简介：ThepurposeofthispaperistostudytheRSdatawebservicesandrelatedsubjectsofdatastorageanddatamanagement.Basedonananalysisofthepresentsituationanddevelopmenttrendofstorageandmanagementofrasterdataandwebservicetechnology,amanagementandservicesystemarchitectureforRemoteSensingrasterdatabasedonwebservicetechnologieswasdeveloped,theimplementationmethodologiesofthekeytechnologyofthesystemwereexploredandaprototypeofthesystemwasillustrated.

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：InordertoprovideascientificbasisforriceyieldestimationandimprovetheacouracyofyieldestimationinZhejiangProvince,Regionalizationindicesforriceyieldestimationbyremoesensing(RS)intheprovinceweredeterminedbyconsjderingthespecialfeaturesofyieldestimationbyRS,andbasedonanalysisofthenaturalconditionsofZhejiangProvince,Theindicesdeterminedincludedricecroppingsystem,agroclimate,landform,surfacefeaturesturctureandriceyieldlevel,wherericeplantingsystemwasconsideredasthemianone,ThenreionalizationfroriceyieldestimationbyRSwascompletedbyspatialneighboringanalysiswiththeGeographicalInfromationSystem(GIS)technologycombinedwithusigoftreealgorithmTheprovincewasdividedintotworegions,i.e.,thesingle-croppingriceregionwhichwassubdividedinto3regionsincludingthoseinmountainsofnorthewstZhejiang,waternetworkareaofnorthZhejiangandmountainsofsothZhejiang,anddouble-croppingriceregionwhichwassudividedinto5regionsincludingthoseonplainofnorthZhejiang,coastalplainsandhillsofsoutheastZhejiang,Jin-QuBasinofmiddleZhejiang,hillsofeastZhejiang,andhillsandmountainsofnorthewatZhejiang,thisregionaliztiontookthecountybordersastheregionboundaries,kepttheregionsconnectiveandmadetheadministrativregionsintegrityand,then,couldmeettherequirementsofriceyieldestimationbyRS,showingthattheresultswerequitesatisfying.

简介：Remotesensingimagesshowaverypromisingperspectivefordistinguishingtreespecies,especiallythosewiththeveryhighresolutionrangingfrom1to4m.However,thetraditionalmethodologyforclassifyinglandcovertypes,solelydependingonspectralfeatures,whiletextureandotherspatialinformationareneglected,hastheweaknesssuchasinadequatelyutilizationofinformation,lowaccuraciesofclassification,etc.Consideringtothetexturedifferencesamongforestspecies,itismoreimportantforspatialinformationdescriptionofhigh-resolutionremotesensingimagetoimprovetheprecisionoftexturalfeatureschoosing.Inthisstudy,thefactorstoinfluencetheninetexturalfeatureschoosingwereanalyzedandtheresultsshowedthatthemovingwindowsizewasthemainfactortoaffecttheobtainingprocessesoftexturalfeaturesbasedonthegraylevelco-occurrencematrix(GLCM)method,andtheimagerywasthenclassifiedcombiningthemaximumlikelihoodclassification(MLC)methodwiththeoriginalspectralvaluesandtexturefeatures.First,thisstudyutilizedacorrelationanalysisoftheimagesfromaprincipalcomponentanalysis.Second,throughmultipleinformationsources,includingtextualfeaturesderivedfromthedata.Forthehigh-resolutionremotesensingimage,themostpropermovingwindowsizewasdeterminedfrom3×3to31×31.Classificationofthemajortreespeciesthroughoutthestudyarea(theSunYat-SenMausoleuminNanjing)wasundertakenusingtheMLC.Third,toaidforestresearch,classificationaccuracywasimprovedusingtheGLCM.Accordingtocorrelationsamongtexturesandrichnessofthedata,GLCMprovidedthebestwindowsizeandtexturalparameters.Resultsindicatedthatthetexturecharacteristicswereaddinthespectralcharacteristicstoimprovetheprecisionoftheresultsoftheclassification,19×19windowforbestwindow.Thetotalprecisioncanreach66.3226%,Kappacoefficientis0.5840.Eachtreespecieshasgreatlyimprovedaccuraciesoftheclassification.Bythecal

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：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:Remotesensing-basedinventoriesareessentialinestimatingforestcoverintropicalandsubtropicalcountries,wheregroundinventoriescannotbeperformedperiodicallyatalargescaleowingtohighcostsandforestinaccessibility(e.g.REDDprojects)andaremandatoryforconstructinghistoricalrecordsthatcanbeusedasforestcoverbaselines.Giventheconditionsofsuchinventories,thesurveyareaispartitionedintoagridofimagerysegmentsofpre-fixedsizewheretheproportionofforestcovercanbemeasuredwithinsegmentsusingacombinationofunsupervised(automatedorsemi-automated)classificationofsatelliteimageryandmanual(i.e.visualon-screen)enhancements.Becausevisualon-screenoperationsaretimeexpensiveprocedures,manualclassificationcanbeperformedonlyforasampleofimagerysegmentsselectedatafirststage,whileforestcoverwithineachselectedsegmentisestimatedatasecondstagefromasampleofpixelsselectedwithinthesegment.Becauseforestcoverdataarisingfromunsupervisedsatelliteimageryclassificationmaybefreelyavailable(e.g.Landsatimagery)overtheentiresurveyarea(wall-to-walldata)andarelikelytobegoodproxiesofmanuallyclassifiedcoverdata(sampledata),theycanbeadoptedassuitableauxiliaryinformation.Methods:Thequestionishowtochoosethesampleareaswheremanualclassificationiscarriedout.Wehaveinvestigatedtheefficiencyofone-per-stratumstratifiedsamplingforselectingsegmentsandpixels,wheretocarryoutmanualclassificationandtodeterminetheefficiencyofthedifferenceestimatorforexploitingauxiliaryinformationattheestimationlevel.Theperformanceofthisstrategyiscomparedwithsimplerandomsamplingwithoutreplacement.Results:OurresultswereobtainedtheoreticallyfromthreeartificialpopulationsconstructedfromtheLandsatclassification(forest/nonforest)availableatpixellevelforastudyarealocatedincentralItaly,assumingthreelevelsoferrorratesoftheuns

简介：在干燥陆地的系统的木质的植物的地位是关键生态系统进程的一个基本决定因素。这地位监视在在干旱、半干旱的生态系统理解木质的植物的动力学起一个重要作用。现在的学习用遥感和地理信息系统技术和统计科学在伊朗决定了Zagros森林的精力。结果证明树的密度从10～53变化了?%根据半干旱的区域的地文学、气候的条件。在植被索引和森林密度之间的最好、最低的关联为全球环境监视索引被获得(GEMI；R2?=?0.94)和土壤调整植被索引(R2?=?0.81),分别地。GEMI被用来监视使用在一个10年的时期上改变的土地。结果显示出那2720?哈森林的2被人的干扰和耕种在也导致了肥沃的土壤层的损失的这个时期期间在陡峭的斜坡上破坏了。GEMI决定了区域与一树的生物资源并且有从没有华盖的区域的树的低生物资源密度的通常分开的边阶区域能盖住。结果用卫星在干旱、半干旱的艰巨森林区域揭示了对森林和植被盖子的那个评价数字数字和平常的采样服从于无常。一个成层的组织过程应该被建立增加评价的精确性。

简介：RemotesensingdatafromtheTerraModerate-ResolutionImagingSpectroradiometer(MODIS)andgeospatialdatawereusedtoestimategrassyieldandlivestockcarryingcapacityintheTibetanAutonomousPrefectureofGolog,Qing-hai,China.TheMODIS-derivednormalizeddifferencevegetationindex(MODIS-NDVI)datawerecorrelatedwiththeabovegroundgreenbiomass(AGGB)datafromtheabovegroundharvestmethod.RegionalregressionmodelbetweentheMODIS-NDVIandthecommonlogarithm(LOG10)oftheAGGBwassignificant(r2=0.51,P<0.001),itwas,there-fore,usedtocalculatethemaximumcarryingcapacityinsheep-unityearperhectare.Themaximumlivestockcarryingcapacitywasthenadjustedtothetheoreticallivestockcarryingcapacitybythereductionfactors(slope,distancetowater,andsoilerosion).Resultsindicatedthatthegrasslandconditionsbecameworse,withlowerabovegroundpalatablegrassyield,plantheight,andcovercomparedwiththeresultsobtainedin1981.Atthesametime,althoughtheactuallivestocknumbersdecreased,theystillexceededthepropertheoreticallivestockcarryingcapacity,andovergrazingratesrangedfrom27.27%inDarlagCountyto293.99%inBaimaCounty.Integratingremotesensingandgeographicalinformationsystemtechnologies,thespatialandtemporalconditionsofthealpinegrassland,trend,andprojectedstockingratescouldbeforecastedfordecisionmaking.

简介：Theradialbasisfunction(RBF)emergedasavariantofartificialneuralnetwork.Generalizedregressionneuralnetwork(GRNN)isonetypeofRBF,anditsprincipaladvantagesarethatitcanquicklylearnandrapidlyconvergetotheoptimalregressionsurfacewithlargenumberofdatasets.Hyperspectralreffectance(350to2500nm)datawererecordedattwodifferentricesitesintwoexperimentfieldswithtwocultivars,threenitrogentreatmentsandoneplantdensity(45plantsm-2).Stepwisemultivariableregressionmodel(SMR)andRBFwereusedtocomparetheirpredictabilityfortheleafareaindex(LAI)andgreenleafchlorophylldensity(GLCD)ofricebasedonreffectance(R)anditsthreedifferenttransformations,thefirstderivativereffectance(D1),thesecondderivativereffectance(D2)andthelog-transformedre?ectance(LOG).GRNNbasedonD1wasthebestmodelforthepredictionofriceLAIandGLCD.TherelationshipsbetweendifferenttransformationsofreffectanceandriceparameterscouldbefurtherimprovedwhenRBFwasemployed.Owingtoitsstrongcapacityfornonlinearmappingandgoodrobustness,GRNNcouldmaximizethesensitivitytochlorophyllcontentusingD1.ItisconcludedthatRBFmayprovideausefulexploratoryandpredictivetoolfortheestimationofricebiophysicalparameters.

简介：PhysiographyandsoilinMaeRimwatershed,ChiangMaiProvince,Thailandwereinvestigatedbyusingaerialphotographsandsatelliteimageinconjunctionwithfieldwork,andsoilinfiltrationrateandsoilshearresistanceweremeasuredinfield.ManyfactorsaffectingrunoffwereanalyzedusigtheIntegratedLandandWaterInformaitonSystem(ILWIS).Asaresult,amodeldeterminingfloodhazarwassetup.Threempsincludingrunoffcurvenumbermap,runoffcoefficentmap,andfloodinumdationmapwerecreated,Inaddition,thetimeofconcentrationwaspredicted.