Unveiling Spatiotemporal Patterns in the Pampa Biome Using Principal Component Analysis
DOI:
https://doi.org/10.26848/rbgf.v18.05.p3872-3890Keywords:
MapBiomas, Google Earth Engine, Landscape, ChangesAbstract
The Pampa biome has been transforming its traditional landscape since the 1950s. Comprehending the spatiotemporal dynamics of land use and land cover change (LUCC) in the Pampa and its primary drivers is crucial in formulating territorial planning and environmental management. Therefore, the purpose of this paper is to use the potential of Principal Components Analysis (PCA) to analyze LUCC in the Pampa biome between 1985-2020, based on the synthesis of the MapBiomas project database. The methodology is divided into three stages, the first is in GEE where pre-processing and PCA are carried out, and the second is in Planetary Computer where correlation coefficients are calculated. Finally, in QGIS 3.10.4, reclassification and cartographic production were conducted. The LUCC processes identified in the Pampa Biome reveal a significant transformation of its landscape, characterized by a reduction in the extent of grassland formations due to the expansion of anthropogenic activities. The conversion of natural grasslands to agricultural land uses, particularly soy, forestry, and farming, has been a prevalent trend. The results of the analysis highlight that the large-scale conversion of grassland areas is a recent phenomenon in the Pampa, occurring mainly between 1995 and 2010, linked to the replacement of cattle ranching by large-scale agriculture.
References
Afaq, Y., & Manocha, A., 2021. Analysis on change detection techniques for remote sensing applications: A review. Ecological Informatics 63, 101310. Available: https://doi.org/10.1016/j.ecoinf.2021.101310. Access: Jun, 2024.
Agresti, A., 2018. An introduction to categorical data analysis. John Wiley & Sons.
Agresti, A., 2010. Analysis of ordinal categorical data. John Wiley & Sons.
Andrade, B., Dröse, W., Aguiar, C., Aires, E., Alvares, D., Barbieri, R., & Mendonça Junior, M., 2023. 12,500+ and counting: Biodiversity of the Brazilian Pampa. Frontiers of Biogeography 15, 2. Available: https://doi.org/10.21425/F5FBG59288. Access: May, 2024.
Antunes, R., 2012. Variação espaço-temporal de NDVI em área de aproveitamento hidroelétrico-UHE Santo Antônio, Porto Velho (RO) (Dissertação de Mestrado). Universidade Federal do Rio Grande do Sul.
Aslam, R., Shu, H., Tariq, A., Naz, I., Ahmad, M., Quddoos, A., & Aeman, H., 2024. Monitoring land use change in Uchhali and Khabeki wetland lakes, Pakistan using remote sensing data. Gondwana Research 129, 252-267. Available: https://doi.org/10.1016/j.gr.2023.12.015. Access: July, 2024.
Beling, H., Cancelier, J., & Flores, C., 2022. Transformações no espaço rural gaúcho: Um olhar para o Pampa Gaúcho. GeoTextos 18. Available: https://doi.org/10.9771/geo.v18i1.48716. Access: Jun, 2024.
Bencke, G., Chomenko, L., & Sant’Anna, D., 2016. O que é o pampa? In L. Chomenko & A. Bencke (Eds.), Nosso Pampa Desconhecido (pp. 16-27). Fundação Zoobotânica do Rio Grande do Sul.
Botelho, R., & Clevelário Júnior, J., 2016. Recursos naturais e questões ambientais. In A. Figueiredo (Ed.), Brasil: Uma visão geográfica e ambiental no início do século XXI (pp. 138-318). IBGE.
Briassoulis, H., 2020. Analysis of land use change: Theoretical and modeling approaches. Regional Research Institute, West Virginia University.
Chomenko, L., 2016. O Pampa em transformação. In L. Chomenko & A. Bencke (Eds.), Nosso Pampa Desconhecido (pp. 188-203). Fundação Zoobotânica do Rio Grande do Sul.
Delgado, R., De Santana, R., Gelsleichter, Y., & Pereira, M., 2022. Degradation of South American biomes: What to expect for the future? Environmental Impact Assessment Review 96, 106815. Available: https://doi.org/10.1016/j.eiar.2022.106815. Access: Jun, 2024.
Deng, J., Wang, K., Deng, Y., & Qi, G., 2008. PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data. International Journal of Remote Sensing 29, 4823-4838. Available: https://doi.org/10.1080/01431160801950162, Access: May, 2024.
Dharani, M., & Sreenivasulu, G., 2021. Land use and land cover change detection by using principal component analysis and morphological operations in remote sensing applications. International Journal of Computers and Applications 43, 462-471. Available: https://doi.org/10.1080/1206212X.2019.1578068. Access: May, 2024.
Ellwanger, J., Ziliotto, M., & Chies, J., 2022. Protect Brazil’s overlooked Pampa biome. Science 377, 720-720. Available: https://doi.org/10.1126/science.ade183. Access: Jun, 2024.
Estornell, J., Martí-Gavilá, J., Sebastiá, M., & Mengual, J., 2013. Principal component analysis applied to remote sensing. Modelling in Science Education and Learning 6, 83-89. Available: https://doi.org/10.4995/msel.2013.1905. Access: Jan, 2024.
Fernando, A., 2014. Análise fatorial para variáveis ordinais (Dissertação de Mestrado). Universidade do Porto.
Freitas, M., Santos, J., & Alves, D., 2013. Land-use and land-cover change processes in the Upper Uruguay Basin: Linking environmental and socioeconomic variables. Landscape Ecology 28, 311-327. Available: https://doi.org/10.1007/s10980-012-9838-9, Access: Jan, 2024.
GEE. Google Earth Engine, 2024. Eigen Analysis. Available: https://developers.google.com/earth-engine/guides/arrays_eigen_analysis. Access: Jan, 2024.
Greenacre, M., Groenen, P., Hastie, T., D’Enza, A., Markos, A., & Tuzhilina, E., 2022. Principal component analysis. Nature Reviews Methods Primers 2, 100. Available: https://doi.org/10.1038/s43586-023-00209-y. Access: Jan, 2024.
Gupta, R., Tiwari, R., Saini, V., & Srivastava, N., 2013. A simplified approach for interpreting principal component images. Advances in Remote Sensing 2, 111-119. Available: https://doi.org/10.4236/ars.2013.22015. Access: Jan, 2024.
Hasenack, H., Weber, E., Vélez-Martin, E., Hofmann, G., & Dewes, H., 2019. Bioma Pampa: Oportunidades e desafios de pesquisa para o desenvolvimento sustentável. In E. Vilela, M. Callegaro, & G. Fernandes (Eds.), Biomas e agricultura: Oportunidades e desafios (pp. 123-140). Vertente Edições.
Hasan, B., & Abdulazeez, A., 2021. A review of principal component analysis algorithm for dimensionality reduction. Journal of Soft Computing and Data Mining 2, 1, 20-30. Available: https://doi.org/10.30880/jscdm.2021.02.01.003. Access: Feb, 2024.
He, F., Yang, J., Zhang, Y., Yu, W., Xiao, X., & Xia, J., 2023. Does partition matter? A new approach to modeling land use change. Computers, Environment and Urban Systems 106, 102041. Available: https://doi.org/10.1016/j.compenvurbsys.2023.102041. Access: Jan, 2024.
BGE. Instituto Brasileiro de Geografia e Estatística, 2019. Biomas do Brasil - 1:250000. Available: https://www.ibge.gov.br/geociencias/informacoes-ambientais/vegetacao/15842-biomas.html?=&t=acesso-ao-produto. Access: Jan, 2024.
Jesudhas, C., Titus, J., & Roy, T., 2024. Remote sensing-based drought hazard monitoring and assessment in a coastal plain: A principal component approach. Environmental Research 243, 117757. Available: https://doi.org/10.1016/j.envres.2023.117757. Access: Jul, 2024.
Jolliffe, I., 2002. Principal component analysis. Springer.
Jolliffe, I., & Cadima, J., 2016. Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 374, 20150202. Available: https://doi.org/10.1098/rsta.2015.0202. Access: Jan, 2024.
Kuplich, T., Capoane, V., & Costa, L., 2018. O avanço da soja no bioma Pampa. Boletim Geográfico do Rio Grande do Sul, 31, 83–100.
Li, L., Ma, H., Zhang, X., Zhao, X., Lv, M., & Jia, Z., 2024. Synthetic aperture radar image change detection based on principal component analysis and two-level clustering. Remote Sensing 16, 11, 1861. Available: https://doi.org/10.3390/rs16111861. Access: Feb, 2025.
Lima, E., Cano, H., & Nascimento, J., 2016. Uma contribuição à geografia dos recursos hídricos. In A. Figueiredo (Ed.), Brasil: Uma visão geográfica e ambiental no início do século XXI (pp. 319–355). IBGE.
Lima, D., Crouzeilles, R., & Vieira, M., 2020. Integrating strict protection and sustainable use areas to preserve the Brazilian Pampa biome through conservation planning. Land Use Policy 99, 104836. Available:https://doi.org/10.1016/j.landusepol.2020.104836. Access: Jan, 2024.
Lopes, Â. L., Schwanz, V., & Simon, A. L., 2024. Uso da Terra e Ameaças à Geodiversidade no Bioma Pampa Brasileiro. Revista Brasileira De Geografia Física 17, 6, 4156–4176. Available: https://doi.org/10.26848/rbgf.v17.6.p4156-4176. Access: Feb, 2025.
Lu, C., 2021. Análise fatorial para dados qualitativos ordinais (Monografia). Universidade Federal de São Carlos, São Carlos.
Maier, É., 2014. Relações espaço-temporais entre dados paleoclimáticos do Nevado Illimani (Bolívia) e a precipitação na América do Sul (Tese de doutorado). Universidade Federal do Rio Grande do Sul, Porto Alegre.
MapBiomas, 2022. MapBiomas General “Handbook” Algorithm Theoretical Basis Document (ATBD) - Collection 6. Available: https://mapbiomas.org/colecoes-mapbiomas-1?cama_set_language=pt-BR. Access: Dec, 2023.
Marques, R. de C. G., Vancine , M., Súarez, Y., Pereira , J., Domingos , J., da Silva , A., & Pereira, Z., 2025. Dinâmica espaço temporal: variações na composição e configuração da vegetação. Revista Brasileira De Geografia Física 18, 2, 1334–1348. Available: https://doi.org/10.26848/rbgf.v18.2.p1334-1348. Access: Feb, 2025.
Martinez-Izquierdo, M., Molina-Sánchez, I., & Morillo-Balsera, M., 2019. Efficient dimensionality reduction using principal component analysis for image change detection. IEEE Latin America Transactions 17, 4, 540–547. Available: https://doi.org/10.1109/TLA.2019.8891877. Access: Jan, 2024.
Mas, J., Lemoine-Rodríguez, R., González-López, R., López-Sánchez, J., Piña-Garduño, A., & Herrera-Flores, E., 2017. Land use/land cover change detection combining automatic processing and visual interpretation. European Journal of Remote Sensing 50, 626–635. Available: https://doi.org/10.1080/22797254.2017.1387505. Access: May, 2024.
Mengue, V., Freitas, M., Silva, T., Fontana, D., & Scottá, F., 2020. Land-use and land-cover change processes in Pampa biome and relation with environmental and socioeconomic data. Applied Geography 125, 102342. Available: https://doi.org/10.1016/j.apgeog.2020.102342. Access: Jan, 2024.
Moreira, J., Conterato, M., & Matte, A., 2019. Transformações produtivas no Pampa brasileiro: Influências do avanço da soja na bovinocultura de corte. Revista Campo-Território 14, 179–207. Available: https://doi.org/10.14393/RCT143308. Access: Jun, 2024.
Mori, Y., Kuroda, M., & Makino, N., 2016. Nonlinear principal component analysis and its applications. Springer.
Oliveira, C., 2020. Análise de mudanças da cobertura e uso do solo no Bioma Pampa com matrizes de transição (Dissertação de mestrado). Universidade Federal do Rio Grande do Sul, Porto Alegre.
Peixoto, D. W. B., Trindade, P. M. P., Kuplich, T. M., & Almeida, C. A. de., 2022. Análise da temperatura de superfície em diferentes coberturas do Bioma Pampa. Revista Brasileira De Geografia Física 15, 1, 268–281. Available: https://doi.org/10.26848/rbgf.v15.1.p268-281. Access: Feb, 2025.
Pessoa, A. M. dos S., da Silva, A., Santos, A. G. dos, Pessoa, R. M. dos S., Santos, L. de F. D. dos, Silveira, M. V. de S., & Santos, B. R. C. dos., 2025. Dinâmica multitemporal da cobertura vegetal do estado da Paraíba. Revista Brasileira De Geografia Física 18, 1, 622–632. Available: https://doi.org/10.26848/rbgf.v18.1.p622-632. Access: Feb, 2025.
Reis, M., Escada, M., Sant’Anna, S., & Dutra, L., 2020. Métodos de classificação e análise de trajetórias de uso e cobertura da terra na Amazônia: Implicações para estudos de regeneração florestal. Revista Brasileira de Cartografia 72, 1087–1113. Available: https://doi.org/10.14393/rbcv72nespecial50anos-56535. Access: May, 2024.
Ribeiro, R., Oliveira, L., Barros, A., Alves, R., Rocha, K., Maltezo, K., … da Silva, J., 2024. Diagnóstico Ambiental e Caracterização da Área de Preservação Permanente do Igarapé Encrenca em Rolim de Moura, Rondônia, Brasil. Revista Brasileira De Geografia Física 17, 5, 3490–507. Available: https://doi.org/10.26848/rbgf.v17.5.p3490-507. Access: Feb, 2025.
Robaina, L., Trentin, R., & Scottá, A., 2021. Geomorphological description of the biome Pampa (Brazil) with support of digital elevation model and geographic information system. Revista do Departamento de Geografia 41, e169605. Available: https://doi.org/10.11606/eISSN.2236-2878.rdg.2021.169605. Access: Jan, 2024.
Souza, C., Shimbo, J., Rosa, M., Parente, L., Alencar, A., Rudorff, B., ... Azevedo, T., 2020. Reconstructing three decades of land use and land cover changes in Brazilian biomes with Landsat archive and Earth Engine. Remote Sensing 12, 1, 27. Available: https://doi.org/10.3390/rs12172735. Access: Jan, 2024.
Thein, A., & Htwe, A., 2023. Based on principal component analysis of land use land cover change detection using Landsat satellite images (Case study Mandalay City). In 2023 IEEE Conference on Computer Applications (ICCA) (pp. 147–152). IEEE. Available: https://doi.org/10.1109/ICCA51723.2023.10181968. Access: Jan, 2024.
Tomazoni, J. C., Guimarães, E., & Tomazoni, E. C., 2025. Vulnerabilidade Ambiental das Unidades Territoriais das Paisagens do Sudoeste do Paraná. Revista Brasileira De Geografia Física 18, 2, 955–972. Available: https://doi.org/10.26848/rbgf.v18.2.p955-972. Access: Feb, 2025.
Trentin, R., Laurent, F., & Robaina, L. E. de S., 2023. Vazão e Balanço Hídrico Relacionado a Mudanças no Uso da Terra em uma Bacia Hidrográfica de Médio Porte no Bioma Pampa do Brasil. Revista Brasileira De Geografia Física 16, 2, 873–893. Available: https://doi.org/10.26848/rbgf.v16.2.p873-893. Access: Feb, 2025.
Verburg, P., Crossman, N., Ellis, E., Heinimann, A., Hostert, P., Mertz, O., & Zhen, L., 2015.
Land system science and sustainable development of the earth system: A global land project perspective. Anthropocene 12, 29–41. Available: https://doi.org/10.1016/j.ancene.2015.09.004. Access: Jan, 2024.
Verburg, P., Soepboer, W., Veldkamp, A., Limpiada, R., Espaldon, V., & Mastura, S., 2002. Modeling the spatial dynamics of regional land use: The CLUE-S model. Environmental Management 30, 391–405. Available: https://doi.org/10.1007/s00267-002-2630-x. Access: Jan, 2024.
Verdum, R., 2016. Paisagem do Pampa: Monotonia que se rompe no espaço e no tempo. In L. Chomenko & A. Bencke (Eds.), Nosso Pampa Desconhecido (pp. 44–59). Fundação Zoobotânica do Rio Grande do Sul.
Zanotta, D., Ferreira, M., & Zortea, M., 2019. Processamento de imagens de satélite. Oficina de Textos.
Zioti, F., Ferreira, K., Queiroz, G., Neves, A., Carlos, F., Souza, F., & Simoes, R., 2022. A platform for land use and land cover data integration and trajectory analysis. International Journal of Applied Earth Observation and Geoinformation 106, 102655. Available: https://doi.org/10.1016/j.jag.2021.102655. Access: Jan, 2024.
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