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Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Iglesias Picazo, Ana LuisaAuthorRecuero, LauraAuthorPalacios-Orueta, AliciaAuthor

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June 10, 2019
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Article

Improving Aboveground Forest Biomass Maps: From High-Resolution to National Scale

Publicated to: Remote Sensing. 11 (7): 795- - 2019-04-01 11(7), DOI: 10.3390/rs11070795

Authors:

Durante, P; Martín-Alcón, S; Gil-Tena, A; Algeet, N; Tomé, JL; Recuero, L; Palacios-Orueta, A; Oyonarte, C
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Affiliations

AGRESTA Soc Cooperat - Author
CAESCG - Author
Univ Almeria, Dept Agron - Author
Univ Politecn Madrid, ETSIMFMN, Dept Sistemas & Recursos Nat - Author
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Abstract

Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km(2)), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R-2 0.71 and RMSE 9.99 tha(-1)) with the normalized difference vegetation index (NDVI) as the main vegetation index, in combination with topographic variables as environmental drivers. An independent validation carried out over the final predicted biomass map showed a satisfactory statistically-robust model (R-2 0.70 and RMSE 10.25 tha(-1)), confirming its applicability at coarser resolutions.
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Keywords

AlsClimate changeClimate-changeCoverField plotsInventory plotsLand-useLidarLife on landMediterranean forestModerate resolutionModisNdviQuantile regression forestTerrestrial ecosystemsUncertaintyVegetation

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 Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2019, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Earth and Planetary Sciences (Miscellaneous). Notably, the journal is positioned above the 90th percentile.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations from Scopus Elsevier, it yields a value for the Field-Weighted Citation Impact from the Scopus agency: 1.59, which indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-25, the following number of citations:

  • WoS: 27
  • Scopus: 30
  • Google Scholar: 35
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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 2026-04-25:

  • 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: 72.
  • 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: 72 (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: 6.
  • The number of mentions on the social network X (formerly Twitter): 7 (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: https://oa.upm.es/94309/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 26
  • Downloads: 2
Continuing with the social impact of the work, it is important to emphasize that, due to its content, it can be assigned to the area of interest of ODS 15 - Life on land, with a probability of 51% according to the mBERT algorithm developed by Aurora University.
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Project objectives

Los objetivos perseguidos en esta aportación se centran en mejorar la estimación de la biomasa aérea forestal a gran escala y alta resolución temporal. Se busca analizar la integración de datos de escaneo láser aerotransportado con medidas de campo y variables espectrales biofísicas derivadas de MODIS. Se pretende evaluar el uso de la regresión cuantílica aleatoria para predecir espacialmente la biomasa y su incertidumbre asociada. Además, se aspira a determinar el desempeño del modelo mediante indicadores estadísticos como R² (0.71) y RMSE (9.99 t/ha) y validar independientemente la aplicabilidad del modelo a resoluciones más gruesas, confirmando resultados con R² de 0.70 y RMSE de 10.25 t/ha.
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Most relevant results

Los resultados más relevantes del estudio se centran en la estimación del biomasa aérea forestal (AGB) en la Región de Murcia, España, utilizando datos de escaneo láser aerotransportado y variables espectrales y topográficas. En primer lugar, el modelo de regresión cuantílica por bosque aleatorio (QRF) mostró un rendimiento satisfactorio con un coeficiente de determinación R² de 0.71 y un error cuadrático medio (RMSE) de 9.99 t ha⁻¹. En segundo lugar, el índice de vegetación de diferencia normalizada (NDVI) fue identificado como el principal predictor vegetal. En tercer lugar, la validación independiente del mapa final de biomasa arrojó un R² de 0.70 y un RMSE de 10.25 t ha⁻¹, confirmando la aplicabilidad del modelo a resoluciones más gruesas.
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Awards linked to the item

Pilar Durante's participation was supported by a predoctoral grant [DI-15-08093] and Nur Algeet [PTQ-14-07206], Santiago Martin-Alcon [PTQ-15-07975] and Assu Gil-Tena [PTQ-16-08741] by postdoctoral grants awarded by the 'National Programme for the Promotion of Talent and Its Employability' of the Ministry of Economy, Industry, and Competitiveness (Torres-Quevedo program), which are partially funded by the European Social Fund (ESF) from the European Commission. Laura Recuero was also supported by a predoctoral grant [FPU014/05633] from the Spanish Ministry of Science, Innovation and Universities.
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