Thermal infrared remote sensing for analysis of landscape ecological processes: methods and applications

Thermal infrared (TIR) remote sensing data can provide important measuremen ts of surface energy fluxes and temperatures, which are integral to underst anding landscape processes and responses. One example of this is the succes sful application of TIR remote sensing data to estimate evapotranspiration and soil moisture, where results from a number of studies suggest that sate llite-based measurements from TIR remote sensing data can lead to more accu rate regional-scale estimates of daily evapotranspiration. With further ref inement in analytical techniques and models, the use of TIR data from airbo rne and satellite sensors could be very useful for parameterizing surface m oisture conditions and developing better simulations of landscape energy ex change over a variety of conditions and space and time scales. Thus, TIR re mote sensing data can significantly contribute to the observation, measurem ent, and analysis of energy balance characteristics (i.e., the fluxes and r edistribution of thermal energy within and across the land surface) as an i mplicit and important aspect of landscape dynamics and landscape functionin g. The application of TIR remote sensing data in landscape ecological studies has been limited, however, for several fundamental reasons that relate prim arily to the perceived difficulty in use and availability of these data by the landscape ecology community, and from the fragmentation of references o n TIR remote sensing throughout the scientific literature. It is our purpos e here to provide evidence from work that has employed TIR remote sensing f or analysis of landscape characteristics to illustrate how these data can p rovide important data for the improved measurement of landscape energy resp onse and energy flux relationships. We examine the direct or indirect use o f TIR remote sensing data to analyze landscape biophysical characteristics, thereby offering some insight on how these data can be used more robustly to further the understanding and modeling of landscape ecological processes .