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Unveiling the Potential of Drone-Borne Optical Imagery in Forest Ecology: A Study on the Recognition and Mapping of Two Evergreen Coniferous Species

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985939%3A_____%2F23%3A00576527" target="_blank" >RIV/67985939:_____/23:00576527 - isvavai.cz</a>

  • Alternative codes found

    RIV/60460709:41320/23:97168 RIV/60076658:12310/23:43906663

  • Result on the web

    <a href="https://doi.org/10.3390/rs15184394" target="_blank" >https://doi.org/10.3390/rs15184394</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.3390/rs15184394" target="_blank" >10.3390/rs15184394</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Unveiling the Potential of Drone-Borne Optical Imagery in Forest Ecology: A Study on the Recognition and Mapping of Two Evergreen Coniferous Species

  • Original language description

    For tasks involving tree crown recognition for counting or mapping multiple tree species, dedicated neural networks designed for object detection and counting, such as the YOLOv8 model, are more suitable and reliable. Although more complex image segmentation algorithms can also yield satisfactory results for mapping, their accuracy may be lower, and the learning process may be longer and computationally intensive. Instance segmentation neural networks are primarily recommended for tasks involving the assessment of separate tree crowns, with results requiring careful expert validation.nWe stress to carefully consider the specific research task and the complexity of object classification when selecting segmentation methods. More complex tasks, such as differentiating between visually similar tree species, may necessitate additional strategies or modifications to existing segmentation algorithms to enhance accuracy. The continuous development of robust and accurate segmentation methods for such intricate tasks is an ongoing focus of research in the fields of remote sensing and computer vision.nSolving practical problems related to tree recognition requires a multi-step process that involves collaboration among experts with different skills and experiences. It is essential to adopt biology- and landscape-oriented approaches when applying remote sensing methods, which requires proficiency not only in remote sensing and deep learning techniques but also in understanding the biological aspects of forest ecosystems. This approach will not only aid in collecting primary remote data but will also significantly enhance the quality of the final recognition results.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    10618 - Ecology

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2023

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Remote Sensing

  • ISSN

    2072-4292

  • e-ISSN

    2072-4292

  • Volume of the periodical

    15

  • Issue of the periodical within the volume

    18

  • Country of publishing house

    CH - SWITZERLAND

  • Number of pages

    16

  • Pages from-to

    4394

  • UT code for WoS article

    001074095800001

  • EID of the result in the Scopus database

    2-s2.0-85172930063