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Generically Dependent Continuant
RDF description of Forest zoning under the concept of homogeneous response areas in the center of Mexico [Zonificación forestal bajo el concepto de Áreas de Respuesta Homogénea en el centro de México] - http://orbis.uaslp.mx/vivo/individual/scopus6084
Article
Forest zoning under the concept of homogeneous response areas in the center of Mexico [Zonificación forestal bajo el concepto de Áreas de Respuesta Homogénea en el centro de México]
The rational use of natural resources makes it necessary to identify and analyze their spatial - temporal condition. There are several techniques to generate information on this subject based on the capabilities of the analysis of a geographic information system (GIS). Considering that the ecosystems and the tree species that compose them are developed in geographic areas with similar environmental requirements, techniques based on the analysis of the information have been developed to look for similar biotic and abiotic conditions. Despite the advantages it represents in saving time and resources the Homogeneous Response Areas (HRA), based on Map algebra has been used in a limited way in Mexico in the evaluation of forest area. The ARH are based on the concept of extrapolation, they allow to use information from easy access areas to areas of difficult access. This can be done in two ways, in both the information of a first ARH is used to estimate a phenomenon or variable in a second ARH. In the first option the results are not validated; in the second a sampling of low intensity is made in the second ARH considering that access to it is difficult. In large areas the mapping of forest areas are required to improve the results of classification using methodologies that consider the information available. The objective of the work was to determine Homogeneous Response Areas (HRA) of natural forest vegetation in the central-northern region of Mexico. To determine the distribution of six classes (types) of vegetation, the following forests were used as the main variable: oak forest, oak-pine forest, pine forest, pine-oak forest, tropical deciduous forest and tropical semi-evergreen forest, with them two topographical variables -exposition and altitude- were analyzed. In the first variable five classes were considered: Zenit, North, East, South and West, and on the second seven intervals of altitude were considered, in meters above sea level (msnm): 0 - 460, 461 - 921, 922 - 1382, 1383 - 1843, 1844 - 2,304, 2,305 - 2,765 and 2,766 - 3,048). In the same way eight types of soil were considered: Xerosol, Litosol, Regosol, Rendzina, Vertisol, Feozem, Chernozem, Luvisol. To determine the ARH, expressions were generated from the combination of the aforementioned classes. In total, 2,016 combinations were developed. Most of the surfaces determined for each type of vegetation were produced as a result of five combinations, in which maps were generated. As a result of the analysis, the pine forest was located mainly in the Feozem soil type and in an altitude range of 2,305 to 2,765 meters above sea level, the pine-oak forest, in S exposure, between 1,383 and 2,304 meters above sea level, the oak forest was located between 922 to 1,843 meters above sea level, the oak-pine forest from 1,383 to 2,304 meters above sea level, tropical deciduous forest in an altitude range of 0 to 921 meters above sea level, with types of lithosol and rendzina soil and tropical semi-evergreen forest was located SE, from 0 to 921 meters above sea level. To validate the results, information was used from 42 sampling clusters of the National Forestry and Soils Inventory 2004 - 2007. A confusion matrix was generated and the Kappa index was calculated, resulting in a Kappa index of 0.886 with respect to the 0.9 presented by the matrix of confusion. The combination of the four variables used in the present study allows to define forest areas adequately and in a reliable way. An important aspect to consider in the definition of ARH is the selection and number of variables that will be used to make up the zoning. Although it is possible to include variables such as precipitation, temperature or slope to determine if these influence in obtaining better results, the increase in the number of variables implies an increase in the number of possible combinations. For the six types of vegetation, areas of easy and difficult access were located. In the former it is possible to obtain a representative sample and to estimate forest variables in the second type. © 2019 Instituto de Geografia. All rights reserved.
Cluster plot sampling; Evaluation of forest areas; Forest ecosystems; Map algebra; San Luis Potosí classification; forest ecosystem; forest soil; GIS; sampling; vegetation mapping; zoning system; Potos
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