Search results for "Engineering"

showing 10 items of 44231 documents

Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016

2016

AbstractThe El Niño-Southern Oscillation (ENSO) is the main driver of interannual climate extremes in Amazonia and other tropical regions. The current 2015/2016 EN event was expected to be as strong as the EN of the century in 1997/98, with extreme heat and drought over most of Amazonian rainforests. Here we show that this protracted EN event, combined with the regional warming trend, was associated with unprecedented warming and a larger extent of extreme drought in Amazonia compared to the earlier strong EN events in 1982/83 and 1997/98. Typical EN-like drought conditions were observed only in eastern Amazonia, whilst in western Amazonia there was an unusual wetting. We attribute this wet…

010504 meteorology & atmospheric sciencesAmazonian0208 environmental biotechnologyClimate change02 engineering and technologyRainforest01 natural sciencesArticle//purl.org/pe-repo/ocde/ford#1.05.00 [http]Environmental impactEcosystem0105 earth and related environmental sciences//purl.org/pe-repo/ocde/ford#1.05.09 [http]MultidisciplinaryAmazon rainforestOcean currentTropics020801 environmental engineeringGeography//purl.org/pe-repo/ocde/ford#1.05.10 [http][SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology13. Climate actionClimatologyAbrupt climate changeENSOClimate-change impacts
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Fractals and geography

2007

010504 meteorology & atmospheric sciencesAnthropology[SHS.GEO] Humanities and Social Sciences/Geography0211 other engineering and technologies021107 urban & regional planning02 engineering and technology[SHS.GEO]Humanities and Social Sciences/Geography01 natural sciences[ SHS.GEO ] Humanities and Social Sciences/GeographyGeographyCartographymodèles mathématiquesanalyse spatiale0105 earth and related environmental sciences
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Modeling the Effects of Climate Change on the Supply of Phosphate-Phosphorus

2009

The transfer of phosphorus from terrestrial to aquatic ecosystems is a key route through which climate can influence aquatic ecosystems. A number of climatic factors interact in complex ways to regulate the transfer of phosphorus and modulate its ecological effects on downstream lakes and reservoirs. Processes influencing both the amount and timing of phosphorus export from terrestrial watersheds must be quantified before we can assess the direct and indirect effects of the weather on the supply and recycling of phosphorus. Simulation of the export of phosphorus from the terrestrial environment is complicated by the fact that it is difficult to describe seasonal and inter-annual variations …

010504 meteorology & atmospheric sciencesAquatic ecosystemPhosphorus0207 environmental engineeringchemistry.chemical_elementSoil science02 engineering and technology15. Life on landAtmospheric sciences01 natural sciences6. Clean waterExtreme weatherchemistry13. Climate actionEffects of global warmingEvapotranspirationEnvironmental scienceTerrestrial ecosystemPrecipitation020701 environmental engineeringSurface runoff0105 earth and related environmental sciences
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Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3

2012

Abstract ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT-5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. By using data from …

010504 meteorology & atmospheric sciencesArtificial neural networkMean squared errorbusiness.industryComputer science0211 other engineering and technologiesSoil ScienceGeology02 engineering and technologyMachine learningcomputer.software_genre01 natural sciencesRegressionSupport vector machineTemporal resolutionGround-penetrating radarCurve fittingArtificial intelligenceComputers in Earth SciencesbusinessImage resolutioncomputer021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingRemote Sensing of Environment
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Coastal precipitation regimes in Kenya.

1997

Kenya is under the influence of the seasonal reversal of the Indian ocean monsoons. However, its coastal belt, up to about 50 km inland, exhibits original climatic features. Hierarchical clustering...

010504 meteorology & atmospheric sciencesAtmospheric circulation[SHS.GEO] Humanities and Social Sciences/GeographyGeography Planning and Development0207 environmental engineeringSoil resilienceGeology02 engineering and technology[SHS.GEO]Humanities and Social Sciences/GeographySeasonality010502 geochemistry & geophysicsMonsoonmedicine.disease01 natural sciences[ SHS.GEO ] Humanities and Social Sciences/GeographyIndian oceanSea breeze13. Climate actionClimatologymedicineEnvironmental sciencePrecipitationSurface runoff020701 environmental engineering0105 earth and related environmental sciences
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Optimized Class-Separability in Hyperspectral Images

2016

International audience; Image visualization techniques are mostly based on three bands as RGB color composite channels for human eye to characterize the scene. This, however, is not effective in case of hyper-spectral images (HSI) because they contain dozens of informative spectral bands. To eliminate redundancy of spectral information among these bands, dimensionality reduction (DR) is applied while at the same trying to retain maximum information. In this paper, we propose a new method of information-preserved hyper-spectral satellite image visualization that is based on fusion of unsupervised band selection techniques and color matching function (CMF) stretching. The results show consist…

010504 meteorology & atmospheric sciencesBand SelectionComputer science0211 other engineering and technologiesComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION[SDU.STU]Sciences of the Universe [physics]/Earth Sciences02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing01 natural sciencesTransformation[SPI]Engineering Sciences [physics][ SPI.NRJ ] Engineering Sciences [physics]/Electric powerDisplay[ SPI ] Engineering Sciences [physics]Computer visionclass separabilityFusion021101 geological & geomatics engineering0105 earth and related environmental sciencesColor imagebusiness.industry[SPI.NRJ]Engineering Sciences [physics]/Electric powerHyperspectral imagingPattern recognition[ SDU.STU ] Sciences of the Universe [physics]/Earth SciencesImage segmentationSpectral bandsDimensionality reductionVisualization[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/ElectronicsImaging spectroscopyFull spectral imagingRGB color modelArtificial intelligencehyper-spectral image visualizationbusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Environmental and biological factors are joint drivers of mercury biomagnification in subarctic lake food webs along a climate and productivity gradi…

2021

Subarctic lakes are getting warmer and more productive due to the joint effects of climate change and intensive land-use practices (e.g. forest clear-cutting and peatland ditching), processes that potentially increase leaching of peat- and soil-stored mercury into lake ecosystems. We sampled biotic communities from primary producers (algae) to top consumers (piscivorous fish), in 19 subarctic lakes situated on a latitudinal (69.0-66.5 degrees N), climatic (+3.2 degrees C temperature and +30% precipitation from north to south) and catchment land-use (pristine to intensive forestry areas) gradient. We first tested how the joint effects of climate and productivity influence mercury biomagnific…

010504 meteorology & atmospheric sciencesBiomagnificationTROPHIC POSITIONmaankäyttö010501 environmental sciencesMETHYLMERCURY01 natural sciencesFood chainBiological FactorsONTARIO LAKESCHAIN STRUCTUREClimate changeympäristömyrkytWaste Management and DisposalLand-useApex predatorTrophic levelkalatStable isotopes2. Zero hungerFRESH-WATEREcologyFishesvesiekosysteemitBIOACCUMULATIONselkärangattomatPollutionSubarctic climateclimate changeProductivity (ecology)Environmental MonitoringFood chain lengthEnvironmental EngineeringFood Chainelohopeachemistry.chemical_elementstable isotopeskasautuminenWHITEFISHland-useEnvironmental ChemistryAnimalsravintoketjutEcosystem1172 Environmental sciences0105 earth and related environmental sciencesfishfood chain lengthLake ecosystemMercury15. Life on landilmastonmuutoksetCHARR SALVELINUS-ALPINUSinvertebratesInvertebratesMercury (element)LakesFishchemistryisotooppianalyysi13. Climate actionEnvironmental scienceMARINEWater Pollutants Chemical
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Smap-based retrieval of vegetation opacity and albedo

2020

Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. The questions addressed in this study are: 1) what is the transparency of the vegetation canopy for different biomes around the Globe at the low-frequency L-band?, 2) what is the seasonal amplitude of vegetation microwave optical depth for different biomes?, 3) what is the effective scattering at this frequency for different vegetation types?, 4) what is the impact of imprecise characterization of vegetation microwave properties on retrieval of soil surface conditions? These questions are addressed based on the recently completed one full annual cycl…

010504 meteorology & atmospheric sciencesBiome0211 other engineering and technologiesFOS: Physical sciences02 engineering and technology15. Life on landAlbedoAnnual cycle01 natural sciencesGeophysics (physics.geo-ph)Physics - GeophysicsMicrowave imaging13. Climate actionBrightness temperaturemedicineEnvironmental sciencemedicine.symptomVegetation (pathology)Water contentOptical depth021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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The roles of microlites and phenocrysts during degassing of silicic magma

2022

Abstract Silicic magmas span a wide range of eruptive styles between explosive and effusive, and transitions between these styles are commonplace. Yet the triggers of switches in eruptive style remain poorly understood. Eruptions are mostly driven by degassing of magmatic water and their eruption style - effusive or explosive - is likely governed by the efficiency of outgassing as well as magma ascent rate. Microlites and phenocrysts are often purported to promote heterogeneous bubble nucleation and outgassing, both key variables in the degassing dynamics that become crucial in controlling the eruptive style. Here, in order to shed light on the role of nature, size and abundance of crystals…

010504 meteorology & atmospheric sciencesBubbleNucleationSilicicengineering.material010502 geochemistry & geophysics01 natural sciencesMicroliteMagmatic waterGeophysicsSpace and Planetary ScienceGeochemistry and PetrologyMagmaRhyoliteEarth and Planetary Sciences (miscellaneous)engineeringPhenocrystPetrologyGeology0105 earth and related environmental sciencesEarth and Planetary Science Letters
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Evaluating roughness effects on C-band AMSR-E observations

2014

International audience; The usefulness of microwave remote sensing to retrieve near-surface soil moisture has already been demonstrated in many studies. However, obtaining high quality estimates of soil moisture is influenced by many effects from soil, vegetation and atmosphere; one of the key parameters is surface roughness. This research focusses on a semi-empirical method to evaluate the roughness effects from space borne observations. Global maps of roughness effects are evaluated at C-band from AMSR-E measurements.

010504 meteorology & atmospheric sciencesC band[SDE.MCG]Environmental Sciences/Global Changes0211 other engineering and technologiessoil surface roughnessAMSR-E02 engineering and technologySurface finish01 natural sciences13. Climate actionEnvironmental sciencesoil moisture[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing2014 IEEE Geoscience and Remote Sensing Symposium
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