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Revista mexicana de ciencias forestales
versión impresa ISSN 2007-1132
Resumen
FLORES-GARNICA, José Germán; REYES-ALVARADO, Ana Gisela y REYES-CARDENAS, Oscar. Time-space relationship between hotspots and agricultural and forest surface areas in San Luis Potosí State, México. Rev. mex. de cienc. forestales [online]. 2021, vol.12, n.64, pp.127-145. Epub 21-Mayo-2021. ISSN 2007-1132. https://doi.org/10.29298/rmcf.v12i64.857.
It is important to examine the dynamics between land use coverages in order to understand the existing relationships in the areas where changes occur. Also, sometimes the cover changes are caused by certain human activities, such as the fires used in agriculture. The objective of this study was to spatiotemporally analyze whether a significant relationship exists between the agricultural, livestock (grasslands) and forest surface areas and the incidence of hotspots, in the state of San Luis Potosí, Mexico. A great number of authors have used remote sensing and GIS for modelling land use cover, and they have applied several algorithms for that purpose. For estimating the coverages (forest, agriculture and livestock), a supervised classification to Landsat 5 TM and Landsat 8 OLI scenes was applied. In addition, agricultural, livestock and forest surface areas were incorporated into non-linear models (Polynomial (2nd order), exponential and potential) and multivariate models for estimating hotspots, which resulted in a substantial increase in the coefficients of determination in the latter. As for the results, it is possible to predict the occurrence of hotspots in a variety of agricultural, livestock and forest areas. In this case, the forest areas were the most significant variable, followed by the livestock areas. The results obtained suggest that there is a relationship between the presence of hotspots and the agricultural land in the study area, and it is possible to predict the occurrence of hotspots based on the variations of agricultural, livestock, and forest lands.
Palabras llave : Supervised classification; correlation; Landsat; nonlinear models; trend; land use.