Taxonomy and remote sensing of leaf mass per area (LMA) in humid tropical forests

Asner, Gregory P.; Martin, Roberta E.; Tupayachi, Raul; Emerson, Ruth; Martinez, Paola; Sinca, Felipe; Powell, George; Wright, S. Joseph; Lugo, Ariel E.

doi:10.1890/09-1999.1

Cited by 149 publications

(125 citation statements)

References 45 publications

(44 reference statements)

Supporting

Mentioning

109

Contrasting

Order By: Relevance

“…Some of most accurately predicted traits have no absorption features in the visible-to-near-infrared, but were instead estimated indirectly via constellation effects. LMA is consistently among the more accurately predicted traits using spectroscopy (Asner and Martin, 2008;Serbin et al, 2014;Chavana-Bryant et al, 2016), but is measured indirectly via its close coupling with water content and structural traits of leaves (Asner et al, 2011b). Silicon (Si) concentrations were well predicted by field spectroscopy, as recently reported by Smis et al (2014).…”

Section: Measuring Interspecific Variation In Leaf Traitssupporting

confidence: 60%

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

2017

View full text Add to dashboard Cite

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.

show abstract

Section: Measuring Interspecific Variation In Leaf Traitssupporting

confidence: 60%

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

2017

View full text Add to dashboard Cite

show abstract

“…For example, species distribution in tropical rain forests may show a weak relationship to environmental gradients at local and regional scales due to stable climatic conditions. Remote sensing of vegetation patterns in such areas hence requires approaches based on the biochemistry of the target species using advanced sensor technologies (e.g., [7]). (2) Indirect relationships as used in this study are complex and hardly transferable.…”

Section: Theoretical Limitations Of the Approachmentioning

confidence: 99%

“…In particular at global and regional scales, where ground-based mapping is inefficient, remote sensing may be a practical alternative approach for vegetation mapping. Environmental conditions, such as climate, also affect the biophysical and biochemical properties of species and hence influence their spectral signal [4,7]. Environmental conditions, especially temperature and precipitation, are further major determinants of species distribution at macroscales [8,9].…”

Section: Introductionmentioning

confidence: 99%

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Feilhauer

Rocchini

2012

Remote Sensing

View full text Add to dashboard Cite

Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species.

show abstract

“…High Fidelity Imaging Spectroscopy (HiFIS) is an emerging technology that utilizes high spectral resolution remotely sensed reflectance data to estimate foliar or canopy characteristics [5,6]. Utilizing reflectance data from the visible to shortwave infrared (400-2500 nm), HiFIS has been used to successfully predict foliar traits from tropical trees at the leaf scale [7][8][9][10]. In addition, it has recently been demonstrated that, in the tropics, community-scale canopy functional traits can be estimated at the hectare level using airborne HiFIS [11,12].…”

Section: Introductionmentioning

confidence: 99%

Organismic-Scale Remote Sensing of Canopy Foliar Traits in Lowland Tropical Forests

Chadwick

Asner

2016

Remote Sensing

View full text Add to dashboard Cite

Airborne high fidelity imaging spectroscopy (HiFIS) holds great promise for bridging the gap between field studies of functional diversity, which are spatially limited, and satellite detection of ecosystem properties, which lacks resolution to understand within landscape dynamics. We use Carnegie Airborne Observatory HiFIS data combined with field collected foliar trait data to develop quantitative prediction models of foliar traits at the tree-crown level across over 1000 ha of humid tropical forest. We predicted foliar leaf mass per area (LMA) as well as foliar concentrations of nitrogen, phosphorus, calcium, magnesium and potassium for canopy emergent trees (R 2 : 0.45-0.67, relative RMSE: 11%-14%). Correlations between remotely sensed model coefficients for these foliar traits are similar to those found in laboratory studies, suggesting that the detection of these mineral nutrients is possible through their biochemical stoichiometry. Maps derived from HiFIS provide quantitative foliar trait information across a tropical forest landscape at fine spatial resolution, and along environmental gradients. Multi-nutrient maps implemented at the fine organismic scale will subsequently provide new insight to the functional biogeography and biological diversity of tropical forest ecosystems.

show abstract

Taxonomy and remote sensing of leaf mass per area (LMA) in humid tropical forests

Cited by 149 publications

References 45 publications

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

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

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Organismic-Scale Remote Sensing of Canopy Foliar Traits in Lowland Tropical Forests

Contact Info

Product

Resources

About