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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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