Of the three types of remotely sensed data discussed here, Landsat data offers the greatest potential for monitoring broad changes in extensive tropical forest environments because of its low-cost, synoptic, repetitive coverage. Scientists from developing countries can choose from a variety of Landsat data classification techniques, thus enabling each country to satisfy limitations on available funding, trained personnel, and equipment.
Factors limiting the application of Landsat data—including relatively low spatial resolution, persistent cloud cover in tropical regions, inadequate coverage of certain areas due to data-acquisition restraints and lack of local Landsat data receiving stations for real-time data recording—must be considered in any proposed study. Future improvements in Landsat capabilities might extend present applications beyond distinction of forest vs. non-forest cover, determination of gross vegetation or forest type, and generalized land use mapping.