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Analysis of land-cover change in the seasonal tropical forests of the Southern Yucatán, Mexico presents a number of significant challenges for the fine-scale land-cover information required of land-change science. Subtle variation in mature forest types across the regional ecocline is compounded by vegetation transitions following agricultural land uses. Such complex mapping environments require innovation in multispectral classification methodologies. This research presents an application of a step-wise maximum likelihood/In-Process Classification Assessment (IPCA) procedure. This hybrid supervised and unsupervised classification methodology allows for exploration of underlying characteristics of Landsat Thematic Mapper (TM) imagery in tropical environments. Once spectrally separable classes have been identified, field data then determine the ecological definition of vegetation types with special attention paid to areas of unknown or mixed classes. A post-field assessment re-classification using the Dempster–Shafer method reduced the original 25 spectral classes to 14 ecologically distinctive classes, providing the fine-tuned land-cover distinctions that are required for both environmental and socioeconomic research questions. The overall map accuracy was 87% with an average per-class accuracy of 86%. Per-class accuracy ranged from as low as 45% for pasture grass to a high of 100% for tall-stature evergreen upland forest, low and medium-stature semi-deciduous upland forest and deciduous forest.
