Results 71 to 80 of about 1,395,220 (301)

Effects of Temperature and Water Availability on Northern European Boreal Forests

Frontiers in Forests and Global Change, 2020
Boreal forests are warming faster than the rest of the planet. Do the benefits of higher temperatures and longer growing seasons for forest productivity exceed the negative effects of more frequent dry spells and heat waves, shifting precipitation ...
Guiomar Ruiz-Pérez, Giulia Vico
doaj   +1 more source

Deciphering Intricacies in Directional CO2 Conversion From Electrolysis to CO2 Batteries

Advanced Energy Materials, EarlyView.
This review will delve into the inherent connections and distinctions of CO2‐directed conversion in ECO2RR and CO2 batteries, in terms of product types, catalyst selection, catalytic mechanisms, and electrochemical performances, while proposing a benchmarking framework for the evaluation of CO2 batteries and innovative CO2 battery configurations for ...
Changfan Xu, Huaping Zhao, Lizhuang Chen, Weidong Shi, Guosheng Shao, Yong Lei   +5 more
wiley   +1 more source

Coherence Tomography for Boreal Forest: Camparison with HUTSCAT Scatterometer Measurements [PDF]

, 2008
In this paper we report on X- and L-band polarization coherence tomography in boreal forest structure mapping. We take advantage of HUTSCAT ranging scatterometer measurements and compare E-SAR derived vertical structure profiles with measured vertical ...
Praks, Jaan, Papathanassiou, Konstantinos P., Kugler, Florian, Hallikainen, Martti   +3 more
core  

Symbioses between cyanobacterial communities and feather mosses in boreal forests and consequences for dinitrogen fixation [PDF]

, 2013
Feather moss-cyanobacteria associations, although still poorly understood, are recognised as essential for the regulation of nitrogen (N) input into N-limited ecosystems such as boreal forests due to their ability to carry out N2-fixation.
Bay, Guillaume
core   +1 more source

Long-Term Record of Sampled Disturbances in Northern Eurasian Boreal Forest from Pre-2000 Landsat Data

Remote Sensing, 2014
Stand age distribution is an important descriptor of boreal forest structure, which is directly linked to many ecosystem processes including the carbon cycle, the land–atmosphere interaction and ecosystem services, among others.
Dong Chen, Tatiana Loboda, Saurabh Channan, Amanda Hoffman-Hall   +3 more
doaj   +1 more source

U.S. Agriculture, Biofuel Markets, and Trade: Challenges and Opportunities

Applied Economic Perspectives and Policy, EarlyView.
ABSTRACT Using an enhanced Computable General Equilibrium (CGE) model (GTAP‐BIO), this study assesses how the expansion in demand for Electric Vehicles (EVs) and the reduction in exports of agricultural products induced by trade disputes negatively affect US agriculture.
Rayan Wolf, Farzad Taheripour
wiley   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

Quantifying the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites (BORTAS) experiment: design, execution and science overview [PDF]

, 2013
We describe the design and execution of the BORTAS (Quantifying the impact of BOReal forest fires on Tropospheric oxidants over the Atlantic using Aircraft and Satellites) experiment, which has the overarching objective of understanding the chemical ...
Taylor, J., L. J. Bailey (7647530), Whaley, C., D. Weaver (7647620), Kliever, J., Trigwell, R., Sakamoto, K. M., Young, J. C., K. M. Rotermund, M. Maurice, J. D. Lee, Dan, L., Matthiesen, S., Strawbridge, K B, D. W. Tarasick, Remedios, J. J., Strawbridge, K. B., Tarasick, D W, Owen, R C, Walker, K a, Franklin, J E, Di Carlo, P., G. Forster (7647548), Lewis, A C, R. Trigwell (7647611), P. F. Bernath (2662423), {Di Carlo}, P, A. M. da Silva (7647605), Aruffo, E, P. Di Carlo (7647539), Maurice, M, Helmig, D., J. Kliever, P. F. Bernath, Kindred, D., Purvis, R. M., Hopper, J T, K. M. Sakamoto (7647602), D. Helmig (1317966), E. Barrett, J. C. Young (7647626), Hopkins, J. R., A. C. Lewis, D. L. Waugh (7647521), Moore, D P, Duck, T J, Rickard, A R, J. Taylor (4601572), D. Kindred (7647566), L. Chisholm (7647542), D. Kindred, Moller, S, Bauguitte, J.-B. S., Duck, T. J., Oram, D E, L. Chisholm, Pawson, S., Lee, J. D., A. M. da Silva, Palmer, P I, M. Parrington, D. W. Tarasick (7647524), Lewis, A. C., Moller, S., Rickard, A. R., Strong, K., D. Griffin (7647557), Parrington, M., Rotermund, K. M., Tereszchuk, K a, K. Strong (7604297), D. E. Oram, J. T. Hopper, R. M. Purvis, Oram, D. E., Barrett, E., Le Breton, M., K. R. Curry (7647536), A. R. Rickard, A. C. Lewis (2034367), Sakamoto, K M, Punjabi, S, J. R. Pierce, Weaver, D, Whaley, C, Forster, G., Andrews, S, Chisholm, L, K. A. Walker, Kliever, J, D. E. Oram (7647578), Andrews, S., S. J. O'Shea (7647581), Le Breton, Michael, S. J-B. Bauguitte (7647533), P. I. Palmer (7616285), M. Maurice (7647575), Hopkins, J R, J. R. Pierce (1832677), Bailey, L J, Percival, Carl, R. Trigwell, E. Aruffo (7647527), Owen, R.C., Trigwell, R, Helmig, D, Chisholm, L., C. M. L. S. Pagniello, A. R. Rickard (7647515), S. Moller (7209410), E. Barrett (3052119), L. J. Bailey, Pagniello, C M L S, Lee, J D, P. Di Carlo, Parrington, M, Bernath, P. F., Pierce, J R, Tarasick, D. W., Tereszchuk, K. A., Taylor, Jonathan, J. R. Hopkins, Strong, K, Franklin, J. E., Walker, K.A., John J. Remedios (7604927), G. Forster, Owen, R. C., K. A. Tereszchuk (7647614), J. J. Remedios, Bailey, L. J., Griffin, D, M. Le Breton (6184571), Moore, D. P., Bauguitte, S J-B, C. J. Percival, Jenkin, M. E., Young, J C, J. Taylor, S. Moller, Punjabi, S., Bernath, P F, S. Punjabi, Kindred, D, R. M. Purvis (7603715), D. P. Moore, Pierce, J. R., S. Matthiesen, S. J. O'Shea, Gibson, M D, Rotermund, K M, K. R. Curry, C. Whaley (7647623), Aruffo, E., Waugh, D L, Weaver, D., E. Aruffo, S. J.-B. Bauguitte, T. J. Duck (7647518), C. J. Percival (7647593), K. M. Rotermund (7647599), K .B. Strawbridge (7647608), M. E. Jenkin, D. Helmig, S. Matthiesen (7647572), M. Le Breton, S. Andrews, J. C. Young, Curry, K R, J. E. Franklin, C. Whaley, da Silva, a M, S. Punjabi (7647596), S. Pawson (7647590), J. D. Lee (1582129), L. Dan (7647545), J. R. Hopkins (2034361), D. Weaver, O'shea, S. J., S. Andrews (7640351), K. B. Strawbridge, K. A. Walker (7647617), Pagniello, C. M. L. S., P. I. Palmer, D. Griffin, Maurice, M., O'Shea, S J, Hopper, J. T., Remedios, J J, J. Kliever (7647569), Purvis, R M, Walker, K. A., S. Pawson, C. M. L. S. Pagniello (7647587), K. Strong, Barrett, E, M. D. Gibson, J. T. Hopper (7647560), R. C. Owen, M. Parrington (7647512), Palmer, P. I., Bauguitte, S. J.-b., R. C. Owen (7647584), Matthiesen, S, Dan, L, Forster, G, Pawson, S, Jenkin, M E, M. D. Gibson (7647554), Curry, K. R., M. E. Jenkin (7647563), K. M. Sakamoto, Waugh, D. L., Percival, C. J., Da Silva, A. M., Gibson, M. D., Purvis, R.M., D. L. Waugh, J. E. Franklin (7647551), T. J. Duck, Griffin, D., L. Dan, K. A. Tereszchuk, D. P. Moore (7582328)   +223 more
core   +2 more sources

Canopy structural modeling using object-oriented image classification and laser scanning [PDF]

, 2008
A terrestrial laser scanning (TLS) experiment was carried out in the EAGLE 2006 campaign to characterize and model the canopy structure of the Speulderbos forest. Semi-variogram analysis was used to describe spatial variability of the surface.
Vekerdy, Zoltán, Dost, R., Timmermans, J., Laake, P. van   +3 more
core   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source