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Impact on the Sustainable Development Goals (SDGs)

Analysis of institutional authors

Estefania-Salazar, EnriqueCorresponding AuthorIglesias, EvaAuthor

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August 4, 2024
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Enhancing spatio-temporal environmental analyses: A machine learning superpixel-based approach

Publicated to: Heliyon. 10 (14): e34711- - 2024-07-30 10(14), DOI: 10.1016/j.heliyon.2024.e34711

Authors:

Estefania-Salazar, E; Iglesias, E
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Affiliations

Univ Politecn Madrid, CEIGRAM, Madrid 28040, Spain - Author

Abstract

The progressive evolution of the spatial and temporal resolutions of Earth observation satellites has brought multiple benefits to scientific research. The increasing volume of data with higher frequencies and spatial resolutions offers precise and timely information, making it an invaluable tool for environmental analysis and enhanced decision-making. However, this presents a formidable challenge for large-scale environmental analyses and socioeconomic applications based on spatial time series, often compelling researchers to resort to lower-resolution imagery, which can introduce uncertainty and impact results. In response to this, our key contribution is a novel machine learning approach for dense geospatial time series rooted in superpixel segmentation, which serves as a preliminary step in mitigating the high dimensionality of data in large-scale applications. This approach, while effectively reducing dimensionality, preserves valuable information to the maximum extent, thereby substantially enhancing data accuracy and subsequent environmental analyses. This method was empirically applied within the context of a comprehensive case study encompassing the 2002-2022 period with 8-d-frequency-normalized difference vegetation index data at 250-m resolution in an area spanning 43,470 km2. The efficacy of this methodology was assessed through a comparative analysis, comparing our results with those derived from 1000-m-resolution satellite data and an existing superpixel algorithm for time series data. An evaluation of the time-series deviations revealed that using coarser-resolution pixels introduced an error that exceeded that of the proposed algorithm by 25 % and that the proposed methodology outperformed other algorithms by more than 9 %. Notably, this methodological innovation concurrently facilitates the aggregation of pixels sharing similar land-cover classifications, thus mitigating subpixel heterogeneity within the dataset. Further, the proposed methodology, which is used as a preprocessing step, improves the clustering of pixels according to their time series and can enhance large-scale environmental analyses across a wide range of applications.
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Keywords

Big dataClimate actionDimension reductionEnvironmental analysisMachine learningSuperpixelVegetation indiceVegetation indices

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Heliyon 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 29/136, thus managing to position itself as a Q1 (Primer Cuartil), in the category Multidisciplinary 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 2026-04-25:

  • WoS: 2
  • Scopus: 3
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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 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: 9 (PlumX).

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/92051/

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: 45
  • Downloads: 29
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 13 - Climate Action, with a probability of 74% according to the mBERT algorithm developed by Aurora University.
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Leadership analysis of institutional authors

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 (ESTEFANIA SALAZAR, ENRIQUE) and Last Author (IGLESIAS MARTINEZ, EVA).

the author responsible for correspondence tasks has been ESTEFANIA SALAZAR, ENRIQUE.

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Project objectives

Los objetivos perseguidos en esta aportación se centran en mejorar el análisis ambiental espacio-temporal mediante técnicas avanzadas. Se propone analizar la evolución de la resolución espacial y temporal en datos satelitales para identificar sus beneficios y limitaciones en estudios ambientales. Se busca desarrollar un enfoque basado en aprendizaje automático y segmentación por superpíxeles para reducir la dimensionalidad de series temporales geoespaciales densas sin perder información relevante. Se pretende evaluar empíricamente la eficacia del método aplicado a datos NDVI normalizados de alta resolución (250 m) entre 2002 y 2022 en una zona de 43,470 km². Finalmente, se aspira a comparar el rendimiento del nuevo algoritmo con datos de menor resolución y métodos existentes, y a mejorar la agregación de píxeles con clasificaciones similares para optimizar análisis ambientales a gran escala.
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Most relevant results

Los resultados más relevantes de este estudio presentan una novedosa metodología basada en aprendizaje automático y segmentación por superpíxeles para series temporales geoespaciales densas. En primer lugar, la aplicación empírica abarcó el periodo 2002-2022 con datos del índice de vegetación de diferencia normalizada a resolución de 250 m y frecuencia de 8 días en un área de 43,470 km². En segundo lugar, la comparación con datos satelitales de resolución 1000 m mostró que el error introducido por estos píxeles de menor resolución superó en un 25 % al del algoritmo propuesto. En tercer lugar, la metodología superó en más de un 9 % a otros algoritmos existentes para series temporales. Finalmente, la técnica mejora la agregación de píxeles con clasificaciones de cobertura terrestre similares, reduciendo la heterogeneidad subpíxel.
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Awards linked to the item

We acknowledge the support from Programa Propio Universidad Politecnica de Madrid.
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