APLICAÇÕES DE APRENDIZADO DE MÁQUINA E SENSORIAMENTO REMOTO NA PESQUISA DE ILHAS DE CALOR URBANO: UMA ANÁLISE BIBLIOMÉTRICA
DOI:
https://doi.org/10.56238/revgeov17n2-098Palavras-chave:
Ilhas de Calor Urbanas, Aprendizado de Máquina, Inteligência Artificial, Sensoriamento Remoto, Análise BibliométricaResumo
As ilhas de calor urbanas (ICU) representam desafios críticos para a adaptação climática conforme a urbanização global se acelera. Embora a inteligência artificial e o sensoriamento remoto tenham surgido como ferramentas poderosas para análise térmica urbana, o crescimento acelerado da pesquisa nesta interseção carece de síntese abrangente. Este estudo bibliométrico examinou 381 publicações (2004–2026) da Web of Science Core Collection para mapear a estrutura, evolução e bases de conhecimento da área. Utilizando o pacote bibliometrix em R, realizamos análise de desempenho (produtividade, citações) e mapeamento científico (coautoria, co-palavras, acoplamento bibliográfico e redes de cocitação). Os resultados revelaram crescimento recente exponencial: as publicações aumentaram de 11 (2019) para 135 (2025), com 95% da pesquisa produzida em sete anos (2019–2025). A concentração geográfica é acentuada: China (33,6%), Índia (9,45%) e EUA (7,61%) dominam a produção, enquanto África Subsaariana e América Latina permanecem sub-representadas, apesar da alta vulnerabilidade ao calor. A pesquisa converge para o sensoriamento remoto térmico aprimorado por aprendizado de máquina, com o Random Forest (7,87% dos artigos) como algoritmo dominante e a temperatura da superfície terrestre (28,87%) como variável principal. As métricas de citação indicam maturidade do campo (índice h = 47, índice g = 75, média de citações = 20,93), consolidando-se sobre fundamentos que abrangem climatologia urbana, metodologia de sensoriamento remoto e aprendizado de máquina. No entanto, a análise temática revelou lacunas críticas: a pesquisa enfatizou a detecção em detrimento dos impactos na saúde, validação de mitigação e integração de políticas públicas.
Downloads
Referências
Ahmed, B., Kamruzzaman, M., Zhu, X., Rahman, M. S., & Choi, K. (2013). Simulating land cover changes and their impacts on land surface temperature in Dhaka, Bangladesh. Remote Sensing, 5(11), 5969–5998. https://doi.org/10.3390/rs5115969
Alabi, G. (1979). Bradford’s law and its application. International Library Review, 11(1), 151–158.
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
Calvin, K., Dasgupta, D., Krinner, G., Mukherji, A., Thorne, P., Trisos, C., Romero, J., Aldunce, P., Barrett, K., Blanco, G. et al. (2023). Climate Change 2023: Synthesis report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC.
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.
Cunha-Melo, J. R., Santos, G. C., & Andrade, M. V. (2006). Brazilian medical publications: Citation patterns for Brazilian-edited and non-Brazilian literature. Brazilian Journal of Medical and Biological Research, 39, 997–1002.
Deilami, K., Kamruzzaman, M., & Liu, Y. (2018). Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures. International Journal of Applied Earth Observation and Geoinformation, 67, 30–42.
Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285–296.
Egghe, L. (2006). Theory and practise of the g-index. Scientometrics, 69(1), 131–152.
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), 16569–16572.
Hu, X., & Weng, Q. (2009). Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113(10), 2089–2102.
IPCC. (2023). Climate Change 2022 – Impacts, adaptation and vulnerability: Working Group II contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (1st ed.). Cambridge University Press.
IPCC. (2022). Global warming of 1.5°C: IPCC special report on impacts of global warming of 1.5°C above pre-industrial levels. Cambridge University Press.
Jiang, Y., Lv, H., Cheng, B., Li, S., & Shao, X. (2024). Climate warming effects on temperature structure in lentic waters: A bibliometric analysis from the recent 20 years. Ecological Indicators, 167, Article 112740.
Kafy, A.-A., Abdullah-Al-Faisal, Rahman, M. S., Hasan, M. M., & Islam, M. (2021). Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh. Sustainable Cities and Society, 64, Article 102542.
Kafy, A.-A., Rahman, M. S., Abdullah-Al-Faisal, Hasan, M. M., & Islam, M. (2020). Modelling future land use land cover changes and their impacts on land surface temperatures in Rajshahi, Bangladesh. Remote Sensing Applications: Society and Environment, 18, Article 100314.
Li, Z.-L., Tang, B., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., & Sobrino, J. A. (2013). Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131, 14–37.
Liu, S., & Shi, Q. (2020). Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 229–242.
Logan, T. M., Zuurmond, M. J., Astell-Burt, T., & Bakula, M. (2020). Night and day: The influence and relative importance of urban characteristics on remotely sensed land surface temperature. Remote Sensing of Environment, 247, Article 111861.
Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323.
Maria, P. F. de. (2019). Mapping relations in researchs about poverty and migration (1980-2017). Mercator, 18, Article e18003.
Menezes, É. M. L., & Macadar, M. A. (2025). Smart city living lab governance paths to sustainability: Bibliometric and content analysis. Revista de Administração Contemporânea, 29, Article e240310.
Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213–228.
Nascimento, R. L., Carvalho, A., Santos, D., Alves, P., & Amaral, F. (2024). Polychaete research in Brazil: A bibliometric analysis. Ocean and Coastal Research, 72, Article e24018.
Newman, M. E. J. (2001). Scientific collaboration networks. I. Network construction and fundamental results. Physical Review E, 64(1), Article 016131.
Oke, T. R. (1982). The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108(455), 1–24.
Peng, S., Piao, S., Ciais, P., Friedlingstein, P., Ottle, C., Breon, F.-M., Nan, H., Zhou, L., & Myneni, R. B. (2012). Surface urban heat island across 419 global big cities. Environmental Science & Technology, 46(2), 696–703.
Shukla, P. R., Skea, J., Slade, R., Al Khourdajie, A., van Diemen, R., McCollum, D., Pathak, M., Some, S., Vyas, P., Fradera, R. (Eds.). (2022). Climate Change 2022: Mitigation of climate change. IPCC.
Stewart, I. D., & Oke, T. R. (2012). Local climate zones for urban temperature studies. Bulletin of the American Meteorological Society, 93(12), 1879–1900.
Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86(3), 370–384.
Weng, Q., Lu, D., & Schubring, J. (2004). Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment, 89(4), 467–483.
Winters, J. R. da F., Prado, M. L. do, Silva, D., Waterkemper, R., & Kempfer, S. S. (2018). Nursing higher education in MERCOSUR: A bibliometric study. Revista Brasileira de Enfermagem, 71, 1732–1739.
Yang, S., Wang, L., Stathopoulos, T., & Marey, A. (2023). Urban microclimate and its impact on built environment – A review. Building and Environment, 238, Article 110334.
Yoo, C., Im, J., Cho, D., Yokoya, N., Xia, J., & Bechtel, B. (2019). Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images. ISPRS Journal of Photogrammetry and Remote Sensing, 157, 155–170.
Zhan, W., Zhou, J., Ju, W., Li, Z.-L., Sobrino, J., Feng, X., & Wu, H. (2013). Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats. Remote Sensing of Environment, 131, 119–139.
Zhou, D., Xiao, J., Bonafoni, S., Berger, C., Deilami, K., Zhou, Y., Furuya, K., Exposito, A., & Luan, J. (2019). Satellite remote sensing of surface urban heat islands: Progress, challenges, and perspectives. Remote Sensing, 11(1), Article 48.
Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472.
