Monitoring the rapid and extensive changes in plant species distributions occurring worldwide requires large-scale, continuous and repeated biodiversity assessments. Imaging spectrometers are at the ...core of novel spaceborne sensor fleets designed for this task, but the degree to which they can capture plant species composition and diversity across ecosystems has yet to be determined. Here we use imaging spectroscopy and vegetation data collected by the National Ecological Observatory Network (NEON) to show that at the landscape level, spectral beta-diversity-calculated directly from spectral images-captures changes in plant species composition across all major biomes in the United States ranging from arctic tundra to tropical forests. At the local level, however, the relationship between spectral alpha- and plant alpha-diversity was positive only at sites with high canopy density and large plant-to-pixel size. Our study demonstrates that changes in plant species composition and diversity can be effectively and reliably assessed with imaging spectroscopy across terrestrial ecosystems at the beta-diversity scale-the spatial scale of spaceborne missions-paving the way for close-to-real-time biodiversity monitoring at the planetary level.
• 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.
Spectroscopy at the leaf and canopy scales has attracted considerable interest in plant ecology over the past decades. Using reflectance spectra, ecologists can infer plant traits and strategies—and ...the community‐ or ecosystem‐level processes they correlate with—at individual or community levels, covering more individuals and larger areas than traditional field surveys.
Because of the complex entanglement of structural and chemical factors that generate spectra, it can be tricky to understand exactly what phenotypic information they contain. We discuss common approaches to estimating plant traits from spectra—radiative transfer and empirical models—and elaborate on their strengths and limitations in terms of the causal influences of various traits on the spectrum. Many chemical traits have broad, shallow and overlapping absorption features, and we suggest that covariance among traits may have an important role in giving empirical models the flexibility to estimate such traits.
While trait estimates from reflectance spectra have been used to test ecological hypotheses over the past decades, there is also a growing body of research that uses spectra directly, without estimating specific traits. By treating positions of species in multidimensional spectral space as analogous to trait space, researchers can infer processes that structure plant communities using the information content of the full spectrum, which may be greater than any standard set of traits. We illustrate this power by showing that co‐occurring grassland species are more separable in spectral space than in trait space and that the intrinsic dimensionality of spectral data is comparable to fairly comprehensive trait datasets. Nevertheless, using spectra this way may make it harder to interpret patterns in terms of specific biological processes.
Synthesis. Plant spectra integrate many aspects of plant form and function. The information in the spectrum can be distilled into estimates of specific traits, or the spectrum can be used in its own right. These two approaches may be complementary—the former being most useful when specific traits of interest are known in advance and reliable models exist to estimate them, and the latter being most useful under uncertainty about which aspects of function matter most.
Plant spectra integrate many aspects of plant form and function. The information in the spectrum can be distilled into estimates of specific traits, or the spectrum can be used in its own right. These two approaches may be complementary—the former being most useful when specific traits of interest are known in advance and reliable models exist to estimate them, and the latter being most useful under uncertainty about which aspects of function matter most.
Bogs, as nutrient-poor ecosystems, are particularly sensitive to atmospheric nitrogen (N) deposition. Nitrogen deposition alters bog plant community composition and can limit their ability to ...sequester carbon (C). Spectroscopy is a promising approach for studying how N deposition affects bogs because of its ability to remotely determine changes in plant species composition in the long term as well as shorter-term changes in foliar chemistry. However, there is limited knowledge on the extent to which bog plants differ in their foliar spectral properties, how N deposition might affect those properties, and whether subtle inter- or intraspecific changes in foliar traits can be spectrally detected. The objective of the study was to assess the effect of N deposition on foliar traits and spectra. Using an integrating sphere fitted to a field spectrometer, we measured spectral properties of leaves from the four most common vascular plant species (Chamaedaphne calyculata, Kalmia angustifolia, Rhododendron groenlandicum and Eriophorum vaginatum) in three bogs in southern Québec and Ontario, Canada, exposed to different atmospheric N deposition levels, including one subjected to a 18-year N fertilization experiment. We also measured chemical and morphological properties of those leaves. We found detectable intraspecific changes in leaf structural traits and chemistry (namely chlorophyll b and N concentrations) with increasing N deposition and identified spectral regions that helped distinguish the site-specific populations within each species. Most of the variation in leaf spectral, chemical, and morphological properties was among species. As such, species had distinct spectral foliar signatures, allowing us to identify them with high accuracy with partial least squares discriminant analyses (PLSDA). Predictions of foliar traits from spectra using partial least squares regression (PLSR) were generally accurate, particularly for the concentrations of N and C, soluble C, leaf water, and dry matter content (<10% RMSEP). However, these multi-species PLSR models were not accurate within species, where the range of values was narrow. To improve the detection of short-term intraspecific changes in functional traits, models should be trained with more species-specific data. Our field study showing clear differences in foliar spectra and traits among species, and some within-species differences due to N deposition, suggest that spectroscopy is a promising approach for assessing long-term vegetation changes in bogs subject to atmospheric pollution.
Plant spectral diversity – how plants differentially interact with solar radiation – is an integrator of plant chemical, structural, and taxonomic diversity that can be remotely sensed. We propose to ...measure spectral diversity as spectral variance, which allows the partitioning of the spectral diversity of a region, called spectral gamma (γ) diversity, into additive alpha (α; within communities) and beta (β; among communities) components. Our method calculates the contributions of individual bands or spectral features to spectral γ‐, β‐, and α‐diversity, as well as the contributions of individual plant communities to spectral diversity. We present two case studies illustrating how our approach can identify 'hotspots’ of spectral α‐diversity within a region, and discover spectrally unique areas that contribute strongly to β‐diversity. Partitioning spectral diversity and mapping its spatial components has many applications for conservation since high local diversity and distinctiveness in composition are two key criteria used to determine the ecological value of ecosystems.
We propose to measure spectral diversity as spectral variance, which allows the partitioning of the spectral diversity of a region, called spectral gamma (γ) diversity, into additive alpha (α; within communities) and beta (β; among communities) components. Our method calculates the contributions of individual bands or spectral features to spectral γ‐, β‐, and α‐diversity, as well as the contributions of individual plant communities to spectral diversity. Partitioning spectral diversity and mapping its spatial components has many applications for conservation since high local diversity and distinctiveness in composition are two key criteria used to determine the ecological value of ecosystems.
Standard and easily accessible cross-thematic spatial databases are key resources in ecological research. In Switzerland, as in many other countries, available data are scattered across computer ...servers of research institutions and are rarely provided in standard formats (e.g., different extents or projections systems, inconsistent naming conventions). Consequently, their joint use can require heavy data management and geomatic operations. Here, we introduce SWECO25, a Swiss-wide raster database at 25-meter resolution gathering 5,265 layers. The 10 environmental categories included in SWECO25 are: geologic, topographic, bioclimatic, hydrologic, edaphic, land use and cover, population, transportation, vegetation, and remote sensing. SWECO25 layers were standardized to a common grid sharing the same resolution, extent, and geographic coordinate system. SWECO25 includes the standardized source data and newly calculated layers, such as those obtained by computing focal or distance statistics. SWECO25 layers were validated by a data integrity check, and we verified that the standardization procedure had a negligible effect on the output values. SWECO25 is available on Zenodo and is intended to be updated and extended regularly.
AIMS: Imaging spectroscopy enables measurement of vegetation optical properties to predict vegetation characteristics that are important for a wide range of ecological applications. Our aim was to ...predict fresh above‐ground biomass of heterogeneous alpine grasslands in two areas and at two ecological scales. We assessed model plausibility for an intensively studied alpine grassland site (plant community scale) having distinct biomass and ungulate grazing patterns. LOCATION: Alpine grasslands in the Swiss National Park. METHODS: Biomass data were collected in 51 plots and combined with imaging spectroscopy data to establish simple ratio models. We analysed the predictive power and transferability of models developed in two areas (Val Trupchun, Il Fuorn) and at two ecological scales (regional, local). In a next step, we compared our results to the broadband normalized difference vegetation index (NDVI). Finally, we assessed the correlations between model predictions and plant biomass distribution at the plant community scale. RESULTS: The best local simple ratio models yielded a model fit of R² = 0.60 and R² = 0.30, respectively, the best regional model a fit of R² = 0.44. NDVI model performance was weaker for the regional and one local area, but slightly better for the other local area. However, at the plant community scale only the local model showed a significant positive correlation (RS = 0.39) with the known biomass distribution. Further, predictive power decreased when models were transferred from one local area to another or from one ecological scale to another. CONCLUSIONS: Our study demonstrated that imaging spectroscopy is generally useful to predict above‐ground plant biomass in alpine grasslands with distinct grazing patterns. Site‐specific local models based on simple ratio indices performed better than the NDVI or regional models, suggesting that standardized approaches might not be adequate, particularly in heterogeneous grasslands inhabited by large ungulates. We emphasize the importance of collecting ground reference data covering the expected range of productivity and plant species composition. Moreover, plant community‐scale data from a previous study proved to be extremely valuable to test model plausibility.
A core goal of the National Ecological Observatory Network (NEON) is to measure changes in biodiversity across the 30‐yr horizon of the network. In contrast to NEON’s extensive use of automated ...instruments to collect environmental data, NEON’s biodiversity surveys are almost entirely conducted using traditional human‐centric field methods. We believe that the combination of instrumentation for remote data collection and machine learning models to process such data represents an important opportunity for NEON to expand the scope, scale, and usability of its biodiversity data collection while potentially reducing long‐term costs. In this manuscript, we first review the current status of instrument‐based biodiversity surveys within the NEON project and previous research at the intersection of biodiversity, instrumentation, and machine learning at NEON sites. We then survey methods that have been developed at other locations but could potentially be employed at NEON sites in future. Finally, we expand on these ideas in five case studies that we believe suggest particularly fruitful future paths for automated biodiversity measurement at NEON sites: acoustic recorders for sound‐producing taxa, camera traps for medium and large mammals, hydroacoustic and remote imagery for aquatic diversity, expanded remote and ground‐based measurements for plant biodiversity, and laboratory‐based imaging for physical specimens and samples in the NEON biorepository. Through its data science‐literate staff and user community, NEON has a unique role to play in supporting the growth of such automated biodiversity survey methods, as well as demonstrating their ability to help answer key ecological questions that cannot be answered at the more limited spatiotemporal scales of human‐driven surveys.
Foliar functional traits refer to a range of biochemical and physiognomic characteristics of vegetation that control photosynthesis, nutrient and water cycling, and can be used to describe the ...functional diversity of ecosystems. In this study, we utilize NASA AVIRIS - Next Generation imaging spectroscopy data to map 15 foliar functional traits in a grassland experiment at the Cedar Creek Ecosystem Science Reserve, a Long-Term Ecological Research site in Central Minnesota, across three years. To estimate traits, we compared the widely used partial least squares regression (PLSR) with Gaussian processes regression (GPR) to assess differences in model performance and uncertainty estimates. PLSR is attractive for its straightforward implementation and interpretation, but requires bootstrapping-based methods to estimate and map prediction uncertainties. On the other hand, GPR is more complex to implement and interpret, but provides explicit estimates of uncertainties. Our results indicated that foliar functional traits can be retrieved in these grasslands with moderate to high accuracies using either method. Highest validation accuracies were obtained for leaf mass per area (LMA), soluble cell contents, hemicellulose and cellulose (all with R2 > 0.8), and lower accuracies for lignin, nitrogen, and some pigments with both techniques. The estimations for the mass-based pigments were more accurate than the area-based pigments (at least 5% improvement in normalized RMSE). Overall, GPR and PLSR performed comparably with respect to both skill of predictions and the selection of most informative spectral regions. Maps of uncertainties corresponded well between the two models, with highest uncertainties related to low vegetation cover, high diversity levels, or under irrigation and nitrogen treatments (not represented in the field sampling). The maps showed that trait values in each plot were relatively stable across three years of managed species richness. Our results provide a template for mapping foliar traits and their uncertainties in grasslands, and point to the need for extensive ground data across time to properly evaluate performance of trait mapping algorithms.
•15 foliar functional traits were mapped in a grassland experiment across three years.•The performances of GPR and PLSR were comparable.•Traits associated with leaf structure were more accurately estimated than pigments.•The trait maps and uncertainties generated by GPR and PLSR were consistent.•Plots with highest uncertainty were due to treatments or low vegetation cover.
The oak wilt disease caused by the invasive fungal pathogen Bretziella fagacearum is one of the greatest threats to oak-dominated forests across the Eastern United States. Accurate detection and ...monitoring over large areas are necessary for management activities to effectively mitigate and prevent the spread of oak wilt. Canopy spectral reflectance contains both phylogenetic and physiological information across the visible near-infrared (VNIR) and short-wave infrared (SWIR) ranges that can be used to identify diseased red oaks. We develop partial least square discriminant analysis (PLS-DA) models using airborne hyperspectral reflectance to detect diseased canopies and assess the importance of VNIR, SWIR, phylogeny, and physiology for oak wilt detection. We achieve high accuracy through a three-step phylogenetic process in which we first distinguish oaks from other species (90% accuracy), then red oaks from white oaks (Quercus macrocarpa) (93% accuracy), and, lastly, infected from non-infected trees (80% accuracy). Including SWIR wavelengths increased model accuracy by ca. 20% relative to models based on VIS-NIR wavelengths alone; using a phylogenetic approach also increased model accuracy by ca. 20% over a single-step classification. SWIR wavelengths include spectral information important in differentiating red oaks from other species and in distinguishing diseased red oaks from healthy red oaks. We determined the most important wavelengths to identify oak species, red oaks, and diseased red oaks. We also demonstrated that several multispectral indices associated with physiological decline can detect differences between healthy and diseased trees. The wavelengths in these indices also tended to be among the most important wavelengths for disease detection within PLS-DA models, indicating a convergence of the methods. Indices were most significant for detecting oak wilt during late August, especially those associated with canopy photosynthetic activity and water status. Our study suggests that coupling phylogenetics, physiology, and canopy spectral reflectance provides an interdisciplinary and comprehensive approach that enables detection of forest diseases at large scales. These results have potential for direct application by forest managers for detection to initiate actions to mitigate the disease and prevent pathogen spread.
•Oak wilt fungus is one of the most lethal threats to oak forests in North America.•Detection of oak wilt across the landscape is possible using spectroscopic imagery.•We achieve high accuracy by distinguishing vulnerable red oaks from other species.•Including short wave infrared wavelengths in models improves detection accuracy.•Spectral indices related to photosynthesis and water status respond to oak wilt.
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