CerrejonValeriaMarchandEtAl2021

Référence

Cerrejón, C., Valeria, O., Marchand, P., Caners, R.T., Fenton, N.J. (2021) No place to hide: Rare plant detection through remote sensing. Diversity and Distributions, 27(6):948-961. (Scopus )

Résumé

Aim: Detection of rare species is limited by their intrinsic nature and by the constraints associated with traditional field surveys. Remote sensing (RS) provides a powerful alternative to traditional detection methods through the increasing availability of RS products. Here, we assess the capacity of RS at high and medium resolution to detect rare plants with direct and indirect approaches, and how the performance of RS can be influenced by the characteristics of species. Methods: An extensive literature review was conducted to synthesize the use of RS to detect or predict rare plant occurrence at high and medium resolution (<30 m and 30–300 m, respectively). The concept of “rarity” was based on Rabinowitz's rare species classification. The literature review was performed in Scopus for the period 1990–2020. Results: While direct detection is often limited, it is possible with high and very high spatial resolution data for rare plants with distinctive traits. RS is also able to capture biophysical conditions driving rare plant distributions, which can indirectly provide accurate predictions for them. Both approaches have the potential to discover new populations of rare plants. RS can also feed SAMs of rare plants, which combined with SDMs can provide a valuable approach for rare plant detection. While direct detection is limited by the space occupied by a species within its habitat and its morphological, phenological and physiological characteristics, the predictive performance of RS-based SDMs (indirect detection) can be influenced by habitat size, habitat specificity and phenological features of rare plants. Similarly, model predictive performance can be influenced by the rarity form of the target species according to the rarity classification criteria. Main conclusions. With this synthesis, the strong potential of RS for the purposes of detection and prediction of rare plant has been highlighted, with practical applications for conservation and management. © 2021 The Authors. Diversity and Distributions published by John Wiley & Sons Ltd.

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@ARTICLE { CerrejonValeriaMarchandEtAl2021,
    AUTHOR = { Cerrejón, C. and Valeria, O. and Marchand, P. and Caners, R.T. and Fenton, N.J. },
    JOURNAL = { Diversity and Distributions },
    TITLE = { No place to hide: Rare plant detection through remote sensing },
    YEAR = { 2021 },
    NOTE = { cited By 1 },
    NUMBER = { 6 },
    PAGES = { 948-961 },
    VOLUME = { 27 },
    ABSTRACT = { Aim: Detection of rare species is limited by their intrinsic nature and by the constraints associated with traditional field surveys. Remote sensing (RS) provides a powerful alternative to traditional detection methods through the increasing availability of RS products. Here, we assess the capacity of RS at high and medium resolution to detect rare plants with direct and indirect approaches, and how the performance of RS can be influenced by the characteristics of species. Methods: An extensive literature review was conducted to synthesize the use of RS to detect or predict rare plant occurrence at high and medium resolution (<30 m and 30–300 m, respectively). The concept of “rarity” was based on Rabinowitz's rare species classification. The literature review was performed in Scopus for the period 1990–2020. Results: While direct detection is often limited, it is possible with high and very high spatial resolution data for rare plants with distinctive traits. RS is also able to capture biophysical conditions driving rare plant distributions, which can indirectly provide accurate predictions for them. Both approaches have the potential to discover new populations of rare plants. RS can also feed SAMs of rare plants, which combined with SDMs can provide a valuable approach for rare plant detection. While direct detection is limited by the space occupied by a species within its habitat and its morphological, phenological and physiological characteristics, the predictive performance of RS-based SDMs (indirect detection) can be influenced by habitat size, habitat specificity and phenological features of rare plants. Similarly, model predictive performance can be influenced by the rarity form of the target species according to the rarity classification criteria. Main conclusions. With this synthesis, the strong potential of RS for the purposes of detection and prediction of rare plant has been highlighted, with practical applications for conservation and management. © 2021 The Authors. Diversity and Distributions published by John Wiley & Sons Ltd. },
    AFFILIATION = { Institut de recherche sur les forêts, Université du Québec en Abitibi-Témiscamingue, Rouyn-Noranda, QC, Canada; Royal Alberta Museum, Edmonton, AB, Canada },
    AUTHOR_KEYWORDS = { direct detection; endemism; new populations; predictive models; rarity; remote sensing predictors; SDMs; sensor; spatial resolution },
    DOCUMENT_TYPE = { Review },
    DOI = { 10.1111/ddi.13244 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85101006402&doi=10.1111%2fddi.13244&partnerID=40&md5=1e2da2d248fe7ad69a3eb15a88b1d489 },
}

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