Despite decades of attention by conservationists, deforestation remains a critical problem in Latin America, particularly in agricultural frontiers like the Amazon and the lowlands of Central America. The limited impact of conservation initiatives in such regions stems partly from a poor understanding of the socioeconomic and land use dynamics that typify frontiers. Moreover, conservation organizations tend to disregard the economic and policy factors at various scales that contribute to deforestation pressures in developing countries.

Population is a key indicator of socioeconomic development, urban planning and environmental protection, particularly for developing countries like China. But, census data for any given area are neither always available nor adequately reflect the internal differences of population. The authors tried to overcome this problem by spatializing the population across China through utilizing integer night-time imagery (Defense Meteorological Satellite Program/Operational Linescan System, DMSP/OLS) and land-use data.

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.