Publications

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.

https://nhess.copernicus.org/articles/21/203/2021/

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: Urban Green Fabric; earth observation; Guatemala City; urban green spaces; spatial pattern; urban planning

https://www.mdpi.com/2073-445X/10/1/18

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; Urban ecology; Systematic literature review; Open science; Ecosystem services; Science policy interface

https://www.sciencedirect.com/science/article/pii/S0169204620308860?via%3Dihub

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: geomorphology; terrain; surface; geodiversity; fluvial; aeolian; coastal; traits; spectral traits; remote sensing; earth observation; DEM; DTM; DSM; monitoring

https://www.mdpi.com/2072-4292/12/22/3690

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 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: Compact city; Dispersed green city; Remote sensing; Spectral unmixing; Landsat; Berlin

https://www.sciencedirect.com/science/article/pii/S0169204619313325 

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 nonlinear 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: Urban green infrastructure (UGI), Ecological flows,  Rise-and-decay functions, Neighbouring effects, Breeding birds, Land surface temperature

https://doi.org/10.1007/s10980-020-01039-z

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: Front and back yard green, Remote sensing, RapidEye, Delineation, Urban green space availability, Spectral unmixing, Sub-pixel mapping, Urban planning

https://doi.org/10.1016/j.ecolind.2019.106029

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: Front and back yard green, Remote sensing, RapidEye, Delineation, Urban green space availability, Spectral unmixing, Sub-pixel mapping, Urban planning

DOI: 10.1016/j.landurbplan.2018.10.010

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: Spectral traits (ST), Spectral trait variations (STV), Urban land-use-intensity (U-LUI), Human-use-intensity, Remote sensing, Hemeroby, NDVI, GLCM

DOI: 10.1016/j.ecolind.2017.10.029

Themen: Stadtklima, Klimawandel, Freiraumplanung, Stadtgrün, Stadtentwicklung, Architektur, Berlin, Städtebau

https://stadtundgruen.de/artikel/umweltgerechtigkeit-aus-buergerschaftlicher-perspektive-15076.html