<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leonairo Pencue-Fierro</style></author><author><style face="normal" font="default" size="100%">Apolinar Figueroa-Casas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Land cover change dynamics and multi-factor analysis in high mountains basins of Colombian Andes</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Magazine</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">clustering analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">high mountain basins</style></keyword><keyword><style  face="normal" font="default" size="100%">Land cover dynamics</style></keyword><keyword><style  face="normal" font="default" size="100%">multi-factor analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/document/7245750/</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><isbn><style face="normal" font="default" size="100%">978-1-4673-7119-3</style></isbn><abstract><style face="normal" font="default" size="100%">&lt;div class=&quot;rtejustify&quot;&gt;
	This paper presents an integrated analysis of factors affecting the dynamics of an Andean basin. The study was conducted by using Landsat images, SRTM DEM data from NASA, infrastructure information (cities and roads proximity) and the resulting hydrological data analysis, over a characteristic basin of Colombian valleys. Land cover recognition was validated with in-situ data, this allowing to build a temporal variation profile of the land cover over a 26 years period. This information was integrated by clustering to obtain uniform regions according to variables used, resulting in the generation of different trend maps. The proposed approach allows to establish the importance of using information as a measure of anthropogenic affectation levels, thus it is possible to make more specific action plans and efficiently compensate the environmental degradation effects.&lt;/div&gt;
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