简介：Thispaperisasummarizationonevaluationofvalueofartificialforest.Themaincontentsinclude:(i)thedifferenceinconceptsbetweenecologicalfunction,ecologicalefficiencyandecologicalbenefitsofartificialforest;(ii)themotiveandseveraltachesofeconomicfeedbackorcompensationforecologicalbenefit;(iii)theecologicalefficienciesofartificialforestandthemaincorrelativefactorswhichincludestheecologicalefficienciesofartificialforestandthemaincorrelationfactorsinfectingtheecologicalefficiency;(iv)thebasicmathcorrelationsbetweenecologicalefficienciesofartificialforestandtherelatedfactors;(v)servicerangeoftheecologicalefficienciesofartificialforest;and(vi)thebasicprincipleofmeasurementofecologicalefficienciesofartificialforest.Attheend,thebasicmethodsofmainecologicalefficienciesofartificialforestareexpatiated.

简介：Inordertostudyseedqualitychangesofmainafforestationspeciesunderhightemperatureandhighrelativehumidity,thedeteriorationmechanismofseedsofRobiniapesudoacaciaandPinustabulaeformisfromaridandsemiaridareasofNorthernChinawaselucidatedinthisstudy.Theseedswereartificiallyagedfor2and6datthetemperatureof45oCandtherelativehumidity(RH)of50%,75%and100%,respectively.Theresultsshowedthatthegerminabilitydecreasedandthecellmembranedeteriorated...

简介：Inordertoraisetheprecisionofstresswaveimagingtechnology(SWIT),undertheconditionsofdifferentareaandoutlineofsimulatedcavitydefectsintimberdiscsofspruce,differentnumberofusedsensors,therelationshipbetweenimaginggraphdefectsandrealdefectsisstudied.Theresultshows:SWITcandisplaygraphofdefects,theprecisionofimaginggraphrelatestorateofrealdefectareaandareaofthetestedwoodcrosssection,thenumberofusedsensorsandoutlineshapeofthedefects.Whentheraterisesfrom1.6%to25.0%,therelativeerrorofgraphdefectareaandrealdefectareadropsfrom22.6%to9.7%.Whenthenumberofusedsensorsisfrom6to24,thegraphofSWITcanshowtheexistenceofrealdefect.ButthenumberofsensorsusedinfluencestheprecisionofSWIT.Outlineshapeofdefectshascertaineffectondetectionofdefects.Undertheconditionofthesamedefectarea,thedefectsoflongandnarrowshapeareeasytobeshownbygraph.Therelationerrorofdefectareaofsuborbicularshapeissmallerthanthatoflongandnarrowshape.

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.