Soil Sealing and the Complex Bundle of Influential Factors: Germany as a Case Study

In order to discuss the impact of land consumption, it is first necessary to localize and quantify the extent of sealed surfaces. Since 2010, the monitoring of land use structures and developments in Germany has been provided by the Monitor of Settlement and Open Space Development at the Leibniz Institute of Ecological Urban and Regional Development (IOR; IOR Monitor), a scientific service operated by the Leibniz Institute of Ecological Urban and Regional Development. The IOR Monitor includes an indicator for soil sealing for the years 2006, 2009 and 2012. Using this new source of data, it is possible for the first time to conduct quantitative studies at the level of Germany’s municipalities with the aim of documenting the extent of soil sealing as a form of spatial classification, as well as to investigate possible correlations with other influential factors. Here, we describe a comprehensive data inspection of soil sealing and potential influential factors. Structural interrelationships are identified under the application of classical and spatial regression methods.

[1]  David C. Wheeler.  Visualizing and Diagnosing Coefficients from Geographically Weighted Regression Models , 2010 .

[2]  R. Scalenghe,et al.  The anthropogenic sealing of soils in urban areas , 2009 .

[3]  C. Lloyd.  Local Models for Spatial Analysis , 2006 .

[4]  Martina Artmann,et al.  SHRINKING CITIES - A SYSTEMIC MULTI-SCALE ANALYSIS IN GERMANY , 2013 .

[5]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[6]  Martin Charlton,et al.  Living with Collinearity in Local Regression Models , 2012 .

[7]  Alfred Ultsch,et al.  Urban data-mining: spatiotemporal exploration of multidimensional data , 2009 .

[8]  S. Mann,et al.  Stand und Dynamik der Flächenversiegelung in der Schweiz , 2009 .

[9]  G. Meinel,et al.  Land-use monitoring by topographic data analysis , 2013 .

[10]  W. Haber,et al.  Nachhaltiges Landmanagement, differenzierte Landnutzung und Klimaschutz , 2013 .

[11]  C. Walsh.  Territorial Agenda of the European Union 2020: Towards an Inclusive, Smart and Sustainable Europe of Diverse Regions , 2012 .

[12]  W. D. Kissling,et al.  Spatial autocorrelation and the selection of simultaneous autoregressive models , 2007 .

[13]  Luc Anselin,et al.  Lagrange Multiplier Test Diagnostics for Spatial Dependence and Spatial Heterogeneity , 2010 .

[14]  L. Salvati,et al.  Complex Patterns, Unpredictable Consequences: The Distribution of Sealed Land along the Urban-rural Gradient in Barcelona , 2015 .

[15]  L. Salvati,et al.  Soil sealing footprint as an indicator of dispersed urban growth: a multivariate statistics approach , 2016 .

[16]  Hans Wackernagel,et al.  Multivariate Geostatistics: An Introduction with Applications , 1996 .

[17]  Odette Kretschmer.  TOWARDS AN UNDERSTANDING OF LAND CONSUMPTION IN GERMANY – OUTLINE OF INFLUENTIAL FACTORS AS A BASIS FOR MULTIDIMENSIONAL ANALYSES , 2015 .

[18]  I. Melendez-Pastor,et al.  Impact of three decades of urban growth on soil resources in Elche (Alicante, Spain) . , 2012 .

[19]  Henning Nuissl,et al.  Does urban sprawl drive changes in the water balance and policy?: The case of Leipzig (Germany) 1870–2003 , 2007 .

[20]  D. Levia.  Farmland conversion and residential development in North Central Massachusetts , 1998 .

[21]  T. Esch,et al.  Large-area assessment of impervious surface based on integrated analysis of single-date Landsat-7 images and geospatial vector data , 2009 .

[22]  Martin Herold,et al.  Mapping imperviousness using NDVI and linear spectral unmixing of ASTER data in the Cologne-Bonn region (Germany) , 2004, SPIE Remote Sensing.

[23]  L. Anselin.  Local Indicators of Spatial Association—LISA , 2010 .

[24]  Gabriela Augusto,et al.  Monitoring Urban Sprawl and the National Ecological Reserve in Sintra-Cascais, Portugal: Multiple OLS Linear Regression Model Evaluation , 2011 .

[25]  J. Fox.  Applied Regression Analysis, Linear Models, and Related Methods , 1997 .

[26]  P. Villa.  Mapping urban growth using Soil and Vegetation Index and Landsat data: The Milan (Italy) city area case study , 2012 .

[27]  Hong S. He,et al.  Imperviousness Change Analysis Tool (I-CAT) for simulating pixel-level urban growth , 2014 .

[28]  M. Goodchild,et al.  Geographic Information Systems and Science (second edition) , 2001 .

[29]  M. Küchler,et al.  Urban growth along motorways in Switzerland , 2010 .

[30]  L. Anselin.  Spatial Econometrics: Methods and Models , 1988 .

[31]  F. Canters,et al.  High resolution modelling and forecasting of soil sealing density at the regional scale , 2015 .

[32]  Manfred M. Fischer,et al.  Spatial Data Analysis: Models, Methods and Techniques , 2011 .

[33]  Nguyen Xuan Thinh,et al.  Evaluation of urban land-use structures with a view to sustainable development , 2002 .

[34]  Ulrich Walz,et al.  Indicators of hemeroby for the monitoring of landscapes in Germany , 2014 .

[35]  Luc Anselin,et al.  Spatial Dependence in Linear Regression Models with an Introduction to Spatial Econometrics , 1995 .

[36]  Yingbin Deng,et al.  Segmentation-based and rule-based spectral mixture analysis for estimating urban imperviousness , 2015 .

[37]  D. Ludlow.  Urban Sprawl in Europe - The Ignored Challenge , 2006 .

[38]  Alan T. Murray,et al.  Estimating impervious surface distribution by spectral mixture analysis , 2003 .

[39]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[40]  D. Griffith.  Spatial Autocorrelation , 2020, Spatial Analysis Methods and Practice.

[41]  L. Salvati,et al.  Soil Sealing in the Growing City: A Survey in Rome, Italy , 2010 .

[42]  A. Páez,et al.  Geographically Weighted Regression , 2021, Handbook of Regional Science.

[43]  G. Carrus,et al.  Benefits and well-being perceived by people visiting green spaces in periods of heat stress. , 2009 .

[44]  Alfred Ultsch,et al.  Knowledge Discovery in Spatial Planning Data: A Concept for Cluster Understanding , 2015 .

[45]  Shiliang Su,et al.  Dynamics of soil sealing and soil landscape patterns under rapid urbanization , 2013 .

[46]  David Wheeler,et al.  Multicollinearity and correlation among local regression coefficients in geographically weighted regression , 2005, J. Geogr. Syst..

[47]  W. Acevedo,et al.  Rates, trends, causes, and consequences of urban land-use change in the United States , 2006 .

[48]  Alfred Ultsch,et al.  Pareto Density Estimation: A Density Estimation for Knowledge Discovery , 2005 .

[49]  Stefan Dech,et al.  Potential of hyperspectral remote sensing for characterisation of urban structure in Munich , 2008 .