简介:Thispaperisasummarizationonevaluationofvalueofartificialforest.Themaincontentsinclude:(i)thedifferenceinconceptsbetweenecologicalfunction,ecologicalefficiencyandecologicalbenefitsofartificialforest;(ii)themotiveandseveraltachesofeconomicfeedbackorcompensationforecologicalbenefit;(iii)theecologicalefficienciesofartificialforestandthemaincorrelativefactorswhichincludestheecologicalefficienciesofartificialforestandthemaincorrelationfactorsinfectingtheecologicalefficiency;(iv)thebasicmathcorrelationsbetweenecologicalefficienciesofartificialforestandtherelatedfactors;(v)servicerangeoftheecologicalefficienciesofartificialforest;and(vi)thebasicprincipleofmeasurementofecologicalefficienciesofartificialforest.Attheend,thebasicmethodsofmainecologicalefficienciesofartificialforestareexpatiated.
简介:详细土壤调查包含昂贵、费时间的工作并且要求专家知识。因为土壤调查提供信息满足大量需要,新方法是必要的快速并且精确地印射土壤。在这研究,多层的视感控器人工的神经网络(ANN)被开发单位使用印射土壤数字举起模型(DEM)属性。几最佳的ANN基于很多个输入数据被生产并且隐藏单位。途径使用了测试和确认区域计算插入内推并且外推的数据的精确性。结果证明采用的土壤分类的系统和水平在结果的精确性上有直接效果。在底层,更小的错误比土壤分类(圣)系统与世界引用库(世界佛教徙联谊会)分类标准被观察,但是当使用圣时,更多的土壤类能被预言(在圣的情况中的7土壤对5与世界佛教徙联谊会)。训练错误为当测试错误(插值错误)和确认错误(推测错误)分别地象50%和70%一样高时,模型使用了的所有ANN低于11%。是期望,用分类的高水平的土壤预言介绍了精确性的更好全面的水平。获得更好的预言除了DEM属性,与是的地形或岩性学有关的数据形成土壤的因素,应该被用作ANN输入数据。
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.