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Close-Range Remote Sensing of Forests: The State of the Art, Challenges, and Opportunities for Systems and Data Acquisitions

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F22%3A92924" target="_blank" >RIV/60460709:41320/22:92924 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9797818" target="_blank" >https://ieeexplore.ieee.org/document/9797818</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/MGRS.2022.3168135" target="_blank" >10.1109/MGRS.2022.3168135</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Close-Range Remote Sensing of Forests: The State of the Art, Challenges, and Opportunities for Systems and Data Acquisitions

  • Original language description

    Remote sensing-based forest investigation and monitoring have become more affordable and applicable in the past few decades. The current bottleneck limiting practical use of the vast volume of remote sensing data lies in the lack of affordable, reliable, and detailed field references, which are required for necessary calibrations of satellite and aerial data and calibrations of relevant allometric models. Conventional field investigations are mostly limited to a small scale, using a small quantity of observations. Rapid development in close-range remote sensing has been witnessed during the past two decades, i.e., in the constant decrease of the costs, size, and weight of sensors, steady improvements in the availability, mobility, and reliability of platforms, and progress in computational capacity and data science. These advances have paved the way for turning conventional expensive and inefficient manual forest in situ data collections into affordable and efficient autonomous observations.

  • 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

    20705 - Remote sensing

Result continuities

  • Project

    <a href="/en/project/EF16_019%2F0000803" target="_blank" >EF16_019/0000803: Advanced research supporting the forestry and wood-processing sector´s adaptation to global change and the 4th industrial revolution</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2022

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    IEEE Geoscience and Remote Sensing Letters

  • ISSN

    1545-598X

  • e-ISSN

  • Volume of the periodical

    10

  • Issue of the periodical within the volume

    3

  • Country of publishing house

    CZ - CZECH REPUBLIC

  • Number of pages

    40

  • Pages from-to

    32-71

  • UT code for WoS article

    000815502600001

  • EID of the result in the Scopus database

    2-s2.0-85132625647