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Analysis of institutional authors

Valero-Jorge, AlexeyAuthor

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May 5, 2024
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Mapping and Monitoring of the Invasive Species Dichrostachys cinerea (Marabu) in Central Cuba Using Landsat Imagery and Machine Learning (1994-2022)

Publicated to:Remote Sensing. 16 (5): 798- - 2024-03-01 16(5), DOI: 10.3390/rs16050798

Authors: Valero-Jorge, Alexey; Zayas, Roberto Gonzalez-De; Matos-Pupo, Felipe; Becerra-Gonzalez, Angel Luis; alvarez-Taboada, Flor

Affiliations

Agrifood Res & Technol Ctr Aragon CITA, Dept Agrarian Forest & Environm Syst, Zaragoza 50059, Spain - Author
Ctr Geomat Environm & Marine Estudies GEOMAR, Mexico City 11560, Mexico - Author
Inst Meteorol, Prov Meteorol Ctr Ciego De Avila, Ave De Los Deportes S-N, Ciego De Avila 65100, Cuba - Author
Moron Geodesy & Cadaster Facil, Moron 67210, Cuba - Author
Univ Ciego De Avila, Fac Tech Sci, Dept Hydraul Engn, Ciego De Avila 65100, Cuba - Author
Univ Leon, Sch Agrarian & Forest Engn, Leon 24404, Spain - Author
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Abstract

Invasive plants are a serious problem in island ecosystems and are the main cause of the extinction of endemic species. Cuba is located within one of the hotspots of global biodiversity, which, coupled with high endemism and the impacts caused by various disturbances, makes it a region particularly sensitive to potential damage by invasive plants like Dichrostachys cinerea (L.) Wight & Arn. (marabu). However, there is a lack of timely information for monitoring this species, as well as about the land use and land cover (LULC) classes most significantly impacted by this invasion in the last few decades and their spatial distribution. The main objective of this study, carried out in Central Cuba, was to detect and monitor the spread of marabu over a 28-year period. The land covers for the years 1994 and 2022 were classified using Landsat 5 TM and 8 OLI images with three different classification algorithms: maximum likelihood (ML), support vector machine (SVM), and random forest (RF). The results obtained showed that RF outperformed the other classifiers, achieving AUC values of 0.92 for 1994 and 0.97 for 2022. It was confirmed that the area covered by marabu increased by 29,555 ha, from 61,977.59 ha in 1994 to 91,533.47 ha in 2022 (by around 48%), affecting key land covers like woodlands, mangroves, and rainfed croplands. These changes in the area covered by marabu were associated, principally, with changes in land uses and tenure and not with other factors, such as rainfall or relief in the province. The use of other free multispectral imagery, such as Sentinel 2 data, with higher temporal and spatial resolution, could further refine the model's accuracy.

Keywords

AccuracyAlgorithmsAreaClassificationCoverDichrostachys cinereaForestLandsatMaximum likelihoodPlantsRandom forestSupport vector machineSupport vector machinesWater

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Remote Sensing due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 47/258, thus managing to position itself as a Q1 (Primer Cuartil), in the category Geosciences, Multidisciplinary.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2025-07-19:

  • WoS: 8
  • Scopus: 8

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2025-07-19:

  • The use, from an academic perspective evidenced by the Altmetric agency indicator referring to aggregations made by the personal bibliographic manager Mendeley, gives us a total of: 34.
  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 46 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

  • The Total Score from Altmetric: 4.55.
  • The number of mentions on the social network X (formerly Twitter): 6 (Altmetric).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: http://hdl.handle.net/10532/6901

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Cuba; Mexico.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Valero Jorge, Alexey) .