IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i5p611-d1387621.html
   My bibliography  Save this article

Estimating the Aboveground Fresh Weight of Sugarcane Using Multispectral Images and Light Detection and Ranging (LiDAR)

Author

Listed:
  • Charot M. Vargas

    (Department of Agricultural Engineering, Instituto Tecnologico de Costa Rica, Cartago 30101, Costa Rica)

  • Muditha K. Heenkenda

    (Department of Geography and the Environment, Lakehead University, Thunder Bay, ON P7B 5E1, Canada)

  • Kerin F. Romero

    (Department of Agricultural Engineering, Instituto Tecnologico de Costa Rica, Cartago 30101, Costa Rica)

Abstract

This study aimed to develop a remote sensing method for estimating the aboveground fresh weight (AGFW) of sugarcane using multispectral images and light detection and ranging (LiDAR). Remotely sensed data were acquired from an unmanned aerial vehicle (drone). Sample plots were harvested and the AGFW of each plot was measured. Sugarcane crown heights and volumes were obtained by isolating individual tree crowns using a LiDAR-derived digital surface model of the area. Multiple linear regression (MLR) and partial least-squares regression (PLSR) models were tested for the field-sampled AGFWs (dependent variable) and individual canopy heights and volumes, and spectral indices were used as independent variables or predictors. The PLSR model showed more promising results than the MLR model when predicting the AGFW over the study area. Although PLSR is well-suited to a large number of collinear predictor variables and a limited number of field samples, this study showed moderate results (R 2 = 0.5). The visual appearance of the spatial distribution of the AGFW map is satisfactory. The limited no. of field samples overfitted the MLR prediction results. Overall, this research highlights the potential of integrating remote sensing technologies in the sugarcane industry, thereby improving yield estimation and effective crop management.

Suggested Citation

  • Charot M. Vargas & Muditha K. Heenkenda & Kerin F. Romero, 2024. "Estimating the Aboveground Fresh Weight of Sugarcane Using Multispectral Images and Light Detection and Ranging (LiDAR)," Land, MDPI, vol. 13(5), pages 1-15, May.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:5:p:611-:d:1387621
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/5/611/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/5/611/
    Download Restriction: no
    ---><---

    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:jlands:v:13:y:2024:i:5:p:611-:d:1387621. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.