Characterizing canopy biochemistry from imaging spectroscopy and its application to ecosystem studies

By R. Kokaly, Gregory P. Asner, Scott V. Ollinger, Mary E. Martin, C. Wessman. 2009

630 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/e7c7c6b6ebdd4a66803868a9395284953116e657

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ecological Modeling

In Ecology

In Global and Planetary Change

Citations in the corpus

The mycorrhizal-associated nutrient economy: a new framework for predicting carbon-nutrient couplings in temperate forests. (2013, background): “This is currently not possible but recent advances in the remote sensing of individual tree species in temperate forests suggests that remote sensing of mycorrhizal association is feasible (Plourde et al., 2007; Kokaly et al., 2009; Ustin & Gamon, 2010).”
Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests. (2016, neutral): “Indeed, each leaf trait has its own spectral fingerprint (Curran, 1989; Kokaly et al., 2009), as we have seen from the VIP values of PLSR models (Fig.”
Observing Changing Ecological Diversity in the Anthropocene (2013, background): “Spectroscopic measurements can also estimate key constraints over decomposition and subsequent soil nutrient cycling, such as nitrogen, lignin and cellulose content (Kokaly et al 2009).”
A visible band index for remote sensing leaf chlorophyll content at the canopy scale (2013, background): “…is the estimation of leaf chlorophyll content, leaf nitrogen content, leaf area index (LAI) and other variables by model inversion, including atmospheric and topographic corrections (Botha et al., 2007; Houborg et al., 2009; Jacquemoud et al., 2009; Kokaly et al., 2009; Vohland et al., 2010).”
Vegetation classification method with biochemical composition estimated from remote sensing data (2011, background): “Kokaly et al. (2009) reviewed research into improving the application of imaging spectrometers to quantify non-pigment biochemical constituents of plants.”
Benefits of hyperspectral remote sensing for tracking plant invasions (2011, background): “It is expected that variations in spectral signatures (shape and the depth of the shape) should be found across environmental gradients or taxonomic lines (Kokaly et al., 2009; Ustin et al., 2009) (Figs 1 & 2).”
Automated mapping of tropical deforestation and forest degradation: CLASlite (2009, background): “, branches) introduce major errors in spectral mixture models and cannot be used [25, 30].”
Urban tree species mapping using hyperspectral and lidar data fusion (2014, background): “This may be a product of consistently high sample LAI and leaf water content dampening the viability of lignin-cellulose absorption features (Kokaly et al., 2009).”
Optimizing spectral indices and chemometric analysis of leaf chemical properties using radiative transfer modeling (2011, background): “Quantifications of Cxc and LMA are much more challenging, because they are masked by stronger absorbers like Cab in the visible and EWT in the infrared (Gitelson et al., 2002; Kokaly et al., 2009; Zur et al., 2000).”
Modeling the carbon cost of plant nitrogen acquisition: Mycorrhizal trade‐offs and multipath resistance uptake improve predictions of retranslocation (2014, background): “Further, recent research is challenging the view that the retranslocation of N from fine roots is negligible [Aerts, 1990; Freschet et al., 2010; Gordon and Jackson, 2000; Kunkle et al., 2009], a paradigm shift that can be quickly assimilated by the cost-benefit framework of FUN 2.0.”
Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection (2013, background): “…the presence of many local spikes in the curve of band selection probability is consistent with the physical basis that the absorption features of a chemical bond comprises a series of vibrational or rotational lines attributable to multiple harmonics and overtones (Kokaly et al., 2009).”
Differentiating plant functional types using reflectance: which traits make the difference? (2018, background): “Assessing the contribution of individual traits is challenging as the canopy reflectance represents the integrated effects of various optical traits (Kokaly et al. 2009; Ollinger 2011).”
PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents (2020, background): “Chlorophyll pigments contain only a small fraction of N in plants, representing less than 2% of the total leaf N (Kokaly et al., 2009).”
Earth system science related imaging spectroscopy — an assessment (2009, background): “Recent findings indicate the need for canopy representation at the shoot (needle) level, allowing proper upscaling approaches from leaves to canopies and biomes (Kokaly et al., 2009-in this issue; Zeng et al., 2008b).”