Resumo

CAMACHO PENA, John Fredy. Logistic Regression Analysis Applied to Spatial Modeling of Deforestation Drivers in Guaviare Department for the Period 2005-2020 and Projection of Deforestation Scenarios to 2030. Cuad. Geogr. Rev. Colomb. Geogr. [online]. 2022, vol.31, n.2, pp.255-280.  Epub 17-Nov-2022. ISSN 0121-215X.  https://doi.org/10.15446/rcdg.v31n2.98012.

Although the identification of causes and agents of deforestation in the Colombian Amazon has been addressed in various studies under a historical-relational approach, there is little geostatistical research aimed at modeling its spatial relationship with the phenomenon and the projection of future scenarios. As a contribution to this field, a geostatistical analysis of the deforestation that occurred in the Colombian Guaviare during the period 2005-2020 is carried out, seeking to identify and model the spatial behavior of its explanatory factors, and based on this, propose three probable deforestation scenarios for the area to 2030 using logistic regression. In the latter case, this analysis was combined with three different deforestation rates to determine the amount of deforestation expected by 2030 and two methods (soft and hard) to assign its location in space. While the degree of forest fragmentation, extensive cattle ranching, coca crops, as well as the accessibility of the area, are largely determinants of deforestation, protected areas and resguardos exert a slight protective effect and in some cases none. By 2030, is expected the loss of between 4.4 % and 8.8 % of the existing forest area on 2020 with a particular impact on the Resguardo Nukak and the Parque Nacional Natural Chiribiquete.

Palavras-chave : cellular automata; Land Use Land Change (LULC); Guaviare; modeling; deforestation drivers; logistic regression.

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