Multitemporal land-cover classification of Mexico using Landsat MSS imagery

Quantifying uncertainty and confusion in land change analyses: a case study from central Mexico using MODIS data

“…With numerous urban centers (INEGI 2005) and Mexico City just beyond the border, the built environment exerts a strong and constant pressure on natural land cover in this region (López et al 2001;Alvarez et al 2003). Urbanization, extensive and intensive agriculture, timber harvesting, and mining fragment the landscape considerably, creating a patchwork of human and natural land covers (Escamilla 1995;Klooster 2003;Cotler, Mazari Hiriart, and de Anda Sanchez 2006).…”

“…The heterogeneity, variability, and rate of land change in the LCS watershed and across Mexico have been observed and demonstrated through multiple sources and methods (de Anda et al 1998;Alvarez et al 2003;Cotler, Mazari Hiriart, and de Anda Sanchez 2006). Yet, the classification of this landscape and the quantitative assessment of discrete land cover change has proven challenging.…”

“…In this study, the quantified potential for error in land cover classifications is referred to as "classification uncertainty" (Chrisman 1991;Eastman 2009), and the compounded effects of classification errors on subsequent change analyses is called "change confusion," in which changing areas may be mistaken for persistence or vice versa, due to potential errors in one or more of the land cover datasets. Mexico has a vast, diverse, and rapidly transforming landscape, subject to both natural and anthropogenic forces (West and Augelli 1976;Mas et al 2002;Alvarez et al 2003;Klooster 2003) The Lerma-Chapala-Santiago (LCS) watershed in central Mexico epitomizes these characteristics (INE 2003;Cotler, Mazari Hiriart, and de Anda Sanchez 2006) and serves as an ideal case for investigation of uncertainty and confusion in land change analyses. The watershed faces human pressures of urban expansion and agricultural transitions and is subject to perennial seasonal fluctuations, interannual climatic variations, and uncertainties of shifting local and global climates (Aguirre Jimenez and Martinez 2007).…”

A step-wise land-cover classification of the tropical forests of the Southern Yucatán, Mexico

“…Typically three to five forest classes are distinguished, such as low-lying, inundated forests from upland or terra firme forests (Podest andSaatchi 2002, Salovaara et al 2005). In Mexico, there has been much advancement in distinguishing between ecological forest types such as tropical, semi-deciduous, and evergreen forests (Alvarez et al 2003, González Murguia et al 2004), but moderate focus on addressing secondary forest types (Ochoa-Gaona and Gonzalez Espinosa 2000, Lunetta et al 2002, Bray et al 2004. In Amazonia, much attention has focused on the latter to address the impact of deforestation and the re-growth of secondary land covers.…”

Distinguishing Land Change from Natural Variability and Uncertainty in Central Mexico with MODIS EVI, TRMM Precipitation, and MODIS LST Data

“…In addition to extensive heterogeneity in its natural cover, the Lerma-Chapala-Santiago watershed includes several major urban centers, including the cities of Guadalajara (1.7 million inhabitants), Toluca (~750,000 inhabitants), Queretaro (~730,000 inhabitants), Aguascalientes (~700,000 inhabitants), Morelia (~640,000 inhabitants), and Guanajuato (~480,000 inhabitants) (INEGI, 2005). With many regional urban centers within the watershed and Mexico City just beyond its limits, the expansion of the built environment exerts a strong and constant pressure on natural land covers [48,49] and water resources [35,50,51]. Processes of urbanization, extensive and intensive agriculture, timber harvesting, and mining fragment the landscape, creating a motley patchwork of human and natural land covers across most of the watershed [3,52,53].…”