Hyperspectral Remote Sensing of Vegetation Bioparameters
5.4 Summary and Future Directions
Hyperspectral remote sensing, or imaging spectroscopy, is a cutting-edge technology that can be utilized in ecological studies for extracting and assessing vegetation characteriza-tion� In this chapter, the spectral characteristics, properties, and/or responses of a set of plant biophysical and biochemical parameters were reviewed� These bioparameters mainly include typical biophysical parameters (LAI, SLA, CC, species/composition, biomass, NPP, and fPAR) and biochemical parameters (plant pigments such as Chl-a and Chl-b, Cars, and Anths, plant nutrients such as N, P, and K, leaf or canopy water content, and other chemicals such as lignin and cellulose; and protein concentration)� To extract and assess typical bioparameters from various hyperspectral data, including laboratory and in situ hyperspectral measurements, spectra synthesized and/or simulated from physically based models, and airborne and spaceborne hyperspectral image data, relatively speaking, a wide range of analysis techniques and approaches that have already been developed and demonstrated are extensively reviewed in this chapter� The spectral analysis techniques cover spectral derivative analysis, spectral matching, spectral index analysis, spectral absorption features and spectral position variables, hyperspectral transformation, spec-tral unmixing analysis, and hyperspecspec-tral classifications; and the two general categories of analysis methods include empirical/statistical methods and physically based models�
Advantages and disadvantages, or merits and drawbacks, for some specific analysis tech-niques and approaches were also discussed here� Data from imaging spectroscopy have repeatedly been shown to produce accurate estimates of many biochemical parameters and physical characteristics related to key ecological processes� Imaging spectroscopy is the only technology available to measure many important environmental properties over large regions, particularly canopy water content, dry plant residues, and soil biochemical properties (Ustin et al� 2004)�
130 Advances in Environmental Remote Sensing
In the future, the richness of information available in the continuous spectral coverage afforded by both airborne and spaceborne imaging spectrometers will make it possible to address questions regarding vegetation bioparameters more correctly and accurately� Since hyperspectral data can provide richer and more delicate spectral information than multi-spectral data, multi-spectral unmixing and automatic target detection remain important infor-mation extraction tasks in hyperspectral data analysis, and the use of PCA, mathematical programming, and factor analysis need to be further assessed in solving the linear mix-ing problem� Inversion of physically based RT models with hyperspectral data assisted by analysis of multiangular data will be useful in solving nonlinear spectral mixing problems because the angular data can be used to retrieve the structural information of vegetation�
When using various spectral VIs to estimate different bioparameters, the use of opti-mized VIs should be considered because there are many potential narrow bands ready to be used for developing various VIs from hyperspectral data� Experience has proven that with some optimized VIs for estimating some bioparameters, the estimation accu-racy can be significantly increased (e�g�, Gong et al� 2003)� When attempting to identify a robust, generic solution, there is currently only limited evidence available with which one can rank the performance of the range of existing hyperspectral analysis approaches in quantifying plant bioparameters� Therefore, it is necessary to conduct intercomparison of hyper spectral approaches (Blackburn 2007b) across a large number of bioparameters using a large number of different analysis techniques� A sensitivity study is needed to determine the set of variables that can be retrieved with a reasonable accuracy for available imaging spectroscopy systems� Finally, although many analysis techniques have been developed and are available in some applications for estimating biochemicals from hyperspectral data at the leaf scale, in order to exploit the opportunities offered by imaging spectrometry for synoptic, consistent, and spatially continuous information, it is important to develop suitable methods that can also derive estimates of foliar biochemical concentrations from canopy-scale reflectance spectra� For this case, several strategies are available for the analysis of canopy spectra (Zarco-Tejada et al� 2001)� This is a scaling issue, a problem encountered frequently in ecological studies�
Acknowledgments
The comments and suggestions of two anonymous reviewers were greatly valuable in improving the chapter� The authors sincerely appreciate their efforts�
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