简介：ThefeasibilityofERSSARTandemdataformappingforestandnon-forestcoverinChinawasevaluatedoverZengchengCountyintheSouthChina.Anaccuracyof75%hasbeenachieved.Then,theMACFERST(MappingChinaForestwithERSSARTandemdata)projectstartedbytheMinistryofScienceandTechnology(MOST)ofChinaandtheEuropeanSpaceAgency(ESA)in1999.Thegenerationofalarge-scaleforestmaprequiressolvingproblemssuchasthegeoreferencingandmosaickingofverylongimagestripscov...

简介：ThepurposeofthispaperistostudytheRSdatawebservicesandrelatedsubjectsofdatastorageanddatamanagement.Basedonananalysisofthepresentsituationanddevelopmenttrendofstorageandmanagementofrasterdataandwebservicetechnology,amanagementandservicesystemarchitectureforRemoteSensingrasterdatabasedonwebservicetechnologieswasdeveloped,theimplementationmethodologiesofthekeytechnologyofthesystemwereexploredandaprototypeofthesystemwasillustrated.

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

简介：Climateisadominantenvironmentalfactorinbuildingecosystemstructureanddrivingbioticdynamicswithtopographicdependenceonspatialdistribution.Thispaperdemonstratestheapplicationofinterpolationtechniquetodescribespatialclimatedistribution.Adigitalelevationmodel(DEM)inresolutionof0.01degreeoflatitudeandlongitudehasbeendevelopedbecausetheincorporationofspatialdependenceontopographyiscrucialtoaccuracyofinterpolation.Climatedatafromsparseanddiscr...

简介：目前，关于防风林的保护的效果的大多数研究根据森林带和网络的二规模。与关于全球环境变化的进一步的研究，然而，更多的注意对防风林的地区性的气候效果被给予。在现在的学习，我们由使用陆地表面温度(LST)在地区性的规模分析了防风林的温度效果从在在到在2008的10月的从三月的中国的吉林省的Yushu，Nongan，Dehui，和Fuyu的中等决定成像分光辐射函数(MODIS)的数据。结果证明防风林能作为与没有防风林地区相比增加保护的农田的土壤温度，与0.57的增长吗？

简介：Forestgrowthismainlycurrentlymonitoredusingin-situmeasurementsinnortheastofChina.Toeffectivelymonitorforestgrowthdisturbanceatlargescale,weattemptedtouseremotesensingtechnique,particularly,timeseriesMODISdatafrom2004to2006.Theannualtimeseriesof8-dayenhancedvegetationindex(EVI)datasetwasgeneratedandsmoothedusingaSavitzky-Golayfilter.TheEVItrajectoryduringgrowthseasonwassimulatedusingalogisticmodel.Fromthesimulatedtrajectory,theEVIareaofgrowthseasonandannualEVIentropywerecalculated.Thesetwofactorswerecombinedtomapthedisturbanceregionsofforestgrowth.Finally,thedisturbanceregionswereverifiedusingasetofrandomsamples.Theresultindicatesthatthedisturbancepointshavedistinctivelyhigherentropyandlowerpeak.SomeofthesepointsalsoshowabruptEVIdeclineduringthemidseasonofthepeakphasesordoublepeaks.Thisapproachisdemonstratedtobefeasiblefordisturbancemonitoringofforestgrowth.

简介：Keerqin沙土地位于在向东北平凡、内部的蒙古之间的过渡地面(42°41′-45°15′N，118°35′-123°30′E)在东北Chinaand，它被沙漠化严重影响。根据地球表面的配置和生态环境，植被，赤裸的砂土的职业比率和土壤质地的范围被使用地调查数据作为评估索引选择。Keerqin沙的沙漠化的评估索引系统被使用遥感数据建立，并且赤裸的砂土的职业比率被混合光谱模型获得。这个索引系统被域调查数据验证，结果显示它对Keerqin的thedesertification评估合适。

简介：作为由地面生产的数据的数量，为根渗透雷达(GPR)大，传播和数据的存储消费大资源。减轻这个问题，我们这里用混乱粒子群建议一个根成像算法最佳(CPSO)压缩了根据根空间的稀少基于GPR数据察觉到。雷达数据以稀少的方式被分解，观察，测量并且代表，因此根图象能与有限数据被重建。第一，雷达信号测量和稀少的表示被实现，并且解决方案空格被小浪基础和高斯随机矩阵建立；第二，匹配的功能被看作健康功能，并且最好的健康价值被一个PSO算法发现；然后，混乱搜索被用来获得全球最佳的操作员；最后，根图象被最佳的操作符重建。分别地，从美国GSSIGPR的A扫描数据，B扫描数据，和复杂数据在试验性的测试被使用。为B扫描数据，计算时间被减少60?%和PSNR被改进5.539?dB；为实际的根数据成像，重建PSNR是26.300?dB，和全部的计算时间仅仅是67.210?s。CPSO-OMP算法克服本地最佳套住的问题并且包括地在重建期间提高精确。

简介：WemappedtheforestcoverofKhadimnagarNationalPark(KNP)ofSylhetForestDivisionandestimatedforestchangeoveraperiodof22years(1988-2010)usingLandsatTMimagesandotherGISdata.SupervisedclassificationandNormalizedDifferenceVegetationIndex(NDVI)imageclassificationapproacheswereappliedtotheimagestoproducethreecoverclasses,viz.denseforest,mediumdenseforest,andbareland.Thechangemapwasproducedbydifferencingclassifiedimageriesof1988and2010asbeforeimageandafterimage,respectively,inERDASIMAGINE.Errormatrixandkappastatisticswereusedtoassesstheaccuracyoftheproducedmaps.Overallmapaccuraciesresultingfromsupervisedclassificationof1988and2010imagerieswere84.6%(Kappa0.75)and87.5%(Kappa0.80),respectively.Forestcoverstatisticsresultingfromsupervisedclassificationshowedthatdenseforestandbarelanddeclinedfrom526ha(67%)to417ha(59%)and105ha(13%)to8ha(1%),respectively,whereasmediumdenseforestincreasedfrom155ha(20%)to317ha(40%).ForestcoverchangestatisticsderivedfromNDVIclassificationshowedthatdenseforestdeclinedfrom525ha(67%)to421ha(54%)whilemediumdenseforestincreasedfrom253ha(32%)to356ha(45%).BothsupervisedandNDVIclassificationapproachesshowedsimilartrendsofforestchange,i.e.decreaseofdenseforestandincreaseofmediumdenseforest,whichindicatesdenseforesthasbeenconvertedtomediumdenseforest.Areaofbarelandwasunchanged.Illicitfelling,encroachment,andsettlementnearforestscausedthedenseforestdeclinewhileshortandlongrotationplantationsraisedinvariousyearscausedtheincreaseinareaofmediumdenseforest.ProtectivemeasuresshouldbeundertakentocheckfurtherdegradationofforestatKNP.

简介：Usingthemulti-temporalLandsatdataandsurveydataofnationalresources,theauthorsstudiedthedynamicsofcultivatedlandandlandcoverchangesoftypicalecologicalregionsinChina.TheresultsofinvestigationshowedthatthewholedistributionofthecultivatedlandshiftedtoNortheastandNorthwestChina,andasaresult,theecologicalqualityofcultivatedlanddroppeddown.TheseacoastandcultivatedlandintheareaofYellowRiverMouthexpandedbyanincreasingrateof0.73km?a-1,withadepositingrateof2.1km?a-1.ThedesertificationareaofthedynamicofHorqinSandyLandincreasedfrom60.02%ofthetotallandareain1970sto64.82%in1980sbutdecreasedto54.90%inearly1990s.AstothechangeofNorthTibetlakes,thewaterareaoftheNamuLakedecreasedby38.58km2fromyear1970to1988,withadecreasingrateof2.14km2?a-1.

简介：Background:Overthelastdecades,manyforestsimulatorshavebeendevelopedfortheforestsofindividualEuropeancountries.Theunderlyinggrowthmodelsareusuallybasedonnationaldatasetsofvaryingsize,obtainedfromNationalForestInventoriesorfromlong-termresearchplots.Manyofthesemodelsincludecountry-andlocation-specificpredictors,suchassitequalityindicesthatmayaggregateclimate,soilpropertiesandtopographyeffects.Consequently,itisnotsensibletocomparesuchmodelsamongcountries,anditisoftenimpossibletoapplymodelsoutsidetheregionorcountrytheyweredevelopedfor.However,thereisaclearneedformoregenericallyapplicablebutstilllocallyaccurateandclimatesensitivesimulatorsattheEuropeanscale,whichrequiresthedevelopmentofmodelsthatareapplicableacrosstheEuropeancontinent.ThepurposeofthisstudyistodeveloptreediameterincrementmodelsthatareapplicableattheEuropeanscale,butstilllocallyaccurate.Wecompiledandusedadatasetofdiameterincrementobservationsofover2.3milliontreesfrom10NationalForestInventoriesinEuropeandasetof99potentialexplanatoryvariablescoveringforeststructure,weather,climate,soilandnutrientdeposition.Results:Diameterincrementmodelsarepresentedfor20species/speciesgroups.Selectionofexplanatoryvariableswasdoneusingacombinationofforwardandbackwardselectionmethods.Theexplainedvariancerangedfrom10%to53%dependingonthespecies.Variablesrelatedtoforeststructure(basalareaofthestandandrelativesizeofthetree)contributedmosttotheexplainedvariance,butenvironmentalvariableswereimportanttoaccountforspatialpatterns.Thetypeofenvironmentalvariablesincludeddifferedgreatlyamongspecies.Conclusions:ThepresenteddiameterincrementmodelsarethefirstoftheirkindthatareapplicableattheEuropeanscale.Thisisanimportantsteptowardsthedevelopmentofanewgenerationofforestdevelopmentsimulatorsthatcan

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：Background:Remotesensing-basedmappingofforestEcosystemService(ES)indicatorshasbecomeincreasinglypopular.TheresultingmapsmayenabletospatiallyassesstheprovisioningpotentialofESsandprioritizethelanduseinsubsequentdecisionanalyses.However,themappingisoftenbasedonreadilyavailabledata,suchaslandcovermapsandotherpubliclyavailabledatabases,andignoringtherelateduncertainties.Methods:Thisstudytestedthepotentialtoimprovetherobustnessofthedecisionsbymeansoflocalmodelfittinganduncertaintyanalysis.Thequalityofforestlanduseprioritizationwasevaluatedundertwodifferentdecisionsupportmodels:eitherusingthedevelopedmodelsdeterministicallyorincorporationwiththeuncertaintiesofthemodels.Results:PredictionmodelsbasedonAirborneLaserScanning(ALS)dataexplainedthevariationinproxiesofthesuitabilityofforestplotsformaintainingbiodiversity,producingtimber,storingcarbon,orprovidingrecreationaluses(berrypickingandvisualamenity)withRMSEsof15%–30%,dependingontheES.TheRMSEsoftheALS-basedpredictionswere47%–97%ofthosederivedfromforestresourcemapswithasimilarresolution.Duetoapplyingasimilarfieldcalibrationsteponbothofthedatasources,thedifferencecanbeattributedtothebetterabilityofALStoexplainthevariationintheESproxies.Conclusions:Despitethedifferentaccuracies,proxyvaluespredictedbyboththedatasourcescouldbeusedforapixel-basedprioritizationoflanduseataresolutionof250m~2,i.e.,inaconsiderablymoredetailedscalethanrequiredbycurrentoperationalforestmanagement.TheuncertaintyanalysisindicatedthatmapsoftheESprovisioningpotentialshouldbepreparedseparatelybasedonexpectedandextremeoutcomesoftheESproxymodelstofullydescribetheproductionpossibilitiesofthelandscapeundertheuncertaintiesinthemodels.

简介：Background:Treelinedynamicshaveinevitableimpactsontheforesttreelinestructureandcomposition.ThepresentresearchsoughttoestimatetreelinemovementandstructuralshiftsinresponsetorecentwarminginCehennemdere,Turkey.Afterimplementinganatmosphericcorrection,thegeo-shiftingofimageswasperformedtomatchimagestogetherforaperpixeltrendanalysis.WedevelopedanewapproachbasedontheNDVI,LST(landsurfacetemperature)data,airtemperaturedata,andforeststandmapsfora43-yearperiod.Theforesttreelineborderwasmappedontheforeststandmapsfor1970,1992,2002,and2013toidentifyshiftsinthetreelinealtitudes,andthenprofilestatisticswerecalculatedforeachperiod.Twentysampleplots(10×10pixels)wereselectedtoestimatetheNDVIandLSTshiftsacrosstheforesttimberlineusingper-pixeltrendanalysisandnon-parametricSpearman’scorrelationanalysis.Inaddition,thespatialandtemporalshiftsintreelinetreespecieswerecomputedwithintheselectedplotsforfourtimeperiodsontheforeststandmapstodeterminethepioneertreespecies.Results:Astatisticallysignificantincreasingtrendinallclimatevariableswasobserved,withthehighestslopeinthemonthlyaveragemeanJulytemperature(tau=0.62,ρ<0.00).Theresultantforeststandmapsshowedageographicalexpansionofthetreelineinboththehighestaltitudes(22m–45m)andthelowestaltitudes(20m–105m)from1970to2013.TheperpixeltrendanalysisindicatedanincreasingtrendintheNDVIandLSTvalueswithintheselectedplots.Moreover,increasesintheLSTwerehighlycorrelatedwithincreasesintheNDVIbetween1984and2017(r=0.75,ρ<0.05).CedruslibaniandJuniperuscommunisapp.weretwopioneertreespeciesthatexpandedandgrewconsistentlyonopenlands,primarilyonrocksandsoil-coveredareas,from1970to2013.Conclusion:Thepresentstudyilustratedthatforesttreelinedynamicsandtreelinestructuralchangescanbedetectedusingtwodata

简介：Background:Overthelastdecadesinteresthasgrownonhowclimatechangeimpactsforestresources.However,oneofthemainconstraintsisthatmeteorologicalstationsarefiddledwithmissingclimaticdata.Thisstudycomparedfiveapproachesforestimatingmonthlyprecipitationrecords:inversedistanceweighting(IDW),amodificationofIDWthatincludeselevationdifferencesbetweentargetandneighboringstations(IDWm),correlationcoefficientweighting(CCW),multiplelinearregression(MLR)andartificialneuralnetworks(ANN).Methods:Acompleteseriesofmonthlyprecipitationrecords(199.5-2012)fromtwentymeteorologicalstationslocatedincentralChilewereused.Twotargetstationswereselectedandtheirneighboringstations,locatedwithinaradiusof25km(3stations)and50km(9stations),wereidentified.Cross-validationwasusedforevaluatingtheaccuracyoftheestimationapproaches.Theperformanceandpredictivecapabilityoftheapproacheswereevaluatedusingtheratiooftherootmeansquareerrortothestandarddeviationofmeasureddata(RSR),thepercentbias(PBIAS),andtheNash-Sutcliffeefficiency(NSE).Fortestingthemainandinteractiveeffectsoftheradiusofinfluenceandestimationapproaches,atwo-levelfactorialdesignconsideringthetargetstationastheblockingfactorwasused.Results:ANNandMLRshowedthebeststatisticsforallthestationsandradiusofinfluence.However,theseapproacheswerenotsignificantlydifferentwithIDWm.InclusionofelevationdifferencesintoIDWsignificantlyimprovedIDWmestimates.Intermsofprecision,similarestimateswereobtainedwhenapplyingANN,MLRorIDWm,andtheradiusofinfluencehadasignificantinfluenceontheirestimates,weconcludethatestimatesbasedonnineneighboringstationslocatedwithinaradiusof50kmareneededforcompletingmissingmonthlyprecipitationdatainregionswithcomplextopography.Conclusions:ItisconcludedthatapproachesbasedonANN,MLRandIDWmhadthebestperformanceintwosectorslocatedinso

简介：Background:Theimportanceofstructurallydiverseforestsfortheconservationofbiodiversityandprovisionofawiderangeofecosystemserviceshasbeenwidelyrecognised.However,toolstoquantifystructuraldiversityofforestsinanobjectiveandquantitativewayacrossmanyforesttypesandsitesarestillneeded,forexampletosupportbiodiversitymonitoring.Theexistingapproachestoquantifyforeststructuraldiversityarebasedonsmallgeographicalregionsorsingleforesttypes,typicallyusingonlysmalldatasets.Results:HerewedevelopedanindexofstructuraldiversitybasedonNationalForestInventory(NFI)dataofBadenWurttemberg,Germany,astatewith1.3millionhaofdiverseforesttypesindifferentownerships.Basedonaliteraturereview,11aspectsofstructuraldiversitywereidentifiedaprioriascruciallyimportanttodescribestructuraldiversity.Aninitialcomprehensivelistof52variablesderivedfromNationalForestInventory(NFI)datarelatedtostructuraldiversitywasreducedbyapplyingfiveselectioncriteriatoarriveatonevariableforeachaspectofstructuraldiversity.Thesevariablescomprise1)quadraticmeandiameteratbreastheight(DBH),2)standarddeviationofDBH,3)standarddeviationofstandheight,4)numberofdecayclasses,5)bark-diversityindex,6)treeswithDBH>40cm,7)diversityoffloweringandfructification,8)averagemeandiameterofdowneddeadwood,9)meanDBHofstandingdeadwood,10)treespeciesrichnessand11)treespeciesrichnessintheregenerationlayer.Thesevariableswerecombinedintoasimple,additiveindextoquantifythelevelofstructuraldiversity,whichassumesvaluesbetween0and1.Weappliedthisindexinanexemplarywaytobroadforestcategoriesandownershipstoassessitsfeasibilitytoanalysestructuraldiversityinlarge-scaleforestinventories.Conclusions:Theforeststructureindexpresentedherecanbederivedinasimilarwayfromstandardinventoryvariablesformostotherlarge-scaleforestin

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：Background:Inthispaper,aregressionmodelforpredictingthespatialdistributionofforestcockchaferlarvaeintheHessianRiedregion(Germany)ispresented.Theforestcockchafer,anativebioticpest,isamajorcauseofdamageinforestsinthisregionparticularlyduringtheregenerationphase.ThemodeldevelopedinthisstudyisbasedonasystematicsampleinventoryofforestcockchaferlarvaebyexcavationacrosstheHessianRied.Theseforestcockchaferlarvaedatawerecharacterizedbyexcesszerosandoverdispersion.Methods:Usingspecificgeneralizedadditiveregressionmodels,differentdiscretedistributions,includingthePoisson,negativebinomialandzero-inflatedPoissondistributions,werecompared.Themethodologyemployedallowedthesimultaneousestimationofnon-linearmodeleffectsofcausalcovariatesand,toaccountforspatialautocorrelation,ofa2-dimensionalspatialtrendfunction.Inthevalidationofthemodels,boththeAkaikeinformationcriterion(AIC)andmoredetailedgraphicalproceduresbasedonrandomizedquantileresidualswereused.Results:ThenegativebinomialdistributionwassuperiortothePoissonandthezero-inflatedPoissondistributions,providinganearperfectfittothedata,whichwasproveninanextensivevalidationprocess.Thecausalpredictorsfoundtoaffectthedensityoflarvaesignificantlyweredistancetowatertableandpercentageofpureclaylayerinthesoiltoadepthof1m.Modelpredictionsshowedthatlarvadensityincreasedwithanincreaseindistancetothewatertableuptoalmost4m,afterwhichitremainedconstant,andwithareductioninthepercentageofpureclaylayer.Howeverthislattercorrelationwasweakandrequiresfurtherinvestigation.The2-dimensionaltrendfunctionindicatedastrongspatialeffect,andthusexplainedbyfarthehighestproportionofvariationinlarvadensity.Conclusions:Assuchthemodelcanbeusedtosupportforestpractitionersintheirdecisionmakingforregenerationandforestprotecti

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