Automated GIS-based derivation of urban ecological indicators using hyperspectral remote sensing and height information

Abstract Urban ecological indicators allow the objective and quantitative characterisation of ecological conditions in a spatially continuous way by evaluating the influence of urban surface types with respect to ecological functions and ecosystem services. Although the concept had already been developed in the 1980s, the variety of existing indicators had not been widely applied yet in urban planning practice, because of the high manual mapping effort that is required for spatially differentiated urban surface mapping. This paper presents a new automated remote sensing and GIS-based system for the flexible and user-defined derivation of urban ecological indicators. The system is based on automated surface material mapping using airborne hyperspectral image data and height information. Because the material classes obtained from remote sensing analysis differ in part from the surface types needed for the calculation of urban ecological indicators, they have been transformed into so-called linking categories representing the basis for the automated GIS-based derivation of urban ecological indicators. For this purpose, a computer-based system for flexible indicator derivation has been developed, allowing the user-defined integration of indicators based on the variable determination of mapping units, linking categories and respective weighting factors. Based on a comprehensive review of existing ecological indicators, 14 indicators have been selected and implemented in the system. To demonstrate the potential of the new system, a variety of indicators has been derived for two test sites situated in the German cities of Dresden and Potsdam, using city blocks defined by the municipal authorities as spatial mapping units. The initial mapping of surface materials was automatically performed on the basis of airborne hyperspectral image data acquired by the HyMAP system. The results of subsequent GIS-based indicator calculation were validated using results from field-based reference mapping that had been carried out for selected city blocks situated in both cities. An accuracy assessment for these reference city blocks has revealed mean errors of approximately 4%, confirming the suitability of the developed automated GIS-based system for flexible and efficient indicator calculation.

[1]  C. Lo.  Application of LandSat TM data for quality of life assessment in an urban environment , 1997 .

[2]  Qihao Weng,et al.  Remote sensing of impervious surfaces in the urban areas: Requirements, methods, and trends , 2012 .

[3]  D. Roberts,et al.  Endmember selection for multiple endmember spectral mixture analysis using endmember average RMSE , 2003 .

[4]  Thomas Esch,et al.  Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey , 2011, Remote. Sens..

[5]  Gregory Asner,et al.  Endmember bundles: a new approach to incorporating endmember variability into spectral mixture analysis , 2000, IEEE Trans. Geosci. Remote. Sens..

[6]  Graham Towers.  An Introduction to Urban Housing Design: At Home in the City , 2005 .

[7]  R. Richter,et al.  Geo-atmospheric processing of airborne imaging spectrometry data. Part 2: Atmospheric/topographic correction , 2002 .

[8]  M. A. Gomarasca,et al.  One century of land use changes in the metropolitan area of Milan (Italy) , 1993 .

[9]  Gustavo Camps-Valls,et al.  Urban Image Classification With Semisupervised Multiscale Cluster Kernels , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  Johannes R. Sveinsson,et al.  Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles , 2008, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Thomas Blaschke,et al.  A “ Green Index ” Incorporating Remote Sensing and Citizen ’ S Perception of Green Space , 2005 .

[12]  S. Pickett,et al.  Spatial heterogeneity in urban ecosystems: reconceptualizing land cover and a framework for classification , 2007 .

[13]  Hermann Kaufmann,et al.  Potential of Hyperspectral Remote Sensing for Analyzing the Urban Environment , 2011 .

[14]  Kenneth Button,et al.  CITY MANAGEMENT AND URBAN ENVIRONMENTAL INDICATORS , 2002 .

[15]  S. M. de Jong,et al.  Imaging spectrometry : basic principles and prospective applications , 2001 .

[16]  A. Pandey,et al.  Built-up and vegetation extraction and density mapping using WorldView-II , 2012 .

[17]  W. Zipperer,et al.  Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas , 2001 .

[18]  K. P. Sharma,et al.  Urban Neighborhood Green Index – A measure of green spaces in urban areas , 2012 .

[19]  P. Bolund,et al.  Ecosystem services in urban areas , 1999 .

[20]  Xiaoma Li,et al.  Spatial pattern of greenspace affects land surface temperature: evidence from the heavily urbanized Beijing metropolitan area, China , 2012, Landscape Ecology.

[21]  P. Böhm.  Urban Structural Units as a Key Indicator for Monitoring and Optimising the Urban Environment , 1998 .

[22]  R. Jenssen,et al.  1 THE HYMAP TM AIRBORNE HYPERSPECTRAL SENSOR : THE SYSTEM , CALIBRATION AND PERFORMANCE , 1998 .

[23]  Ingegärd Eliasson,et al.  Spatial air temperature variations and urban land use — a statistical approach , 2003 .

[24]  S. Dicks,et al.  Association of urban temperatures with land use and surface materials , 1987 .

[25]  G. F. Hepner,et al.  Investigation of imaging spectroscopy for discriminating urban land covers and surface materials. , 2001 .

[26]  Margaret E. Gardner,et al.  Spectrometry for urban area remote sensing—Development and analysis of a spectral library from 350 to 2400 nm , 2004 .

[27]  Martin Herold,et al.  Spectral resolution requirements for mapping urban areas , 2003, IEEE Trans. Geosci. Remote. Sens..

[28]  S. Pauleit,et al.  Assessing the environmental performance of land cover types for urban planning , 2000 .

[29]  Jon Atli Benediktsson,et al.  Recent Advances in Techniques for Hyperspectral Image Processing , 2009 .

[30]  Hermann Kaufmann,et al.  Towards an Automated Update of Urban Biotope Maps Using Remote Sensing Data: What is Possible? , 2010 .

[31]  Nina Schwarz.  Urban form revisited—Selecting indicators for characterising European cities , 2010 .

[32]  Daniel Schläpfer,et al.  Geo-atmospheric processing of airborne imaging spectrometry data. Part 1: Parametric orthorectification , 2002 .

[33]  Deepak Khare,et al.  Monitoring and modelling of urban sprawl using remote sensing and GIS techniques , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[34]  Alexander F. H. Goetz,et al.  Three decades of hyperspectral remote sensing of the Earth: a personal view. , 2009 .

[35]  K. Kraus,et al.  ADVANCED DTM GENERATION FROM LIDAR DATA , 2001 .

[36]  J. C. Taylor,et al.  Sensitivity of mixture modelling to end‐member selection , 2003 .

[37]  E. Gómez‐Baggethun,et al.  Classifying and valuing ecosystem services for urban planning , 2013 .

[38]  Jin Chen,et al.  Mapping impervious surface expansion using medium-resolution satellite image time series: a case study in the Yangtze River Delta, China , 2012 .

[39]  Tobia Lakes,et al.  The urban environmental indicator “Biotope Area Ratio”—An enhanced approach to assess and manage the urban ecosystem services using high resolution remote-sensing , 2012 .

[40]  Hany M. Ayad,et al.  The urban structural units method: a basis for evaluating environmental prospects for sustainable development , 2002 .

[41]  S. Tiesdell,et al.  Public Places Urban Spaces , 2003 .

[42]  P. Cabral,et al.  Délimitation d‘aires urbaines à partir d‘une image Landsat ETM+ : comparaison de méthodes de classification , 2007 .

[43]  Rusong Wang,et al.  Measurement indicators and an evaluation approach for assessing urban sustainable development: A case study for China's Jining City , 2009 .

[44]  F. Gómez,et al.  Green Areas, the Most Significant Indicator of the Sustainability of Cities: Research on Their Utility for Urban Planning , 2011 .

[45]  Hermann Kaufmann,et al.  Fusion of spectral and shape features for identification of urban surface cover types using reflective and thermal hyperspectral data , 2003 .

[46]  Manfred F. Buchroithner,et al.  Estimation of Urban Green Volume Based on Single-Pulse LiDAR Data , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[47]  Xiaojun Yang,et al.  Urban remote sensing : monitoring, synthesis and modeling in the urban environment , 2011 .

[48]  D. Roberts,et al.  Hierarchical Multiple Endmember Spectral Mixture Analysis (MESMA) of hyperspectral imagery for urban environments , 2009 .

[49]  John Handley,et al.  Characterising the urban environment of UK cities and towns: A template for landscape planning , 2008 .

[50]  T. Esch,et al.  Urban structure type characterization using hyperspectral remote sensing and height information , 2012 .

[51]  Ellen Banzhaf,et al.  Monitoring Urban Structure Types as Spatial Indicators With CIR Aerial Photographs for a More Effective Urban Environmental Management , 2008, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[52]  N. Munier,et al.  Methodology to select a set of urban sustainability indicators to measure the state of the city, and performance assessment , 2011 .

[53]  Martin Sandtner.  Die Erfassung von städtischen Oberflächen mittels EDV-gestützter Luftbildauswertung : ein Beitrag zur klimatologischen und luftklimatischen Bewertung von städtischen Teilflächen , 1998 .

[54]  Toby N. Carlson,et al.  ANALYSIS AND PREDICTION OF SURFACE RUNOFF IN AN URBANIZING WATERSHED USING SATELLITE IMAGERY 1 , 2004 .

[55]  Hermann Kaufmann,et al.  Automated differentiation of urban surfaces based on airborne hyperspectral imagery , 2001, IEEE Trans. Geosci. Remote. Sens..

[56]  D. Lu,et al.  Spectral Mixture Analysis of the Urban Landscape in Indianapolis with Landsat ETM+ Imagery , 2004 .

[57]  Thomas Blaschke,et al.  Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems - An Overview , 2011, Remote. Sens..

[58]  M. Bauer,et al.  Photogrammetric Engineering & Remote Sensing Comparison of Spectral Analysis Techniques for Impervious Surface Estimation Using Landsat Imagery , 2022 .

[59]  Hermann Kaufmann,et al.  Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data , 2007 .

[60]  Qihao Weng,et al.  Modeling Urban Heat Islands and Their Relationship With Impervious Surface and Vegetation Abundance by Using ASTER Images , 2011, IEEE Transactions on Geoscience and Remote Sensing.