NALC/Mexico land-cover mapping results: implications for assessing landscape condition
An inventory of land-cover conditions throughout Mexico was performed using North American Landscape Characterization (NALC) Landsat Multi-Spectral Scanner (MSS) 'triplicate' images, corresponding to the 1970s, 1980s and 1990s epoch periods. The equivalent of 300 image scenes were analysed using an unsupervised classification approach by a consortium of 13 universities and institutes across Mexico. Accuracy assessments were conducted to validate the 1970s and 1990s results using independent land-cover classifications (reference data) developed from the interpretation of 1:100 000-scale aerial photography collected in 1973, and Landsat Thematic Mapper (TM) imagery collected between 1990 and 1993. The 1980s epoch classifications were compared with both reference datasets, collectively. The relative accuracy of the classifications results were 60% for both the 1970s and 1990s epoch and 67% for the 1980s epoch. The significantly (p =0.05) higher accuracy for the 1980s epoch (67%) was thought to be an aberration resulting from the combined application of two reference datasets, resulting in a random compensation of reference data error. Significantly different (p =0.05) results were documented for a subset of Mexico's major habitat regions. Desert and xeric shrublands were most accurate (74%), followed by conifer and xeric dominated habitats (64%) and other mixed habitats (54%). Scenes representing the highest accuracies (15 percentile) almost exclusively represented desert and xeric shrub habitat regions, and the lowest (17 percentile) represented predominantly mixed habitat regions. Significant differences among the 13 member consortium universities and institutes were attributed to habitat region assignments. Results indicated that large area spectral based land-cover categorizations should be stratified and processed on a habitat or ecoregion basis. Results also suggested that any future land-cover conversion analysis for Mexico would probably best be accomplished using a post-classification approach, based on major habitat regions, rather than on a scene-by-scene or pixel-wise basis.