SPATIAL AND TEMPORAL DYNAMICS OF LAND COVER CHANGES IN THE WATERSHED OF THE CAUAMÉ RIVER (1988-2023), RORAIMA STATE, BRAZIL
Abstract
El estudio de la dinámica espacio - temporal en cualquier lugar de la superficie terrestre es muy importante, permitiendo el seguimiento y evaluación de los impactos socioambientales generados por las actividades antrópicas. El objetivo de esta investigación fue evaluar la dinámica espacio - temporal de los cambios en las coberturas de la tierra para la Cuenca Hidrográfica del Río Cauamé, Roraima, Brasil, para los años 1988, 2003 y 2023, con técnicas de Geoprocesamiento en imágenes Landsat, estimando las áreas que fueron afectadas por las actividades humanas durante este período, analizando también los factores que están incidiendo en estos cambios. Como resultados obtuvimos la presencia de un fuerte proceso de cambios, especialmente las coberturas de sabanas, sustituidas por cultivos y construcciones. Las sabanas ocupaban para el año 1988 un 72,27 % de la superficie total, disminuyendo en 2023 en 60,47 % de la superficie total, con una pérdida de 37.556 ha., Finalmente, se afirma que los cambios en las coberturas, los procesos de deforestación y la amplia remoción de coberturas de sabanas, son producto de actividades antropogénicas, debido a la presión que la población está ejerciendo dentro y fuera del área de la cuenca.
Palabras-clave: Palabras-Claves: Geoprocesamiento; Sabanas de Roraima; Recursos Naturales; Clasificaciones Digitales; Fotointerpretación.
References
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CHEN, T. et al. China and India lead in greening of the word through land-use management. Nature Sustainability, London-New York, v.2, n.1, p. 122-129, 2019.
CONGEDO, L. Complemento de clasificación semiautomática: una herramienta de Python para la descarga y el procesamiento de imágenes de detección remota en QGIS. Revista de software de código abierto, v. 6, n. 64, 2021, Disponível em: https://doi.org/10.21105/joss.03172. Acesso em: 4 de jul. 2023
COSTA, J.A.V.; SCHAEFER, C. E.; VALE JÚNIOR, J.F. Aspectos geológicos geomorfológicos do Estado de Roraima. Revista Ação Ambiental, Viçosa, n. 32, p. 11-14, 2005.
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ELLIS, E.C. et al., 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology Biogeography, v. 19, p. 589–606, 2010. Disponível em: https://doi.org/10.1111/j.1466-8238.2010.00540.x. Acesso em: 4 jul 2023.
ESRI (2023). Environmental Systems Research Institute, Inc. https://pro.arcgis.com/en/pro-app/latest/help/analysis/raster-functions/segment-mean-shift-function.htm#ESRI_SECTION1_833311C0BD9A4231B4105F8888ECDDE1
FAO. Global Forest Resources Assessment, Roma-Itália: Food and Agriculture Organization of the United Nations. 2020. 190 p.
FUKUNAGA, E. ; HOSTETLER, L. The Estimation of the Gradient of a Density Function, with Applications in Pattern – Recognition. IEEE TRANSACTIONS ON INFORMATION THEORY, v. 21, n.1. p. 32 - 40, 1975.
GOLDEWIJK, K. K. et al. Anthropogenic land use estimates for the Holocene–HYDE 3.2. Earth Syst Sci, v. 9, n. 1, p. 927–953, 2017.
GUTMAN, G. et al. (Ed.). Land change science: observing, monitoring and understanding trajectories of change on the earth’s surface. Springer Science & Business Media, 2004.
HANEWINKEl, M. et al. Climate change may cause several loss in the economic value of European forest. Nature Climatic Change, London, v.3, n. 3, p. 203-207, 2013.
HANSEN, M.C.; LOVELAND, T.R., 2012. A review of large area monitoring of land cover change using Landsat data. Remote Sens. Environment, v. 122, p. 66–74, 2012. Disponível em: https://doi.org/ 10.1016/j.rse.2011.08.024. Acesso em: 10 Jul. 2023.
HARRIS, N. L. et al. Global maps of twenty – first century forest carbon fluxes. Nature Climatic Change, London, v.11, n.1, p. 234-240, 2021.
IBGE. Manual Técnico de Vegetação Brasileira. 1 ed. Rio de Janeiro: IBGE. 2012. 271 p.
JUSTICE, C.O., et al. The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research. IEEE Trans. Geoscience of Remote Sensing, v. 36, p. 1228–1249, 1998.
LILLESAND, T.; KIEFER, R.; CHIPMAM, J. Remote Sensing and Image Interpretation. 7. ed. New York: John Wiley & Sons. 2015. 770 p.
MARQUINA, J; MOGOLLÓN, A. Niveles y escalas de levantamiento de información geográfica en sensores remotos. Revista Geográfica Venezolana. Mérida, v.50, n.1, p. 42 – 52, 2018. Disponível em: http://www.saber.ula.ve/handle/123456789/44693. Acesso em: 5 de jul. 2023
MELO, L.H. Interpretación visual de imágenes de sensores remotos y su aplicación en levantamientos de cobertura y uso de la tierra. Bogotá: Instituto Geográfico Agustín Codazzi (IGAC). 2005, 156 p.
POTTER, C. et al. Major disturbance events in terrestrial ecosystems detected using global satellite data sets. Global Change Biology, v. 9, p. 1005–1021, 2003. Disponível em: https://doi.org/10.1046/j.1365- 2486.2003.00648.x. Acesso em: 5 jul. 2023.
PRICOPE, N.G., MAPES, K.L., WOODWARD, K.D. Remote sensing of human–environment interactions in global change research: a review of advances, challenges and future directions. Remote Sensing, v.11. n. 23, p. 2783, 2019. Disponível em: https://doi.org/10.3390/rs11232783 .
REIS NETO, R. A.; COSTA, J. A. MAPEAMENTO DE UNIDADES GEOMORFOLÓGICAS DA BACIA DO RIO CAUAMÉ – RR. Revista Brasileira de Geomorfologia. Curitiva-Paraná. v. 11, nº 2, p. 1 – 8, 2010.
REIS NETO, Rainmundo Alves. Geomorfologia e neotectônica da Bacia Hidrográfica do Rio Cauamé. 2007. 86 f. Dissertação (Mestrado em Recursos Naturais) – Programa de Pós-graduação em Recursos Naturais, Universidade Federal de Roraima, Boa Vista, 2007.
RINDFUSS, R.R. et al. Developing a science of land change: challenges and methodological issues. Proceeding of Natural Academy of Science., v.101, n, 1, p. 13976–13981, 2004. Disponível em: https://doi.org/10.1073/pnas.0401545101. Acesso em: 3 Jul 2023.
ROY, D.P., et al. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, v.145, p. 154–172, 2014. Disponível em: https://doi.org/ 10.1016/j.rse.2014.02.001. Acesso em: 5 jul. 2023.
SELLERS, P.J. et al. Remote sensing of the land surface for studies of global change: Models—algorithms—experiments. Remote Sensing of Environment. 51, 3–26, 1995. Disponível em: https://doi.org/10.1016/0034-4257(94)00061-Q. Acesso em: 5 jul. 2023.
SHANG, R., 2022. Near-real-time monitoring of land disturbance with harmonized Landsats 7–8 and Sentinel-2 data. Remote Sensing of Environment, v. 278, n. 1, p. 1 -15, 2022. Disponível em: https://doi.org/10.1016/j.rse.2022.113073. Acesso em: 10 Jul. 2023.
SIMÕES-FILHO, F.L; TURCQ, B; SIFEDDINE, A. Mudanças paleoambientais do contato Floresta-Savana de Roraima durante o Holoceno. En: BARBOSA, R. I.; MELO, V. F. (org). Roraima: homem, ambiente e ecologia. Boa Vista: FEMACT, 2010. p. 1-170.
STRUKER, D.; BESERRA, L. C. N.; TAVAREZ. E. S. J. Compartimentação da paisagem da Bacia Hidrográfica do Rio Cauamé, RR: uma análise da fragilidade á erosão do solo. En: WANKLER, F. et al (Org.). A Bacia Hidrográfica do Rio Cauamé. Boa Vista: ProfAgua/UFRR. 2022. p. 21 – 37.
SOUZA, Sidney Araújo. Efeito do plantio de Acacia Mangium Willd. (fabaceae) sobre a riqueza e diversidade arbórea em áreas de savana na amazônia setentrional. Dissertação (mestrado em Recursos Naturais), Programa de Pós-graduação em Recursos Naturais, Universidade Federal de Roraima, Roraima, 2014.
TANG, X., et al. Can VIIRS continue the legacy of MODIS for near real-time monitoring of tropical forest disturbance?. Remote Sensing of Environment, v. 249, 2020. Disponível em: https://doi.org/10.1016/j.rse.2020.112024. Acesso em: 10 Jul. 2023.
TURNER, B.L. The sustainability principle in global agendas: implications for understanding land-use/cover change. The Geography Journal, v. 163, n. 2, p. 133–140, 1997. Disponível em: https://doi.org/10.2307/3060176. Acesso em: 10 Jul. 2023.
TURNER, B.L., LAMBIN, E.F., REENBERG, A. The emergence of land change science for global environmental change and sustainability. Proc. Natl. Acad. Sci., v. 104, n.1, p. 20666–20671, 2007. Disponível em: https://doi.org/10.1073/pnas.0704119104. Acesso em: 4 de Jul 2023.
USTIN, S.L.; MIDDLETON, E.M. Current and near-term advances in Earth observation for ecological applications. Ecol. Process. 10, 1–57, 2012. Disponível em: https://doi.org/10.1186/ s13717-020-00255-4. Acesso em: 5 jul. 2023.
VERBESSELT, J., ZEILEIS, A., HEROLD, M. Near real-time disturbance detection using satellite image time series. Remote Sensing of Environment, v. 123, p. 98–108, 2012. Disponível em: https://doi.org/10.1016/j.rse.2012.02.022. Acesso em: 10 Jul. 2023.
WANG, M. et al. Assessing Landsat-8 and Sentinel-2 spectral-temporal features for mapping tree species of northern plantation forests in Heilongjiang Province, China. Forest Ecosystems, v. 9, n. 1, 2022. Disponível em: https://www.sciencedirect.com/science/article/pii/S219756202200032X . Acesso em: 5 de jul. 2023.
WINGFIELD. M.J ; BROCKERHOFF, B.D ; WINGFIELD, B. S. Plantation forest health : the need for a global strategy. Science, v. 349, n. 6250, p. 832-836, 2015.
WOODCOCK, C.E. et al. Free access to Landsat imagery. Science, v. 320, n. 5879, p, 1011, 2008. Disponível em: https://doi.org/10.1126/ science.320.5879.1011a. Acesso em: 5 de jul. 2023.
WULDER, M.A. et al. Opening the archive: How free data has enabled the science and monitoring promise of Landsat. Remote Sensing of Environment, v. 122, p. 2–10, 2012. Disponível em: https://doi.org/10.1016/j. rse.2012.01.010. Acesso em: 5 de jul. 2023.
XIN, Q.C., et al. Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data. Remote Sensing of Environment, v. 135, p. 234–247, 2013. Disponível em: https://doi.org/10.1016/J.Rse.2013.04.002. Acesso em: 10 Jul. 2023.
YE, S., et al. A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection. Remote Sensing of Environment, v. 252, p. 1-17, 2021.Disponível em: https://doi.org/10.1016/j.rse.2020.112167. Acesso em: 10 Jul. 2023.
ZHE, Z.; SHI, Q.; SU, Y. Remote sensing of land change: A multifaceted perspective. Remote Sensing of Environment, v. 282, n., p. 1-20, 2022. Disponível em: https://www.sciencedirect.com/science/article/pii/S0034425722003728 . Acesso em: 5 de jul. 2023
ZHU, Z. Change detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS J. Photogrammetry and Remote Sensing, v. 130, p. 370–384, 2017. Disponível em: https://doi.org/10.1016/j.isprsjprs.2017.06.013. Acesso em: 05 jul. 2023.
ZHU, Z. Change detection using Landsat time series: A review of frequencies, preprocessing, algorithms, and applications. ISPRS J. Photogrammetry and Remote Sensing, v. 130, p. 370–384, 2017. Disponível em: https://doi.org/10.1016/j.isprsjprs.2017.06.013. Acesso em: 10 Jul. 2023.
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Published
02/12/2023
How to Cite
MARQUINA VERA, Jesús Jordán; TAVARES, Stelio Soares; BECERRA NETA, Luiza Câmara.
SPATIAL AND TEMPORAL DYNAMICS OF LAND COVER CHANGES IN THE WATERSHED OF THE CAUAMÉ RIVER (1988-2023), RORAIMA STATE, BRAZIL.
Mercator, Fortaleza, v. 22, dec. 2023.
ISSN 1984-2201.
Available at: <http://www.mercator.ufc.br/mercator/article/view/e22028>. Date accessed: 27 apr. 2024.
doi: https://doi.org/10.4215/rm2023.e22028.
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ARTICLES
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