Contemporary and emerging remote sensing technologies, combined with biophysical first principles and modern spatial statistics allow for novel landscapes analyses at a range of spatial and temporal scales. In the past, supervised or un-supervised classification methods and the development of indices of landscape degradation and other derived products based on multi-spectral imagery of various resolutions has become a standard. Biophysical indices, such as leaf area index, fraction of photosynthetically-active radiation, phytomass or canopy chemistry, can be derived from the spectral properties of satellite imagery. Indices of changes in landscape composition and structure can be measured from the thematic maps originating from remotely-sensed imagery. Additionally, 30-year or longer time series of historical remote sensing archives (Landsat, AVHRR) allow retrospective studies of the historical range of variability and the trajectories of both landscape elements and biophysical properties.
A trade-off exists between high spatial and high temporal resolution when comparing platforms. Development of new, improved sensors and analysis techniques, such as sub-pixel classifications resulting in the development of continuous fields for formerly discrete classes, has reduced this trade-off. High spectral resolution and multiple view angles even enhance the potential for accurate retrieval of variables such as Albedo and chlorophyll concentration. Thus, powerful monitoring tools for land use/cover change detection are arising from such analyses. They can lead to an improved understanding of landscape states and processes. Finally, this evolution allows for mapping and monitoring of new landscape features that were not much used to date.