简介：Basedonthecurrentstatusofnature,economyandsociety,andinthelightofinterconnectedpatternsofmaterial,energyandinformationflows,theForestEco-NetworkSysteminChina(CFENS)istobeestablishedtoharmonizesthedevelopmentofhuman,natureandsocietyinthiscountry,whichisofintegrity,multi-function,highefficiencyandoperability,andviewsthewholemainlandasanecosystemwithdifferentbigparchesconsistingofdifferenttypesofforests,grasslands,flelds,barrenhill...

简介：植物在最佳的季节的条件下面有花的一个能力保证繁殖成功。光周期和温度是植物flowering的二个重要季节依赖者因素。植物的花的转变在光周期和温度取决于变化的精确测量。在Arabidopsis和米饭上的分子的生物学和遗传的最近的进展表明由光周期和温度的植物flowering的规定与不同规章的小径涉及一个复杂基因网络，并且新证据和理解在米饭flowering的规定被提供。这里，我们总结并且分析不同flowering规章的小径详细在米饭基于以前的研究和我们的结果包括短天的提升，长天的抑制，flowering的长天的正式就职，夜里裂缝，不同轻质量并且温度规定小径。

简介：ChineseForestEcosystemResearchNetwork,estabfishedinlate1950'sanddirectlyconstructedandadministeredbytheScienceandTechnologyDepartmentofStateForestryAdministrationofChina,isalargeecologyresearchnetworkfocusesonlong-termecosystemfixed-observation.Itembodies15sitesthatrepresentdiverseecosystemsandresearchpriorities,including6state-levelsites.CFERNOfficecoordinatescommunications,networkpublications,andresearch-planningactivities.CFERNusestheadvancedgroundandspatialobservationtechnologiessuchasRS,GPS,GIStostudythestructure,functionallawsandfeedbackmechanismofChineseforestecosystem,aswellasitseffectsonChina'ssocialandeconomicdevelopment.ThemaintaskscarriedoutbyCFERNare:(1)constructionofthedatabaseonthestructureandfunctionsofChineseforestecosystemanditsecologicalenvironmentalfactors;(2)thedatabaseconstructionofforestresources,ecologicalenvironment,waterresourcesandrelatedsocialeconomyinbothregionalandnationalscales;(3)theestablishmentofanevaluationsystemofforestecologicaleffectsinChina'smaindrainageareas;(4)theestabfishmentofaforestenvironmentmonitoringnetworkandadynamicpredictionandalarmsystem.

简介：BPandRBFneuralnetworktopredictforeststockvolumewerestudied,butthestudyinevaluatingbothnetworks’applicationeffectswasnotconducted.Inordertofindahigherforecastprecision,morestrongapplicativemethod,thecomprehensiveanalysisandevaluationonthetwomethodswerecarriedoutinthepracticalapplication.Bythecorrelationanalysis,crowndensity,shady-slopeandsunny-slope,TM1,TM2,TM3,TM5,TM7,NDVI,TM,(4-3),TM4/3wereselectedasinputvariables,andtheforestvolumeofMiyunCountyasoutputvariables,RBFandBPneuralnetworkmodelsforforecastingtheforestvolumewereestablished.Andtheneuralnetworktrainingsteplength,trainingtime,predictionaccuracyandtheapplicabilitymodelofthetwomethodswerecomprehensivelyanalyzed.TheresultsshowthattheRBFneuralnetworkmodelissuperiortotheBPneuralnetworkmodel.

简介：ＦＯＲＵＭＯＮ“ＷＯＭＥＮＡＮＤＳＯＣＩＡＬＦＯＲＥＳＴＲＹ”ＨＥＬＤＢＹＴＨＥＦＯＲＥＳＴＲＹＡＮＤＳＯＣＩＥＴＹＮＥＴＷＯＲＫ￥ＢｙＬｉＷｅｉｃｈａｎｇＡｆｏｒｕｍｏｎ＂ＷｏｍｅｎａｎｄＳｏｃｉａｌＦｏｒｅｓｔｒｙ＂ｗａｓｈｅｌｄａｔｔｈｅＩｎｓ...

简介：Urbanforestisanimportantcompositionandthewindowandsoulofmoderncities,whichhasacloserelationshipwithecologicalenvironmentandinvestingenvironment.SourbanforesthasbeenconstructedinChina.HuainingCountycouldholdofthehistoricalopportunityandcomeupwiththegreatblueprintofforestecologicalnetworksystemconstructionforthenewtown.Thispapermainlyintroducestheguidingideas,principles,goalsandoveralllayoutsoftheconstructioninthenewtown,andhopethatitwillbeamodelforothercounty-levelforestecologicalnetworksystemconstructioninChina.

简介：Moreaccurateestimationofcropevapotranspiration(ETc)inaregionalscalehasalwaysbeenoneofthemostimportantchallenges.TemporalandspatialmonitoringofETcusingsatelliteimagescanhelptoenhanceaccuracyofestimations.Inthisstudy,the(ETc)ricemapswereproducedbyusingstatistical/experimentalmethodsbasedoncropcoefficient(Kc)mapsderivedfromvegetationindex(VI).Kcwasestimatedusingfourmethods,includinglinearrelationshipbetweenKcandVI(Kc-VI),calibratedmodelofKc-VI,linearrelationshipbetweenKcb(thebasalcropcoefficient)andVI(Kcb-VI),andcalibratedmodelofKcb-VI.TheresultsshowedthatcalibratedmodelofKc-VIhadabetterperformancecomparedtotheothermethods,withnormalizedrootmeansquareerrors(NRMSE),meanabsoluteerrorandrootmeansquareerrorbeing5.7%,0.05mm/dand0.06mm/d,respectively.(ETc)ricemapswereproducedbyusingcalibratedmodelofKc-VIandreferenceevapotranspiration(ET0)fromFAOPenman-Monteithmethod.TheNRMSEwas21.3%forusingFAOPenman-Monteithmethod.Therefore,calibratedKc-VImodelincombiningwithET0basedontheLandsat7ETM+imagescouldbeprovidedagoodestimationof(ETc)riceinregionalscale,andcanbeappliedtoestimatewaterrequirementduetothefreeandfacilitateaccess.

简介：Theradialbasisfunction(RBF)emergedasavariantofartificialneuralnetwork.Generalizedregressionneuralnetwork(GRNN)isonetypeofRBF,anditsprincipaladvantagesarethatitcanquicklylearnandrapidlyconvergetotheoptimalregressionsurfacewithlargenumberofdatasets.Hyperspectralreffectance(350to2500nm)datawererecordedattwodifferentricesitesintwoexperimentfieldswithtwocultivars,threenitrogentreatmentsandoneplantdensity(45plantsm-2).Stepwisemultivariableregressionmodel(SMR)andRBFwereusedtocomparetheirpredictabilityfortheleafareaindex(LAI)andgreenleafchlorophylldensity(GLCD)ofricebasedonreffectance(R)anditsthreedifferenttransformations,thefirstderivativereffectance(D1),thesecondderivativereffectance(D2)andthelog-transformedre?ectance(LOG).GRNNbasedonD1wasthebestmodelforthepredictionofriceLAIandGLCD.TherelationshipsbetweendifferenttransformationsofreffectanceandriceparameterscouldbefurtherimprovedwhenRBFwasemployed.Owingtoitsstrongcapacityfornonlinearmappingandgoodrobustness,GRNNcouldmaximizethesensitivitytochlorophyllcontentusingD1.ItisconcludedthatRBFmayprovideausefulexploratoryandpredictivetoolfortheestimationofricebiophysicalparameters.

简介：Background:LeafAreaIndex(LAI)isanimportantparameterusedinmonitoringandmodelingofforestecosystems.Theaimofthisstudywastoevaluateperformanceoftheartificialneuralnetwork(ANN)modelstopredicttheLAIbycomparingtheregressionanalysismodelsastheclassicalmethodinthesepureandeven-agedCrimeanpineforeststands.Methods:OnehundredeighttemporarysampleplotswerecollectedfromCrimeanpineforeststandstoestimatestandparameters.EachsampleplotwasimagedwithhemisphericalphotographstodetecttheLAI.ThepartialcorrelationanalysiswasusedtoassesstherelationshipsbetweenthestandLAIvaluesandstandparameters,andthemultivariatelinearregressionanalysiswasusedtopredicttheLAIfromstandparameters.DifferentartificialneuralnetworkmodelscomprisingdifferentnumberofneuronandtransferfunctionsweretrainedandusedtopredicttheLAIofforeststands.Results:ThecorrelationcoefficientsbetweenLAIandstandparameters(standnumberoftrees,basalarea,thequadraticmeandiameter,standdensityandstandage)weresignificantatthelevelof0.01.Thestandage,numberoftrees,siteindex,andbasalareawereindependentparametersinthemostsuccessfulregressionmodelpredictedLAIvaluesusingstandparameters(/?;adj=0.5431).AscorrespondingmethodtopredicttheinteractionsbetweenthestandLAIvaluesandstandparameters,theneuralnetworkarchitecturebasedontheRBF4-19-1withGaussianactivationfunctioninhiddenlayerandtheidentityactivationfunctioninoutputlayerperformedbetterinpredictingLAI(SSE(12.1040),MSE(0.1223),RM5E(0.3497),AIC(0.1040),BIC(-777310)andR2(0.6392))comparedtotheotherstudiedtechniques.Conclusion:TheANNoutperformedthemultivariateregressiontechniquesinpredictingLAIfromstandparameters.TheANNmodels,developedinthisstudy,mayaidinmakingforestmanagementplanninginstudyforeststands.

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