Using Imaging Spectroscopy to Study Ecosystem Processes and Properties

By Susan L. Ustin, Dar A. Roberts, John A. Gamon, Gregory P. Asner, Robert O. Green. 2004

615 citations recorded by the source corpus.

Abstract Remote sensing data provide essential input for today's climate and ecosystem models. It is generally agreed that many model processes are not accurately depicted by current remotely sensed indices of vegetation and that new observational capabilities are needed at different spatial and spectral scales to reduce uncertainty. Recent advances in materials and optics have allowed the development of smaller, more stable, accurately calibrated imaging spectrometers that can quantify biophysical properties on the basis of the spectral absorbing and scattering characteristics of the land surface. Airborne and spaceborne imaging spectrometers, which measure large numbers (hundreds) of narrow spectral bands, are becoming more widely available from government and commercial sources; thus, it is increasingly feasible to use data from imaging spectroscopy for environmental research. In contrast to multispectral sensors, imaging spectroscopy produces quantitative estimates of biophysical absorptions, which can be used to improve scientific understanding of ecosystem functioning and properties. We present the recent advances in imaging spectroscopy and new capabilities for using it to quantify a range of ecological variables.

Subfields: Analytical Chemistry, Ecology

View Paper: https://www.semanticscholar.org/paper/7185256b94f723d973c65e004fd86e5c219b0134

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In Ecology

Citations in the corpus

Benefits of hyperspectral remote sensing for tracking plant invasions (2011, methodology): “Typically, hyperspectral sensors measure the reflected spectrum at wavelengths between 350 and 2500 nm using 150–300 contiguous bands of 5- to 10-nm bandwidths (Ustin et al., 2004).”
Canopy spectral invariants, Part 2: Application to classification of forest types from hyperspectral data (2011, background): “It has been shown that hyperspectral data convey information about leaf physiological state [12], water content and evapotranspiration [13–15], vegetation chemical constituents such as nitrogen, lignin, and cellulose [13,16–19].”
NEON: the first continental-scale ecological observatory with airborne remote sensing of vegetation canopy biochemistry and structure (2009, background): “The hyperspectral data provided by the imaging spectrometer provides the capability to assess vegetative species diversity and classify vegetation to plant functional types or species levels [19].”
Comparison of spectral indices obtained using multiple spectroradiometers (2006, background): “Similarly, measurement of the ‘blue shift’ requires 1–5 nm resolution ( Ustin et al., 2004 )a nd thus may be more accurately tracked with the ASD HH or FR. To what degree instrument affects canopy-level spectra will also be important to determine.”
Estimating foliar biochemistry from hyperspectral data in mixed forest canopy (2008, background): “With the advent of imaging spectroscopy, also known as hyperspectral remote sensing, in the mid-1980s, a significant advancement was achieved in monitoring quantitative characteristics of forested ecosystems (Wessman et al., 1988; Ollinger et al., 2002; Asner et al., 2003; Ustin et al., 2004).”
Mapping invasive plants using hyperspectral imagery and Breiman Cutler classifications (RandomForest) (2006, background): “An alternative to multispectral imagery is hyperspectral imagery (Ustin et al., 2004).”
Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests (2009, background): “…and photoprotective pigments, water, nitrogen, and carbon constituents, can be remotely measured from the airborne vantage point (reviewed in Ustin et al. 2004), rarely has the chemical information derived from HiFIS measurements been used to estimate the taxonomic composition of plant…”
Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data (2008, background): “Photochemical Reflectance Index (PRI)] and were, with the advent of spaceborne imaging spectrometry (Ustin et al., 2004), upscaled to canopy level (e.”
Prediction of sweetness and amino acid content in soybean crops from hyperspectral imagery (2007, background): “Ustin et al. (2004) also suggest that the measurement of a broader spectral range may possibly provide a better basis for predicting chemical concentrations.”
Habitat suitability modelling of an invasive plant with advanced remote sensing data (2009, background): “Yet remote sensing is proven capable of mapping vegetation to the species level (Underwood et al., 2003; Ustin et al., 2004).”
‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging (2014, background): “Different remote sensing techniques, such as hyperspectral (HS) data from spectrometers, airborne and satellite HS and multispectral (MS) images (Ustin et al. 2004; Zhang et al. 2003; Ye et al. 2008; Plaza et al. 2009), are widely used for various agricultural applications.”