An alternative approach to using LiDAR remote sensing data to predict stem diameter distributions across a temperate forest landscape

© 2017 by the authors. We apply a spatially-implicit, allometry-based modelling approach to predict stem diameter distributions (SDDs) from low density airborne LiDAR data in a heterogeneous, temperate forest in Ontario, Canada. Using a recently published algorithm that relates the density, size, and species of individual trees to the height distribution of first returns, we estimated parameters that succinctly describe SDDs that are most consistent with each 0.25-ha LiDAR tile across a 30,000 ha forest landscape. Tests with independent validation plots showed that the diameter distribution of stems was predicted with reasonable accuracy in most cases (half of validation plots had R2 ≥ 0.75, and another 23% had 0.5 ≤ R2 < 0.75). The predicted frequency of larger stems was much better than that of small stems (8 ≤ x < 11 cm diameter), particularly small conifers. We used the predicted SDDs to calculate aboveground carbon density (ACD; RMSE = 21.4 Mg C/ha), quadratic mean diameter (RMSE = 3.64 cm), basal area (RMSE = 6.99 m2/ha) and stem number (RMSE = 272 stems/ha). The accuracy of our predictions compared favorably with previous studies that have generally been undertaken in simpler conifer-dominated forest types. We demonstrate the utility of our results to spatial forest management planning by mapping SDDs, the proportion of broadleaves, and ACD at a 0.25 ha resolution

A platform for efficient, thiol-stable conjugation to albumin's native single accessible cysteine

Herein we report the use of bromomaleimides for the construction of stable albumin conjugates via conjugation to its native, single accessible, cysteine followed by hydrolysis. Advantages over the classical maleimide approach are highlighted in terms of quantitative hydrolysis and absence of undesirable retro-Michael deconjugation

Effects of growth rate, size, and light availability on tree survival across life stages: a demographic analysis accounting for missing values and small sample sizes.

The data set supporting the results of this article is available in the Dryad repository, http://dx.doi.org/10.5061/dryad.6f4qs. Moustakas, A. and Evans, M. R. (2015) Effects of growth rate, size, and light availability on tree survival across life stages: a demographic analysis accounting for missing values.Plant survival is a key factor in forest dynamics and survival probabilities often vary across life stages. Studies specifically aimed at assessing tree survival are unusual and so data initially designed for other purposes often need to be used; such data are more likely to contain errors than data collected for this specific purpose

Monitoring and modelling landscape dynamics

International audienceChanges in land cover and land use are among the most pervasive and important sources of recent alterations of the Earth's land surface.This special issue also presents new directions in modelling landscape dynamics. Agent-based models have primarily been used to simulate local land use and land cover changes processes with a focus on decision making (Le 2008; Matthews et al. 2007; Parker et al. 2003; Bousquet and Le Page 2001)

Contributions of nitrogen deposition and forest regrowth to terrestrial carbon uptake

<p>Abstract</p> <p>Background</p> <p>The amount of reactive nitrogen deposited on land has doubled globally and become at least five-times higher in Europe, Eastern United States, and South East Asia since 1860 mostly because of increases in fertilizer production and fossil fuel burning. Because vegetation growth in the Northern Hemisphere is typically nitrogen-limited, increased nitrogen deposition could have an attenuating effect on rising atmospheric CO₂by stimulating the vegetation productivity and accumulation of carbon in biomass.</p> <p>Results</p> <p>This study shows that elevated nitrogen deposition would not significantly enhance land carbon uptake unless we consider its effects on re-growing forests. Our results suggest that nitrogen enriched land ecosystems sequestered 0.62–2.33 PgC in the 1980s and 0.75–2.21 PgC in the 1990s depending on the proportion and age of re-growing forests. During these two decades land ecosystems are estimated to have absorbed 13–41% of carbon emitted by fossil fuel burning.</p> <p>Conclusion</p> <p>Although land ecosystems and especially forests with lifted nitrogen limitations have the potential to decelerate the rise of CO₂concentrations in the atmosphere, the effect is only significant over a limited period of time. The carbon uptake associated with forest re-growth and amplified by high nitrogen deposition will decrease as soon as the forests reach maturity. Therefore, assessments relying on carbon stored on land from enhanced atmospheric nitrogen deposition to balance fossil fuel emissions may be inaccurate.</p

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

Interatomic potentials: predicting phase transformations in zirconium Machine learning leads to a new interatomic potential for zirconium that can predict phase transformations. A team led by Hongxian Zong at Xi’an Jiaotong University, China, and Turab Lookman at Los Alamos National Laboratory, U.S.A, used a Gaussian-type machine learning approach to produce an interatomic potential that predicted phase transformations in zirconium. They expressed each atomic energy contribution via changes in the local atomic environment, such as bond length, shape, and volume. The resulting machine-learning potential successfully described pure zirconium’s physical properties. When used in molecular dynamics simulations, it predicted a zirconium phase diagram as a function of both temperature and pressure that agreed well with previous experiments and simulations. Developing learnt interatomic potentials in phase-transforming systems could help us better simulate complex systems

Crown Plasticity and Competition for Canopy Space: A New Spatially Implicit Model Parameterized for 250 North American Tree Species

BACKGROUND: Canopy structure, which can be defined as the sum of the sizes, shapes and relative placements of the tree crowns in a forest stand, is central to all aspects of forest ecology. But there is no accepted method for deriving canopy structure from the sizes, species and biomechanical properties of the individual trees in a stand. Any such method must capture the fact that trees are highly plastic in their growth, forming tessellating crown shapes that fill all or most of the canopy space. METHODOLOGY/PRINCIPAL FINDINGS: We introduce a new, simple and rapidly-implemented model--the Ideal Tree Distribution, ITD--with tree form (height allometry and crown shape), growth plasticity, and space-filling, at its core. The ITD predicts the canopy status (in or out of canopy), crown depth, and total and exposed crown area of the trees in a stand, given their species, sizes and potential crown shapes. We use maximum likelihood methods, in conjunction with data from over 100,000 trees taken from forests across the coterminous US, to estimate ITD model parameters for 250 North American tree species. With only two free parameters per species--one aggregate parameter to describe crown shape, and one parameter to set the so-called depth bias--the model captures between-species patterns in average canopy status, crown radius, and crown depth, and within-species means of these metrics vs stem diameter. The model also predicts much of the variation in these metrics for a tree of a given species and size, resulting solely from deterministic responses to variation in stand structure. CONCLUSIONS/SIGNIFICANCE: This new model, with parameters for US tree species, opens up new possibilities for understanding and modeling forest dynamics at local and regional scales, and may provide a new way to interpret remote sensing data of forest canopies, including LIDAR and aerial photography

Vitamin G: effects of green space on health, well-being, and social safety

BACKGROUND: Looking out on and being in the green elements of the landscape around us seem to affect health, well-being and feelings of social safety. This article discusses the design of a research program on the effects of green space in the living environment on health, well-being and social safety. METHODS/DESIGN: The program consists of three projects at three different scales: at a macro scale using data on the Netherlands as a whole, at an intermediate scale looking into the specific effect of green space in the urban environment, and at micro scale investigating the effects of allotment gardens. The projects are observational studies, combining existing data on land use and health interview survey data, and collecting new data through questionnaires and interviews. Multilevel analysis and GIS techniques will be used to analyze the data. DISCUSSION: Previous (experimental) research in environmental psychology has shown that a natural environment has a positive effect on well-being through restoration of stress and attentional fatigue. Descriptive epidemiological research has shown a positive relationship between the amount of green space in the living environment and physical and mental health and longevity. The program has three aims. First, to document the relationship between the amount and type of green space in people's living environment and their health, well-being, and feelings of safety. Second, to investigate the mechanisms behind this relationship. Mechanisms relate to exposure (leading to stress reduction and attention restoration), healthy behavior and social integration, and selection. Third, to translate the results into policy on the crossroads of spatial planning, public health, and safety. Strong points of our program are: we study several interrelated dependent variables, in different ordinary settings (as opposed to experimental or extreme settings), focusing on different target groups, using appropriate multilevel methods