The 2012–2015 drought has left California with severely reduced snowpack, soil moisture, ground water, and reservoir stocks, but the impact of this estimated millennial-scale event on forest health ...is unknown. We used airborne laser-guided spectroscopy and satellite-based models to assess losses in canopy water content of California’s forests between 2011 and 2015. Approximately 10.6 million ha of forest containing up to 888 million large trees experienced measurable loss in canopy water content during this drought period. Severe canopy water losses of greater than 30% occurred over 1 million ha, affecting up to 58 million large trees. Our measurements exclude forests affected by fire between 2011 and 2015. If drought conditions continue or reoccur, even with temporary reprieves such as El Niño, we predict substantial future forest change.
The functional biogeography of tropical forests is expressed in foliar chemicals that are key physiologically based predictors of plant adaptation to changing environmental conditions including ...climate. However, understanding the degree to which environmental filters sort the canopy chemical characteristics of forest canopies remains a challenge. Here, we report on the elevation and soil‐type dependence of forest canopy chemistry among 75 compositionally and environmentally distinct forests in nine regions, with a total of 7819 individual trees representing 3246 species collected, identified and assayed for foliar traits. We assessed whether there are consistent relationships between canopy chemical traits and both elevation and soil type, and evaluated the general role of phylogeny in mediating patterns of canopy traits within and across communities. Chemical trait variation and partitioning suggested a general model based on four interconnected findings. First, geographic variation at the soil‐Order level, expressing broad changes in fertility, underpins major shifts in foliar phosphorus (P) and calcium (Ca). Second, elevation‐dependent shifts in average community leaf dry mass per area (LMA), chlorophyll, and carbon allocation (including nonstructural carbohydrates) are most strongly correlated with changes in foliar Ca. Third, chemical diversity within communities is driven by differences between species rather than by plasticity within species. Finally, elevation‐ and soil‐dependent changes in N, LMA and leaf carbon allocation are mediated by canopy compositional turnover, whereas foliar P and Ca are driven more by changes in site conditions than by phylogeny. Our findings have broad implications for understanding the global ecology of humid tropical forests, and their functional responses to changing climate.
Leaf economics spectrum (LES) theory suggests a universal trade-off between resource acquisition and storage strategies in plants, expressed in relationships between foliar nitrogen (N) and ...phosphorus (P), leaf mass per area (LMA), and photosynthesis. However, how environmental conditions mediate LES trait interrelationships, particularly at large biospheric scales, remains unknown because of a lack of spatially explicit data, which ultimately limits our understanding of ecosystem processes, such as primary productivity and biogeochemical cycles. We used airborne imaging spectroscopy and geospatial modeling to generate, to our knowledge, the first biospheric maps of LES traits, here centered on 76 million ha of Andean and Amazonian forest, to assess climatic and geophysical determinants of LES traits and their interrelationships. Elevation and substrate were codominant drivers of leaf trait distributions. Multiple additional climatic and geophysical factors were secondary determinants of plant traits. Anticorrelations between N and LMA followed general LES theory, but topo-edaphic conditions strongly mediated and, at times, eliminated this classic relationship. We found no evidence for simple P–LMA or N–P trade-offs in forest canopies; rather, we mapped a continuum of N–P–LMA interactions that are sensitive to elevation and temperature. Our results reveal nested climatic and geophysical filtering of LES traits and their interrelationships, with important implications for predictions of forest productivity and acclimation to rapid climate change.
Multi-method ensembles are generally believed to return more reliable results than the application of one method alone. Here, we test if for the quantification of leaf traits an ensemble of ...regression models, consisting of Partial Least Squares (PLSR), Random Forest (RFR), and Support Vector Machine regression (SVMR) models, is able to improve the robustness of the spectral band selection process compared to the outcome of a single technique alone. The ensemble approach was tested using one artificial and five measured data sets of leaf level spectra and corresponding information on leaf chlorophyll, dry matter, and water content. PLSR models optimized for the goodness of fit, an established approach for band selection, were used to evaluate the performance of the ensemble. Although the fits of the models within the ensemble were poorer than the fits achieved with the reference approach, the ensemble was able to provide a band selection with higher consistency across all data sets. Due to the selection characteristics of the methods within the ensemble, the ensemble selection is moderately narrow and restrictive but in good agreement with known absorption features published in literature. We conclude that analyzing the range of agreement of different model types is an efficient way to select a robust set of spectral bands related to the foliar properties under investigation. This may help to deepen our understanding of the spectral response of biochemical and biophysical traits in foliage and canopies.
Spatial and temporal information on plant functional traits are lacking in ecology, which limits our understanding of how plant communities and ecosystems are changing. This problem is acute in ...remote tropical regions, where information on plant functional traits is difficult to ascertain. We used Carnegie Airborne Observatory visible-to-shortwave infrared (VSWIR) imaging spectroscopy with light detection and ranging (LiDAR) to assess the foliar traits of Amazonian and Andean tropical forest canopies. We calibrated and validated the retrieval of 15 canopy foliar chemicals and leaf mass per area (LMA) across a network of 79 1-hectare field plots using a new VSWIR-LiDAR fusion approach designed to accommodate the enormous scale mismatch between field and remote sensing studies. The results indicate that sparse and highly variable field sampling can be integrated with VSWIR-LiDAR data to yield demonstrably accurate estimates of canopy foliar chemical traits. This new airborne approach addresses the inherent limitations and sampling biases associated with field-based studies of forest functional traits, particularly in structurally and floristically complex tropical canopies.
•Canopy functional trait mapping is needed for tropical forest ecology.•Imaging spectroscopy and LiDAR were fused to estimate 16 Amazon canopy traits.•The new approach addresses current limitations of field plots in tropical forests.
Tree canopies play an enormous role in the maintenance of tropical forest diversity and ecosystem function, and are therefore central to conservation, management, and resource policy development in ...tropical regions. However, high-resolution mapping of tropical forest canopies is very difficult, because traditional field, airborne, and satellite measurements cannot resolve the number of canopy species, or particular species of interest, over the large regional scales commensurate with conservation goals and strategies. Newer technologies, such as imaging spectroscopy and light detection and ranging (lidar), are just now reaching performance levels that will allow monitoring of tropical forest diversity from the air, but the methods for applying these technologies are not yet ready. Here, we present concepts that combine chemical and spectral remote sensing perspectives to facilitate canopy diversity mapping. Using examples from our ongoing work in the Hawaiian Islands, we demonstrate how a new "airborne spectranomics" approach could revolutionize tropical forest monitoring in the future.
• Canopy chemistry and spectroscopy offer insight into community assembly and ecosystem processes in high-diversity tropical forests, but phylogenetic and environmental factors controlling chemical ...traits underpinning spectral signatures remain poorly understood. • We measured 21 leaf chemical traits and spectroscopic signatures of 594 canopy individuals on high-fertility Inceptisols and low-fertility Ultisols in a lowland Amazonian forest. The spectranomics approach, which explicitly connects phylogenetic, chemical and spectral patterns in tropical canopies, provided the basis for analysis. • Intracrown and intraspecific variation in chemical traits varied from 1.4 to 36.7% (median 9.3%), depending upon the chemical constituent. Principal components analysis showed that 14 orthogonal combinations were required to explain 95% of the variation among 21 traits, indicating the high dimensionality of canopy chemical signatures among taxa. Inceptisols and lianas were associated with high leaf nutrient concentrations and low concentrations of defense compounds. Independent of soils or plant habit, an average 70% (maximum 89%) of chemical trait variation was explained by taxonomy. At least 10 traits were quantitatively linked to remotely sensed signatures, which provided highly accurate species classification. • The results suggest that taxa found on fertile soils carry chemical portfolios with a deep evolutionary history, whereas taxa found on low-fertility soils have undergone trait evolution at the species level. Spectranomics provides a new connection between remote sensing and community assembly theory in high-diversity tropical canopies.
Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest ...machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including--in the latter case--x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1) when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.
• Leaf reflectance spectra have been increasingly used to assess plant diversity. However, we do not yet understand how spectra vary across the tree of life or how the evolution of leaf traits ...affects the differentiation of spectra among species and lineages.
• Here wedescribe a framework that integrates spectra with phylogenies and apply it to a global dataset of over 16 000 leaf-level spectra (400–2400 nm) for 544 seed plant species. Wetest for phylogenetic signal in spectra, evaluate their ability to classify lineages, and characterize their evolutionary dynamics.
• We show that phylogenetic signal is present in leaf spectra but that the spectral regions most strongly associated with the phylogeny vary among lineages. Despite among-lineage heterogeneity, broad plant groups, orders, and families can be identified from reflectance spectra. Evolutionary models also reveal that different spectral regions evolve at different rates and under different constraint levels, mirroring the evolution of their underlying traits.
• Leaf spectra capture the phylogenetic history of seed plants and the evolutionary dynamics of leaf chemistry and structure. Consequently, spectra have the potential to provide breakthrough assessments of leaf evolution and plant phylogenetic diversity at global scales.
The PROSPECT leaf optical model has, to date, combined the effects of photosynthetic pigments, but a finer discrimination among the key pigments is important for physiological and ecological ...applications of remote sensing. Here we present a new calibration and validation of PROSPECT that separates plant pigment contributions to the visible spectrum using several comprehensive datasets containing hundreds of leaves collected in a wide range of ecosystem types. These data include leaf biochemical (chlorophyll
a, chlorophyll
b, carotenoids, water, and dry matter) and optical properties (directional–hemispherical reflectance and transmittance measured from 400 nm to 2450 nm). We first provide distinct
in vivo specific absorption coefficients for each biochemical constituent and determine an average refractive index of the leaf interior. Then we invert the model on independent datasets to check the prediction of the biochemical content of intact leaves. The main result of this study is that the new chlorophyll and carotenoid specific absorption coefficients agree well with available
in vitro absorption spectra, and that the new refractive index displays interesting spectral features in the visible, in accordance with physical principles. Moreover, we improve the chlorophyll estimation (RMSE
=
9 µg/cm
2) and obtain very encouraging results with carotenoids (RMSE
=
3 µg/cm
2). Reconstruction of reflectance and transmittance in the 400–2450 nm wavelength domain using PROSPECT is also excellent, with small errors and low to negligible biases. Improvements are particularly noticeable for leaves with low pigment content.
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