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Revista de Ciencias Ambientales

versión On-line ISSN 2215-3896versión impresa ISSN 1409-2158

Resumen

PEREA-ARDILA, Mauricio Alejandro; VAQUIRO, Jair Ricardo  y  RODRIGUEZ-VALENZUELA, Jeisson. Land cover and land use determination using RapidEye in Los Nevados National Park and its buffer zone in Colombia. Ciencias Ambientales [online]. 2022, vol.56, n.2, pp.146-174. ISSN 2215-3896.  http://dx.doi.org/10.15359/rca.56/2.8.

(Introduction):

Los Nevados National Natural Park (PNNN) is one of the main protected areas of the Colombian Andean region, it is home to a large paramo ecosystem that provides different ecosystem services to the region, quantifying and analyzing natural cover and land use is essential for conservation processes.

(Objective):

To determine the main land cover and land use (CT-US) of the PNNN and its buffer area in an area of 1 250 km2 located on the right flank of the department of Tolima in Colombia, standardizing the land cover classification scheme for Colombia.

(Methodology):

It involved the use of Remote Sensing techniques for the processing of a RapidEye image from 2010, as well as image interpretation techniques, supervised classification, field work for the validation of results and the development of thematic cartography at a scale of 1:25 000.

(Results):

A radiometric improvement of the RapidEye image was obtained, also, 10 CT-US patterns were generated for the supervised classification, within the validation of results an overall reliability of 89.52 % and a Kappa coefficient of 0.88 were obtained. Additionally, 14 homologated categories were generated under the land cover classification scheme for Colombia and 1:25 000 scale cartography of the PNNN and its buffer zone was generated.

(Conclusions):

This study made it possible to obtain the context of the coverages of the PNNN for 2010 and its buffer zone, which will serve as a background and support for future research involving natural resource planning in Latin America and the Andean region.

Palabras clave : Andean ecosystems; land classification remote sensing; scheme; supervised classification.

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