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Grant support

This work was supported by the territorial project "Gestion ambiental integradora con enfoque ecosistemico en el Gran Humedal del Norte de Ciego de & Aacute;vila para su adaptacion al cambio climatico" of the Cuban government (Code: PT121CA003-003).

Analysis of institutional authors

Valero-Jorge, AlexeyAuthor

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November 8, 2024
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An Innovative Tool for Monitoring Mangrove Forest Dynamics in Cuba Using Remote Sensing and WebGIS Technologies: SIGMEM

Publicated to:Remote Sensing. 16 (20): 3802- - 2024-10-01 16(20), DOI: 10.3390/rs16203802

Authors: Valero-Jorge, Alexey; Gonzalez-Lozano, Raul; Gonzalez De Zayas, Roberto; Matos-Pupo, Felipe; Sori, Rogert; Stojanovic, Milica

Affiliations

Ctr Agrifood Res & Technol Aragon CITA, Dept Agr Syst Forestry & Environm, Unit Associated EEAD CSIC Soils & Irrigat, Zaragoza 50059, Spain - Author
Ctr Geomat Environm & Marine Estudies GEOMAR, Ciudad De Mexico 11560, Mexico - Author
Ctr Meteorol Prov Ciego De Avila, Inst Meteorol, Ave Deportes S-N, Ciego De Avila 65100, Cuba - Author
Univ Ciego De Avila, Fac Tech Sci, Dept Hydraul Engn, Ciego De Avila 65100, Cuba - Author
Univ Vigo, Ctr Invest Marina, Environm Phys Lab EPhysLab, Campus As Lagoas S N, Orense 32004, Spain - Author
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Abstract

The main objective of this work was to develop a viewer with web output, through which the changes experienced by the mangroves of the Gran Humedal del Norte de Ciego de Avila (GHNCA) can be evaluated from remote sensors, contributing to the understanding of the spatiotemporal variability of their vegetative dynamics. The achievement of this objective is supported by the use of open-source technologies such as MapStore, GeoServer and Django, as well as Google Earth Engine, which combine to offer a robust and technologically independent solution to the problem. In this context, it was decided to adopt an action model aimed at automating the workflow steps related to data preprocessing, downloading, and publishing. A visualizer with web output (Geospatial System for Monitoring Mangrove Ecosystems or SIGMEM) is developed for the first time, evaluating changes in an area of central Cuba from different vegetation indices. The evaluation of the machine learning classifiers Random Forest and Naive Bayes for the automated mapping of mangroves highlighted the ability of Random Forest to discriminate between areas occupied by mangroves and other coverages with an Overall Accuracy (OA) of 94.11%, surpassing the 89.85% of Naive Bayes. The estimated net change based on the year 2020 of the areas determined during the classification process showed a decrease of 5138.17 ha in the year 2023 and 2831.76 ha in the year 2022. This tool will be fundamental for researchers, decision makers, and students, contributing to new research proposals and sustainable management of mangroves in Cuba and the Caribbean.

Keywords

AlgorithmsAprendizaje automáticoClassificationCloudCoasCoverDifferenceDrivenImageryIndexMachine learninManglesMangroveSentinel-2Teledetección espacialVegetationWebvisor

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 110/358, thus managing to position itself as a Q1 (Primer Cuartil), in the category Environmental Sciences.

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 2025-07-10:

  • Scopus: 2

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 2025-07-10:

  • 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: 14.
  • 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: 24 (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: 0.5.
  • The number of mentions on the social network X (formerly Twitter): 1 (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.

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) .