Nitrogen cycling, forest canopy reflectance, and emergent properties of ecosystems

Leaf Photosynthetic Capacity of Sunlit and Shaded Mature Leaves in a Deciduous Forest

A clear understanding of the dynamics of photosynthetic capacity is crucial for accurate modeling of ecosystem carbon uptake. However, such dynamical information is hardly available and has dramatically impeded our understanding of carbon cycles. Although tremendous efforts have been made in coupling the dynamic information of photosynthetic capacity into models, using “proxies” rooted from the close relationships between photosynthetic capacity and other available leaf parameters remains the popular selection. Unfortunately, no consensus has yet been reached on such “proxies”, leading them only applicable to limited cases. In this study, we aim to identify if there are close relationships between the photosynthetic capacity (represented by the maximum carboxylation rate, Vcmax) and leaf traits for mature broadleaves within a cold temperature deciduous forest. This is based on a long-term in situ dataset including leaf chlorophyll content (Chl), leaf nitrogen concentration (Narea, Nmass), leaf carbon concentration (Carea, Cmass), equivalent water thickness (EWT), leaf mass per area (LMA), and leaf gas exchange measurements from which Vcmax was derived, for both sunlit and shaded leaves during leaf mature periods from 2014 to 2019. The results show that the Vcmax values of sunlit and shaded leaves were relatively stable during these periods, and no statistically significant interannual variations occurred (p > 0.05). However, this is not applicable to specific species. Path analysis revealed that Narea was the major contributor to Vcmax for sunlit leaves (0.502), while LMA had the greatest direct relationship with Vcmax for shaded leaves (0.625). The LMA has further been confirmed as a primary proxy if no leaf type information is available. These findings provide a promising way to better understand photosynthesis and to predict carbon and water cycles in temperate deciduous forests.

Towards comparable assessment of the soil nutrient status across scales-Review and development of nutrient metrics

Nutrient availability influences virtually every aspect of an ecosystem, and is a critical modifier of ecosystem responses to global change. Although this crucial role of nutrient availability in regulating ecosystem structure and functioning has been widely acknowledged, nutrients are still often neglected in observational and experimental synthesis studies due to difficulties in comparing the nutrient status across sites. In the current study, we explain different nutrient-related concepts and discuss the potential of soil-, plant- and remote sensing-based metrics to compare the nutrient status across space. Based on our review and additional analyses on a dataset of European, managed temperate and boreal forests (ICP [International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests] Forests dataset), we conclude that the use of plant- and remote sensing-based metrics that rely on tissue stoichiometry is limited due to their strong dependence on species identity. The potential use of other plant-based metrics such as Ellenberg indicator values and plant-functional traits is also discussed. We conclude from our analyses and review that soil-based metrics have the highest potential for successful intersite comparison of the nutrient status. As an example, we used and adjusted a soil-based metric, previously developed for conifer forests across Sweden, against the same ICP Forests data. We suggest that this adjusted and further adaptable metric, which included the organic carbon concentration in the upper 20 cm of the soil (including the organic fermentation-humus [FH] layer), the C:N ratio and pH CaCl 2 of the FH layer, can be used as a complementary tool along with other indicators of nutrient availability, to compare the background nutrient status across temperate and boreal forests dominated by spruce, pine or beech. Future collection and provision of harmonized soil data from observational and experimental sites is crucial for further testing and adjusting the metric.

Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest

Changes in plant phenology affect the carbon flux of terrestrial forest ecosystems due to the link between the growing season length and vegetation productivity. Digital camera imagery, which can be acquired frequently, has been used to monitor seasonal and annual changes in forest canopy phenology and track critical phenological events. However, quantitative assessment of the structural and biochemical controls of the phenological patterns in camera images has rarely been done. In this study, we used an NDVI (Normalized Difference Vegetation Index) camera to monitor daily variations of vegetation reflectance at visible and near-infrared (NIR) bands with high spatial and temporal resolutions, and found that the infrared camera based NDVI (camera-NDVI) agreed well with the leaf expansion process that was measured by independent manual observations at Harvard Forest, Massachusetts, USA. We also measured the seasonality of canopy structural (leaf area index, LAI) and biochemical properties (leaf chlorophyll and nitrogen content). We found significant linear relationships between camera-NDVI and leaf chlorophyll concentration, and between camera-NDVI and leaf nitrogen content, though weaker relationships between camera-NDVI and LAI. Therefore, we recommend ground-based camera-NDVI as a powerful tool for long-term, near surface observations to monitor canopy development and to estimate leaf chlorophyll, nitrogen status, and LAI.

Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (M(area)), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ15N) as well as pixel-wise uncertainties in their estimates based on error propagation in the analytical methods. Both M(area) and %N PLSR models had a R2 > 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with R2 > 0.65 and carbon and cellulose with R2 > 0.45. Although R2 of %C and cellulose were lower than M(area) and %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ15N was the lowest (R2 = 0.48, RMSE = 0.95 per thousand), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first-of-its-kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub-boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non-discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.