简介：

简介：Thesimilaritytheorywassystematicallyintroduced,bycombiningthetheoryandtheanalytichierarchyprocess(AHP),andtakingthedynamicchangesoftwo-stagegreenlandpatchesinShenzhenasanexample,thepatchessimilarityofeachdistrictandeachgreenlandtypewereestimated.Thelocation,shapeandareasofgreenlandunitwereusedasthesimilarityelements.Thenthesimilaritycoefficientscanbedefined.Theoverlappingnumberofgreenlandpatchesindicatedthelocationvariationofgreenland.Theratioofminimumandmaximumshapeindexofgreenlandindicatedtheshapevariationofgreenland.Withthesamemethod,theareasvariationcoefficientwasalsoobtained.Theresultsshowsthatbasedonsimilaritytheoryandmethodtheestimationofgreenlandvariationisfeasible,whichcanprovideeffectivemethodsandaccordanceforthefurtherassessmentofgreenlanddevelopmentinShenzhenSpecialEconomicZone.

简介：Manystudiesindicatedthattheproductsofbiosphereburninghaveshortandlong-termeffectsontheatmosphere.Vegetationburningcanproducesomegaseswhichhavesignificantinfluenceonenvironment,includingsomegreenhousegasesasCO2andCH4,etc.Smokeaerosolsproducedfromburningalsoinfluenceglobalclimateandatmosphericchemistry.Thepapercalculatestheconsumedbiomassduetoforestfiresaccordingtothestatisticsofforestfiresfrom1991to2000andresearchresultsofbiomassofChineseforests.Duringthestudyperiod,forestfiresburnedaverage5Tg-7Tgbiomasseachyearanddirectlyemitted20.24Tg-28.56Tgcarbon.In1991-2000,averageemissionofcarbondioxideandCH4accountfor2.7%-3.9%and3.3%-4.7%ofthetotalemissionofChina(calculatingwiththedataof2000),respectively.

简介：Basedonsixthandseventhnationalforestryinventorydataofthesixprovinces,includingGuangdong,Jiangxi,Guizhou,Shaanxi,JilinandBeijing,thethreemethods(IPCC,continuousfunctionforbiomassexpansionfactorandweightedbiomassregressionmodel)wereselectedtoestimatewoodbiomassinthispaper.Theestimationofthethreemethodswerecomparedandanalyzedfromcalculatingprocess,methodcharacters,repeatabilityandverifiabilitytostabilityofgrowthrateofbiomassbetweentwoperiods.TheresultsshowedthetotalbiomassestimatedbyIPCCmethodwithvariableBEF2waslarge,thetotalbiomassestimatedbyIPCCmethodwithconstantBEF2wassmallandthetotalbiomassesestimatedbycontinuousfunctionforbiomassexpansionfactorandweightedbiomassregressionmodelweremiddle.Thebiomassexpansionfactorderivedfromweightedregressionmodelwasmoststableinthedifferentprovinces.Basedontheseventhnationalforestryinventorydata,thebiomassexpansionfactorsofvariouskindsoftreespeciesderivedfromIPCCandtheweightedregressionmodelweremorestablethanthebiomassexpansionfactorsderivedfromcontinuousfunctionmethod.Thegrowthrateofbiomassbetweentwoperiodswasthesameregularpatternasthebiomassexpansionfactors.

简介：Bytakingtheurbangreenlandastestingobjective,theGeoEye-1fusionimageasbackground,andusingMoran’sindex,thecorrelativityofgreenlandunitshasbeenmeasured.Then,thepresamplingwasconductedbasedonthemeasuredcorrelativity,thesystematicsamplingdesignhasbeencarriedoutattheprecision95%and85%.ByusingofKriginginterpolationmethod,theweightofdifferenttypeofgreenlandhasbeendividedintothestratifications.Throughthestratifiedsampling,theestimationofgreenlandcoveragehasbeenobtained.Theresultsshowthattheurbangreenlandcoverageareais14.81km~2,accountingfor27.73%ofallurbanlandareas.Comparedwiththecommonsamplingmethod,theprecisionofthatincreasedabout3%.Thismethodcanalsobeutilizedinmonitoringdynamicvariationofurbangreenlandwiththerealtimeimages.

简介：Thedevelopmentofhigh-resolutionremotesensingimagingtechnologyprovidesanewwaytothelarge-scaleestimationofforestcanopydensity.Thetraditionalinversionmethodsforcanopydensityonlyusespectralortopographicalfeaturesofremotesensingimages.However,duetotheexistenceofthedifferentthingwithsamespectrumandthesamethingwithdifferentspectrumphenomena,itisdifficulttoimprovetheestimationaccuracyofcanopydensity.Basedonspectrumandothertraditionalfeatures,thispapercombinestexturefeaturesofremotesensingimagestoestimatecanopydensity.Firstly,thegraylevelco-occurrencematrix(GLCM)texturefeaturesarecomputedusingobjectbasedmethod.Then,theprincipalcomponentanalysis(PCA)methodisappliedincorrelationanalysisanddimensionreductionoftexturefeatures.Finally,spectrumandtopographicalfeaturestogetherwithtexturefeaturesareintroducedintostepwiseregressionmodeltoestimatecanopydensity.Theexperimentalresultsshowedthatcomparedwiththetraditionalmethodonlybasedonspectrumortopographicalfeatures,themethodcombinedwithtexturefeaturesgreatlyimprovedtheestimationaccuracy.Thecoefficientofdetermination(adjustedR~2)increasedfrom0.737to0.805.Theestimationaccuracyincreasedfrom81.03%to84.32%.

简介：Asanewlycurrentadvancedanalysistechnology,thenear-infrared(NIR)spectroscopypossessesadvantagesofeasyoperation,fastandaccuratedetection,lowcostandnon-destructivetest,hasbeenwidelyusedinthefieldsincludingpulpmanufacturingandpaper-making,woodpropertiesestimation,woodprogressing,woodcompositesproducingandwoodprotection.Inpresentwork,basedonintroductionofthebasicprinciplesofNIRanditsmaincharacteristics,anoverviewwasconductedfocusingontheresearchstatusofwoodanatomicalcharacteristics(includingcellulosecrystallinity,microfibrilangleandfibermorphology)estimationbyusingNIRspectroscopy.Moreover,theapplicationtrendswereprospected.

简介：Background:Informationonabove-groundbiomass(AGB)isimportantformanagingforestresourceuseatlocallevels,landmanagementplanningatregionallevels,andcarbonemissionsreportingatnationalandinternationallevels.Inmanytropicaldevelopingcountries,thisinformationmaybeunreliableoratascaletoocoarseforuseatlocallevels.ThereisavitalneedtoprovideestimatesofAGBwithquantifiableuncertaintythatcanfacilitatelandusemanagementandpolicydevelopmentimprovements.Model-basedmethodsprovideanefficientframeworktoestimateAGB.Methods:UsingNationalForestInventory(NFI)datafora~1,000,000hastudyareainthemiomboecoregion,Zambia,weestimatedAGBusingpredictedcanopycover,environmentaldata,disturbancedata,andLandsat8OLIsatelliteimagery.Weassesseddifferentcombinationsofthesedatasetsusingthreemodels,asemiparametricgeneralizedadditivemodel(GAM)andtwononlinearmodels(sigmoidalandexponential),employingageneticalgorithmforvariableselectionthatminimizedrootmeansquarepredictionerror(RMSPE),calculatedthroughcross-validation.Wecomparedmodelfitstatisticstoanullmodelasabaselineestimationmethod.Usingbootstrapresamplingmethods,wecalculated95%confidenceintervalsforeachmodelandcomparedresultstoasimpleestimateofmeanAGBfromtheNFIgroundplotdata.Results:Canopycover,soilmoisture,andvegetationindiceswereconsistentlyselectedaspredictorvariables.ThesigmoidalmodelandtheGAMperformedsimilarly;forbothmodelstheRMSPEwas-36.8tonnesperhectare(i.e.,57%ofthemean).However,thesigmoidalmodelwasapproximately30%moreefficientthantheGAM,assessedusingbootstrappedvarianceestimatesrelativetoanullmodel.Afterselectingthesigmoidalmodel,weestimatedtotalAGBforthestudyareaat64,526,209tonnes(+/-477,730),withaconfidenceinterval20timesmoreprecisethanasimpledesignbasedestimate.Conclusions:OurfindingsdemonstratethatNFIdatamaybecombinedwithfreelyavailablesate

简介：Background:Currently,thecommonandfeasiblewaytoestimatethemostaccurateforestbiomassrequiresgroundmeasurementsandallometricmodels.Previousstudieshavebeenconductedonallometricequationsdevelopmentforestimatingtreeabovegroundbiomass(AGB)oftropicaldipterocarpforests(TDFs)inKalimantan(IndonesianBorneo).However,beforetheuseofexistingequations,avalidationfortheselectionofthebestallometricequationisrequiredtoassessthemodelbiasandprecision.Thisstudyaimsatevaluatingthevalidityoflocalandpantropicalequations;developingnewallometricequationsforestimatingtreeAGBinTDFsofKalimantan;andvalidatingthenewequationsusingindependentdatasets.Methods:Weused108treesamplesfromdestructivesamplingtodeveloptheallometricequations,withmaximumtreediameterof175cmandanother109samplesfrompreviousstudiesforvalidatingourequations.WeperformedordinaryleastsquareslinearregressiontoexploretherelationshipbetweentheAGBandthepredictorvariablesinthenaturallogarithmicform.Results:Thisstudyfoundthatmostoftheexistinglocalequationstendedtobebiasedandimprecise,withmeanrelativeerrorandmeanabsoluterelativeerrormorethan0.1and0.3,respectively.WedevelopednewallometricequationsfortreeAGBestimationintheTDFsofKalimantan.Throughavalidationusinganindependentdataset,wefoundthatourequationswerereliableinestimatingtreeAGBinTDF.Thepantropicalequation,whichincludestreediameter,wooddensityandtotalheightaspredictorvariablesperformedonlyslightlyworsethanournewmodels.Conclusions:OurequationsimprovetheprecisionandreducethebiasofAGBestimatesofTDFs.Localmodelsdevelopedfromsmallsamplestendtosystematicallybias.AvalidationofexistingAGBmodelsisessentialbeforetheuseofthemodels.

简介：Forestsareamongthemostimportantcarbonsinksonearth.However,theircomplexstructureandvastareasprecludeaccurateestimationofforestcarbonstocks.Datasetsfromforestmonitoringusingadvancedsatelliteimageryarenowusedininternationalpolicyagreements.DatasetsenabletrackingofemissionsofCO2intotheatmospherecausedbydeforestationandothertypesofland-usechanges.TheaimofthisstudyistodeterminethecapabilityofSPOT-HRGSatellitedatatoestimateabovegroundcarbonstockinadistrictofDarabkolaresearchandtrainingforest,Iran.Preprocessingtoeliminateorreducegeometricerrorandatmosphericerrorwereperformedontheimages.Usingclustersampling,165sampleplotsweretaken.Of165plots,81wereinnaturalhabitats,and84wereinforestplantations.Followingthecollectionofgrounddata,biomassandcarbonstockswerequantifiedforthesampleplotsonaperhectarebasis.Nonparametricregressionmodelssuchassupportvectorregressionwereusedformodelingpurposeswithdifferentkernelsincludinglinear,sigmoid,polynomial,andradialbasisfunction.Theresultsshowedthatathird-degreepolynomialwasthebestmodelfortheentirestudiedareashavinganrootmeansquareerror,biasandaccuracy,respectively,of38.41,5.31,and62.2;42.77,16.58,and57.3%forthebestpolynomialfornaturalforest;and44.71,2.31,and64.3%forafforestation.Overall,theseresultsindicatethatSPOTHRGsatellitedataandsupportvectormachinesareusefulforestimatingabovegroundcarbonstock.

简介：这研究的目的是在不平的里海的森林里为单个树估计一个基础区域生长模型。调查被进行以便发现没有任何收获活动，一个那么叫的未触动过的森林和一个区域的一个自然森林从伊朗的里海的森林被选择。在一样的方面并且一样的森林类型的三个样品阴谋被选择。在每个阴谋，总计树高度，在胸高度的直径，邻居树和方位角的距离被测量。三十棵树被选择并且与增长borer钻了决定增长模型。回归分析被用来估计生长模型。结果证明为单个树，在年度基础区域增长之间有一种重要非线性的关系，作为依赖变量，和基础区域。结果也证明竞争的树的基础区域在生长上有积极影响。增长与更多是更高的竞争附近的树可能因为阴谋与每公顷和更多的竞争大量，也很可能每公顷与更低的体积比阴谋有更高的地点索引或良土或更好的地点生产率。

简介：Weusedgeographicinformationsystemapplicationsandstatisticalanalysestoclassifyyoung,prematureforestareasinsoutheasternGeorgiausingcombineddatafromLandsatTM5satelliteimageryandgroundinventorydata.Wedefinedprematurestandsasforestswithtreesupto15yearsold.Weestimatedtheprematureforestareasusingthreemethods:maximumlikelihoodclassification(MLC),regressionanalysis,andk-nearestneighbor(kNN)modeling.Overallaccuracy(OA)ofclassifyingtheprematureforestusingMLCwas82%andtheKappacoefficientofagreementwas0.63,whichwasthehighestamongthemethodsthatwehavetested.ThekNNapproachrankedsecondinaccuracywithOAof61%andaKappacoefficientofagreementof0.22.RegressionanalysisyieldedanOAof57%andaKappacoefficientof0.14.WeconcludethatLandsatimagerycanbeeffectivelyusedforestimatingprematureforestareasincombinationwithimageprocessingclassifierssuchasMLC.

简介：Thispaperfocusesontheuseofmodelsforincreasingtheprecisionofestimatorsinlarge-areaforestsurveys.Itismotivatedbytheincreasingavailabilityofremotelysenseddata,whichfacilitatesthedevelopmentofmodelspredictingthevariablesofinterestinforestsurveys.Wepresent,reviewandcomparethreedifferentestimationframeworkswheremodelsplayacorerole:model-assisted,model-based,andhybridestimation.Thefirsttwoarewellknown,whereasthethirdhasonlyrecentlybeenintroducedinforestsurveys.Hybridinferencemixesdesignbasedandmodel-basedinference,sinceitreliesonaprobabilitysampleofauxiliarydataandamodelpredictingthetargetvariablefromtheauxiliarydata.Wereviewstudiesonlarge-areaforestsurveysbasedonmodel-assisted,modelbased,andhybridestimation,anddiscussadvantagesanddisadvantagesoftheapproaches.Weconcludethatnogeneralrecommendationscanbemadeaboutwhethermodel-assisted,model-based,orhybridestimationshouldbepreferred.Thechoicedependsontheobjectiveofthesurveyandthepossibilitiestoacquireappropriatefieldandremotelysenseddata.Wealsoconcludethatmodellingapproachescanonlybesuccessfullyappliedforestimatingtargetvariablessuchasgrowingstockvolumeorbiomass,whichareadequatelyrelatedtocommonlyavailableremotelysenseddata,andthuspurelyfieldbasedsurveysremainimportantforseveralimportantforestparameters.

简介：Background:Around2000plantspeciesoccurnaturallyinLorestanProvinceofwhich250speciesaremedicinalandmyrtleisoneofthem.Myrtleisashrubwhoseleavesandfruitshavemedicinalvalueandthus,ifmanagedandharvestedproperly,couldproducesustainedeconomicbenefits.Inrecentyears,however,overhalfofthemyrtlesiteareaswasdestroyed,duetoinappropriatemanagementandexcessiveharvestingpractices.Thus,comingupwithapracticalharvestingapproachalongwithidentifyingthosefactorsdamagingthesites,seemstobeverycrucial.Methods:Inourinvestigation,wecalculatedtheconservationvalueperhectareofmyrtleintheDoorehforestareainLorestanProvince.UsingtheContingentValuation(CV)andDoubleBoundedDichotomousChoice(DBDC)methods,wedeterminedthewillingnesstopay(WTP)formyrtleconservation.TheWTPwasestimatedwithalogitmodelforwhichindiceswereobtainedbasedonamaximumprecisioncriterion.Results:Theresultsshowedthat86.67percentofpeoplewerewillingtopayfortheconservationofthesemyrtlesites.AveragemonthlyWTPperfamilywascalculatedas$0.79.TheannualconservationvalueintermsofWTPforthepreservationofthemyrtlesitesinDoorehwasestimatedas$102,525.Amongthevariablesofthemodelpresented,educationhadapositiveimpact,whiletheamountproposedforpaymentandfamilysizehadanegativeimpactontheWTP.Conclusions:Ourestimateofthevalueofmyrtleconservationshouldprovidejustificationforpolicymakersanddecisionmakingbodiesofnaturalresourcestoimplementpoliciesinordertoconservethenaturalsitesofthisspeciesmoreeffectively.

简介：Pinusdensifloravar。zhangwuensis变得快，并且它的干旱和咸度抵抗比Pinussylvestrisvar好。mongolica。我们比较了在二种之间的冷强壮的冷强壮和机制，到为支持并且适用P提供一个理论基础。densifloravar。在冷区域的zhangwuensis。一个冷压力实验在P的3岁的小植物上被执行。densifloravar。zhangwuensis和P。sylvestrisvar。在在五温度政体变硬以后的mongolica，5，10，20，40，并且60?瑩??欠灡慰愠??杭洿????猯灵￣桳睯摥愠猠浩汩牡椠据敲獡?湩栠楥桧?畢?楤灳慬敹??楨桧牥椠据敲獡摥椠?潴慴?档潬潲桰汹?愠瑣癩瑩敩?景爠'T獩潣???湡??呓?湡?敬敶?景??愠摮?????猯'T￣桴湡琠潨敳琠敲瑡摥眠瑩??欠灡慰愠??杭洿????猯灵？