia Wang and Dagmar Haase and Thilo Wellmann and Angela Lausch},
url = {https://www.sciencedirect.com/science/article/pii/S0048969722057072},
doi = {https://doi.org/10.1016/j.scitotenv.2022.158608},
issn = {0048-9697},
year = {2023},
date = {2023-01-01},
journal = {Science of The Total Environment},
volume = {855},
pages = {158608},
abstract = {Urban green space (UGS) is a complex and highly dynamic interface between people and nature. The existing methods of quantifying and evaluating UGS are mainly implemented on the surface features at a landscape scale, and most of them are insufficient to thoroughly reflect the spatial-temporal relationships, especially the internal characteristics changes at a small scale and the neighborhood spatial relationship of UGS. This paper thus proposes a method to evaluate the internal dynamics and neighborhood heterogeneity of different types of UGS in Leipzig using the gray level co-occurrence matrix (GLCM) index. We choose GLCM variance, contrast, and entropy to analyze five main types of UGS through a holistic description of their vegetation growth, spatial heterogeneity, and internal orderliness. The results show that different types of UGS have distinct characteristics due to the changes of surrounding buildings and the distance to the built-up area. Within a one-year period, seasonal changes in UGS far away from built-up areas are more obvious. As for the larger and dense urban forests, they have the lowest spatial heterogeneity and internal order. On the contrary, the garden areas present the highest heterogeneity. In this study, the GLCM index depicts the seasonal alternation of UGS on the temporal scale and shows the spatial form of each UGS, being in line with local urban planning contexts. The correlation analysis of indices also proves that each type of UGS has its distinct temporal and spatial characteristics. The GLCM is valid in assessing the internal characteristics and relationships of various UGS at the neighborhood scales, and using the methodology developed in our study, more studies and field experiments could be fulfilled to investigate the assessment accuracy of our GLCM index approach and to further enhance the scientific understanding on the internal features and ecological functions of UGS.},
keywords = {Internal orderliness, RapidEye data, Spatial heterogeneity, Urban green space, Urban planning, Vegetation management},
pubstate = {published},
tppubtype = {article}
}
Urban green space (UGS) is a complex and highly dynamic interface between people and nature. The existing methods of quantifying and evaluating UGS are mainly implemented on the surface features at a landscape scale, and most of them are insufficient to thoroughly reflect the spatial-temporal relationships, especially the internal characteristics changes at a small scale and the neighborhood spatial relationship of UGS. This paper thus proposes a method to evaluate the internal dynamics and neighborhood heterogeneity of different types of UGS in Leipzig using the gray level co-occurrence matrix (GLCM) index. We choose GLCM variance, contrast, and entropy to analyze five main types of UGS through a holistic description of their vegetation growth, spatial heterogeneity, and internal orderliness. The results show that different types of UGS have distinct characteristics due to the changes of surrounding buildings and the distance to the built-up area. Within a one-year period, seasonal changes in UGS far away from built-up areas are more obvious. As for the larger and dense urban forests, they have the lowest spatial heterogeneity and internal order. On the contrary, the garden areas present the highest heterogeneity. In this study, the GLCM index depicts the seasonal alternation of UGS on the temporal scale and shows the spatial form of each UGS, being in line with local urban planning contexts. The correlation analysis of indices also proves that each type of UGS has its distinct temporal and spatial characteristics. The GLCM is valid in assessing the internal characteristics and relationships of various UGS at the neighborhood scales, and using the methodology developed in our study, more studies and field experiments could be fulfilled to investigate the assessment accuracy of our GLCM index approach and to further enhance the scientific understanding on the internal features and ecological functions of UGS.
2022
Scheuer, Sebastian; Jache, Jessica; Kičić, Martina; Wellmann, Thilo; Wolff, Manuel; Haase, Dagmar
A trait-based typification of urban forests as nature-based solutions Journal Article
In: Urban Forestry & Urban Greening, vol. 78, pp. 127780, 2022, ISSN: 1618-8667.
Abstract | Links | BibTeX | Tags: Nature-based solution, Ontology, Semantics, Trait-based modelling, Typology, Urban forest
@article{SCHEUER2022127780,
title = {A trait-based typification of urban forests as nature-based solutions},
author = {Sebastian Scheuer and Jessica Jache and Martina Kičić and Thilo Wellmann and Manuel Wolff and Dagmar Haase},
url = {https://www.sciencedirect.com/science/article/pii/S1618866722003235},
doi = {https://doi.org/10.1016/j.ufug.2022.127780},
issn = {1618-8667},
year = {2022},
date = {2022-01-01},
journal = {Urban Forestry & Urban Greening},
volume = {78},
pages = {127780},
abstract = {Urban forests as nature-based solutions (UF-NBS) are important tools for climate change adaptation and sustainable development. However, achieving both effective and sustainable UF-NBS solutions requires diverse knowledge. This includes knowledge on UF-NBS implementation, on the assessment of their environmental impacts in diverse spatial contexts, and on their management for the long-term safeguarding of delivered benefits. A successful integration of such bodies of knowledge demands a systematic understanding of UF-NBS. To achieve such an understanding, this paper presents a conceptual UF-NBS model obtained through a semantic, trait-based modelling approach. This conceptual model is subsequently implemented as an extendible, re-usable and interoperable ontology. In so doing, a formal, trait-based vocabulary on UF-NBS is created, that allows expressing spatial, morphological, physical, functional, and institutional UF-NBS properties for their typification and a subsequent integration of further knowledge and data. Thereby, ways forward are opened for a more systematic UF-NBS impact assessment, management, and decision-making.},
keywords = {Nature-based solution, Ontology, Semantics, Trait-based modelling, Typology, Urban forest},
pubstate = {published},
tppubtype = {article}
}
Urban forests as nature-based solutions (UF-NBS) are important tools for climate change adaptation and sustainable development. However, achieving both effective and sustainable UF-NBS solutions requires diverse knowledge. This includes knowledge on UF-NBS implementation, on the assessment of their environmental impacts in diverse spatial contexts, and on their management for the long-term safeguarding of delivered benefits. A successful integration of such bodies of knowledge demands a systematic understanding of UF-NBS. To achieve such an understanding, this paper presents a conceptual UF-NBS model obtained through a semantic, trait-based modelling approach. This conceptual model is subsequently implemented as an extendible, re-usable and interoperable ontology. In so doing, a formal, trait-based vocabulary on UF-NBS is created, that allows expressing spatial, morphological, physical, functional, and institutional UF-NBS properties for their typification and a subsequent integration of further knowledge and data. Thereby, ways forward are opened for a more systematic UF-NBS impact assessment, management, and decision-making.
Wellmann, Thilo; Andersson, Erik; Knapp, Sonja; Scheuer, Sebastian; Lausch, Angela; Palliwoda, Julia; Haase, Dagmar
Reinforcing nature-based solutions through tools providing social-ecological-technological integration Journal Article
In: Ambio, 2022.
Links | BibTeX | Tags: Climate Change Adaptation, functional diversity, Nature-based solutions, nbs, Remote Sensing, resilience, sets, social-ecological-technological systems
@article{Wellmann2022,
title = {Reinforcing nature-based solutions through tools providing social-ecological-technological integration},
author = {Thilo Wellmann and Erik Andersson and Sonja Knapp and Sebastian Scheuer and Angela Lausch and Julia Palliwoda and Dagmar Haase},
doi = {10.1007/s13280-022-01801-4},
year = {2022},
date = {2022-01-01},
journal = {Ambio},
keywords = {Climate Change Adaptation, functional diversity, Nature-based solutions, nbs, Remote Sensing, resilience, sets, social-ecological-technological systems},
pubstate = {published},
tppubtype = {article}
}
2021
Scheuer, Sebastian; Jache, Jessica; Wellmann, Thilo; Wolff, Manuel; Haase, Dagmar
Outlining a semantics-based Sino- European UF-NBS typology Technical Report
2021.
Abstract | Links | BibTeX | Tags: Nature-based solutions, Semantics, Typology, Urban forest, Urban green infrastructure, Web application, Web Ontology Language
@techreport{Thilo_Wellmann_107218460,
title = {Outlining a semantics-based Sino- European UF-NBS typology},
author = {Sebastian Scheuer and Jessica Jache and Thilo Wellmann and Manuel Wolff and Dagmar Haase},
url = {https://clearinghouseproject.eu/wp-content/uploads/2021/11/D1_1_Report_on_a_novel_standardised_Sino__European_UFBS_typology_V1.pdf},
year = {2021},
date = {2021-03-31},
urldate = {2021-01-01},
journal = {H2020 project CLEARING HOUSE},
abstract = {This deliverable outlines the CLEARING HOUSE typology of urban forests as nature-based solutions (UF-NBS). The typology thus conceptualizes entities relevant to UF-NBS. To do so, elements of greenblue infrastructure (GBI) are defined in the typology. Contrary to purely textual representations of knowledge, CLEARING HOUSE proposes a definition of GBI elements through traits, i.e., characteristic and defining morphological, physical, functional, and institutional attributes, including for example the composition, spatial grouping, and topology of UF-NBS elements, and the ecosystem services and benefits provided them. CLEARING HOUSE proposes a semantic approach to express this knowledge, i.e., a formalization of knowledge as an ontology using the Web Ontology Language. Such ontologies are machineinterpretable series of statements
of facts to define a taxonomy (a vocabulary). The definitions of GBI elements are embedded within a formalization of overarching concepts, particularly, of urban forest, nature-based solutions (NBS), and of UF-NBS. Here, urban forest is conceptually understood as the entirety of trees within an urban-ecological system. NBS are perceived in CLEARING HOUSE as an overarching concept that embraces natural and semi-natural elements of the GBI such as forests, engineered solutions such as permeable pavements, as well as actions inspired by nature. UF-NBS are then conceptualized as the intersection of the two previous entities, i.e., as the intersection of urban forest and NBS, and thus include any tree-related NBS. The proposed typology will provide the grounding knowledge of the comparative case study analysis to be conducted by CLEARING HOUSE, and will serve as a basis for the development of the CLEARING HOUSE benchmarking tool.},
howpublished = {Clearing House Research and Innovation Action (RIA) This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 821242},
keywords = {Nature-based solutions, Semantics, Typology, Urban forest, Urban green infrastructure, Web application, Web Ontology Language},
pubstate = {published},
tppubtype = {techreport}
}
This deliverable outlines the CLEARING HOUSE typology of urban forests as nature-based solutions (UF-NBS). The typology thus conceptualizes entities relevant to UF-NBS. To do so, elements of greenblue infrastructure (GBI) are defined in the typology. Contrary to purely textual representations of knowledge, CLEARING HOUSE proposes a definition of GBI elements through traits, i.e., characteristic and defining morphological, physical, functional, and institutional attributes, including for example the composition, spatial grouping, and topology of UF-NBS elements, and the ecosystem services and benefits provided them. CLEARING HOUSE proposes a semantic approach to express this knowledge, i.e., a formalization of knowledge as an ontology using the Web Ontology Language. Such ontologies are machineinterpretable series of statements
of facts to define a taxonomy (a vocabulary). The definitions of GBI elements are embedded within a formalization of overarching concepts, particularly, of urban forest, nature-based solutions (NBS), and of UF-NBS. Here, urban forest is conceptually understood as the entirety of trees within an urban-ecological system. NBS are perceived in CLEARING HOUSE as an overarching concept that embraces natural and semi-natural elements of the GBI such as forests, engineered solutions such as permeable pavements, as well as actions inspired by nature. UF-NBS are then conceptualized as the intersection of the two previous entities, i.e., as the intersection of urban forest and NBS, and thus include any tree-related NBS. The proposed typology will provide the grounding knowledge of the comparative case study analysis to be conducted by CLEARING HOUSE, and will serve as a basis for the development of the CLEARING HOUSE benchmarking tool.
of facts to define a taxonomy (a vocabulary). The definitions of GBI elements are embedded within a formalization of overarching concepts, particularly, of urban forest, nature-based solutions (NBS), and of UF-NBS. Here, urban forest is conceptually understood as the entirety of trees within an urban-ecological system. NBS are perceived in CLEARING HOUSE as an overarching concept that embraces natural and semi-natural elements of the GBI such as forests, engineered solutions such as permeable pavements, as well as actions inspired by nature. UF-NBS are then conceptualized as the intersection of the two previous entities, i.e., as the intersection of urban forest and NBS, and thus include any tree-related NBS. The proposed typology will provide the grounding knowledge of the comparative case study analysis to be conducted by CLEARING HOUSE, and will serve as a basis for the development of the CLEARING HOUSE benchmarking tool.
Chen, Shanshan; Haase, Dagmar; Xue, Bing; Wellmann, Thilo; Qureshi, Salman
Integrating Quantity and Quality to Assess Urban Green Space Improvement in the Compact City Journal Article
In: Land, 2021.
Abstract | Links | BibTeX | Tags: Berlin, Greening City, Land surfacae temperature, Landsat, Public engagement, Remote Sensing, Urban governance
@article{Thilo_Wellmann_104658268,
title = {Integrating Quantity and Quality to Assess Urban Green Space Improvement in the Compact City},
author = {Shanshan Chen and Dagmar Haase and Bing Xue and Thilo Wellmann and Salman Qureshi},
url = {http://doi.org/10.3390/land10121367},
doi = {10.3390/land10121367},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Land},
abstract = {Urban green space (UGS) has gained much attention in terms of urban ecosystems and human health. Measures to improve green space in compact cities are important for urban sustainability. However, there is a knowledge gap between UGS improvement and planning management. Based on the integration of quantity and quality, this research aims to identify UGS changes during urban development and suggest ways to improve green space. We analyse land use changes, conduct a hotspot analysis of land surface temperature (LST) between 2005 and 2015 at the city scale, and examine the changes in small, medium and large patches at the neighbourhood scale to guide decision-makers in UGS management. The results show that (i) the redevelopment of urban brownfields is an effective method for increasing quantity, with differences depending on regional functions; (ii) small, medium and large patches of green space have significance in terms of improving the quality of temperature mitigation, with apparent coldspot clustering from 2005 to 2015; and (iii) the integration of UGS quality and quantity in planning management is beneficial to green space sustainability. Green space improvement needs to emphasize the integration of UGS quantity and quality to accommodate targeted planning for local conditions.},
keywords = {Berlin, Greening City, Land surfacae temperature, Landsat, Public engagement, Remote Sensing, Urban governance},
pubstate = {published},
tppubtype = {article}
}
Urban green space (UGS) has gained much attention in terms of urban ecosystems and human health. Measures to improve green space in compact cities are important for urban sustainability. However, there is a knowledge gap between UGS improvement and planning management. Based on the integration of quantity and quality, this research aims to identify UGS changes during urban development and suggest ways to improve green space. We analyse land use changes, conduct a hotspot analysis of land surface temperature (LST) between 2005 and 2015 at the city scale, and examine the changes in small, medium and large patches at the neighbourhood scale to guide decision-makers in UGS management. The results show that (i) the redevelopment of urban brownfields is an effective method for increasing quantity, with differences depending on regional functions; (ii) small, medium and large patches of green space have significance in terms of improving the quality of temperature mitigation, with apparent coldspot clustering from 2005 to 2015; and (iii) the integration of UGS quality and quantity in planning management is beneficial to green space sustainability. Green space improvement needs to emphasize the integration of UGS quantity and quality to accommodate targeted planning for local conditions.
Scheuer, Sebastian; Jache, Jessica; Sumfleth, Luca; Wellmann, Thilo; Haase, Dagmar
Creating accessible evidence bases: Opportunities through the integration of interactive tools into literature review synthesis Journal Article
In: MethodsX, vol. 8, pp. 101558, 2021.
Abstract | Links | BibTeX | Tags: Dashboard, Data science, Dissemination, Structured data, Systematic literature review, Visualisation techniques, Web application
@article{Scheuer_2021b,
title = {Creating accessible evidence bases: Opportunities through the integration of interactive tools into literature review synthesis},
author = {Sebastian Scheuer and Jessica Jache and Luca Sumfleth and Thilo Wellmann and Dagmar Haase},
url = {https://doi.org/10.1016%2Fj.mex.2021.101558},
doi = {10.1016/j.mex.2021.101558},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {MethodsX},
volume = {8},
pages = {101558},
publisher = {Elsevier BV},
abstract = {The COVID-19 pandemic has shown that an immediate access to relevant information is key for timely interventions and forming of public opinion and discourse. In this regard, dashboards present themselves as invaluable tools for the democratization of data and for the creation of accessible evidence bases. Building on this momentum, it is proposed to integrate interactive means such as dashboards into academic literature review synthesis, in order to support the summarization, narration, and dissemination of findings, and furthermore, to increase transparency and support the transferability and comparability of findings. Exemplified for a systematic literature review on urban forests as nature-based solutions,
•Key functionalities, requirements and design considerations for the development of dashboards for use in academic literature reviews synthesis are identified.
•An application architecture that embeds dashboard development into an R workflow is presented, with emphasis on the steps needed to transform the data collected during the review process into a structured form.
•Technical and methodological means for the actual dashboard implementation are highlighted, considering the identified key functionalities and requirements.},
keywords = {Dashboard, Data science, Dissemination, Structured data, Systematic literature review, Visualisation techniques, Web application},
pubstate = {published},
tppubtype = {article}
}
The COVID-19 pandemic has shown that an immediate access to relevant information is key for timely interventions and forming of public opinion and discourse. In this regard, dashboards present themselves as invaluable tools for the democratization of data and for the creation of accessible evidence bases. Building on this momentum, it is proposed to integrate interactive means such as dashboards into academic literature review synthesis, in order to support the summarization, narration, and dissemination of findings, and furthermore, to increase transparency and support the transferability and comparability of findings. Exemplified for a systematic literature review on urban forests as nature-based solutions,
•Key functionalities, requirements and design considerations for the development of dashboards for use in academic literature reviews synthesis are identified.
•An application architecture that embeds dashboard development into an R workflow is presented, with emphasis on the steps needed to transform the data collected during the review process into a structured form.
•Technical and methodological means for the actual dashboard implementation are highlighted, considering the identified key functionalities and requirements.
•Key functionalities, requirements and design considerations for the development of dashboards for use in academic literature reviews synthesis are identified.
•An application architecture that embeds dashboard development into an R workflow is presented, with emphasis on the steps needed to transform the data collected during the review process into a structured form.
•Technical and methodological means for the actual dashboard implementation are highlighted, considering the identified key functionalities and requirements.
Lessel, Tilia; Wellmann, Thilo
Umweltgerechtigkeit aus bürgerschaftlicher Perspektive: Handlungsempfehlung am Beispiel Berlin-Schöneberg Journal Article
In: Stadt+Grün, vol. 01, 2021.
Abstract | Links | BibTeX | Tags: Berlin, Climate Change, Climate Change Adaptation, Environmental justice, Urban development, Urban green infrastructure, Urban planning
@article{Thilo_Wellmann_107218183,
title = {Umweltgerechtigkeit aus bürgerschaftlicher Perspektive: Handlungsempfehlung am Beispiel Berlin-Schöneberg},
author = {Tilia Lessel and Thilo Wellmann},
url = {https://stadtundgruen.de/artikel/umweltgerechtigkeit-aus-buergerschaftlicher-perspektive-15076.html},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Stadt+Grün},
volume = {01},
abstract = {Städte sind für die Umsetzung von Umweltgerechtigkeit von zentraler Bedeutung. Rund drei Viertel der EuropäerInnen leben in urbanen Räumen, so dass Fragen von Gerechtigkeit und Zugang zu Umweltqualitäten besonders hier entschieden werden. Zudem schaffen Städte durch ihre Baumasse Wärme- und Trockeninseln und damit ein besonders extremes, umwelt- und gesundheitsbelastendes Lokalklima. Vor diesem Hintergrund ist absehbar, dass die Effekte des Klimawandels die Städte besonders betreffen.},
keywords = {Berlin, Climate Change, Climate Change Adaptation, Environmental justice, Urban development, Urban green infrastructure, Urban planning},
pubstate = {published},
tppubtype = {article}
}
Städte sind für die Umsetzung von Umweltgerechtigkeit von zentraler Bedeutung. Rund drei Viertel der EuropäerInnen leben in urbanen Räumen, so dass Fragen von Gerechtigkeit und Zugang zu Umweltqualitäten besonders hier entschieden werden. Zudem schaffen Städte durch ihre Baumasse Wärme- und Trockeninseln und damit ein besonders extremes, umwelt- und gesundheitsbelastendes Lokalklima. Vor diesem Hintergrund ist absehbar, dass die Effekte des Klimawandels die Städte besonders betreffen.
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.
Andersson, Erik; Haase, Dagmar; Anderson, Pippin; Cortinovis, Chiara; Goodness, Julie; Kendal, Dave; Lausch, Angela; McPhearson, Timon; Sikorska, Daria; Wellmann, Thilo
What are the traits of a social-ecological system: towards a framework in support of urban sustainability Journal Article
In: npj Urban Sustainability, 2021.
Abstract | Links | BibTeX | Tags: Ecosystem services, Environmental impact, Environmental studies, Human behaviour, Social-Ecological System, Sustainability, Traits, Urban ecology, Urban governance
@article{Thilo_Wellmann_91204221,
title = {What are the traits of a social-ecological system: towards a framework in support of urban sustainability},
author = {Erik Andersson and Dagmar Haase and Pippin Anderson and Chiara Cortinovis and Julie Goodness and Dave Kendal and Angela Lausch and Timon McPhearson and Daria Sikorska and Thilo Wellmann},
url = {http://doi.org/10.1038/s42949-020-00008-4},
doi = {10.1038/s42949-020-00008-4},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {npj Urban Sustainability},
abstract = {To ensure that cities and urban ecosystems support human wellbeing and overall quality of life we need conceptual frameworks that can connect different scientific disciplines as well as research and practice. In this perspective, we explore the potential of a traits framework for understanding social-ecological patterns, dynamics, interactions, and tipping points in complex urban systems. To do so, we discuss what kind of framing, and what research, that would allow traits to (1) link the sensitivity of a given environmental entity to different globally relevant pressures, such as land conversion or climate change to its social-ecological consequences; (2) connect to human appraisal and diverse bio-cultural sense-making through the different cues and characteristics people use to detect change or articulate value narratives, and (3) examine how and under what conditions this new approach may trigger, inform, and support decision making in land/resources management at different scales.},
keywords = {Ecosystem services, Environmental impact, Environmental studies, Human behaviour, Social-Ecological System, Sustainability, Traits, Urban ecology, Urban governance},
pubstate = {published},
tppubtype = {article}
}
To ensure that cities and urban ecosystems support human wellbeing and overall quality of life we need conceptual frameworks that can connect different scientific disciplines as well as research and practice. In this perspective, we explore the potential of a traits framework for understanding social-ecological patterns, dynamics, interactions, and tipping points in complex urban systems. To do so, we discuss what kind of framing, and what research, that would allow traits to (1) link the sensitivity of a given environmental entity to different globally relevant pressures, such as land conversion or climate change to its social-ecological consequences; (2) connect to human appraisal and diverse bio-cultural sense-making through the different cues and characteristics people use to detect change or articulate value narratives, and (3) examine how and under what conditions this new approach may trigger, inform, and support decision making in land/resources management at different scales.
2020
Lausch, Angela; Schaepman, Michael E.; Skidmore, Andrew K.; Truckenbrodt, Sina C.; Hacker, Jörg M.; Baade, Jussi; Bannehr, Lutz; Borg, Erik; Bumberger, Jan; Dietrich, Peter; Gläßer, Cornelia; Haase, Dagmar; Heurich, Marco; Jagdhuber, Thomas; Jany, Sven; Krönert, Rudolf; Möller, Markus; Mollenhauer, Hannes; Montzka, Carsten; Pause, Marion; Rogass, Christian; Salepci, Nesrin; Schmullius, Christiane; Schrodt, Franziska; Schütze, Claudia; Schweitzer, Christian; Selsam, Peter; Spengler, Daniel; Vohland, Michael; Volk, Martin; Weber, Ute; Wellmann, Thilo; Werban, Ulrike; Zacharias, Steffen; Thiel, Christian
Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces Journal Article
In: Remote Sensing, vol. 12, no. 22, pp. 3690, 2020.
Abstract | Links | BibTeX | Tags: Earth observation, Geodiversity, Geomorphology, Monitoring, Remote Sensing, Spectral traits, Traits
@article{Lausch_2020,
title = {Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces},
author = {Angela Lausch and Michael E. Schaepman and Andrew K. Skidmore and Sina C. Truckenbrodt and Jörg M. Hacker and Jussi Baade and Lutz Bannehr and Erik Borg and Jan Bumberger and Peter Dietrich and Cornelia Gläßer and Dagmar Haase and Marco Heurich and Thomas Jagdhuber and Sven Jany and Rudolf Krönert and Markus Möller and Hannes Mollenhauer and Carsten Montzka and Marion Pause and Christian Rogass and Nesrin Salepci and Christiane Schmullius and Franziska Schrodt and Claudia Schütze and Christian Schweitzer and Peter Selsam and Daniel Spengler and Michael Vohland and Martin Volk and Ute Weber and Thilo Wellmann and Ulrike Werban and Steffen Zacharias and Christian Thiel},
url = {https://doi.org/10.3390%2Frs12223690},
doi = {10.3390/rs12223690},
year = {2020},
date = {2020-11-01},
urldate = {2020-11-01},
journal = {Remote Sensing},
volume = {12},
number = {22},
pages = {3690},
publisher = {MDPI AG},
abstract = {The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.},
keywords = {Earth observation, Geodiversity, Geomorphology, Monitoring, Remote Sensing, Spectral traits, Traits},
pubstate = {published},
tppubtype = {article}
}
The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.
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.
Castillo-Cabrera, Fernando; Wellmann, Thilo; Haase, Dagmar
Urban Green Fabric Analysis Promoting Sustainable Planning in Guatemala City Journal Article
In: Land, 2020.
Abstract | Links | BibTeX | Tags: Guatemala City, Remote Sensing, Urban green infrastructure, Urban planning, Urbanisation
@article{Thilo_Wellmann_85962105,
title = {Urban Green Fabric Analysis Promoting Sustainable Planning in Guatemala City},
author = {Fernando Castillo-Cabrera and Thilo Wellmann and Dagmar Haase},
url = {http://doi.org/10.3390/land10010018},
doi = {10.3390/land10010018},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Land},
abstract = {Urbanization rate in Central America is the second fastest worldwide and its major cities face challenges regarding urban sustainability. Urban Green Fabric (UGF) is an important material condition for the urban quality of life and, therefore, key to planning processes. We performed an analysis of the UGF of Guatemala City including the identification and classification of UGF, their spatial pattern analysis, construction of ensembles of districts (zones) and revealing citizen’s interactions with UGF. We used remote sensing and land use mapping techniques, spatial metrics and a questionnaire survey. Main results are the UGF map of Guatemala City and six ensembles of zones based on a set of indicators. We further revealed citizens’ recognition of green spaces, their perceptions about green space amount and availability as well as their support for UGF future interventions. Finally, we discuss the implications for planning promoted by our results and suggest three actions for UGF sustainability: Creation of new green spaces, protecting existing green spaces and enhancing the mosaic with different green spaces types. UGF is an essential decision support tool for a diversity of actors.},
keywords = {Guatemala City, Remote Sensing, Urban green infrastructure, Urban planning, Urbanisation},
pubstate = {published},
tppubtype = {article}
}
Urbanization rate in Central America is the second fastest worldwide and its major cities face challenges regarding urban sustainability. Urban Green Fabric (UGF) is an important material condition for the urban quality of life and, therefore, key to planning processes. We performed an analysis of the UGF of Guatemala City including the identification and classification of UGF, their spatial pattern analysis, construction of ensembles of districts (zones) and revealing citizen’s interactions with UGF. We used remote sensing and land use mapping techniques, spatial metrics and a questionnaire survey. Main results are the UGF map of Guatemala City and six ensembles of zones based on a set of indicators. We further revealed citizens’ recognition of green spaces, their perceptions about green space amount and availability as well as their support for UGF future interventions. Finally, we discuss the implications for planning promoted by our results and suggest three actions for UGF sustainability: Creation of new green spaces, protecting existing green spaces and enhancing the mosaic with different green spaces types. UGF is an essential decision support tool for a diversity of actors.
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}
}
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.
Wellmann, Thilo; Lausch, Angela; Andersson, Erik; Knapp, Sonja; Cortinovis, Chiara; Jache, Jessica; Scheuer, Sebastian; Kremer, Peleg; Mascarenhas, André; Kraemer, Roland; Schug, Franz; Haase, Annegret; Haase, Dagmar
Remote sensing in urban planning: Contributions towards ecologically sound policies? Journal Article
In: Landscape and Urban Planning, vol. 204, pp. 103921, 2020.
Abstract | Links | BibTeX | Tags: Earth observation, Ecosystem services, Open science, Remote Sensing, Science policy interface, Systematic literature review, Urban ecology
@article{wellmann2020remote,
title = {Remote sensing in urban planning: Contributions towards ecologically sound policies?},
author = {Thilo Wellmann and Angela Lausch and Erik Andersson and Sonja Knapp and Chiara Cortinovis and Jessica Jache and Sebastian Scheuer and Peleg Kremer and André Mascarenhas and Roland Kraemer and Franz Schug and Annegret Haase and Dagmar Haase},
doi = {10.1016/j.landurbplan.2020.103921},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
journal = {Landscape and Urban Planning},
volume = {204},
pages = {103921},
publisher = {Elsevier},
abstract = {Remote sensing has evolved to become a key tool for various fields of environmental analysis, thus actively informing policy across areas and domains. To evaluate the degree to which remote sensing is contributing to the science of ecologically-oriented urban planning, we carried out a systematic literature review using the SCOPUS database, searching for articles integrating knowledge in urban planning, remote sensing and ecology. We reviewed 186 articles, analysing various issues in urban environments worldwide. Key findings include that the level of integration between the three disciplines is limited, with only 12% of the papers fully integrating ecology, remote sensing and planning while 24% of the studies use specific methods from one domain only. The vast majority of studies is oriented towards contributing to the knowledge base or monitoring the impacts of existing policies. Few studies are directly policy relevant by either contributing to direct issues in planning and making specific design suggestions or evaluations. The accessibility of the scientific findings remains limited, as the majority of journal articles are not open access and proprietary software and data are frequently used. To overcome these issues, we suggest three future avenues for science as well as three potential entry points for remote sensing into applied urban planning. By doing so, remote sensing data could become a vital tool actively contributing to policies, civil engagement and concrete planning measures by providing independent and cost effective environmental analyses.},
keywords = {Earth observation, Ecosystem services, Open science, Remote Sensing, Science policy interface, Systematic literature review, Urban ecology},
pubstate = {published},
tppubtype = {article}
}
Remote sensing has evolved to become a key tool for various fields of environmental analysis, thus actively informing policy across areas and domains. To evaluate the degree to which remote sensing is contributing to the science of ecologically-oriented urban planning, we carried out a systematic literature review using the SCOPUS database, searching for articles integrating knowledge in urban planning, remote sensing and ecology. We reviewed 186 articles, analysing various issues in urban environments worldwide. Key findings include that the level of integration between the three disciplines is limited, with only 12% of the papers fully integrating ecology, remote sensing and planning while 24% of the studies use specific methods from one domain only. The vast majority of studies is oriented towards contributing to the knowledge base or monitoring the impacts of existing policies. Few studies are directly policy relevant by either contributing to direct issues in planning and making specific design suggestions or evaluations. The accessibility of the scientific findings remains limited, as the majority of journal articles are not open access and proprietary software and data are frequently used. To overcome these issues, we suggest three future avenues for science as well as three potential entry points for remote sensing into applied urban planning. By doing so, remote sensing data could become a vital tool actively contributing to policies, civil engagement and concrete planning measures by providing independent and cost effective environmental analyses.
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.
2018
Wellmann, Thilo; Haase, Dagmar; Knapp, Sonja; Salbach, Christoph; Selsam, Peter; Lausch, Angela
Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing Journal Article
In: Ecological Indicators, vol. 85, pp. 190–203, 2018.
Abstract | Links | BibTeX | Tags: GLCM, Hemeroby, Human-use-intensity, NDVI, RapidEye, Remote Sensing, Spectral trait variations, Spectral traits, Urban land-use-intensity
@article{wellmann2018urban,
title = {Urban land use intensity assessment: The potential of spatio-temporal spectral traits with remote sensing},
author = {Thilo Wellmann and Dagmar Haase and Sonja Knapp and Christoph Salbach and Peter Selsam and Angela Lausch},
url = {https://thilowellmann.de/wp/wp-content/uploads/2022/03/Wellmann_etal_Manuscript_U-LUI_2018.pdf},
doi = {10.1016/j.ecolind.2017.10.029},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
journal = {Ecological Indicators},
volume = {85},
pages = {190--203},
publisher = {Elsevier},
abstract = {By adding attributes of space and time to the spectral traits (ST) concept we developed a completely new way of quantifying and assessing land use intensity and the hemeroby of urban landscapes. Calculating spectral traits variations (STV) from remote sensing data and regressing STV against hemeroby, we show how to estimate human land use intensity and the degree of hemeroby for large spatial areas with a dense temporal resolution for an urban case study. We found a linear statistical significant relationship (p=0.01) between the annual amplitude in spectral trait variations and the degree of hemeroby. It was thereof possible to separate the different types of land use cover according to their degree of hemeroby and land use intensity, respectively. Moreover, since the concept of plant traits is a functional framework in which each trait can be assigned to one or more ecosystem functions, the assessment of STV is a promising step towards assessing the diversity of spectral traits in an ecosystem as a proxy of functional diversity.},
keywords = {GLCM, Hemeroby, Human-use-intensity, NDVI, RapidEye, Remote Sensing, Spectral trait variations, Spectral traits, Urban land-use-intensity},
pubstate = {published},
tppubtype = {article}
}
By adding attributes of space and time to the spectral traits (ST) concept we developed a completely new way of quantifying and assessing land use intensity and the hemeroby of urban landscapes. Calculating spectral traits variations (STV) from remote sensing data and regressing STV against hemeroby, we show how to estimate human land use intensity and the degree of hemeroby for large spatial areas with a dense temporal resolution for an urban case study. We found a linear statistical significant relationship (p=0.01) between the annual amplitude in spectral trait variations and the degree of hemeroby. It was thereof possible to separate the different types of land use cover according to their degree of hemeroby and land use intensity, respectively. Moreover, since the concept of plant traits is a functional framework in which each trait can be assigned to one or more ecosystem functions, the assessment of STV is a promising step towards assessing the diversity of spectral traits in an ecosystem as a proxy of functional diversity.