Search results for "volution"
showing 10 items of 11678 documents
Annually resolved δ13Cshell chronologies of long-lived bivalve mollusks (Arctica islandica) reveal oceanic carbon dynamics in the temperate North Atl…
2011
Abstract The ability of the ocean to absorb carbon dioxide is likely to be adversely affected by recent climate change. However, relatively little is known about the spatiotemporal variability in the oceanic carbon cycle due to the lack of long-term, high-resolution dissolved inorganic carbon isotope ( δ 13 C DIC ) data, especially for the temperate North Atlantic, which is the major oceanic sink for anthropogenic CO 2 . Here, we report shell carbon isotope values ( δ 13 C shell ), a potential proxy for δ 13 C DIC , of old-grown specimens of the long-lived bivalve mollusk, Arctica islandica . This paper presents the first absolutely dated, annually resolved δ 13 C shell record from surface …
An Ecohydrological Cellular Automata Model Investigation of Juniper Tree Encroachment in a Western North American Landscape
2016
Woody plant encroachment over the past 140 years has substantially changed grasslands in western North American. We studied encroachment of western juniper (Juniperus occidentalis var. occidentalis) into a previously mixed shrubâgrassland site in central Oregon (USA) using a modified version of Cellular Automata TreeâGrassâShrub Simulator (CATGraSS) ecohydrological model. We developed simple algorithms to simulate three encroachment factors (grazing, fire frequency reduction, and seed dispersal by herbivores) in CATGraSS. Local ecohydrological dynamics represented by the model were first evaluated using satellite-derived leaf area index and measured evapotranspiration data. Reconstruc…
Partitioning of nitrogen during melting and recycling in subduction zones and the evolution of atmospheric nitrogen
2019
Abstract The subduction of sediment connects the surface nitrogen cycle to that of the deep Earth. To understand the evolution of nitrogen in the atmosphere, the behavior of nitrogen during the subduction and melting of subducted sediments has to be estimated. This study presents high-pressure experimental measurements of the partitioning of nitrogen during the melting of sediments at sub-arc depths. For quantitative analysis of nitrogen in minerals and glasses, we calibrated the electron probe micro-analyzer on synthetic ammonium feldspar to measure nitrogen concentrations as low as 500 μg g−1. Nitrogen abundances in melt and mica are used together with mass balance calculations to determi…
Inter-annual climate variability in Europe during the Oligocene icehouse
2017
Abstract New sclerochronological data suggest that a variability comparable to the North Atlantic Oscillation (NAO) was already present during the middle Oligocene, about 20 Myr earlier than formerly assumed. Annual increment width data of long-lived marine bivalves of Oligocene (30–25 Ma) strata from Central Europe revealed a distinct quasi-decadal climate variability modulated on 2–12 (mainly 3–7) year cycles. As in many other modern bivalves, these periodic changes in shell growth were most likely related to changes in primary productivity, which in turn, were coupled to atmospheric circulation patterns. Stable carbon isotope values of the shells (δ 13 C shell ) further corroborated the …
Developing an indicator-modelling approach to forecast changes in nitrogen critical load exceedance across Europe arising from agricultural reform
2011
International audience; Atmospheric nitrogen (N) deposition above the critical load causes eutrophication with adverse impacts on biodiversity. Average Accumulated critical load Exceedance (AAE) is a measure of the amount of critical load exceedance and the area of habitat which is affected, and has been adopted in Europe as a pressure indicator for biodiversity. In Europe, AAE is calculated by the Coordination Centre for Effects (CCE) of the United Nations Economic Commission for Europe based on modelled nitrogen deposition and country-level reporting of critical load thresholds and ecosystem area. Due to differences in country-level reporting, AAE values for semi-natural habitats may show…
Statistical retrieval of atmospheric profiles with deep convolutional neural networks
2019
Abstract Infrared atmospheric sounders, such as IASI, provide an unprecedented source of information for atmosphere monitoring and weather forecasting. Sensors provide rich spectral information that allows retrieval of temperature and moisture profiles. From a statistical point of view, the challenge is immense: on the one hand, “underdetermination” is common place as regression needs to work on high dimensional input and output spaces; on the other hand, redundancy is present in all dimensions (spatial, spectral and temporal). On top of this, several noise sources are encountered in the data. In this paper, we present for the first time the use of convolutional neural networks for the retr…
Convolutional Neural Networks for Cloud Screening: Transfer Learning from Landsat-8 to Proba-V
2018
Cloud detection is a key issue for exploiting the information from Earth observation satellites multispectral sensors. For Proba-V, cloud detection is challenging due to the limited number of spectral bands. Advanced machine learning methods, such as convolutional neural networks (CNN), have shown to work well on this problem provided enough labeled data. However, simultaneous collocated information about the presence of clouds is usually not available or requires a great amount of manual labor. In this work, we propose to learn from the available Landsat −8 cloud masks datasets and transfer this learning to solve the Proba-V cloud detection problem. CNN are trained with Landsat images adap…
First Results of Hyperspectral Scene Generation in Preparation of the Chime Imaging Spectrometer Mission
2021
End-To-End mission performance simulators (E2Es) are software tools developed to support satellite mission preparatory activities. For passive remote sensing missions, E2Es generate synthetic scenes simulating the interaction of the solar radiation between the atmosphere and the surface; therefore allowing the estimation of the mission performance before its launch. In this paper, we present the CHIME Scene Generator Module (SGM) as part of CHIME E2Es, with state-of-the-art parallelization and optimization that give a performance allowing to obtain a whole year of daily worldwide Top-Of-Atmosphere radiance images in a matter of hours. The CHIME SGM generates 100x200km hyperspectral scenes w…
Tree Species Classification of Drone Hyperspectral and RGB Imagery with Deep Learning Convolutional Neural Networks
2020
Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were emp…
Estimating Missing Information by Cluster Analysis and Normalized Convolution
2018
International audience; Smart city deals with the improvement of their citizens' quality of life. Numerous ad-hoc sensors need to be deployed to know humans' activities as well as the conditions in which these actions take place. Even if these sensors are cheaper and cheaper, their installation and maintenance cost increases rapidly with their number. We propose a methodology to limit the number of sensors to deploy by using a standard clustering technique and the normalized convolution to estimate environmental information whereas sensors are actually missing. In spite of its simplicity, our methodology lets us provide accurate assesses.