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Low Density ALS Data to Support Forest Management Plans: The Alta Val Di Susa Forestry Consortium (NW Italy) Case Study

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Geomatics for Green and Digital Transition (ASITA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1651))

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Abstract

LiDAR systems are evolving very rapidly. In recent years, in fact, the forest sector is largely taking advantage of such evolving progress. Aerial LiDAR (ALS) capability of collecting large amounts of data can directly influence the cost of ordinary in-field forest measurements. A great availability of freely accessible LiDAR data archives from public institutions, often obtained for different purposes than the forestry one, can, however, enormously contribute to forests management. The present study, based on pre-processed and freely available LiDAR-derived DTM and DSM from the Piemonte Region (NW Italy), is a further demonstration that forest planning can be valuable supported by this type of data, that proved to be able to support Forest Settlement Plans redaction. In this study, an estimate (and mapping) of the main forest structural parameters over a test area was achieved with an accuracy consistent with the one ordinarily required by planners when reviewing/setting up a new forest management plan. Moreover, this work proved that free official open data coupled with the current availability of free advanced software for data processing can make this technology easily transferrable to professionals and territory managers.

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Authors and Affiliations

  1. ITHACA - Information Technology for Humanitarian Assistance Cooperation and Action, Turin, Italy

    E. Ilardi & V. Fissore

  2. Department of Agricultural, Forest and Food Sciences, University of Torino, Turin, Italy

    R. Berretti & E. Borgogno-Mondino

  3. Val Di Susa Forestry Consortium, Susa, Italy

    A. Dotta

  4. DIST - Interuniversity Department of Regional and Urban Studies and Planning, Politecnico of Turin, Turin, Italy

    P. Boccardo

Authors
  1. E. Ilardi
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  2. V. Fissore
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  4. A. Dotta
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  5. P. Boccardo
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  6. E. Borgogno-Mondino
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Correspondence to E. Ilardi .

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Editors and Affiliations

  1. University of Torino, Grugliasco, Italy

    Enrico Borgogno-Mondino

  2. University of Pisa, Pisa, Italy

    Paola Zamperlin

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Ilardi, E., Fissore, V., Berretti, R., Dotta, A., Boccardo, P., Borgogno-Mondino, E. (2022). Low Density ALS Data to Support Forest Management Plans: The Alta Val Di Susa Forestry Consortium (NW Italy) Case Study. In: Borgogno-Mondino, E., Zamperlin, P. (eds) Geomatics for Green and Digital Transition. ASITA 2022. Communications in Computer and Information Science, vol 1651. Springer, Cham. https://doi.org/10.1007/978-3-031-17439-1_19

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  • DOI: https://doi.org/10.1007/978-3-031-17439-1_19

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  • Print ISBN: 978-3-031-17438-4

  • Online ISBN: 978-3-031-17439-1

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