How Much Would You Pay for a Satellite Image?: Lessons Learned From French Spatial-Data Infrastructure

Satellite imagery is increasingly employed for land-use analysis and planning. In this article, we examine the economic value of high-resolution (HR) satellite images as perceived by direct users. Drawing on a French spatial-data infrastructure (SDI), the direct users of which are mostly from public bodies, we used a contingent-valuation method to evaluate their willingness to pay (WTP) for satellite imagery. A clear understanding of the value of these images is critical for justifying the large investments made in this sector and supporting policies that aim to develop and sustain these resources. We analyzed the differences in the stated values according to the various types of users. A survey of the registered users on the Geo Information for Sustainable Development (GEOSUD) platform found a mean value of €1,696 for a 60 × 60-km2 HR image. Charging this amount leads to an acceptance rate of 43%, with 57% of users no longer acquiring the imagery. Furthermore, we noticed significant differences in values for images among the sectors. The results show that users are more willing to pay a fixed yearly amount to join an HR-image pooling system than to be charged per image. Hence, we recorded a mean membership value of €3,022, with 12% of users willing to pay up to €15,000 to join such a service. For the 7,500 HR images available on the platform, the total user benefits amount to €12.7 million.

[1]  Robert Gilmore Pontius,et al.  Recommendations for using the relative operating characteristic (ROC) , 2013, Landscape Ecology.

[2]  Christina M. Kennedy,et al.  High-Resolution Satellite Imagery Is an Important yet Underutilized Resource in Conservation Biology , 2014, PloS one.

[3]  Naohiko Kohtake,et al.  Application of satellite navigation system for emergency warning and alerting , 2016, Comput. Environ. Urban Syst..

[4]  Olga V. Wilhelmi,et al.  Designing a Geospatial Information Infrastructure for Mitigation of Heat Wave Hazards in Urban Areas , 2004 .

[5]  Brett House,et al.  Creating a Learning Society: a New Approach to Growth, Development, and Social Progress , 2014 .

[6]  Zhi Liu,et al.  Use of satellite-derived landscape imperviousness index to characterize urban spatial growth , 2005, Comput. Environ. Urban Syst..

[7]  Geoffrey G. Parker,et al.  Innovation, Openness, and Platform Control , 2018, Manag. Sci..

[8]  Isabelle Reginster,et al.  Urban Environmental Quality in Two Belgian Cities, Evaluated on the Basis of Residential Choices and GIS Data , 2005 .

[9]  M. Storper,et al.  Better Rules or Stronger Communities? On the Social Foundations of Institutional Change and Its Economic Effects , 2006 .

[10]  John R. Weeks,et al.  Spatial Variability in Fertility in Menoufia, Egypt, Assessed through the Application of Remote-Sensing and GIS Technologies , 2000 .

[11]  Renata Ribeiro do Valle Gonçalves,et al.  Coffee Crop's Biomass and Carbon Stock Estimation With Usage of High Resolution Satellites Images , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[12]  Martha S. Feldman,et al.  Dynamics of Organizational Routines: A Generative Model , 2012 .

[13]  Alice Lam.  Tacit Knowledge, Organizational Learning and Societal Institutions: An Integrated Framework , 2000 .

[14]  A. S. Belward,et al.  Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites , 2015 .

[15]  Peijun Du,et al.  A review of supervised object-based land-cover image classification , 2017 .

[16]  S. Wolf,et al.  Knowledge and the politics of land , 2016 .

[17]  W. Hanemann.  Discrete-Continuous Models of Consumer Demand , 1984 .

[18]  Richard T. Carson,et al.  Incentive and informational properties of preference questions , 2007 .

[19]  Chunming Wu,et al.  A Review of Geological Applications of High-Spatial-Resolution Remote Sensing Data , 2018, J. Circuits Syst. Comput..

[20]  Michael Storper,et al.  Why do regions develop and change?: the challenge for geography and economics , 2011 .

[21]  H. Etzkowitz,et al.  Triple Helix Systems: An Analytical Framework for Innovation Policy and Practice in the Knowledge Society , 2013 .

[22]  James A. Robinson.  ECONOMIC DEVELOPMENT AND DEMOCRACY , 2006 .

[23]  Pierre Maurel,et al.  Mesurer les impacts des infrastructures de données géographiques (IDG) et des observatoires - Application à l'IDG SIG-LR , 2017, Rev. Int. Géomatique.

[24]  Raj Chetty.  Behavioral Economics and Public Policy: A Pragmatic Perspective , 2015 .

[25]  Patrick Cohendet,et al.  The creation of knowledge: local building, global accessing and economic development—toward an agenda , 2014 .

[26]  M. Herold,et al.  The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses , 2002 .

[27]  Rodney X. Sturdivant,et al.  Introduction to the Logistic Regression Model , 2005 .

[28]  P. Samuelson.  The Pure Theory of Public Expanditure , 1954 .

[29]  O. Kuik,et al.  Assessing the value of information for water quality management in the North Sea. , 2009, Journal of environmental management.

[30]  Philipp Olbrich.  Open space: The global effort for open access to environmental satellite data , 2018 .

[31]  Thomas Frei.  Economic and social benefits of meteorology and climatology in Switzerland , 2010 .

[32]  Molly K. Macauley,et al.  The value of information: Measuring the contribution of space-derived earth science data to resource management , 2006 .

[33]  Oliver E. Williamson,et al.  The Logic of Economic Organization , 1988 .

[34]  H. Nagendra,et al.  High resolution satellite imagery for tropical biodiversity studies: the devil is in the detail , 2008, Biodiversity and Conservation.

[35]  John Bessant,et al.  Introduction: Innovation and economic geography: a review and analysis , 2012 .

[36]  Ruggero G. Pensa,et al.  $M^3\text{Fusion}$: A Deep Learning Architecture for Multiscale Multimodal Multitemporal Satellite Data Fusion , 2018, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  Markus C. Becker,et al.  Applying Organizational Routines in understanding organizational change , 2005 .

[38]  H. Garretsen,et al.  Geography and development , 2011 .

[39]  PaikowskyDeganit.  What Is New Space? The Changing Ecosystem of Global Space Activity , 2017 .

[40]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[41]  Edward E. Leamer,et al.  Report of the NOOA Panel on Contingent Valuation , 1993 .

[42]  John B. Loomis,et al.  Valuing Geospatial Information: Using the Contingent Valuation Method to Estimate the Economic Benefits of Landsat Satellite Imagery , 2015 .

[43]  Juan Carlos Duque,et al.  A review of regional science applications of satellite remote sensing in urban settings , 2013, Comput. Environ. Urban Syst..

[44]  P. Maurel,et al.  Satellite Imagery: a Tool for Territorial Development , 2016 .

[45]  Michal Kawulok,et al.  Evaluating super-resolution reconstruction of satellite images , 2018, Acta Astronautica.

[46]  Pierre Maurel,et al.  Spatial data infrastructure management: A two-sided market approach for strategic reflections , 2019, Int. J. Inf. Manag..

[47]  Nicolas Peter,et al.  The changing geopolitics of space activities , 2006 .

[48]  R. Kingston,et al.  Smart Cities and Green Growth: Outsourcing Democratic and Environmental Resilience to the Global Technology Sector , 2014 .

[49]  Luke Bateson,et al.  Identifying natural and anthropogenically-induced geohazards from satellite ground motion and geospatial data: Stoke-on-Trent, UK , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[50]  Molly K. Macauley,et al.  The value of information : methodological frontiers and new applications in environment and health , 2012 .

[51]  Richard Bernknopf,et al.  Economic Assessment of the Use Value of Geospatial Information , 2015, ISPRS Int. J. Geo Inf..

[52]  C. Lo,et al.  Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area , 2002 .

[53]  Robert Cameron Mitchell,et al.  Using Surveys to Value Public Goods: The Contingent Valuation Method , 1989 .

[54]  Gil Denis,et al.  Towards disruptions in Earth observation? New Earth Observation systems and markets evolution: Possible scenarios and impacts ☆ , 2017 .

[55]  D. Cox.  Two further applications of a model for binary regression , 1958 .

[56]  L. G. Pee,et al.  Interactions among factors influencing knowledge management in public-sector organizations: A resource-based view , 2016, Gov. Inf. Q..

[57]  Michael Förster,et al.  Remote sensing for mapping natural habitats and their conservation status - New opportunities and challenges , 2015, Int. J. Appl. Earth Obs. Geoinformation.