Remote sensing of plant functional types.

By Susan L. Ustin, John A. Gamon. New Phytologist, 2010

616 citations recorded by the source corpus.

Subfields: Ecology, Nature and Landscape Conservation, Plant Science

View Paper: https://www.semanticscholar.org/paper/787098b18be717c661ab2e51c861b92fb675729d

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Citations in the corpus

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms (2021, background): “…function (e. g., NDVI, NIRv, PRI, CCI; Gamon et al., 1992, 2016; Campbell et al., 2013) to statistical classifiers of plant distributions (Ustin and Gamon, 2010; Fassnacht et al., 2016; Meerdink et al., 2019) and predictive models of continuous properties (Asner and Martin, 2015; Serbin et al.,…”
Optical remote sensing and the retrieval of terrestrial vegetation bio-geophysical properties - A review (2015, methodology): “For an overview of retrievable leaf biochemical and canopy bio-geophysical variables from superspectral or hyperspectral sensors we refer to Ustin and Gamon (2010) and Malenovsky et al. (2012).”
Current knowledge and future research opportunities for modeling annual crop mixtures. A review (2019, positive): “…filter (Keddy 1992) guides the choice of the mixture components and occurs at the species level and not the genotype level because it is influenced by general properties of a species that are shared by all of its genotypes, thus defining coarse plant functional types (Ustin and Gamon 2010).”
Will remote sensing shape the next generation of species distribution models? (2015, methodology): “For example Buermann et al. (2008) used RADAR data from QuikSCAT as a proxy for Amazonian forest canopy roughness and found that it improved habitat suitability models for several species of birds.”
Observing Changing Ecological Diversity in the Anthropocene (2013, methodology): “…sensing techniques produce data on the distributions of species and their traits (Table 1) by directly measuring the chemical composition of plant canopies (Figure 5, Asner and Martin 2009) and by retrieving signatures linked to water, light and nutrient use (Ustin and Gamon 2010, Ollinger 2011).”
Review of optical-based remote sensing for plant trait mapping (2013, background): “Such an interdisciplinary cooperation 664 resulted recently in a novel concept of “optical traits”, i.e. assessing combined effects of 665 vegetation physiological, structural and phenological properties on reflectance measurements 666 (Ustin and Gamon, 2010).”
Linking plant strategies and plant traits derived by radiative transfer modelling (2017, methodology): “The RTM Inversion andmapping of plant traits The RTM PROSAIL links the leaf reflectance model PROSPECT-5 (Feret et al. 2008) and the canopy reflectance model 4SAIL (Verhoef et al. 2007).”
Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. (2012, background): “Those can be used to determine structural and physiological traits of vegetation (Malenovský et al. 2009; Rascher et al. 2010; Ustin and Gamon 2010).”
Differentiating plant functional types using reflectance: which traits make the difference? (2018, methodology): “The resulting combinations of trait expressions were used as input for PROSAIL 5B (Verhoef et al. 2007; Feret et al. 2008) to simulate canopy spectra in the wavelength range of 400–2500 nm.”
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).”
Sources of variability in canopy reflectance and the convergent properties of plants. (2011, background): “Ustin & Gamon (2010) even suggest extending the functional type concept to include plant ‘optical types’ through integration of remote sensing and ecological theory.”