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Empirical models relating forest attributes to remotely sensed metrics are widespread in the literature and underpin many of our efforts to map forest structure across complex landscapes. In this study we compared empirical models relating Landsat reflectance to forest age across Oregon using two alternate sets of ground data: one from a large (n ~ 1500) systematic forest inventory and another from a smaller set of plots (n < 50) deliberately selected to represent pure conditions along predefined structural gradients. Models built with the smaller set of targeted ground data resulted in lower plot-level mapping error (root mean square error) and higher apparent explanatory power (R2) than those built with the larger, more widely distributed inventory data. However, in two of the three ecoregions considered, predictions derived from models built with the smaller ground data set displayed a bias relative to those built with the larger but noisier inventory data. A modeling exercise, wherein mapped forest age was translated into carbon, demonstrated how nonlinear ecological models can magnify these prediction biases over landscapes. From this study, it is clear that for mapping purposes, inventory data are superior to project-specific data sets if those data sets are not representative of the full region over which mapping is to be done. [ABSTRACT FROM AUTHOR]

Remote sensing, geographic information systems, and modeling have combined to produce a virtual explosion of growth in ecological investigations and applications that are explicitly spatial and temporal Of all remotely sensed data, those acquired by Landsat sensors have played the most pivotal role in spatial and temporal scaling. Modern terrestrial ecology relies on remote sensing for modeling biogeochemical cycles and for characterizing land cover, vegetation biophysical attributes, forest structure, and fragmentation in relation to biodiversity. Given the more than 30-year record of Landsat data, mapping land and vegetation cover change and using the derived surfaces in ecological models is becoming commonplace. In this article, we summarize this large body of work, highlighting the unique role of Landsat. [ABSTRACT FROM AUTHOR]

Discusses land-use changes in rural areas in the western United States which threaten biodiversity and challenge efforts to sustain local communities and ecosystems. Topic of urban sprawl and growth in suburban communities which threaten biodiversity and habitat; Focus on the Greater Yellowstone Ecosystem (GYE) and quantification of socioeconomic and ecological change.

Presents a study on lidar, an emerging remote sensing technology which directly measures the three-dimensional distribution of plant canopies, and which can accurately estimate vegetation structural attributes. Outlook for uses of laser altimetry, or lidar, in biophysical measurements; Uses for remote sensing of topography, measurement of three-dimensional structure and function of vegetation canopies, and prediction of forest stand attributes.

Presents estimates of the levels of atmospheric carbon associated with deforestation based on a carbon-flux modeling strategy. The effects of carbon dioxide concentrations in the atmosphere on the Earth's climate; The demonstration of the modeling strategy; The significance to the regional and global carbon cycle; The implications on resource management.

Introduces a series of article on remote sensing and ecology, published in the June 2004 issue of "BioScience."