Imaging Spectroscopy of Forest Ecosystems: Perspectives for the Use of Space-borne Hyperspectral Earth Observation Systems

摘要

The emerging challenges in preserving and managing forest ecosystems are multiscale in terms of space and time, and therefore require spatially and temporally contiguous information sources. Imaging spectroscopy has the potential to contribute information that cannot be raised by other Earth Observation Systems. In particular, the spectral capacity to monitor the distributions of chemical traits, such as canopy foliar nitrogen distribution, and to track changes in water content or the percentage water in plants, has already opened novel pathways toward assessing the global variability of ecosystem functions and services. However, there is an ongoing debate on how to best extract this type of information from the spectral measurements. Empirical approaches have demonstrated their efficiency in a multitude of local studies, but are criticized with respect to poor generalization capacities. Alternative strategies, such as the use of physically based models of leaf or canopy reflectance, or hybrid approaches, have the potential advantage to be more widely applicable. This paper attempts to assess achievements and shortcomings of these strategies and finds that the often-cited disadvantages of using empirical approaches are becoming less pronounced in the light of recent research results. While retrievals based on physically based models on leaf/needle level are close to laboratory quality, results on canopy level available to date still have considerable deficits. Owing to improved instrumental designs, better data calibration, new approaches for compensating canopy effects, and the use of increasingly efficient methods for establishing data-driven models, the scope of empirical approaches has considerably widened and they have been successfully applied to large areas. The future availability of regularly acquired hyperspectral imagery from Earth orbits will substantially contribute to their generalizability.