CCA Fuzzy Land Cover: a new method for classifying vegetation types and coverages and its implications for deforestation analysis
Land cover has been evaluated and classified on the basis of general features using reflectance or digital levels of photographic or satellite data. One of the most common methodologies based on CORINE land cover (Coordination of Information on the Environment) data, which classifies natural cover according to a small number of categories. This method produces generalizations about the inventoried areas, resulting in the loss of important floristic and structural information about vegetation types present (such as palm groves, tall dense mangroves, and dense forests). This classification forfeits relevant information on sites with high heterogeneity and diversity. Especially in the tropics, simplification of coverage types reaches its maximum level with the use of deforestation analysis, particularly when it is reduced to the two classes of forests and nonforests. As this paper demonstrates, these results have considerable consequences for political efforts to conserve the biodiversity of megadiverse countries. We designed a new methodological approach that incorporates biological distinctiveness combined with phytosociological classification of vegetation and its relation to physical features. This approach is based on parameters obtained through canonical correspondence analysis on a fuzzy logic model, which are used to construct multiple coverage maps. This tool is useful for monitoring and analyzing vegetation dynamics, since it maintains the typological integrity of a cartographic series. The methodology creates cartographic series congruent in time and scale, can be applied to multiple and varied satellite inputs, and always evaluates the same model parameters. We tested this new method in the southwestern Colombian Caribbean region and compared our results with those from what we believe are outdated tools used in other analyses of deforestation around the world.