Confounding effects of snow cover on remotely sensed vegetation indices of evergreen and deciduous trees: An experimental study.

Located at northern latitudes and subject to large seasonal temperature fluctuations, boreal forests are sensitive to the changing climate, with evidence for both increasing and decreasing productivity, depending upon conditions. Optical remote sensing of vegetation indices based on spectral reflectance offers a means of monitoring vegetation photosynthetic activity and provides a powerful tool for observing how boreal forests respond to changing environmental conditions. Reflectance-based remotely sensed optical signals at northern latitude or high-altitude regions are readily confounded by snow coverage, hampering applications of satellite-based vegetation indices in tracking vegetation productivity at large scales. Unraveling the effects of snow can be challenging from satellite data, particularly when validation data are lacking. In this study, we established an experimental system in Alberta, Canada including six boreal tree species, both evergreen and deciduous, to evaluate the confounding effects of snow on three vegetation indices: the normalized difference vegetation index (NDVI), the photochemical reflectance index (PRI), and the chlorophyll/carotenoid index (CCI), all used in tracking vegetation productivity for boreal forests. Our results revealed substantial impacts of snow on canopy reflectance and vegetation indices, expressed as increased albedo, decreased NDVI values and increased PRI and CCI values. These effects varied among species and functional groups (evergreen and deciduous) and different vegetation indices were affected differently, indicating contradictory, confounding effects of snow on these indices. In addition to snow effects, we evaluated the contribution of deciduous trees to vegetation indices in mixed stands of evergreen and deciduous species, which contribute to the observed relationship between greenness-based indices and ecosystem productivity of many evergreen-dominated forests that contain a deciduous component. Our results demonstrate confounding and interacting effects of snow and vegetation type on vegetation indices and illustrate the importance of explicitly considering snow effects in any global-scale photosynthesis monitoring efforts using remotely sensed vegetation indices.

Revisiting the carbon-biodiversity connection.

Ecosystem studies often consider the co-benefits of biodiversity and carbon sequestration, but these carbon-biodiversity links can be complex and multifaceted. Recent findings in forest ecosystems emphasize the importance of looking beyond single trophic levels and the more visible, above-ground portions to consider the full range of relationships between all ecosystem components when evaluating carbon sequestration potential. Simple engineered solutions to carbon storage based on monocultures that fail to consider all costs and benefits may be deceiving and lead to inappropriate management practices. Regenerating natural ecosystems may best enhance the co-benefits of carbon sequestration and biodiversity.

Contrasting photoprotective responses of forest trees revealed using PRI light responses sampled with airborne imaging spectrometry.

The Photochemical Reflectance Index (PRI) provides an optical indicator of photosynthetic light-use efficiency, photoprotection, and stress in plants. Although PRI can be applied in remote sensing, its interpretation depends on irradiance, which is hard to obtain from satellite or airborne imagery. To quantify forest photoprotective responses remotely, we developed a framework for modeling and interpreting PRI-light responses of individual trees and species using airborne imaging spectrometry coupled with georeferenced forest inventory data from a temperate broad-leaved forest. We derived an irradiance proxy, used hierarchical modeling to analyze PRI-light responses, and developed a framework of physiological interpretations of model parameters as facultative and constitutive components of photoprotection. Photochemical Reflectance Index declined with illumination, and PRI-light relationships varied with landscape position and among tree crowns and species. More sun-exposed foliage had lower intercepts and slopes of the relationship, indicating greater constitutive, but less facultative, photoprotection. We show that tree photoprotective strategies can be quantified at multiple scales using airborne hyperspectral data in structurally complex forests. Our findings and approach have important implications for the remote sensing of forest stress by offering a new way to assess functional diversity through dynamic differences in photoprotection and photosynthetic downregulation and providing previsual indicators of forest stress.

Recent Advances Toward Transparent Methane Emissions Monitoring: A Review.

Given that anthropogenic greenhouse gas (GHG) emissions must be immediately reduced to avoid drastic increases in global temperature, methane emissions have been placed center stage in the fight against climate change. Methane has a significantly larger warming potential than carbon dioxide. A large percentage of methane emissions are in the form of industry emissions, some of which can now be readily identified and mitigated. This review considers recent advances in methane detection that allow accurate and transparent monitoring, which are needed for reducing uncertainty in source attribution and evaluating progress in emissions reductions. A particular focus is on complementary methods operating at different scales with applications for the oil and gas industry, allowing rapid detection of large point sources and addressing inconsistencies of emissions inventories. Emerging airborne and satellite imaging spectrometers are advancing our understanding and offer new top-down assessment methods to complement bottom-up methods. Successfully merging estimates across scales is vital for increased certainty regarding greenhouse gas emissions and can inform regulatory decisions. The development of comprehensive, transparent, and spatially resolved top-down and bottom-up inventories will be crucial for holding nations accountable for their climate commitments.

Vegetation type is an important predictor of the arctic summer land surface energy budget.

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994-2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm-2) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

Remotely detected aboveground plant function predicts belowground processes in two prairie diversity experiments.

Imaging spectroscopy provides the opportunity to incorporate leaf and canopy optical data into ecological studies, but the extent to which remote sensing of vegetation can enhance the study of belowground processes is not well understood. In terrestrial systems, aboveground and belowground vegetation quantity and quality are coupled, and both influence belowground microbial processes and nutrient cycling. We hypothesized that ecosystem productivity, and the chemical, structural and phylogenetic-functional composition of plant communities would be detectable with remote sensing and could be used to predict belowground plant and soil processes in two grassland biodiversity experiments: the BioDIV experiment at Cedar Creek Ecosystem Science Reserve in Minnesota and the Wood River Nature Conservancy experiment in Nebraska. We tested whether aboveground vegetation chemistry and productivity, as detected from airborne sensors, predict soil properties, microbial processes and community composition. Imaging spectroscopy data were used to map aboveground biomass, green vegetation cover, functional traits and phylogenetic-functional community composition of vegetation. We examined the relationships between the image-derived variables and soil carbon and nitrogen concentration, microbial community composition, biomass and extracellular enzyme activity, and soil processes, including net nitrogen mineralization. In the BioDIV experiment-which has low overall diversity and productivity despite high variation in each-belowground processes were driven mainly by variation in the amount of organic matter inputs to soils. As a consequence, soil respiration, microbial biomass and enzyme activity, and fungal and bacterial composition and diversity were significantly predicted by remotely sensed vegetation cover and biomass. In contrast, at Wood River-where plant diversity and productivity were consistently higher-belowground processes were driven mainly by variation in the quality of aboveground inputs to soils. Consequently, remotely sensed functional, chemical and phylogenetic composition of vegetation predicted belowground extracellular enzyme activity, microbial biomass, and net nitrogen mineralization rates but aboveground biomass (or cover) did not. The contrasting associations between the quantity (productivity) and quality (composition) of aboveground inputs with belowground soil attributes provide a basis for using imaging spectroscopy to understand belowground processes across productivity gradients in grassland systems. However, a mechanistic understanding of how above and belowground components interact among different ecosystems remains critical to extending these results broadly.

Downscaling of far-red solar-induced chlorophyll fluorescence of different crops from canopy to leaf level using a diurnal data set acquired by the airborne imaging spectrometer HyPlant.

Remote sensing-based measurements of solar-induced chlorophyll fluorescence (SIF) are useful for assessing plant functioning at different spatial and temporal scales. SIF is the most direct measure of photosynthesis and is therefore considered important to advance capacity for the monitoring of gross primary production (GPP) while it has also been suggested that its yield facilitates the early detection of vegetation stress. However, due to the influence of different confounding effects, the apparent SIF signal measured at canopy level differs from the fluorescence emitted at leaf level, which makes its physiological interpretation challenging. One of these effects is the scattering of SIF emitted from leaves on its way through the canopy. The escape fraction ( f esc ) describes the scattering of SIF within the canopy and corresponds to the ratio of apparent SIF at canopy level to SIF at leaf level. In the present study, the fluorescence correction vegetation index (FCVI) was used to determine f esc of far-red SIF for three structurally different crops (sugar beet, winter wheat, and fruit trees) from a diurnal data set recorded by the airborne imaging spectrometer HyPlant. This unique data set, for the first time, allowed a joint analysis of spatial and temporal dynamics of structural effects and thus the downscaling of far-red SIF from canopy ( SIF 760 canopy ) to leaf level ( SIF 760 leaf ). For a homogeneous crop such as winter wheat, it seems to be sufficient to determine f esc once a day to reliably scale SIF760 from canopy to leaf level. In contrast, for more complex canopies such as fruit trees, calculating f esc for each observation time throughout the day is strongly recommended. The compensation for structural effects, in combination with normalizing SIF760 to remove the effect of incoming radiation, further allowed the estimation of SIF emission efficiency ( ε SIF ) at leaf level, a parameter directly related to the diurnal variations of plant photosynthetic efficiency.

Coupling spectral and resource-use complementarity in experimental grassland and forest communities.

Reflectance spectra provide integrative measures of plant phenotypes by capturing chemical, morphological, anatomical and architectural trait information. Here, we investigate the linkages between plant spectral variation, and spectral and resource-use complementarity that contribute to ecosystem productivity. In both a forest and prairie grassland diversity experiment, we delineated n-dimensional hypervolumes using wavelength bands of reflectance spectra to test the association between the spectral space occupied by individual plants and their growth, as well as between the spectral space occupied by plant communities and ecosystem productivity. We show that the spectral space occupied by individuals increased with their growth, and the spectral space occupied by plant communities increased with ecosystem productivity. Furthermore, ecosystem productivity was better explained by inter-individual spectral complementarity than by the large spectral space occupied by productive individuals. Our results indicate that spectral hypervolumes of plants can reflect ecological strategies that shape community composition and ecosystem function, and that spectral complementarity can reveal resource-use complementarity.

A unified vegetation index for quantifying the terrestrial biosphere.

Empirical vegetation indices derived from spectral reflectance data are widely used in remote sensing of the biosphere, as they represent robust proxies for canopy structure, leaf pigment content, and, subsequently, plant photosynthetic potential. Here, we generalize the broad family of commonly used vegetation indices by exploiting all higher-order relations between the spectral channels involved. This results in a higher sensitivity to vegetation biophysical and physiological parameters. The presented nonlinear generalization of the celebrated normalized difference vegetation index (NDVI) consistently improves accuracy in monitoring key parameters, such as leaf area index, gross primary productivity, and sun-induced chlorophyll fluorescence. Results suggest that the statistical approach maximally exploits the spectral information and addresses long-standing problems in satellite Earth Observation of the terrestrial biosphere. The nonlinear NDVI will allow more accurate measures of terrestrial carbon source/sink dynamics and potentials for stabilizing atmospheric CO2 and mitigating global climate change.

Leaf reflectance spectra capture the evolutionary history of seed plants.

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 we describe 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. We test 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.

Multi-temporal assessment of grassland α- and ß-diversity using hyperspectral imaging.

While more and more studies are exploring the application of remote sensing in assessing biodiversity for different ecosystems, most consider biodiversity at one point in time. Using several remote-sensing-based metrics, we asked how well remote sensing can detect biodiversity (both α- and ß-diversity) in a prairie grassland across time using airborne hyperspectral data collected in two successive years (2017 and 2018) and at different periods in the growing season (2018). The ability to detect biodiversity using "spectral diversity" and "spectral species" types indeed varied significantly over a 2-yr timespan. Toward the end of the growing season in 2018, the relationship between field- and remote-sensing-based α- and ß-diversity weakened compared to data collected from the same season in the previous year. This contrasting pattern between the two years was likely influenced by prescribed fire, altered weather, and the resulting shifting species composition and phenology. These findings indicate that direct detection of α- and ß-diversity in grasslands should be multi-temporal when possible and should consider the effect of disturbances, climate variables, and phenology. We demonstrate an essential role for airborne platforms in developing a global biodiversity monitoring system involving forthcoming space-borne hyperspectral sensors.

Spring warming in Yukon mountains is not amplified by the snow albedo feedback.

Decreasing spring snow cover may amplify Arctic warming through the snow albedo feedback. To examine the impact of snowmelt on increasing temperature we used a 5,000 m elevation gradient in Yukon, Canada, extending from valley-bottom conifer forests, through middle elevation tundra, to high elevation icefields, to compare validated downscaled reanalysis air temperature patterns across elevational bands characterized by different patterns of spring snowmelt. From 2000 to 2014 we observed surface warming of 0.01 °C/a·1,000 m in May (0.14 °C/a at 1,000 m to 0.19 °C/a at 5,000 m), and uniform cooling of 0.09 °C/a in June at all elevations. May temperature trends across elevationally dependent land cover types were highly correlated with each other despite large variations in albedo and snow cover trends. Furthermore, a clear dependency of infrared skin temperature on snow cover mediated albedo decline was observed in tundra, but this was insufficient to influence average diurnal air temperature. We observed negative June temperature trends which we attribute to increasing daytime cloud cover because albedo and snow cover trends were unchanging. We conclude that 8-day and monthly averaged Spring air temperature trends are responding to a synoptic external forcing that is much stronger than the snow albedo feedback in sub-Arctic mountains.

Plant spectral diversity integrates functional and phylogenetic components of biodiversity and predicts ecosystem function.

Biodiversity promotes ecosystem function as a consequence of functional differences among organisms that enable resource partitioning and facilitation. As the need for biodiversity assessments increases in the face of accelerated global change, novel approaches that are rapid, repeatable and scalable are critical, especially in ecosystems for which information about species identity and the number of species is difficult to acquire. Here, we present 'spectral diversity'-a spectroscopic index of the variability of electromagnetic radiation reflected from plants measured in the visible, near-infrared and short-wave infrared regions (400-2,400 nm). Using data collected from the Cedar Creek biodiversity experiment (Minnesota, USA), we provide evidence that the dissimilarity of species' leaf spectra increases with functional dissimilarity and evolutionary divergence time. Spectral diversity at the leaf level explains 51% of total variation in productivity-a proportion comparable to taxonomic (47%), functional (51%) or phylogenetic diversity (48%)-and performs similarly when calculated from high-resolution canopy image spectra. Spectral diversity is an emerging dimension of plant biodiversity that integrates trait variation within and across species even in the absence of taxonomic, functional, phylogenetic or abundance information, and has the potential to transform biodiversity assessment because of its scalability to remote sensing.

The spatial sensitivity of the spectral diversity-biodiversity relationship: an experimental test in a prairie grassland.

Remote sensing has been used to detect plant biodiversity in a range of ecosystems based on the varying spectral properties of different species or functional groups. However, the most appropriate spatial resolution necessary to detect diversity remains unclear. At coarse resolution, differences among spectral patterns may be too weak to detect. In contrast, at fine resolution, redundant information may be introduced. To explore the effect of spatial resolution, we studied the scale dependence of spectral diversity in a prairie ecosystem experiment at Cedar Creek Ecosystem Science Reserve, Minnesota, USA. Our study involved a scaling exercise comparing synthetic pixels resampled from high-resolution images within manipulated diversity treatments. Hyperspectral data were collected using several instruments on both ground and airborne platforms. We used the coefficient of variation (CV) of spectral reflectance in space as the indicator of spectral diversity and then compared CV at different scales ranging from 1 mm2 to 1 m2 to conventional biodiversity metrics, including species richness, Shannon's index, Simpson's index, phylogenetic species variation, and phylogenetic species evenness. In this study, higher species richness plots generally had higher CV. CV showed higher correlations with Shannon's index and Simpson's index than did species richness alone, indicating evenness contributed to the spectral diversity. Correlations with species richness and Simpson's index were generally higher than with phylogenetic species variation and evenness measured at comparable spatial scales, indicating weaker relationships between spectral diversity and phylogenetic diversity metrics than with species diversity metrics. High resolution imaging spectrometer data (1 mm2 pixels) showed the highest sensitivity to diversity level. With decreasing spatial resolution, the difference in CV between diversity levels decreased and greatly reduced the optical detectability of biodiversity. The optimal pixel size for distinguishing α diversity in these prairie plots appeared to be around 1 mm to 10 cm, a spatial scale similar to the size of an individual herbaceous plant. These results indicate a strong scale-dependence of the spectral diversity-biodiversity relationships, with spectral diversity best able to detect a combination of species richness and evenness, and more weakly detecting phylogenetic diversity. These findings can be used to guide airborne studies of biodiversity and develop more effective large-scale biodiversity sampling methods.

A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers.

In evergreen conifers, where the foliage amount changes little with season, accurate detection of the underlying "photosynthetic phenology" from satellite remote sensing has been difficult, presenting challenges for global models of ecosystem carbon uptake. Here, we report a close correspondence between seasonally changing foliar pigment levels, expressed as chlorophyll/carotenoid ratios, and evergreen photosynthetic activity, leading to a "chlorophyll/carotenoid index" (CCI) that tracks evergreen photosynthesis at multiple spatial scales. When calculated from NASA's Moderate Resolution Imaging Spectroradiometer satellite sensor, the CCI closely follows the seasonal patterns of daily gross primary productivity of evergreen conifer stands measured by eddy covariance. This discovery provides a way of monitoring evergreen photosynthetic activity from optical remote sensing, and indicates an important regulatory role for carotenoid pigments in evergreen photosynthesis. Improved methods of monitoring photosynthesis from space can improve our understanding of the global carbon budget in a warming world of changing vegetation phenology.

Phenology and species determine growing-season albedo increase at the altitudinal limit of shrub growth in the sub-Arctic.

Arctic warming is resulting in reduced snow cover and increased shrub growth, both of which have been associated with altered land surface-atmospheric feedback processes involving sensible heat flux, ground heat flux and biogeochemical cycling. Using field measurements, we show that two common Arctic shrub species (Betula glandulosa and Salix pulchra), which are largely responsible for shrub encroachment in tundra, differed markedly in albedo and that albedo of both species increased as growing season progressed when measured at their altitudinal limit. A moveable apparatus was used to repeatedly measure albedo at six precise spots during the summer of 2012, and resampled in 2013. Contrary to the generally accepted view of shrub-covered areas having low albedo in tundra, full-canopy prostrate B. glandulosa had almost the highest albedo of all surfaces measured during the peak of the growing season. The higher midsummer albedo is also evident in localized MODIS albedo aggregated from 2000 to 2013, which displays a similar increase in growing-season albedo. Using our field measurements, we show the ensemble summer increase in tundra albedo counteracts the generalized effect of earlier spring snow melt on surface energy balance by approximately 40%. This summer increase in albedo, when viewed in absolute values, is as large as the difference between the forest and tundra transition. These results indicate that near future (<50 years) changes in growing-season albedo related to Arctic vegetation change are unlikely to be particularly large and might constitute a negative feedback to climate warming in certain circumstances. Future efforts to calculate energy budgets and a sensible heating feedback in the Arctic will require more detailed information about the relative abundance of different ground cover types, particularly shrub species and their respective growth forms and phenology.

Net ecosystem exchange of CO2 with rapidly changing high Arctic landscapes.

High Arctic landscapes are expansive and changing rapidly. However, our understanding of their functional responses and potential to mitigate or enhance anthropogenic climate change is limited by few measurements. We collected eddy covariance measurements to quantify the net ecosystem exchange (NEE) of CO2 with polar semidesert and meadow wetland landscapes at the highest latitude location measured to date (82°N). We coupled these rare data with ground and satellite vegetation production measurements (Normalized Difference Vegetation Index; NDVI) to evaluate the effectiveness of upscaling local to regional NEE. During the growing season, the dry polar semidesert landscape was a near-zero sink of atmospheric CO2 (NEE: -0.3 ± 13.5 g C m(-2) ). A nearby meadow wetland accumulated over 300 times more carbon (NEE: -79.3 ± 20.0 g C m(-2) ) than the polar semidesert landscape, and was similar to meadow wetland NEE at much more southerly latitudes. Polar semidesert NEE was most influenced by moisture, with wetter surface soils resulting in greater soil respiration and CO2 emissions. At the meadow wetland, soil heating enhanced plant growth, which in turn increased CO2 uptake. Our upscaling assessment found that polar semidesert NDVI measured on-site was low (mean: 0.120-0.157) and similar to satellite measurements (mean: 0.155-0.163). However, weak plant growth resulted in poor satellite NDVI-NEE relationships and created challenges for remotely detecting changes in the cycling of carbon on the polar semidesert landscape. The meadow wetland appeared more suitable to assess plant production and NEE via remote sensing; however, high Arctic wetland extent is constrained by topography to small areas that may be difficult to resolve with large satellite pixels. We predict that until summer precipitation and humidity increases enough to offset poor soil moisture retention, climate-related changes to productivity on polar semideserts may be restricted.

The photochemical reflectance index provides an optical indicator of spring photosynthetic activation in evergreen conifers.

In evergreens, the seasonal down-regulation and reactivation of photosynthesis is largely invisible and difficult to assess with remote sensing. This invisible phenology may be changing as a result of climate change. To better understand the mechanism and timing of these hidden physiological transitions, we explored several assays and optical indicators of spring photosynthetic activation in conifers exposed to a boreal climate. The photochemical reflectance index (PRI), chlorophyll fluorescence, and leaf pigments for evergreen conifer seedlings were monitored over 1 yr of a boreal climate with the addition of gas exchange during the spring. PRI, electron transport rate, pigment levels, light-use efficiency and photosynthesis all exhibited striking seasonal changes, with varying kinetics and strengths of correlation, which were used to evaluate the mechanisms and timing of spring activation. PRI and pigment pools were closely timed with photosynthetic reactivation measured by gas exchange. The PRI provided a clear optical indicator of spring photosynthetic activation that was detectable at leaf and stand scales in conifers. We propose that PRI might provide a useful metric of effective growing season length amenable to remote sensing and could improve remote-sensing-driven models of carbon uptake in evergreen ecosystems.

Three causes of variation in the photochemical reflectance index (PRI) in evergreen conifers.

The photochemical reflectance index (PRI) reflects diurnal xanthophyll cycle activity and is also influenced by seasonally changing carotenoid : Chl pigment ratios. Both changing pigment pools and xanthophyll cycle activity contribute to photoprotection in evergreen conifers exposed to boreal winters, but they operate over different timescales, and their relative contribution to the PRI signal has often been unclear. To clarify these responses and their contribution to the PRI signal, leaf PRI, pigment composition, temperature and irradiance were monitored over 2 yr for two evergreen conifers (Pinus contorta and Pinus ponderosa) in a boreal climate. PRI was affected by three distinct processes operating over different timescales and exhibiting contrasting spectral responses. Over the 2 yr study period, the greatest change in PRI resulted from seasonally changing carotenoid : Chl pigment ratios, followed by a previously unreported shifting leaf albedo during periods of deep cold. Remarkably, the smallest change was attributable to the xanthophyll cycle. To properly distinguish these three effects, interpretation of PRI must consider temporal context, physiological responses to evolving environmental conditions, and spectral response. Consideration of the separate mechanisms affecting PRI over different timescales could greatly improve efforts to monitor changing photosynthetic activity using optical remote sensing.