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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Henao-Cespedes, Vladimir
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Computer Science & IT Electrical & Electronics Engineering

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Remote sensing in the analysis between forest cover and COVID-19 cases in Colombia Henao-Céspedes, Vladimir; Garcés-Gómez, Yeison Alberto
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp732-740

Abstract

This article explores the relationship between forest cover and coronavirus disease 2019 (COVID-19) cases in Colombia using remote sensing techniques and data analysis. The study focuses on the CORINE land cover methodology's five main land cover categories: artificial territory, agricultural territories, forests and semi-natural areas, humid areas, and water surfaces. The research methodology involves several phases of the unified method of analytical solutions for data mining (ASUM-DM). Data on COVID-19 cases and forest cover are collected from the Colombian National Institute of Health and Advanced Land Observation Satellite (ALOS PALSAR), respectively. Land cover data is processed using QGIS software. The results indicate an inverse relationship between forest cover and COVID-19 cases, as evidenced by Pearson's index ρ of -0.439 (p-value <0.012). In addition, a negative correlation is observed between case density and forests and semi-natural areas, one of the land cover categories. The findings of this study suggest that higher forest cover is associated with lower numbers of COVID-19 cases in Colombia. The results could potentially inform government organizations and policymakers in implementing strategies and policies for forest conservation and the inclusion of green areas in densely populated urban areas.
Remote sensing in the analysis of the behavior of CO associated with confinement due to COVID-19, in the city of Manizales Henao-Céspedes, Vladimir; Garcés-Gómez, Yeison Alberto; Cardona-Morales, Oscar
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.7441

Abstract

This article analyzed the behavior of carbon monoxide (CO) levels in Manizales during pre-lockdown, lockdown, and post-lockdown, as a response to the coronavirus disease (COVID-19) pandemic. The analysis focuses on the data of CO levels obtained from the tropospheric monitoring instrument (TROPOMI), precipitation, and temperature (T) recorded by the network of stations of Caldas. The data allowed us to find that during the lockdown, the average value of CO was 9.92% lower than the value registered before the lockdown, and it was 11.75% lower after the lockdown. On the other hand, the correlation between CO levels and population density during the three periods was analyzed, obtaining an ?2 = 0.816 after lockdown. Finally, considering other possible variables that can affect the CO levels, an analysis of the behavior of CO was carried out concerning the temperature and precipitation of the city registered before, during, and after the lockdown. Regarding CO and temperature, the correlation was inverse with Pearson’s ? = −0.599 (Fisher’s ? = −0.692), which also supports the decreasing trend of the value measured, and that the variation of CO levels does not depend only on lockdown but also on other factors. Regarding CO and precipitation, a positive correlation of Pearson’s ? = 0.165 (Fisher’s ? = 0.167) was obtained.
Agrivoltaic systems: a literature review Durán-Cabezas, Mariajosé; Gómez-Gaviria, Catalina; Henao-Céspedes, Vladimir
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9964

Abstract

Agrivoltaic systems integrate solar energy generation with agricultural production to achieve efficient land use and mitigate climate change. This study presents a bibliometric analysis of the scientific literature on this topic, published from 2013 to 2023, to identify key trends, research areas, and emerging topics. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology and data from the Scopus database, the analysis was conducted with the R package bibliometrix and VOSviewer software. The results show remarkable growth in scientific output since 2020, with the United States, China, and Germany as the leading countries. The findings reveal the benefits of agrivoltaic systems, such as increased crop productivity, water-use efficiency, and income diversification for farmers. Emerging topics include the optimization of panel configurations and socioeconomic implications. Despite these benefits, challenges like high initial costs, social acceptance, and the need for adaptable designs persist. The conclusions underscore the importance of specific policies and incentives to support the adoption of these technologies. This analysis provides a comprehensive overview of the state of agrivoltaic systems, offering valuable insights for researchers, policymakers, and other stakeholders.
Bibliometrics on the use of remote sensing and machine learning in crop classification Sánchez-Chavez, Andrea del Pilar; Henao-Cespedes, Vladimir; Garcés-Gómez, Yeison Alberto
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10476

Abstract

This study presents a bibliometric analysis of global research on crop classification using remote sensing and machine learning (ML), a field critical to advancing precision agriculture. A systematic search in Scopus identified 2,122 peer-reviewed articles published between 2014 and 2023. The analysis employed VOSviewer and the Bibliometrix package in R to assess publication trends, citation impact, and keyword co-occurrence networks. Results reveal a marked increase in scientific production after 2017, coinciding with the availability of high-resolution satellite imagery and the adoption of deep learning algorithms, particularly convolutional neural networks (CNNs). China emerged as the leading contributor, followed by the United States and India, reflecting strong investments in agricultural modernization and remote sensing infrastructure. Thematic mapping highlights both traditional research areas, such as vegetation indices and land cover classification, and emerging themes, including AI-supported algorithms and food security. Despite this growth, disparities persist, with most countries contributing fewer than 100 publications, underscoring the need to promote participation in underrepresented regions. Findings demonstrate the field’s rapid evolution, emphasize the integration of AI-driven methods in crop monitoring, and suggest future directions combining remote sensing, ML, and internet of things (IoT) technologies to address global challenges in food security and sustainable agricultural management.
Co-Authors Cardona-Morales, Oscar Durán-Cabezas, Mariajosé Garces-Gomez, Yeison Alberto Gómez-Gaviria, Catalina Sánchez-Chavez, Andrea del Pilar