IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v6y2021i10p108-d654818.html
   My bibliography  Save this article

A Principal Components Analysis-Based Method for the Detection of Cannabis Plants Using Representation Data by Remote Sensing

Author

Listed:
  • Carmine Gambardella

    (Benecon University Consortium, 80138 Naples, Italy)

  • Rosaria Parente

    (Benecon University Consortium, 80138 Naples, Italy)

  • Alessandro Ciambrone

    (Benecon University Consortium, 80138 Naples, Italy)

  • Marialaura Casbarra

    (Benecon University Consortium, 80138 Naples, Italy)

Abstract

Integrating the representation of the territory, through airborne remote sensing activities with hyperspectral and visible sensors, and managing complex data through dimensionality reduction for the identification of cannabis plantations, in Albania, is the focus of the research proposed by the multidisciplinary group of the Benecon University Consortium. In this study, principal components analysis (PCA) was used to remove redundant spectral information from multiband datasets. This makes it easier to identify the most prevalent spectral characteristics in most bands and those that are specific to only a few bands. The survey and airborne monitoring by hyperspectral sensors is carried out with an Itres CASI 1500 sensor owned by Benecon, characterized by a spectral range of 380–1050 nm and 288 configurable channels. The spectral configuration adopted for the research was developed specifically to maximize the spectral separability of cannabis. The ground resolution of the georeferenced cartographic data varies according to the flight planning, inserted in the aerial platform of an Italian Guardia di Finanza’s aircraft, in relation to the orography of the sites under investigation. The geodatabase, wherein the processing of hyperspectral and visible images converge, contains ancillary data such as digital aeronautical maps, digital terrain models, color orthophoto, topographic data and in any case a significant amount of data so that they can be processed synergistically. The goal is to create maps and predictive scenarios, through the application of the spectral angle mapper algorithm, of the cannabis plantations scattered throughout the area. The protocol consists of comparing the spectral data acquired with the CASI1500 airborne sensor and the spectral signature of the cannabis leaves that have been acquired in the laboratory with ASD Fieldspec PRO FR spectrometers. These scientific studies have demonstrated how it is possible to achieve ex ante control of the evolution of the phenomenon itself for monitoring the cultivation of cannabis plantations.

Suggested Citation

  • Carmine Gambardella & Rosaria Parente & Alessandro Ciambrone & Marialaura Casbarra, 2021. "A Principal Components Analysis-Based Method for the Detection of Cannabis Plants Using Representation Data by Remote Sensing," Data, MDPI, vol. 6(10), pages 1-13, October.
  • Handle: RePEc:gam:jdataj:v:6:y:2021:i:10:p:108-:d:654818
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/6/10/108/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/6/10/108/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marco Dugato & Francesco Calderoni & Gian Maria Campedelli, 2020. "Measuring Organised Crime Presence at the Municipal Level," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 147(1), pages 237-261, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Carmine Gambardella & Rosaria Parente & Anna Scotto di Santolo & Giuseppe Ciaburro, 2022. "New Digital Field of Drawing and Survey for the Automatic Identification of Debris Accumulation in Flooded Areas," Sustainability, MDPI, vol. 15(1), pages 1-23, December.
    2. Benjamin Costello & Olusegun O. Osunkoya & Juan Sandino & William Marinic & Peter Trotter & Boyang Shi & Felipe Gonzalez & Kunjithapatham Dhileepan, 2022. "Detection of Parthenium Weed ( Parthenium hysterophorus L.) and Its Growth Stages Using Artificial Intelligence," Agriculture, MDPI, vol. 12(11), pages 1-23, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francesca Maria Calamunci & Federico Fabio Frattini, 2023. "When Crime Tears Communities Apart: Social Capital and Organised Crime," Working Papers 2023.08, Fondazione Eni Enrico Mattei.
    2. Maria Rosaria Carillo & Tiziana Venittelli & Alberto Zazzaro, 2024. "Immigrants’ Social Identity, Racial Hate Crimes and Public Backlash: Evidence from The "San Gennaro Massacre"," CSEF Working Papers 727, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    3. Campedelli, Gian Maria & Daniele, Gianmarco & Martinangeli, Andrea F.M. & Pinotti, Paolo, 2023. "Organized crime, violence and support for the state," Journal of Public Economics, Elsevier, vol. 228(C).
    4. Giovanni Bernardo & Irene Brunetti & Mehmet Pinar & Thanasis Stengos, 2021. "Measuring the presence of organized crime across Italian provinces: a sensitivity analysis," European Journal of Law and Economics, Springer, vol. 51(1), pages 31-95, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:6:y:2021:i:10:p:108-:d:654818. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.