John P. Caspersen

Projecting the future of the U.S. carbon sink

Atmospheric and ground-based methods agree on the presence of a carbon sink in the coterminous United States (the United States minus Alaska and Hawaii), and the primary causes for the sink recently have been identified. Projecting the future behavior of the sink is necessary for projecting future net emissions. Here we use two models, the Ecosystem Demography model and a second simpler empirically based model (Miami Land Use History), to estimate the spatio-temporal patterns of ecosystem carbon stocks and fluxes resulting from land-use changes and fire suppression from 1700 to 2100. Our results are compared with other historical reconstructions of ecosystem carbon fluxes and to a detailed carbon budget for the 1980s. Our projections indicate that the ecosystem recovery processes that are primarily responsible for the contemporary U.S. carbon sink will slow over the next century, resulting in a significant reduction of the sink. The projected rate of decrease depends strongly on scenarios of future land use and the long-term effectiveness of fire suppression.

Plant functional traits have globally consistent effects on competition

Phenotypic traits and their associated trade-offs have been shown to have globally consistent effects on individual plant physiological functions, but how these effects scale up to influence competition, a key driver of community assembly in terrestrial vegetation, has remained unclear. Here we use growth data from more than 3 million trees in over 140,000 plots across the world to show how three key functional traits—wood density, specific leaf area and maximum height—consistently influence competitive interactions. Fast maximum growth of a species was correlated negatively with its wood density in all biomes, and positively with its specific leaf area in most biomes. Low wood density was also correlated with a low ability to tolerate competition and a low competitive effect on neighbours, while high specific leaf area was correlated with a low competitive effect. Thus, traits generate trade-offs between performance with competition versus performance without competition, a fundamental ingredient in the classical hypothesis that the coexistence of plant species is enabled via differentiation in their successional strategies. Competition within species was stronger than between species, but an increase in trait dissimilarity between species had little influence in weakening competition. No benefit of dissimilarity was detected for specific leaf area or wood density, and only a weak benefit for maximum height. Our trait-based approach to modelling competition makes generalization possible across the forest ecosystems of the world and their highly diverse species composition.

Successional diversity and forest ecosystem function

Forest inventory data was used to examine the relationship between successional diversity and forest ecosytem function. The inventory data show that stands composed of early successional species are more productive than stands composed of late successional species, whereas stands composed of late successsional species have lower turnover than stands composed of early successional species. Taken alone, these results would suggest that forests should be managed in a way that favors the most productive early successional species or longest-lived late successional species, depending on whether the goal is to maximize productivity or maximize carbon storage. However, the inventory data also show that stands with low successional diversity fix and store less carbon than stands with high successional diversity. This result suggests that forests should be managed in such a way as to retain species diversity while also favoring species that maximize the ecosystem function of interest.

Allometric equations for integrating remote sensing imagery into forest monitoring programmes

Abstract Remote sensing is revolutionizing the way we study forests, and recent technological advances mean we are now able – for the first time – to identify and measure the crown dimensions of individual trees from airborne imagery. Yet to make full use of these data for quantifying forest carbon stocks and dynamics, a new generation of allometric tools which have tree height and crown size at their centre are needed. Here, we compile a global database of 108753 trees for which stem diameter, height and crown diameter have all been measured, including 2395 trees harvested to measure aboveground biomass. Using this database, we develop general allometric models for estimating both the diameter and aboveground biomass of trees from attributes which can be remotely sensed – specifically height and crown diameter. We show that tree height and crown diameter jointly quantify the aboveground biomass of individual trees and find that a single equation predicts stem diameter from these two variables across the worlds forests. These new allometric models provide an intuitive way of integrating remote sensing imagery into large‐scale forest monitoring programmes and will be of key importance for parameterizing the next generation of dynamic vegetation models.

How Stand Productivity Results from Size- and Competition-Dependent Growth and Mortality

Background A better understanding of the relationship between stand structure and productivity is required for the development of: a) scalable models that can accurately predict growth and yield dynamics for the worlds forests; and b) stand management regimes that maximize wood and/or timber yield, while maintaining structural and species diversity. Methods We develop a cohort-based canopy competition model (“CAIN”), parameterized with inventory data from Ontario, Canada, to examine the relationship between stand structure and productivity. Tree growth, mortality and recruitment are quantified as functions of diameter and asymmetric competition, using a competition index (CAIh) defined as the total projected area of tree crowns at a given trees mid-crown height. Stand growth, mortality, and yield are simulated for inventoried stands, and also for hypothetical stands differing in total volume and tree size distribution. Results For a given diameter, tree growth decreases as CAIh increases, whereas the probability of mortality increases. For a given CAIh, diameter growth exhibits a humped pattern with respect to diameter, whereas mortality exhibits a U-shaped pattern reflecting senescence of large trees. For a fixed size distribution, stand growth increases asymptotically with total density, whereas mortality increases monotonically. Thus, net productivity peaks at an intermediate volume of 100–150 m3/ha, and approaches zero at 250 m3/ha. However, for a fixed stand volume, mortality due to senescence decreases if the proportion of large trees decreases as overall density increases. This size-related reduction in mortality offsets the density-related increase in mortality, resulting in a 40% increase in yield. Conclusions Size-related variation in growth and mortality exerts a profound influence on the relationship between stand structure and productivity. Dense stands dominated by small trees yield more wood than stands dominated by fewer large trees, because the relative growth rate of small trees is higher, and because they are less likely to die.

Unlocking the forest inventory data: relating individual tree performance to unmeasured environmental factors.

Geographically extensive forest inventories, such as the USDA Forest Services Forest Inventory and Analysis (FIA) program, contain millions of individual tree growth and mortality records that could be used to develop broad-scale models of forest dynamics. A limitation of inventory data, however, is that individual-level measurements of light (L) and other environmental factors are typically absent. Thus, inventory data alone cannot be used to parameterize mechanistic models of forest dynamics in which individual performance depends on light, water, nutrients, etc. To overcome this limitation, we developed methods to estimate species-specific parameters (thetaG) relating sapling growth (G) to L using data sets in which G, but not L, is observed for each sapling. Our approach involves: (1) using calibration data that we collected in both eastern and western North America to quantify the probability that saplings receive different amounts of light, conditional on covariates x that can be obtained from inventory data (e.g., sapling crown class and neighborhood crowding); and (2) combining these probability distributions with observed G and x to estimate thetaG using Bayesian computational methods. Here, we present a test case using a data set in which G, L, and x were observed for saplings of nine species. This test data set allowed us to compare estimates of thetaG obtained from the standard approach (where G and L are observed for each sapling) to our method (where G and x, but not L, are observed). For all species, estimates of thetaG obtained from analyses with and without observed L were similar. This suggests that our approach should be useful for estimating light-dependent growth functions from inventory data that lack direct measurements of L. Our approach could be extended to estimate parameters relating sapling mortality to L from inventory data, as well as to deal with uncertainty in other resources (e.g., water or nutrients) or environmental factors (e.g., temperature).

Spatial ecology of mating success in a sexually polymorphic plant.

The spatial context of reproduction is of crucial importance to plants because of their sessile habit. Since pollen and seed dispersal is often restricted, mating success is likely to depend on the quantity and quality of mates in local neighbourhoods. Here we use neighbourhood models to investigate the spatial ecology of pollination and mating in Narcissus assoanus, a sexually polymorphic plant with two mating morphs that differ in style length. By mapping individuals in eight populations from southwestern France, we investigated the influence of the density and morph identity of plants at different spatial scales on variation in female fertility. By using inferences on the expected patterns of pollen transfer based on floral morphology, we were able to predict the quantitative relations between local morph ratios and variation in fertility. Our analyses revealed differences in the spatial clustering of morphs and in their response to plant density and morph identity within local neighbourhoods. Mating success in N. assoanus was characterized by both density- and frequency-dependent processes, a condition that may be a general feature of the spatial ecology of plant mating.

A multi-band watershed segmentation method for individual tree crown delineation from high resolution multispectral aerial image

A comprehensive forest resource inventory needs more detailed species information at individual tree level. Although conventional ground-based measurement fails to achieve this target in an efficient way, the emergence of high resolution remote sensing images has made it possible in the past decade. Individual tree crown delineation is one of the most critical steps for tree species classification from remote sensing images. However, it is still challenging to delineate individual tree crowns in deciduous forests because of the continuous canopy. In this study, a multi-band watershed segmentation method is proposed to delineate deciduous tree crowns by constructing a spectral angle space. The proposed algorithm is further examined by a high resolution multispectral aerial image of a deciduous forested area in Haliburton Forest, Ontario, Canada. Results demonstrate that, the proposed multi-band watershed segmentation method outperforms the existing valley-following based ITC map, in terms of visual interpretation and quantitative evaluation.

Object-Based Canopy Gap Segmentation and Classification: Quantifying the Pros and Cons of Integrating Optical and LiDAR Data

Delineating canopy gaps and quantifying gap characteristics (e.g., size, shape, and dynamics) are essential for understanding regeneration dynamics and understory species diversity in structurally complex forests. Both high spatial resolution optical and light detection and ranging (LiDAR) remote sensing data have been used to identify canopy gaps through object-based image analysis, but few studies have quantified the pros and cons of integrating optical and LiDAR for image segmentation and classification. In this study, we investigate whether the synergistic use of optical and LiDAR data improves segmentation quality and classification accuracy. The segmentation results indicate that the LiDAR-based segmentation best delineates canopy gaps, compared to segmentation with optical data alone, and even the integration of optical and LiDAR data. In contrast, the synergistic use of two datasets provides higher classification accuracy than the independent use of optical or LiDAR (overall accuracy of 80.28% ± 6.16% vs. 68.54% ± 9.03% and 64.51% ± 11.32%, separately). High correlations between segmentation quality and object-based classification accuracy indicate that classification accuracy is largely dependent on segmentation quality in the selected experimental area. The outcome of this study provides valuable insights of the usefulness of data integration into segmentation and classification not only for canopy gap identification but also for many other object-based applications.

Fully constrained linear spectral unmixing based global shadow compensation for high resolution satellite imagery of urban areas

Abstract Shadows commonly exist in high resolution satellite imagery, particularly in urban areas, which is a combined effect of low sun elevation, off-nadir viewing angle, and high-rise buildings. The presence of shadows can negatively affect image processing, including land cover classification, mapping, and object recognition due to the reduction or even total loss of spectral information in shadows. The compensation of spectral information in shadows is thus one of the most important preprocessing steps for the interpretation and exploitation of high resolution satellite imagery in urban areas. In this study, we propose a new approach for global shadow compensation through the utilization of fully constrained linear spectral unmixing. The basic assumption of the proposed method is that the construction of the spectral scatter plot in shadows is analogues to that in non-shadow areas within a two-dimension spectral mixing space. In order to ensure the continuity of land covers, a smooth operator is further used to refine the restored shadow pixels on the edge of non-shadow and shadow areas. The proposed method is validated using the WorldView-2 multispectral imagery collected from downtown Toronto, Ontario, Canada. In comparison with the existing linear-correlation correction method, the proposed method produced the compensated shadows with higher quality.