简介:ABSTRACT AGISisproposedasatoolforthemanagingplanfortheAntarcticspeciallymanagedarea(ASMA)inAdmiraltyBay.TheASMAcomprisestheareaconsideredtobewithintheglacialdrainagebasinofthebay.Furthermore,itincludespartofSSSINo.8adjacenttotheareabutoutsideoftheglacialdrainagebasin.Threestationsandsixrefugesarelocatedinthearea.UsingaSPOTsatelliteimagemap,thelimitsoftheASMAaremarkedanditsareaisre_calculated.Itconsistsof362km2,including186km2islandicefieldandsmallcirqueglaciersand32km2ice_freefield.Therestcompriseswaterofthebayandasmalladjacentarea(8km2)oftheBransfieldStrait.TheASMA_GISwillconsistsof12datalayersrangingfromthephysiographicsettingstothebiologicalandadministrativefeatures.AlldatawillbeimplementedintoArc/InfoGISaccordingtothecartographicguidelinesoftheSCARWG_GGI.First,fiveplansofinformationwillberealisedusingatopographicdatabasecompiledfromvarioussourcesanddatafromtherevisedbathymetricchartpublishedbytheBrazilianNavyHydrographicSurveyandalsoincluding:1)LimitsoftheASMAandprotectedareas;2)Glaciologicalfeatures(e.g.drainagebasinlimits)and3)Humanpresence(e.g.stationsandhistoricalsites).ThesebasicGISlayerswillbeoperationalinearly2001.Then,additionaldataontheremaininglayers(e.g.hydrology,geologyandgeomorphology)willbeincludedfrompublishedsources.TheASMA_GISwillformanimportantdatabaseforenvironmentalmonitoringandstudiessurveyingtemporalchangesoffeaturessuchasglacierfrontpositionsorbirdbreadingsites.
简介:有为在农业分水岭的环境恢复的长期的植被盖子的河边的缓冲区的地理设计需要估计多少农田位于一个担心的分水岭的缓冲区。传统地,这个评价被地调查并且用手的印射做,它是为一个大区域的一个费时间、昂贵的过程。在这份报纸,遥感(RS)和地理信息系统(GIS)作为划算的技术被用来为识别农业河边的缓冲区恢复的批评地点开发一条基于集水的途径。方法与11集水通过分水岭的案例研究被解释,结果仅仅显示出那四集水以为河边的缓冲区恢复的更高的优先级是合格的。这研究在集水以内越过一个分水岭并且到可变缓冲情形的地理图案在基于集水的河边的缓冲区有方法学的贡献到对耕作紧张的空间评价。前者使基于集水的管理策略可能,并且后者提供其他的恢复情形遇见不同管理目的,哪个在真实世界上有直接实现到河边的缓冲区的环境恢复。因此,这研究加亮RS和GIS应用程序的大潜力到在农业分水岭计划和河边的缓冲区恢复的管理。
简介:IthaslongbeenacknowledgedthatGISdatacanbeusedasauxiliaryinformationtoimproveremotesensingimageclassification.Inpreviousstudies,GISdatawereoftenusedintrainingareaselectionandpostprocessingofclassificationresultoractedasadditionalbands.Generally,itisfulfilledinastatisticalorinteractivemanner,soitisdifficulttousetheauxiliarydataautomaticallyandintelligently. Furthermore,iftheclassifierrequestscertainstatisticalcharacteristics,theadditionalbandmethodcannotbeusedbecausemostauxiliarydatadonotmeettherequirementsofstatisticalcharacteristics.Ontheotherhand,expertsystemtechniqueswereincorporatedinremotesensingimageclassificationtomakeuseofdomainknowledgeandlogicalreasoning.Butbuildinganimageclassificationexpertsystemwasverydifficultbecauseofthe“knowledgeacquisitionbottleneck”. Spatialdataminingandknowledgediscovery(SDMKD),istheextractionofimplicit,interestingspatialornon_spatialpatternsandgeneralcharacteristics.Weproposedatheoreticalandtechnicalframeworkofspatialdataminingandknowledgediscovery(Lietal.,1997).Andspatialdataminingissupposedtobeusedintwoaspects,oneisintelligentanalysisofGISdata,theotheristosupportknowledgedriveninterpretationandanalysisofremotesensingimages.SDMKDprovidesanewwayofknowledgeacquisitionforremotesensingimageclassification.Severalresearchershavedonesomeworkinthisfield.Eklundetal.(1998)extractedknowledgefromTMimagesandgeographicdatainsoilsalinityanalysisusinginductivelearningalgorithmC4.5.Huangetal.(1997)extractedknowledgefromGISdataandSPOTmultispectralimageinwetlandclassificationusingC4.5too.Inthesetwostudies,geographicdatawereconvertedfromvectortorasterformatinwhichthesamplingsizeisequaltoimagepixelsize.Theimplementationofdataminingtechniquesinspatialdatabase,especiallyinductivelearningmethod,andthecombinationo
简介:ThispaperdiscussestheplacementofChineseannotationfrompointofviewofgraphics.AreaFeatureisclassifiedassimplepolygon,complexpolygonandspecialpolygon.Forsimpleones,annotationsareplacedalongthelongestedge.Forcomplexones,firstlythepolygonaresimplifiedaccordingtoclosepoints,thenthelongestdiagonalisgottenbycomparinglength,lastly,annotationsareplacedalonglong-diagonal.Forspecialones,thepolygonarepartitionedintoseveralpartsbyacertainruleforgettingtheirsub-diagonals,thentheirannotationareplacedbymeansofthesecond.
简介:Theobjectivesofthisstudyaretoassesslandsuitabilityandtopredictthespatialandtemporalchangesinlandusetypes(LUTs)byusingGIS-basedlandusemanagementdecisionsupportsystem.AGISdatabasewithdataonclimate,topography,soilcharacteristic,irrigationcondition,fertilizerapplication,andspecialsocioeconomicactivitieshasbeendevelopedandusedfortheevaluationoflandproductivityfordifferentcropsbyintegratingwithacropgrowthmodel--theerosionproductivityimpactcalculator(EPIC).Internationalfoodpolicysimulationmodel(IFPSIM)isalsoembeddedintoGISforthepredictionsofhowcropdemandsandcropmarketpriceswillchangeunderalternativepolicyscenarios.Aninferenceengine(IE)includinglandusechoicemodelisdevelopedtoillustratelandusechoicebehaviorbasedonlogitmodels,whichallowstoanalyzehowdiversifiedfactorsrangingfromclimatechanges,croppricechangestolandmanagementchangescanaffectthedistributionofagriculturallanduse.Atestforintegratedsimulationistakenineach0.1°by0.1°gridcelltopredictthechangeofagriculturallandusetypesatgloballevel.Globallandusechangesaresimulatedfrom1992to2050.