Evolution of Soil Consumption in the Municipality of Melfi (Southern Italy) in Relation to Renewable Energy | SpringerLink
Skip to main content
Springer Nature Link
Log in

Evolution of Soil Consumption in the Municipality of Melfi (Southern Italy) in Relation to Renewable Energy

  • Conference paper
  • First Online:
Computational Science and Its Applications – ICCSA 2019 (ICCSA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11621))

Included in the following conference series:

Abstract

Soil consumption represent an important indicator of soil management, in last few years the European States have been promoted the use and installation of renewable energy sources, with a consequent soil consumption increase. The aim of this work is to implement a procedure that analyzes the change detection of the soil consumption and discriminate those related to soil consumption due to installation of renewable energy sources from that due to built-up areas. The select test site is the Municipality of Melfi (Southern Italy) because is highly significant because is characterized by fragmented and various environments. The increase of urbanization is due to the growth of built-up areas and the exponential development of renewable sources installation. The work herein presented concerns an application study on these processes with the images of Sentinel-2 satellite. In order to produce a synthetic map of soil consumption, the Sentinel-2 images were classified using a supervised classification. A first map of soil consumption was obtained divided the area characterized by urbanization from the area with the presence of the renewable energy sources. Eolic class have been subdivided and reclassified, divided the relevant street from the turbine pad. Eolic class have been reclassified discriminate the relevant street from the turbine pad and subdivided into other subclasses referred to the power wind turbines, in order to quantify the soil consumption related to each one. All processes have been processes developed integrating Remote Sensing and Geographic Information System (GIS), using open source software.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 5719
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 7149
Price includes VAT (Japan)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Level 2 https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/product-types/level-2a.

References

  1. COM(2016) 0767

    Google Scholar 

  2. 2014/C 200/01

    Google Scholar 

  3. 2009/28/CE

    Google Scholar 

  4. 2007/2/CE

    Google Scholar 

  5. Dixon, B., Candade, N.: Multispectral landuse classification using neural networks and support vector machines: one or the other, or both? Int. J. Remote Sens. 29(4), 1185–1206 (2008)

    Article  Google Scholar 

  6. Mountrakis, G., Im, J., Ogole, C.: Support vector machines in remote sensing: a review. ISPRS J. Photogramm. 66(3), 247–259 (2011)

    Article  Google Scholar 

  7. Murgante, B., Borruso, G., Lapucci, A.: Sustainable development: concepts and methods for its application in urban and environmental planning. In: Murgante, B., Borruso, G., Lapucci, A. (eds.) Geocomputation, Sustainability and Environmental Planning. Studies in Computational Intelligence, vol. 348, pp. 1–15. Springer, Berlin (2011). https://doi.org/10.1007/978-3-642-19733-8_1

    Chapter  Google Scholar 

  8. Nolè, G., Murgante, B., Calamita, G., Lanorte, A., Lasaponara, R.: Evaluation of urban sprawl from space using open sources technologies. Ecol. Inform. 26(2015), 151–161 (2015)

    Article  Google Scholar 

  9. Rapporto Ambiente – SNPA (2018)

    Google Scholar 

  10. Romano, B., Zullo, F.: Models of urban land use in Europe: assessment tools and criticalities. Int. J. Agric. Environ. Inf. Syst. 4(3), 80–97 (2013). https://doi.org/10.4018/ijaeis.2013070105

    Article  Google Scholar 

  11. Saganeiti, L., Favale, A., Pilogallo, A., Scorza, F., Murgante, B.: Assessing urban fragmentation at regional scale using sprinkling indexes. Sustainability, 4 (2018)

    Google Scholar 

  12. Zhu, G., Blumberg, D.G.: Classification using ASTER data and SVM algorithms; the case study of Beer Sheva. Israel Remote Sens. Environ. 80, 233–240 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IMAA-CNR, C.da Santa Loja, Zona Industriale, Tito Scalo, 85050, Potenza, Italy

    Valentina Santarsiero, Gabriele Nolè, Antonio Lanorte, Biagio Tucci & Pasquale Baldantoni

  2. School of Engineering, University of Basilicata, Viale dell’Ateneo Lucano 10, 85100, Potenza, Italy

    Beniamino Murgante

  3. Environmental Observatory Foundation of Basilicata Region (FARBAS), Corso Vittorio Emanuele II n. 3, 85052, Marsico Nuovo, PZ, Italy

    Beniamino Murgante

Authors
  1. Valentina Santarsiero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gabriele Nolè
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Antonio Lanorte
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Biagio Tucci
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pasquale Baldantoni
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Beniamino Murgante
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Valentina Santarsiero .

Editor information

Editors and Affiliations

  1. Covenant University, Ota, Nigeria

    Sanjay Misra

  2. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  3. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Saint Petersburg State University, Saint Petersburg, Russia

    Vladimir Korkhov

  6. Polytechnic University of Bari, Bari, Italy

    Carmelo Torre

  7. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  8. Monash University, Clayton, VIC, Australia

    David Taniar

  9. Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  10. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Santarsiero, V., Nolè, G., Lanorte, A., Tucci, B., Baldantoni, P., Murgante, B. (2019). Evolution of Soil Consumption in the Municipality of Melfi (Southern Italy) in Relation to Renewable Energy. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11621. Springer, Cham. https://doi.org/10.1007/978-3-030-24302-9_48

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-24302-9_48

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24301-2

  • Online ISBN: 978-3-030-24302-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation