Skip to main navigation menu Skip to main content Skip to site footer

Land covers map using Sentinel-2 and Landsat-8 satellite images of the municipality of Covarachía - Colombia

Mapa de coberturas del suelo utilizando imágenes satelitales Sentinel-2 y Landsat-8 del municipio de Covarachía – Colombia




Section
Artículos

How to Cite
Leon Leon, J., Medina, R. J., & Ovalle, D. M. (2021). Land covers map using Sentinel-2 and Landsat-8 satellite images of the municipality of Covarachía - Colombia. #ashtag, 2(19), 8-27. https://doi.org/10.52143/2346139X.930

Dimensions
PlumX
Citations

How to Cite

Leon Leon, J., Medina, R. J., & Ovalle, D. M. (2021). Land covers map using Sentinel-2 and Landsat-8 satellite images of the municipality of Covarachía - Colombia. #ashtag, 2(19), 8-27. https://doi.org/10.52143/2346139X.930

Download Citation

Jose Leon Leon
Sin roles de crédito asignados.
Ruben Javier Medina
Sin roles de crédito asignados.
Diana Marcela Ovalle
Sin roles de crédito asignados.

Agriculture is one of the fields in which the use
of soils is of importance, since having adequate
information makes it possible to demonstrate
the management of agroecosystems that is of
importance in mitigating climatic and environmental
impacts (Rega et al., 2020). Given the
different applications that need updated information
on land cover, it is difficult to have solutions
to all the needs due to the great variety of
users (Szantoi et al., 2020). In this article, Sentinel-
2 and Landsat-8 satellite images are used to
which supervised and unsupervised classifying
algorithms are applied to generate a map of the
land cover of the municipality of Covarachía
Colombia.


Article visits 223 | PDF visits 539


Downloads

Download data is not yet available.
  1. Abbas, A., Minallh, N., Ahmad, N., Rehman, S., y Khan, M. (2016). K-Means and ISODATA Clustering Algorithms for Landcover Classification Using Remote Sensing. ResearchGate, 48, 315-318. https://www.researchgate.net/publication/303971825_K-Means_and_ISODATA_Clustering_Algorithms_for_Landcover_Classification_Using_Remote_Sensing
  2. Fisterra. (2020). Medidas de concordancia: El índice Kappa. https://www.fisterra.com/formacion/metodologia-investigacion/medidas-concordancia-indice-kappa/
  3. García, D., Camacho, M., y Paegelow, M. (2019). Sensitivity of a common Land Use Cover Change (LUCC) model to the Minimum Mapping Unit (MMU) and Minimum Mapping Width (MMW) of input maps. Computers, Environment and Urban Systems, 78, 101389. https://doi.org/10.1016/j.compenvurbsys.2019.101389
  4. Gisadminbeers. (26 de marzo de 2017). Combinaciones RGB de imágenes satélite Landsat y Sentinel. Gis&Beers. http://www.gisandbeers.com/combinacion-de-imagenes-satelite-landsat-sentinel-rgb/
  5. He, Y., Lee, E., y Warner, T. A. (2017). A time series of annual land use and land cover maps of China from 1982 to 2013 generated using AVHRR GIMMS NDVI3g data. Remote Sensing of Environment, 199, 201-217. https://doi.org/10.1016/j.rse.2017.07.010
  6. Kiswanto, Tsuyuki, S., Mardiany, y Sumaryono. (2018). Completing yearly land cover maps for accurately describing annual changes of tropical landscapes. Global Ecology and Conservation, 13. https://doi.org/10.1016/j.gecco.2018.e00384
  7. NV5 Geospatial. (2020). K-Means. https://www.harrisgeospatial.com/docs/KMeansClassification.html
  8. Li, X., Ling, F., Foody, G., Ge, Y., Zhang, Y., y Du, Y. (2017). Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps. Remote Sensing of Environment, 196, 293-311. https://doi.org/10.1016/j.rse.2017.05.011
  9. Misra, M., Kumar, D., y Shekhar, S. (2020). Assessing Machine Learning Based Supervised Classifiers For Built-Up Impervious Surface Area Extraction From Sentinel-2 Images. Urban Forestry & Urban Greening, 53. https://doi.org/10.1016/j.ufug.2020.126714
  10. Nuestro municipio - Alcaldía Municipal de Covarachía en Boyacá. (2020). http://www.covarachia-boyaca.gov.co/municipio/nuestro-municipio
  11. Pérez, A., Udías, F., y Rembold, F. (2020). Integrating Multiple Land Cover Maps through a Multi-Criteria Analysis to Improve Agricultural Monitoring in Africa. International Journal of Applied Earth Observation and Geoinformation, 88. https://doi.org/10.1016/j.jag.2020.102064
  12. Rega, C., Short, C., Pérez, M., y Paracchini, M. (2020). A classification of European agricultural land using an energy-based intensity indicator and detailed crop description. Landscape and Urban Planning, 198. https://doi.org/10.1016/j.landurbplan.2020.103793
  13. Renza, D., Martinez, E., Molina, I., y Ballesteros, D. (2017). Unsupervised change detection in a particular vegetation land cover type using spectral angle mapper. Advances in Space Research, 59(8), 2019-2031. https://doi.org/10.1016/j.asr.2017.01.027
  14. Saah, D., Tenneson, K., Poortinga, A., Nguyen, Q., Chishtie, F., … Ganz, D. (2020). Primitives as building blocks for constructing land cover maps. International Journal of Applied Earth Observation and Geoinformation, 85. https://doi.org/10.1016/j.jag.2019.101979
  15. Satélite Sentinel-2. Flota de satélites europeos de vigilancia medioambiental del programa Copernicus
  16. Satélite Landsat-8. Satélite estadounidense para estudios cartográficos y de características de temperatura de la superficie
  17. Stéphane, D., Laurence, D., Raffaele, G., Valérie, A., y Eloise, R. Land Cover Maps of Antananarivo (Capital of Madagascar) Produced by Processing Multisource Satellite Imagery and Geospatial Reference Data. Data in Brief, 31. https://doi.org/10.1016/j.dib.2020.105952
  18. Humboldt State University. (2019). Supervised Classification. http://gsp.humboldt.edu/olm/Courses/GSP_216/lessons/Classification/supervised.html
  19. Szantoi, Z., Geller, G., Tsendbazar, N., See, L., Griffiths, P., Fritz, S., Gong, P., Herold, M., Mora, B., y Obregón, A. (2020). Addressing the need for improved land cover map products for policy support. Environmental Science & Policy, 112, 28-35. https://doi.org/10.1016/j.envsci.2020.04.005
  20. Vilar, L., Garrido, J., Echavarría, P., Martínez, J., y Martín, M. (2019). Comparative analysis of CORINE and climate change initiative land cover maps in Europe: Implications for wildfire occurrence estimation at regional and local scales. International Journal of Applied Earth Observation and Geoinformation, 78, 102-117. https://doi.org/10.1016/j.jag.2019.01.019
Sistema OJS 3.4.0.9 - Metabiblioteca |