Remote Sensing-Based Characterization of Settlement Structures for Assessing Local Potential of District Heat

In Europe, heating of houses and commercial areas is one of the major contributors to greenhouse gas emissions. When considering the drastic impact of an increasing emission of greenhouse gases as well as the finiteness of fossil resources, the usage of efficient and renewable energy generation technologies has to be increased. In this context, small-scale heating networks are an important technical component, which enable the efficient and sustainable usage of various heat generation technologies. This paper investigates how the potential of district heating for different settlement structures can be assessed. In particular, we analyze in which way remote sensing and GIS data can assist the planning of optimized heat allocation systems. In order to identify the best suited locations, a spatial model is defined to assess the potential for small district heating networks. Within the spatial model, the local heat demand and the economic costs of the necessary heat allocation infrastructure are compared. Therefore, a first and major step is the detailed characterization of the settlement structure by means of remote sensing data. The method is developed on the basis of a test area in the town of Oberhaching in the South of Germany. The results are validated through detailed in situ data sets and demonstrate that the model facilitates both the calculation of the required input parameters and an accurate assessment of the district heating potential. The described method can be transferred to other investigation areas with a larger spatial extent. The study underlines the range of applications for remote sensing-based analyses with respect to energy-related planning issues.

[1]  Peter Reinartz,et al.  Near Real Time Processing of DSM from Airborne Digital Camera System for Disaster Monitoring , 2008 .

[2]  John A. Richards,et al.  Remote Sensing Digital Image Analysis , 1986 .

[3]  Franz Kurz,et al.  Accuracy assessment of the DLR 3K camera system , 2009 .

[4]  Monika Sester Creating a digital thermal map using laser scanning and GIS , 2006 .

[5]  R. Barthelmie,et al.  Can Satellite Sampling of Offshore Wind Speeds Realistically Represent Wind Speed Distributions , 2003 .

[6]  Stefan Dech,et al.  Fernerkundung im urbanen Raum - Erdbeobachtung auf dem Weg zur Planungspraxis , 2010 .

[7]  Qihao Weng Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling. , 2002, Journal of environmental management.

[8]  E. LeDrew,et al.  Remote sensing of aquatic coastal ecosystem processes , 2006 .

[9]  Xiaojun Yang What is Urban Remote Sensing , 2011 .

[10]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[11]  Manfred Fischedick,et al.  Potenziale von Nah- und Fernwärmenetzen für den Klimaschutz bis zum Jahr 2020 , 2007 .

[12]  D. Lu The potential and challenge of remote sensing‐based biomass estimation , 2006 .

[13]  S. Ventura,et al.  THE INTEGRATION OF GEOGRAPHIC DATA WITH REMOTELY SENSED IMAGERY TO IMPROVE CLASSIFICATION IN AN URBAN AREA , 1995 .

[14]  Avery Sen The benefits of remote sensing for energy policy , 2004 .

[15]  Christian Schill,et al.  A new thinking for renewable energy model: Remote sensing‐based renewable energy model , 2009 .

[16]  T. Esch,et al.  Object-based feature extraction using high spatial resolution satellite data of urban areas , 2010 .

[17]  K. Shadan,et al.  Available online: , 2012 .

[18]  Thomas Esch,et al.  Object-based image information fusion using multisensor earth observation data over urban areas , 2011 .

[19]  H. Böhnisch,et al.  Nahwärme im Gebäudebestand - Anlagenaspekte und Umsetzung , 2001 .

[20]  Thomas Esch,et al.  Improvement of Image Segmentation Accuracy Based on Multiscale Optimization Procedure , 2008, IEEE Geoscience and Remote Sensing Letters.

[21]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[22]  Manfred Fischedick,et al.  Nutzung von Satellitendaten für die Regionalisierung des regenerativen Nahwärmepotenzials in Deutschland , 2007 .

[23]  Hannes Taubenböck,et al.  Linking structural urban characteristics derived from high resolution satellite data to population distribution , 2007 .

[24]  Rudolf O. Weber,et al.  Automated Classification Scheme for Wind Fields , 1995 .

[25]  Claus Brenner,et al.  AUTOMATIC CALCULATION OF BUILDING VOLUMES FOR AN AREA-WIDE DETERMINATION OF HEAT REQUIREMENTS , 2003 .

[26]  Ian Witten,et al.  Data Mining , 2000 .

[27]  H. C. Hillmann,et al.  Statistisches Jahrbuch Für die Bundesrepublik Deutschland 1952 , 1953 .

[28]  Hendrik Herold,et al.  Analyzing building stock using topographic maps and GIS , 2009 .

[29]  Giorgio Guariso,et al.  Methods and tools to evaluate the availability of renewable energy sources , 2011 .

[30]  Lucien Wald,et al.  The European Solar Radiation Atlas: a valuable digital tool , 2001 .

[31]  M. Nast Chancen und Perspektiven der Nahwärme im zukünftigen Energiemarkt , 2004 .

[32]  Markus Blesl Räumlich hoch aufgelöste Modellierung leitungsgebundener Energieversorgungssysteme zur Deckung des Niedertemperaturwärmebedarfs , 2002 .

[33]  Peter Reinartz,et al.  ARGOS - Near Real Time Airborne Monitoring System for Disaster and Traffic Applications , 2010 .

[34]  J. E. Estes,et al.  Distributed parameter modelling of urban residential energy demand , 1979 .