Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation

Asner, Gregory P.; Martin, Robert E.; Knapp, David; Tupayachi, Raul; Anderson, Christopher B.; Sinca, Felipe; Vaughn, Nicholas R.; Llactayo, William

doi:10.1126/science.aaj1987

Cited by 212 publications

(182 citation statements)

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“…although we have found little evidence to suggest their presence in this analysis, even in comparison with detailed regional studies (SI Appendix, Fig. S26) (49).…”

Section: Methodsmentioning

confidence: 56%

Mapping local and global variability in plant trait distributions

Butler

Datta

Flores‐Moreno

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

174

185

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Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration-specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50 × 50-km cells across the entire vegetated land surface. We do this in several ways-without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.odeling global climate and the carbon cycle with Earth system models (ESMs) requires maps of plant traits that play key roles in leaf-and ecosystem-level metabolic processes (1-4). Multiple traits are critical to both photosynthesis and respiration, foremost leaf nitrogen concentration (Nm ) and specific leaf area (SLA) (5-7). More recently, variation in leaf phosphorus concentration (Pm ) has also been linked to variation in photosynthesis and foliar respiration (7-12). Estimating detailed global geographic patterns of these traits and corresponding trait-environment relationships has been hampered by limited measurements (13), but recent improvements in data coverage (14) allow for greater detail in spatial estimates of these key traits.Previous work has extrapolated trait measurements across continental or larger regions through three methodologies: (i) grouping measurements of individuals into larger categories that share a set of properties [a working definition of plant functional types (PFTs)] (4, 15), (ii) exploiting trait-environment relationships (e.g., leaf Nm and mean annual temperature) (1,(16)(17)(18)(19)(20), or (iii) restricting the analysis to species whose presence has been widely estimated on the ground (21-24). Each of these methods has limitations-for example, trait-environment relationships do not well explain observed trait spatial patterns (1, 25), while species-based approaches limit the scope of extrapolation to only areas with well-measured species abundance. More critically, the first two global methodologies emp...

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“…although we have found little evidence to suggest their presence in this analysis, even in comparison with detailed regional studies (SI Appendix, Fig. S26) (49).…”

Section: Methodsmentioning

confidence: 56%

Mapping local and global variability in plant trait distributions

Butler

Datta

Flores‐Moreno

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

174

185

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“…In addition, the precise sun-to-canopy-to-sensor geometry provides a means to locate and remove canopy gaps and their associated shadows that interfere with the chemical signal [15]. We call this laser-guided imaging spectroscopy [16].…”

Section: Spectral Qualitymentioning

confidence: 99%

An Approach for Foliar Trait Retrieval from Airborne Imaging Spectroscopy of Tropical Forests

et al. 2018

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Spatial information on forest functional composition is needed to inform management and conservation efforts, yet this information is lacking, particularly in tropical regions. Canopy foliar traits underpin the functional biodiversity of forests, and have been shown to be remotely measurable using airborne 350-2510 nm imaging spectrometers. We used newly acquired imaging spectroscopy data constrained with concurrent light detection and ranging (LiDAR) measurements from the Carnegie Airborne Observatory (CAO), and field measurements, to test the performance of the Spectranomics approach for foliar trait retrieval. The method was previously developed in Neotropical forests, and was tested here in the humid tropical forests of Malaysian Borneo. Multiple foliar chemical traits, as well as leaf mass per area (LMA), were estimated with demonstrable precision and accuracy. The results were similar to those observed for Neotropical forests, suggesting a more general use of the Spectranomics approach for mapping canopy traits in tropical forests. Future mapping studies using this approach can advance scientific investigations and applications based on imaging spectroscopy.

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“…Ever larger areas of earth are being mapped with airborne spectrometers (e.g. Asner et al, 2017) and the anticipated launch of satellite-borne sensors (e.g. En-MAP was approved by the DLR, with launch planned for mid-2018; Guanter et al, 2015) will soon enable vegetation and ecosystem function to be characterised at the global scale.…”

Section: Difficulties In Measuring Intraspecific Variation By Field Smentioning

confidence: 99%

“…There is currently great interest in using hyperspectroscopy as a tool for studying the chemical and structural traits of leaves, particularly because improved airborne sensors and faster computing make it possible to map functional traits from the air (Ustin et al, 2009;Asner and Martin, 2016b;Jetz et al, 2016;Asner et al, 2017). Plans to put hyperspectral sensors into space (e.g.…”

Section: Introductionmentioning

confidence: 99%

On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

2017

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Abstract. Understanding the causes of variation in functional plant traits is a central issue in ecology, particularly in the context of global change. Spectroscopy is increasingly used for rapid and non-destructive estimation of foliar traits, but few studies have evaluated its accuracy when assessing phenotypic variation in multiple traits. Working with 24 chemical and physical leaf traits of six European tree species growing on strongly contrasting soil types (i.e. deep alluvium versus nearby shallow chalk), we asked (i) whether variability in leaf traits is greater between tree species or soil type, and (ii) whether field spectroscopy is effective at predicting intraspecific variation in leaf traits as well as interspecific differences. Analysis of variance showed that interspecific differences in traits were generally much stronger than intraspecific differences related to soil type, accounting for 25 % versus 5 % of total trait variation, respectively. Structural traits, phenolic defences and pigments were barely affected by soil type. In contrast, foliar concentrations of rock-derived nutrients did vary: P and K concentrations were lower on chalk than alluvial soils, while Ca, Mg, B, Mn and Zn concentrations were all higher, consistent with the findings of previous ecological studies. Foliar traits were predicted from 400 to 2500 nm reflectance spectra collected by field spectroscopy using partial least square regression, a method that is commonly employed in chemometrics. Pigments were best modelled using reflectance data from the visible region (400-700 nm), while all other traits were best modelled using reflectance data from the shortwave infrared region (1100-2500 nm). Spectroscopy delivered accurate predictions of species-level variation in traits. However, it was ineffective at detecting intraspecific variation in rock-derived nutrients (with the notable exception of P). The explanation for this failure is that rock-derived elements do not have absorption features in the 400-2500 nm region, and their estimation is indirect, relying on elemental concentrations covarying with structural traits that do have absorption features in that spectral region ("constellation effects"). Since the structural traits did not vary with soil type, it was impossible for our regression models to predict intraspecific variation in rock-derived nutrients via constellation effects. This study demonstrates the value of spectroscopy for rapid, non-destructive estimation of foliar traits across species, but highlights problems with predicting intraspecific variation indirectly. We discuss the implications of these findings for mapping functional traits by airborne imaging spectroscopy.

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Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation

Cited by 212 publications

References 50 publications

Mapping local and global variability in plant trait distributions

Mapping local and global variability in plant trait distributions

An Approach for Foliar Trait Retrieval from Airborne Imaging Spectroscopy of Tropical Forests

On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

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