IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i12p9618-d1171806.html
   My bibliography  Save this article

Landolt Indicator Values in Modern Research: A Review

Author

Listed:
  • Natalya Ivanova

    (Institute Botanic Garden Ural Branch of RAS, 8 Marta Street, 202a, 620144 Yekaterinburg, Russia)

  • Ekaterina Zolotova

    (Zavaritsky Institute of Geology and Geochemistry, Ural Branch of Russian Academy of Sciences, 15 Akad. Vonsovsky Street, 620010 Yekaterinburg, Russia)

Abstract

The conservation of biodiversity and ecosystem sustainability is essential for human well-being. An important tool for addressing this issue is ecological indicators. This overview document examines recent studies covering the period 2018–2022 that use the values of the Landolt indicator as one of the analysis methods. The total number of records examined was 8910. After excluding irrelevant, inconsistent and duplicate records, 91 records were selected. The selection criteria were the presence of the English abstract and a digital object identifier (DOI). We chose the VOSviewer software for data analysis and visualization. The analysis of research geography, types of plant communities under research, research topics, the network of co-authorship and relationship papers, as well as the publication activity and citation rate of the authors, is carried out. The results revealed that the geography of using Landolt indicator values is quite wide, while they are more often used in Switzerland, Italy and Russia. The most important and frequently cited studies were carried out in the context of large international projects, confirming the importance of scientific cooperation in the development of environmental indicators. The Landolt indicator values showed effectiveness in the study of forests, bogs, riparian vegetation, a coarse quarry waste dump, and in assessing the urban environment. At the same time, the vegetation dynamics and influence of various factors on plants were studied most often. It was revealed that Landolt indicator values can serve as an effective universal method that integrates many aspects of the environment and allows for a comprehensive multicriteria analysis of environmental factors, habitat stability, vegetation diversity and dynamics for different plant communities at different spatial scales over a wide geographical area. This is confirmed by the high citation rate of the papers. We recommend the Landolt indicator values for a wider use, including in the monitoring of ecosystems and individual species for their conservation and sustainable management. In order to achieve this goal, it is necessary to extend the network of relationships between the authors, which is not very well developed at the moment. The research results obtained are useful for the further successful development not only of the Landolt indicator values but also of environmental indicators in general.

Suggested Citation

  • Natalya Ivanova & Ekaterina Zolotova, 2023. "Landolt Indicator Values in Modern Research: A Review," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9618-:d:1171806
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/12/9618/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/15/12/9618/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carly R. Cowell & Leigh-Anne Bullough & Sonia Dhanda & Vicki Harrison Neves & Ed Ikin & Jessica Moore & Rachel Purdon & China Williams & Julia Willison & Sharon J. Willoughby, 2022. "Fortuitous Alignment: The Royal Botanic Gardens, Kew and the Sustainable Development Goals," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    2. Ludo Waltman & Nees Eck, 2013. "A smart local moving algorithm for large-scale modularity-based community detection," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(11), pages 1-14, November.
    3. Simone Ravetto Enri & Fabio Petrella & Fabrizio Ungaro & Laura Zavattaro & Andrea Mainetti & Giampiero Lombardi & Michele Lonati, 2021. "Relative Importance of Plant Species Composition and Environmental Factors in Affecting Soil Carbon Stocks of Alpine Pastures (NW Italy)," Agriculture, MDPI, vol. 11(11), pages 1-24, October.
    4. Ludo Waltman & Nees Jan Eck, 2012. "A new methodology for constructing a publication-level classification system of science," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2378-2392, December.
    5. Katarzyna Krasowska & Zbigniew W. Paszkowski, 2023. "The Need to Maintain Sustainability in the Dynamic Anthropogenic Changes in the Natural Landscape of the Bay of Pomerania in Poland," Sustainability, MDPI, vol. 15(3), pages 1-19, January.
    6. Small, Henry & Boyack, Kevin W. & Klavans, Richard, 2014. "Identifying emerging topics in science and technology," Research Policy, Elsevier, vol. 43(8), pages 1450-1467.
    7. Boyack, Kevin W. & Klavans, Richard, 2014. "Including cited non-source items in a large-scale map of science: What difference does it make?," Journal of Informetrics, Elsevier, vol. 8(3), pages 569-580.
    8. Kaihan Yang & Ai Chin Thoo, 2023. "Visualising the Knowledge Domain of Reverse Logistics and Sustainability Performance: Scientometric Mapping Based on VOSviewer and CiteSpace," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    9. Nees Jan Eck & Ludo Waltman, 2010. "Software survey: VOSviewer, a computer program for bibliometric mapping," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 523-538, August.
    10. Enrico Feoli & Paola Ganis, 2019. "The Use of the Evenness of Eigenvalues of Similarity Matrices to Test for Predictivity of Ecosystem Classifications," Mathematics, MDPI, vol. 7(3), pages 1-6, March.
    11. Alessandro Liberati & Douglas G Altman & Jennifer Tetzlaff & Cynthia Mulrow & Peter C Gøtzsche & John P A Ioannidis & Mike Clarke & P J Devereaux & Jos Kleijnen & David Moher, 2009. "The PRISMA Statement for Reporting Systematic Reviews and Meta-Analyses of Studies That Evaluate Health Care Interventions: Explanation and Elaboration," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-28, July.
    12. Haitham Nobanee & Fatima Youssef Al Hamadi & Fatma Ali Abdulaziz & Lina Subhi Abukarsh & Aysha Falah Alqahtani & Shayma Khalifa AlSubaey & Sara Mohamed Alqahtani & Hamama Abdulla Almansoori, 2021. "A Bibliometric Analysis of Sustainability and Risk Management," Sustainability, MDPI, vol. 13(6), pages 1-16, March.
    13. Manuel J. Steinbauer & John-Arvid Grytnes & Gerald Jurasinski & Aino Kulonen & Jonathan Lenoir & Harald Pauli & Christian Rixen & Manuela Winkler & Manfred Bardy-Durchhalter & Elena Barni & Anne D. Bj, 2018. "Accelerated increase in plant species richness on mountain summits is linked to warming," Nature, Nature, vol. 556(7700), pages 231-234, April.
    Full references (including those not matched with items on IDEAS)

    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. Nees Jan Eck & Ludo Waltman, 2017. "Citation-based clustering of publications using CitNetExplorer and VOSviewer," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1053-1070, May.
    2. Kevin W. Boyack, 2017. "Investigating the effect of global data on topic detection," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 999-1015, May.
    3. Sjögårde, Peter & Ahlgren, Per, 2018. "Granularity of algorithmically constructed publication-level classifications of research publications: Identification of topics," Journal of Informetrics, Elsevier, vol. 12(1), pages 133-152.
    4. Lovro Šubelj & Nees Jan van Eck & Ludo Waltman, 2016. "Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-23, April.
    5. R. Fileto Maciel & P. Saskia Bayerl & Marta Macedo Kerr Pinheiro, 2019. "Technical research innovations of the US national security system," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 539-565, August.
    6. Francesco Pasimeni, 2020. "The Origin of the Sharing Economy Meets the Legacy of Fractional Ownership," SPRU Working Paper Series 2020-19, SPRU - Science Policy Research Unit, University of Sussex Business School.
    7. van Eck, Nees Jan & Waltman, Ludo, 2014. "CitNetExplorer: A new software tool for analyzing and visualizing citation networks," Journal of Informetrics, Elsevier, vol. 8(4), pages 802-823.
    8. Qi Wang & Tobias Jeppsson, 2022. "Identifying benchmark units for research management and evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7557-7574, December.
    9. Zang, Yuzhu & Yang, Yuanyuan & Liu, Yansui, 2021. "Toward serving land consolidation on the table of sustainability: An overview of the research landscape and future directions," Land Use Policy, Elsevier, vol. 109(C).
    10. Paola Bernardi & Alberto Bertello & Canio Forliano & Ludovico Bullini Orlandi, 2022. "Beyond the “ivory tower”. Comparing academic and non-academic knowledge on social entrepreneurship," International Entrepreneurship and Management Journal, Springer, vol. 18(3), pages 999-1032, September.
    11. Daniele Rotolo & Ismael Rafols & Michael Hopkins & Loet Leydesdorff, 2014. "Scientometric Mapping as a Strategic Intelligence Tool for the Governance of Emerging Technologies," SPRU Working Paper Series 2014-10, SPRU - Science Policy Research Unit, University of Sussex Business School.
    12. Fang Han & Christopher L. Magee, 2018. "Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 767-796, August.
    13. Anthony F. J. Raan, 2021. "Laudation on the occasion of the presentation of the Derek de Solla Price Award 2021 to Prof. Ludo Waltman at the ISSI conference, Leuven, 2021," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8235-8238, October.
    14. Xu, Haiyun & Winnink, Jos & Yue, Zenghui & Zhang, Huiling & Pang, Hongshen, 2021. "Multidimensional Scientometric indicators for the detection of emerging research topics," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    15. Peter Sjögårde & Per Ahlgren & Ludo Waltman, 2021. "Algorithmic labeling in hierarchical classifications of publications: Evaluation of bibliographic fields and term weighting approaches," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(7), pages 853-869, July.
    16. Lutz Bornmann & Robin Haunschild & Sven E. Hug, 2018. "Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 427-437, February.
    17. María Pinto & Rosaura Fernández-Pascual & David Caballero-Mariscal & Dora Sales, 2020. "Information literacy trends in higher education (2006–2019): visualizing the emerging field of mobile information literacy," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1479-1510, August.
    18. Giovanni Matteo & Pierfrancesco Nardi & Stefano Grego & Caterina Guidi, 2018. "Bibliometric analysis of Climate Change Vulnerability Assessment research," Environment Systems and Decisions, Springer, vol. 38(4), pages 508-516, December.
    19. Loredana Canfora & Corrado Costa & Federico Pallottino & Stefano Mocali, 2021. "Trends in Soil Microbial Inoculants Research: A Science Mapping Approach to Unravel Strengths and Weaknesses of Their Application," Agriculture, MDPI, vol. 11(2), pages 1-21, February.
    20. Ignacio Rodríguez-Rodríguez & José-Víctor Rodríguez & Niloofar Shirvanizadeh & Andrés Ortiz & Domingo-Javier Pardo-Quiles, 2021. "Applications of Artificial Intelligence, Machine Learning, Big Data and the Internet of Things to the COVID-19 Pandemic: A Scientometric Review Using Text Mining," IJERPH, MDPI, vol. 18(16), pages 1-29, August.

    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:jsusta:v:15:y:2023:i:12:p:9618-:d:1171806. 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.