2022
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
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.
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.
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; 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.