A multi-criterion index for the evaluation of local tropical forest conditions in Mexico

Despite the ecological and economical importance of tropical forests they are currently affected by human activities, mainly through deforestation and selective extraction. With the aim of making an opportune diagnosis of the condition of forests, we developed an ecological index based on qualitative and semi-quantitative data, allowing a quick diagnosis in order to manage and conserve tropical forests. We evaluated 44 plots of tree vegetation, measuring canopy height, number of strata, tree cover, dominant trees, number of tree species, as well as the management of and damage to the forest. The data of each parameter was classified in categories of 3, 4 or 5, which were normalized between 0 and 1 for the worst and best characteristics, respectively. For the purpose of analysis, the average, a set of IF–THEN rules, and fuzzy logic were applied and as a result we obtained a model that measures the ecological condition of the tropical forests. The model has the advantages of having an ecological basis, allows data to be gathered quickly and is clear and easy to manage and interpret. When running the model, the value of each intermediate variable is displayed, thus allowing the detection of where necessary action is required to improve the ecological condition of the forest. We expect this index to contribute in evaluating the effectiveness of forest management and possibly offer advice for the short-term management and conservation of the remnants of tropical forests.
Despite the ecological and economical importance of tropical forests they are currently affected by human activities, mainly through deforestation and selective extraction. With the aim of making an opportune diagnosis of the condition of forests, we developed an ecological index based on qualitative and semi-quantitative data, allowing a quick diagnosis in order to manage and conserve tropical forests. We evaluated 44 plots of tree vegetation, measuring canopy height, number of strata, tree cover, dominant trees, number of tree species, as well as the management of and damage to the forest. The data of each parameter was classified in categories of 3, 4 or 5, which were normalized between 0 and 1 for the worst and best characteristics, respectively. For the purpose of analysis, the average, a set of IF–THEN rules, and fuzzy logic were applied and as a result we obtained a model that measures the ecological condition of the tropical forests. The model has the advantages of having an ecological basis, allows data to be gathered quickly and is clear and easy to manage and interpret. When running the model, the value of each intermediate variable is displayed, thus allowing the detection of where necessary action is required to improve the ecological condition of the forest. We expect this index to contribute in evaluating the effectiveness of forest management and possibly offer advice for the short-term management and conservation of the remnants of tropical forests.