Klaus Steinnocher

Integrating earth observation and GIScience for high resolution spatial and functional modeling of urban land use

Integrative analysis of remote sensing data and socioeconomic information enables the transition of land cover and urban structures into a detailed functional model of urban land use. In this paper object based image analysis is used to derive a classification of urban structures. The implementation of ALS (Airborne Laser Scanning) significantly enhances the classification of optical imagery both in terms of accuracy as well as automation. Land cover types are additionally differentiated based on their relative height above ground resulting in a 3D building model. This model forms the basis for the integration of socioeconomic data for identifying urban functions. Buildings are split into sub-buildings by creating Thiessen polygons based on geocoded address point data. Company data is linked to this address information resulting in significant refinement of the functional classification and concrete identification of building use. By means of spatial disaggregation, raster population data is distributed to potential residential buildings. The relevant potential residential capacity is calculated under consideration of building use and ALS-based height information. These additional information sources guarantee a high accuracy of disaggregation and a further refinement of the functional 3D city model, independently confirmed by a quantitative accuracy assessment.

Multi-level geospatial modeling of human exposure patterns and vulnerability indicators

In the context of disaster risk management and in particular for improving preparedness and mitigation of potential impacts, information on socioeconomic characteristics including aspects of situation-specific human exposure and vulnerability is considered vital. This paper provides an overview on available multi-level geospatial information and modeling approaches from global to local scales that could serve as inventory for people involved in disaster-related areas. Concepts and applications related to the human exposure and social vulnerability domains are addressed by illustrating the varying dimensions and contextual implications. Datasets and methods are highlighted that can be applied to assess earthquake-related population exposure, ranging from global and continental-scale population grids (with a focus on recent developments for Europe) to high-resolution functional urban system models and space–time variation aspects. In a further step, the paper elaborates on the integration of social structure on regional scale and the development of aggregative social and economic vulnerability indicators which would eventually enable the differentiation of situation-specific risk patterns. The presented studies cover social vulnerability mapping for selected US federal states in the New Madrid seismic zone as well as the advancement of social vulnerability analysis through integration of additional economic features in the index construction by means of a case study for Turkey’s provinces.

Object-oriented classification of orthophotos to support update of spatial databases

Interpretation of aerial images is normally carried out by visual interpretation as traditional classification routines are too limited in dealing with the complexity of very high resolution data. Segmentation based classifiers can overcome this limitation by dividing images into homogenous segments and using them as basis for further classification procedures. In this paper this approach is examined in view of its potential to support the update of existing land use data bases. A workflow was developed that allows the classification of high-resolution aerial images, the subsequent comparison with land use data and the assessment of identified changes. Special emphasis is put on the transferability of the procedure in terms of study area as well as image and land use data.

Long-term spatio-temporal social vulnerability variation considering health-related climate change parameters particularly affecting elderly

For assessing the social dimension of vulnerability, population exposure mapping is usually considered the essential starting point. Integration of social structure then further differentiates situation-specific vulnerability patterns on a local scale. Census data available in heterogeneous spatial reference units are still considered the standard information input for assessing potentially affected people, for example, in case of an emergency. There is a strong demand for population data in homogeneous spatial units that are independent from administrative areas. Raster representations meet this demand but are not yet available for all European countries. In this paper, we present an approach of spatial disaggregation of population data for a European transect referring to current population statistics and anticipated future prospects. Recently published data providing the degree of soil sealing are applied as basic proxy for population density in the spatial disaggregation model. In order to assess future patterns of climate change-related vulnerability, results of a European regional climate model are considered for projecting the situation in the 2030s. “Heat wave frequency” is accounted for as climate variable featuring conditions regarded as especially strenuous for elderly or physically weak persons. Integrated analysis of the population and climate prospects enables identification of hot spots in the European transect examined, that is, regions of particularly demanding projected climatic patterns as well as high population density and case-specific vulnerable structure (elderly people). Integrated and consistent spatial analyses on European scale are essential for decision support in the context of climate change impact mitigation as well as for risk communication and future safety and security considerations.

Rural–Urban Regions: A Spatial Approach to Define Urban–Rural Relationships in Europe

The previous chapter introduced the range of issues associated with the peri-urban, the subject of this book. The peri-urban as a specific morphological type was defined and the different dimensions of its dynamics were explored. This peri-urban zone is intimately associated with the transition from a dense urban structure to that of a rural character and since it also involves movements into, out of and across it from both these extremes, it is difficult to consider it properly without understanding the broader regional context and dynamics across the urban–rural gradient. Therefore, this chapter will focus on the broader context of urban–rural relationships. Based on recent scientific debates concerning the concept of functional regions and urban–rural relationships, both current and previous definitions and their political implementations are introduced before presenting a new typology to represent Rural–urban Regions (RUR) spatially. Covering the territory of European Union (EU), this typology classifies regions into different types, considering city size, degree of regional mono- and poly-centricity, as well as their urban, peri-urban or rural predominance. The development of the typology includes a further delineation of regions into urban, peri-urban and rural sub-regions, all based on land use patterns and population distribution and density. The typology was subsequently used throughout the PLUREL project and each of the case studies presented in Part Two refers to one of these types, although not all are represented there, since the case studies were unavoidably selected before the typology was developed.

Population Exposure and Impact Assessment: Benefits of Modeling Urban Land Use in Very High Spatial and Thematic Detail

This paper highlights the benefits that high-level geospatial modeling of urban patterns can provide for real-world applications in the field of population exposure and impact assessment. A set of techniques is described leading to identification of functional and socioeconomic relationships in a suburban environment. Diverse high resolution remote sensing data are classified using Object-Based Image Analysis in order to derive structural land cover information. Georeferenced address data then serve as essential link between this geometric framework and ancillary space-related information such as company and census data. The final very high resolution functional population model (i.e. broken down to address-based building part objects) is consulted for exposure and impact assessments exemplarily shown in two different fictitious scenarios: (1) earthquake hazard and (2) traffic noise propagation. High-detail spatial data sets including functional and socioeconomic information as derived in this study can be of great value in disaster risk management and simulation, but also in regional and environmental planning as well as geomarketing analyses.

VGDI – Advancing the Concept: Volunteered Geo‐Dynamic Information and its Benefits for Population Dynamics Modeling

The concept of Volunteered Geographic Information (VGI) has progressed from being an exotic prospect to making a profound impact on GIScience and geography in general, as initially anticipated. However, while massive and manifold data is continuously produced voluntarily and applications are built for information and knowledge extraction, the initially introduced concept of VGI lacks certain methodological perspectives in this regard which have not been fully elaborated. In this article we highlight and discuss an important gap in this concept, i.e. the lack of formal acknowledgment of temporal aspects. By coining the proposed advanced framework ‘Volunteered Geo-Dynamic Information’ (VGDI), we attempt to lay the ground for full conceptual and applied spatio-temporal integration. To illustrate that integrative approach of VGDI and its benefits, we describe the potential impact on the field of dynamic population distribution modeling. While traditional approaches in that domain rely on survey-based data and statistics as well as static geographic information, the use of VGDI enables a dynamic setup. Foursquare venue and user check-in data are presented for a test site in Lisbon, Portugal. Two core modules of spatio-temporal population assessment are thereby addressed, namely time use profiling and target zone characterization, motivated by the potential integration in existing population dynamics frameworks such as the DynaPop model.

GEOLAND spatial planning observatory: How remote sensing data can serve the needs of urban and regional planning

The objective of the geoland is to develop and demonstrate reliable, affordable and cost efficient European geo-information services, supporting the implementation of European directives and their national, regional and local implementation, as well as European and international policies. The observatory for spatial planning (OSP) generates products and services based on earth observation (EO) data, other geo-spatial as well as socioeconomic data contributing to the information needs of spatial planning. In this paper we will focus on presenting the results of the two first years of geoland OSP, namely indicators and urban growth scenarios and on analysing their usefulness in regional and urban planning. The process of calculating indicators on the basis of combining earth observation data with socio-economic data has been carried out at three levels: European level, national/transnational level and subnational level. The indicators are linked to urban issues, urban-rural interaction and environmental topics. In addition to making the spatial data more useful for users e.g. urban and regional planners, the indicators have been developed and calculated following fully scientific criteria. Many of the developed indicators have been devised bearing in mind issues linked to various aspects of sustainable development. Landscape fragmentation, access to green urban areas and degree of urban sprawl yield useful information for planners about the present state of the environment and if the direction of the development is towards a more sustainable future or not. Urban planning is very future oriented task. Most available tools such as indicators are linked to past and present. There is a clear need for development and testing of models which would help urban and regional planners to simulate in an easy and reliable way the future development of the city and more over what kinds of impacts different development scenarios induce. In order to bridge this gap geoland spatial observatory has done various tests with two types of land use models: settlement growth model and cellular automata model. Both models have proved to produce reliable simulations for urban land use changes. Urban and regional planners participating in the project are convinced about the added value these types of models can provide with both in the planning process and in political decision-making.

Image classification with LiDAR and GIS-data: moving from land cover to land use

Using image and LiDAR data the identification of image objects can be improved over using image data alone but the classification will still be mainly on the level of land cover. In order to move from land cover to land use it is suggested to include data stored in databases which can supply some information on land use. Although some of these will be stored in a GIS and can thus be linked more easily, others will typically only be indexed by street addresses with very little other geographical references. With the availability of georeferenced postal addresses these data can now be linked to image objects and used for classification purposes. An example is presented where a land cover classification is linked to the information taken from the yellow pages. This allows the identification of buildings used for commercial as well as public services down the specific branches of business. Inversely this also allows the identification of all residential buildings or those that have some kind of mixed use.

Earth observation for Low Carbon Action plan development - A concept for EO supported energy planning

The paper discusses the challenges and chances for the use of Earth Observation (EO) as a supportive information source in the development of Low Carbon Action Plans. The process of Low Carbon Action Planning is described and analyzed with regard to information gaps that can be bridged using the support of EO derived information products. In addition, the potentials for utilization of urban remote sensing are discussed and related to the requirements of Low Carbon Action Plans. A case study is presented showing EO based information products for the cities of Ahmedabad and Gandhinagar.