Application of techniques for detecting changes in land use and cover in the Pao River basin, Venezuela, using Landsat satellite images
DOI:
https://doi.org/10.24850/j-tyca-2026-03-09Keywords:
land use, remote sensing, river basins, cartography, water resources, environmental degradation, VenezuelaAbstract
In this investigation, the spatio-temporal variation techniques were applied on the Pao river basin, located at the Venezuelan territory, through the satellite image processing. Eleven satellite images were acquired from Landsat satellite: L5TM, L7ETM SLC:on, and L8OLI. The change detection techniques were: post-classification and pre-classification applied on images from two dates: 1986-2016; 1990-2016; 1991-2016, 1998-2016, 1999-2016, 2000 SLC:on -2016, 2001-2016, 2002 SLC:on -2016, 2003 SLC:on-2016, 2015-2016. Seven pre-classification and a post-classification methods were applied. The categorization of the pre-classification methods in order of the more or less exact for estimating of the area proportion of change / no change in land cover and land uses from the Pao river basin obtained from the error matrix and by adjusting the methods to the local characteristics of the scene in the Landsat satellite image gave as a result: 1) principal component analysis 2) reflectance image differencing, 3) Kauth-Thomas transformation, 4) change vector, 5) reflectance image ratioing, 6) differencing of images of normalized difference of vegetation index and 7) reflectance image regression. In the post-classification method, the area difference corresponding to the Pao river basin between 1986 (date 1) and 2016 (date 2) gave the results about the classes following: Urban, -1.37 %; rangeland, -22.99 %; agricultural, 1.12 %; water, 0.55 %; vegetation, 8.1 %; degraded land, 9.66 %; clouds, 2.49 %; shadows, 2.28 %. The post-classification method has been the predominant option regarding to pre-classification method.
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