Airborne spectranomics: mapping canopy chemical and taxonomic diversity in tropical forests

By Gregory P. Asner, Roberta E. Martin. 2009

407 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/cfd53bcfdb814cc084d7b863b24965863be30d3b

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 Environmental Engineering

Citations in the corpus

Carbon Content of Tree Tissues: A Synthesis (2012, background): “Such technologies may be able to discriminate species in temperate forests [75], but are likely to perform only limited species- or tree functional type discrimination in diverse tropical forests [76].”
Testing the performance of a novel spectral reflectance sensor, built with light emitting diodes (LEDs), to monitor ecosystem metabolism, structure and function (2010, background): “Valuable information can be inferred from multispectral eflectance in ecological studies because numerous aspects of cosystem structure, function and metabolism show wavelengthpecific responses (Asner and Martin, 2009; Sellers, 1985; Ustin et l., 2004).”
High spatial resolution three-dimensional mapping of vegetation spectral dynamics using computer vision (2013, background): “Recent advances in 3D remote sensing have combined 3D measurements with rich spectral information, yielding unprecedented capabilities for observing biodiversity and ecosystem functioning (Asner & Martin, 2009).”
The potential ecological costs and cobenefits of REDD: a critical review and case study from the Amazon region (2009, background): “Techniques for remote monitoring of population-level plant biodiversity, including invasions, are also under development (Asner & Vitousek, 2005; Nagendra & Rocchini, 2008; Asner & Martin, 2009; Krishnaswamy et al., 2009).”
Differentiating plant functional types using reflectance: which traits make the difference? (2018, background): “Several authors (Asner and Martin 2009; Ustin and Gamon 2010; Jetz et al. 2016) list plant traits (e.g. pigment, dry matter, nitrogen or phosphorus content), which are supposedly important to optically differentiate plant functioning.”
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).”
Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure (2015, background): “the last four decades by remote sensing technologies that map the extent and dynamics of structural, spectral, and even taxonomic traits of forests at spatial scales ranging from the single leaf to the entire planet [3–9].”
Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping (2014, methodology): “This filtering technique can remove artifacts caused by differences in canopy architecture and has been explored at the airborne level [119] over natural vegetation; therefore, the same techniques that are applied with sub-metre imagery could be applied to sub-centimetre datasets and single plants i”
Linking earth observation and taxonomic, structural and functional biodiversity: local to ecosystem perspectives (2016, background): “The comprehensive studies by Asner (1998), Asner et al. (2008, 2012a,b), and Asner and Martin (2009) in the area of measuring biochemical diversity by means of airborne hyperspectral EO show that the quantifying of different biochemical characteristics directly depends on the spectral EO…”
PROSPECT-D: towards modeling leaf optical properties through a complete lifecycle (2017, methodology): “It includes univariate statistical models derived from spectral indices (e.g., Gitelson et al., 2006), multivariate statistical models such as partial least squared regressions (e.g., Asner and Martin, 2009) and machine learning algorithms (e.g., Verrelst et al., 2015).”
PROSPECT-PRO for estimating content of nitrogen-containing leaf proteins and other carbon-based constituents (2020, background): “, 2007; Clevers and Kooistra, 2012; Vos and Bom, 1993; Yoder and Pettigrew-Crosby, 1995), this relationship does not hold during their senescence and does not appear to be universal, as it is relatively weak across species and ecosystems (Asner and Martin, 2009; Homolová et al., 2013).”
Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection (2013, background): “…nature, has played and will continue to play an indispensible role in forging a predictive science for hyperspectral remote sensing of vegetation (Asner & Martin, 2009; Feret et al., 2011), even given that recent advances in modeling leaf and canopy spectra have facilitated physically-based…”