Advertisement

Analysis of the Evolution of a Rural Landscape by Combining SAR Geodata with GIS Techniques

  • Giuseppe CillisEmail author
  • Aimé Lay-Ekuakille
  • Vito Telesca
  • Dina Statuto
  • Pietro Picuno
Conference paper
  • 27 Downloads
Part of the Lecture Notes in Civil Engineering book series (LNCE, volume 67)

Abstract

In the last decades, Mediterranean rural landscapes have undergone significant changes, with relevant considerable environmental and socio-economic impacts. These phenomena are often triggered by agricultural abandonment, especially in environmentally-sensitive areas, which are usually located in marginal and less profitable regions, and which could indeed irremediably compromise the identity and role of these Mediterranean landscapes. On the other hand, the progressive increase of available multi-source geodata allows to reconstruct the landscape original structure, providing new tools able to prevent negative impacts on environment. Hence, thanks to the development of increasingly advanced and open-source GIS tools, it is possible to implement several geodata typologies that can be mutually integrated in an increasingly efficient approach. In this paper the process of landscape reshaping pattern is analyzed in a study area of Basilicata region (Southern Italy) using remote sensing. In particular, the vegetation component of a landscape has been assessed by means of SAR images by using an artificial intelligence approach, that is machine learning to understand landscape dynamics in two different time periods. In this way, it has been possible to integrate data of different source and composition into landscape analysis methodologies, hence developing a suitable tool for planning and managing the rural landscape.

Keywords

Remote sensing SAR Geographical information system Rural landscape Artificial intelligence Machine learning 

Notes

Acknowledgements

Authors gracefully thank Geocart Srl company for providing the SAR images used in this work.

References

  1. Andria, G., D’orazio, A., Lay Ekuakille, A., Moretti, M., Pieri, P., Tralli, F., & Tropeano, M. (2000). Accuracy assessment in photo interpretation of remote sensing ERS-2/SAR images. In 17th IEEE—IMTC2000, Baltimore, USA, May 1–4.Google Scholar
  2. Bhateja, V., Tripathi, A., Gupta, A., & Lay-Ekuakille (2015). Speckle suppression in SAR images employing modified anisotropic diffusion filtering in wavelet domain for environment monitoring. Measurement, 74, 246–254.Google Scholar
  3. Lay-Ekuakille, A., Pelillo, V., Dellisanti, C., & Tralli, F. (2002). Sar aided method for rural soil evaluation. SPIE2002 Remote Sensing, 22–27 September in Crete, Greece.Google Scholar
  4. Liu, S., Qi, Z., Li, X. & AndYeh, A. G. (2019). Integration of convolutional neural networks and object-based post-classification refinement for land use and land cover mapping with optical and SAR data. Remote Sens, 11, 690.Google Scholar
  5. Losurdo, A., Pacifico, S., Sarli, V., Colangelo, A., & Leggeri, M. (2008). Integration of differential SAR interferometry and ancillary GIS data for the study of superficialdeformations. EARSeLe Proceedings 7, 1/2008.Google Scholar
  6. Otero, I., Marull, J., Tello, E., Diana, G. L., Pons, M., Coll, F., et al. (2015). Land abandonment, landscape, and biodiversity:questioning the restorative character of the forest transition in the Mediterranean. Ecology and Society, 20(2), 7.CrossRefGoogle Scholar
  7. Queiroz, C., Beilin, R., Folke, C., & Lindborg, R. (2014). Farmland abandonment: Threat or opportunity for biodiversity conservation? a global review. Frontiers in Ecology and the Environment, 12(5), 288–296.CrossRefGoogle Scholar
  8. Scheiber, R., Hajnsek, I., Horn, R., Papathanassiou, K. P., Prats, P., & Moreira, A. (2008). Recent developments and applications of multi-pass airborne interferometric SAR using the E-SAR system. In 7th European Conference on Synthetic Aperture Radar, Friedrichshafen, Germany, 2008, pp. 1–4.Google Scholar
  9. Statuto, D., Cillis, G., & Picuno, P. (2016). Analysis of the effects of agricultural land use change on rural environment and landscape through historical cartography and GIS tools. Journal of Agricultural Engineering, 47, 28–39.CrossRefGoogle Scholar
  10. Statuto, D., Cillis, G., & Picuno, P. (2017). Using historical maps within a GIS to analyze two centuries of rural landscape changes in Southern Italy. Land, 6, 65.CrossRefGoogle Scholar
  11. Statuto, D., Cillis, G., & Picuno, P. (2018). GIS-based analysis of temporal evolution of rural landscape: a case study in southern italy. Natural Resources Research,  https://doi.org/10.1007/s11053-018-9402-7.CrossRefGoogle Scholar
  12. Tan, X., & Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing, 19, 1635–1650.MathSciNetCrossRefGoogle Scholar
  13. Yin, H., Prishchepov, A. V., Kuemmerle, T., Bleyhl, B., Buchner, J., & Radeloff, V. C. (2018). Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series. Remote Sensing of Environment, 210, 12–24.CrossRefGoogle Scholar
  14. Zhu, X. X., Tuia, D., Mou, L., Xia, G., Zhang, L., Xu, F., & Fraundorfer, F. (2017). Deep learning in remote sensing: A comprehensive review and list of resources. In IEEE Geoscience and Remote Sensing Magazine, pp. 8–36.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Giuseppe Cillis
    • 1
    Email author
  • Aimé Lay-Ekuakille
    • 2
  • Vito Telesca
    • 3
  • Dina Statuto
    • 1
  • Pietro Picuno
    • 1
  1. 1.School of Agricultural, Forest, Food and Environmental Sciences - SAFEUniversity of BasilicataPotenzaItaly
  2. 2.Department of Innovation EngineeringUniversity of SalentoLecceItaly
  3. 3.School of EngineeringUniversity of BasilicataPotenzaItaly

Personalised recommendations