Search results for "metsätyypit"

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Comparing the Climatic and Landscape Risk Factors for Lyme Disease Cases in the Upper Midwest and Northeast United States

2020

Lyme disease, recognized as one of the most important vector-borne diseases worldwide, has been increasing in incidence and spatial extend in United States. In the Northeast and Upper Midwest, Lyme disease is transmitted by Ixodes scapularis. Currently, many studies have been conducted to identify factors influencing Lyme disease risk in the Northeast, however, relatively few studies focused on the Upper Midwest. In this study, we explored and compared the climatic and landscape factors that shape the spatial patterns of human Lyme cases in these two regions, using the generalized linear mixed models. Our results showed that climatic variables generally had opposite correlations with Lyme d…

esiintyvyysympäristötekijätHealth Toxicology and MutagenesisClimate030231 tropical medicinelcsh:MedicineEnvironmentForestsmaisemaArticleMidwestern United Statesmetsätyypit03 medical and health sciences0302 clinical medicineLyme diseaseNew EnglandRisk FactorsLymen borrelioosimedicineLyme diseaseAnimalsHumans030212 general & internal medicineBorrelia burgdorfericlimateforest fragmentationLyme DiseasebiologyIxodespaikallisilmastoIncidence (epidemiology)lcsh:RPublic Health Environmental and Occupational HealthClimatic variablesmedicine.diseasebiology.organism_classificationbacterial infections and mycosesLYMEBorrelia-bakteerit<i>Borrelia burgdorferi</i>DeciduousGeographyborrelioosiIxodes scapularisBorrelia burgdorferiSpatial ecologyLinear ModelsDemographyInternational Journal of Environmental Research and Public Health
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Coupling high-resolution satellite imagery with ALS-based canopy height model and digital elevation model in object-based boreal forest habitat type …

2014

We developed a classification workflow for boreal forest habitat type mapping. In object-based image analysis framework, Fractal Net Evolution Approach segmentation was combined with random forest classification. High-resolution WorldView-2 imagery was coupled with ALS based canopy height model and digital terrain model. We calculated several features (e.g. spectral, textural and topographic) per image object from the used datasets. We tested different feature set alternatives; a classification accuracy of 78.0 % was obtained when all features were used. The highest classification accuracy (79.1 %) was obtained when the amount of features was reduced from the initial 328 to the 100 most imp…

ta1172Multispectral imageforest classifierta1171Feature selectionboreaaliset metsätData typeAtomic and Molecular Physics and OpticsComputer Science ApplicationsRandom forestmetsätyypitFeature (computer vision)Satellite imagerySegmentationboreal forestComputers in Earth SciencesDigital elevation modelEngineering (miscellaneous)Remote sensingbiologia
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