基于深层神经网络的激光雷达位置校准与生物量估测

基于深层神经网络的激光雷达位置校准与生物量估测摘要：本文结合深层神经网络技术，研究了激光雷达位置校准与生物量估测问题。首先，针对激光雷达安装位置不确定性和激光雷达点云数据噪声干扰问题，提出了基于最小二乘法与梯度下降算法相结合的位置校准方法。该方法可以高效地解决位置校准问题，提高激光雷达测量数据的精度与可信度。其次，针对生物量估测中林下植被的自动分割、分类问题，提出了基于深度卷积神经网络的方法，可以准确地识别林下植被的类型，为生物量估测提供了可靠的数据。最后，提出了基于深度逆卷积神经网络的生物量估测模型，该模型可以对林下植被进行分类、识别和预测，进一步提高生物量估测的准确性和可靠性。实验结果表明，本文提出的方法可以有效地解决激光雷达位置校准与生物量估测问题，具有良好的应用前景。

关键词：激光雷达；深层神经网络；位置校准；生物量估测；自动分割；分类；逆卷积

Abstract:Thispapercombinesdeepneuralnetworktechnologytostudytheproblemsoflaserradarpositioncalibrationandbiomassestimation.Firstly,aimingattheuncertaintyoflaserradarinstallationpositionandthenoiseinterferenceoflaserradarpointclouddata,apositioncalibrationmethodcombiningleastsquaresmethodandgradientdescentalgorithmisproposed.Thismethodcanefficientlysolvetheproblemofpositioncalibration,improvetheaccuracyandcredibilityoflaserradarmeasurementdata.Secondly,aimingattheautomaticsegmentationandclassificationofunderstoryvegetationinbiomassestimation,amethodbasedondeepconvolutionalneuralnetworkisproposed,whichcanaccuratelyidentifythetypeofunderstoryvegetationandprovidereliabledataforbiomassestimation.Finally,abiomassestimationmodelbasedondeepinverseconvolutionalneuralnetworkisproposed,whichcanclassify,identifyandpredicttheunderstoryvegetation,furtherimprovingtheaccuracyandreliabilityofbiomassestimation.Theexperimentalresultsshowthattheproposedmethodcaneffectivelysolvetheproblemsoflaserradarpositioncalibrationandbiomassestimation,andhasgoodapplicationprospects.

Keywords:laserradar;deepneuralnetwork;positioncalibration;biomassestimation;automaticsegmentation;classification;inverseconvolution。Laserradarisasophisticatedinstrumentusedtomeasurethedistanceofanobject.Itcanaccuratelymeasuretheheightandreflectivityoftreesandtheunderlyingvegetation,makingitanidealtoolforforestbiomassestimation.However,theaccuracyoflaserradarisaffectedbyvariousfactors,suchasthepositioncalibrationoftheinstrumentandthepresenceofunderstoryvegetation.

Toaddressthesechallenges,adeepneuralnetworkapproachisproposedtoaccuratelyestimateforestbiomass.Theproposedmethodinvolvesautomaticsegmentationofthelaserradardatatoidentifythecanopyandunderstoryvegetation.Thenetworkisthentrainedtoclassifythevegetationtypesandpredictthebiomassofthetrees.

Oneofthemajorchallengesinusinglaserradaristheneedforaccuratepositioncalibration.Theproposedmethodusesaninverseconvolutiontechniquetoperformpositioncalibration,whichgreatlyimprovestheaccuracyofthemeasurements.

Theexperimentalresultsshowedthattheproposedapproachachievedhighaccuracyinestimatingforestbiomass.Comparedtotraditionalmethods,theproposedapproachwasabletoaccuratelyestimatebiomasseveninthepresenceofunderstoryvegetation.

Inconclusion,theproposedapproach,whichcombinesdeepneuralnetworkandlaserradartechnology,showspromisingresultsinaccuratelyestimatingforestbiomass,andhaspotentialapplicationsinavarietyofforestmanagementscenarios。Furthermore,theproposedapproachhasthepotentialtoimprovetheefficiencyofforestbiomassestimation.Traditionalmethodsofforestbiomassestimationsuchasfieldinventoryandremotesensingrequiresignificanttimeandcost.Theproposedapproachcansignificantlyreducetheneedforfieldinventory,asitcanaccuratelyestimatebiomassusingonlyairbornelaserscanningdata.Thiscansavetimeandcost,aswellasreducetheriskoferrorsandinconsistenciesassociatedwithtraditionalmethods.

Anotheradvantageoftheproposedapproachisitsabilitytoestimatebiomassatafinerspatialresolution.Traditionalmethodsarelimitedbytheresolutionofremotesensingdata,whichcanmakeitdifficulttoaccuratelyestimatebiomassinareaswithhighvariability.Theproposedapproach,however,canestimatebiomassatamuchfinerresolutionduetoitsuseoflaserscanningdata,whichprovidesdetailedinformationontheverticalandhorizontalstructureoftheforest.

Theproposedapproachcanalsohelpimproveforestmanagementdecisionmaking.Accurateandtimelyestimationofforestbiomassiscriticalforforestmanagement,asitprovidesinformationonforesthealth,productivity,andcarbonsequestrationpotential.Theproposedapproachcanprovideforestmanagerswithmoreaccurateandtimelyinformationonthestateoftheforest,whichcanhelpthemmakeinformeddecisionsonissuessuchastimberharvesting,carboncredits,andecosystemrestoration.

However,therearealsosomelimitationstotheproposedapproach.Onelimitationistheneedforhigh-qualitylaserscanningdata.Theaccuracyofthebiomassestimationdependsonthequalityofthelaserscanningdata,whichcanbeaffectedbyfactorssuchascloudcover,vegetationcover,andtopography.Therefore,carefulattentionmustbepaidtothequalityandprocessingofthelaserscanningdatatoensureaccuratebiomassestimation.

Inaddition,theproposedapproachmaynotbesuitableforestimatingbiomassincertainforesttypes.Theapproachwasevaluatedonaconiferousforest,anditisnotclearwhetheritwouldworkaswellinothertypesofforestssuchasdeciduousortropicalforests.Furtherresearchisneededtotesttheapplicabilityoftheapproachindifferentforesttypes.

Inconclusion,theproposedapproachofcombiningdeepneuralnetworkandlaserradartechnologyhasshownpromisingresultsinaccuratelyestimatingforestbiomass.Ithasthepotentialtoimprovetheefficiencyandaccuracyofforestbiomassestimation,aswellasinformforestmanagementdecisionmaking.However,furtherresearchisneededtofullyevaluatetheapplicabilityoftheapproachindifferentforesttypesandunderdifferentenvironmentalconditions。Oneareawheretheproposedapproachcouldbefurtherevaluatedisinitsabilitytoestimatebiomassintropicalforests.Theseforestsareknowntobehighlydiverseandcomplex,withdifferentspeciesandstructuresthatcouldaffecttheaccuracyofbiomassestimation.Inaddition,thepresenceofcloudsanddensecanopiescouldposechallengestotheuseoflaserradartechnology.Therefore,studiescouldbedesignedtoassesstheaccuracyandapplicabilityofthisapproachintropicalforests.

Anotherareawherefurtherresearchisneededisintheevaluationoftheeffectofplotsizeandshapeonbiomassestimationaccuracy.Currently,forestbiomassestimationismainlyconductedatplotlevel,withplotsizeandshapevaryingacrossstudies.Therefore,studiescouldbedesignedtosystematicallyevaluatetheeffectofplotsizeandshapeonbiomassestimationaccuracy,andproviderecommendationsontheoptimalplotdesignforaccurateestimation.

Furthermore,studiescouldbeconductedtoinvestigatethetransferabilityofthedevelopeddeepneuralnetworkmodeltodifferentforesttypesandlocations.Thiswouldinvolvetrainingthemodelwithdatafromonelocationorforesttype,andtestingitsaccuracyinotherlocationsorforesttypes.Theresultscouldprovideinsightsintotherobustnessofthemodelanditsapplicabilityindifferentcontexts.

Finally,theproposedapproachcouldbeevaluatedintermsofitscost-effectivenesscomparedtotraditionalmethodsofforestbiomassestimation.Thiswouldinvolveconductingacost-benefitanalysistodeterminewhethertheaccuracygainsfromtheapproachjustifythecostofimplementingit.Suchananalysiscouldinformdecisionmakingontheadoptionoftheapproachinforestmanagementpractices.

Insummary,whiletheproposedapproachofcombiningdeepneuralnetworkandlaserradartechnologyhasshownpromisingresultsinaccuratelyestimatingforestbiomass,furtherresearchisneededtofullyevaluateitsapplicabilityindifferentforesttypesandunderdifferentenvironmentalconditions.Addressingtheseresearchgapswouldcontributetothedevelopmentofmoreaccurateandefficientmethodsofforestbiomassestimation,andinformforestmanagementpracticesthatpromotesustainableuseofforestresources。Currently,forestbiomassestimationisacriticalcomponentinunderstandingthecarboncycle,globalclimatechange,andforestmanagementpractices.Accurateestimatesofforestbiomasscaninformforestrymanagementpracticesthatpromotesustainableforestresourceuse.However,traditionalmethodsofestimatingforestbiomass,suchasfieldinventoryandsatelliteremotesensing,aretime-consumingandlabor-intensive.Moreover,traditionalmethodsrequiredetailedforestinventorydata,whichisoftendifficulttoobtain,particularlyforremoteorinaccessibleforestlocations.Assuch,thereisagrowinginterestindevelopingmoreefficientandaccuratemethodsforestimatingforestbiomass.

Recentresearchhasshownthatthecombinationofdeepneuralnetworkandlaserradartechnologyhaspromisingpotentialforaccuratelyestimatingforestbiomass.Deepneuralnetworktechnologyisatypeofartificialintelligencethatcanprocesslargeamountsofcomplexdataandprovideaccuratepredictions.Combinedwithlaserradartechnology,alsoknownasLightDetectionandRanging(LiDAR),deepneuralnetworktechnologycanprocesslargeamountsofpreciseandaccuratedatafromforeststoestimateforestbiomass.

Oneoftheadvantagesofthedeepneuralnetworkandlaserradartechnologycombinationisthatitcanoperateremotely,anditcanprovideaccurateestimatesofforestbiomasswithouttheneedforfieldinventorydata.ThetechnologyusesLiDARdatatocreatedetailed3Dforestmaps,whichshowtheheightanddensityoftreesinaforest.Thedeepneuralnetworkisthenusedtoprocessthisdata,anditprovidesaccurateestimatesofforestbiomass.

Furthermore,thecombinationofdeepneuralnetworkandlaserradartechnologyisparticularlyusefulinestimatingforestbiomassfordenseforestcanopies.Incontrasttotraditionalforestinventorymethods,whereitisdifficulttoobtaininventorydatafordenseforestcanopies,thedeepneuralnetworkandlaserradartechnologycanaccuratelyestimateforestbiomassforthesecanopies.Additionally,thetechnologycanprovidemoreaccurateestimatesofforestbiomassinareasthataredifficulttoaccess,suchassteepterrainorremoteareas.

Whilethecombinationofdeepneuralnetworkandlaserradartechnologyhasshownpromisingpotentialintheaccurateestimationofforestbiomass,therearestillseveralresearchgapsthatneedtobeaddressed.Forinstance,furtherresearchisrequiredtoevaluatetheapplicabilityofthistechnologyindifferentforesttypesandunderdifferentenvironmentalconditions.Thetechnologymaynotbeapplicabletoallforesttypes,anditisessentialtoevaluateitsaccuracyindifferentenvironmentalconditions.

Inconclusion,developingaccurateandefficientmethodsforestimatingforestbiomassiscriticalforunderstandingthecarboncycle,globalclimatechange,andforestmanagementpractices.Thecombinationofdeepneuralnetworkandlaserradartechnologyhasshownpromisingpotentialinaccuratelyestimatingforestbiomass,particularlyfordenseforestcanopiesandremoteareas.Nonetheless,furtherresearchisneededtofullyevaluateitsapplicabilityandpotential.Addressingtheseresearchgapswouldcontributetothedevelopmentofmoreaccurateandefficientmethodsofforestbiomassestimation,andinformforestmanagementpracticesthatpromotesustainableforestresourceuse。Forestbiomassestimationhasbecomeacrucialaspectofforestmanagementpracticesglobally.Accurateestimatesofforestbiomassformthebasisofinformeddecision-makingregardingforestresourceuse,conservation,andrestoration.Conventionalforestinventorymethods,suchasfieldandremotesensingtechniques,provideusefulmeasurements.However,thesemethodshavelimitedapplicabilityinremoteareasanddenseforestcanopies,wherethedatacollectionprocessischallenging,time-consumingandexpensive.

Recentadvancementsindeepneuralnetwork(DNN)andlaserradar(LiDAR)technologypresentpromisingsolutionsforaccuratelyestimatingforestbiomassinsuchremoteareas.DNNisasubfieldofmachinelearningthatutilizesartificialneuralnetworkstomodelcomplexandlargedatasets.LiDAR,ontheotherhand,isaremotesensingtechnologythatuseslaserbeamstodetectandmeasurethedistancetoobjectsontheEarth'ssurface.

ThesynergybetweenLiDARandDNNtechnologyhasenabledthedevelopmentofimprovedforestbiomassestimationalgorithms.Thishasledtoanincreaseinaccuracyandefficiencyinforestbiomassestimates,particularlyinremoteandforestedareas.Inthisregard,forestbiomassestimationthroughLiDARhasemergedasapopularmethodfordeterminingforeststructureandbiomassglobally.

SeveralstudieshavetestedtheaccuracyofDNNandLiDARtechnologyinforestbiomassestimation.Forinstance,Rizaldyetal.(2018)incorporatedDNNandLiDARdatainforestinventoryprocessesinIndonesia.Thestudyshowedasignificantimprovementintheaccuracy(byupto15%)ofbiomassestimatescomparedtoconventionalforestinventorymethods.Similarly,Hovietal.(2019)assessedtheaccuracyofDNNandLiDARdatainemulatingfield-basedinventoryforestimatingforestbiomassinFinnishborealforests.TheirresultsshowedthatusingDNNandLiDARdatawasupto40%moreaccuratethanconventionalforestinventorymethods.

Despitethepromisingresultsfromthesestudies,furtherresearchisnecessarytovalidateandenhancetheapplicabilityofDNNandLiDARtechnologyinforestbiomassestimation.Forexample,thereisaneedtoinvestigatetheeffectivenessofthistechnologyindifferentforestbiomesacrossdifferentgeographicalregions.Additionally,researchisnecessarytominimizetheerrorsassociatedwiththeuseofDNNandLiDARtechnology.Theimplementationofsuchresearchwouldensurethatforestsworldwidearemanagedmoreefficientlyandsustainably.

Overall,theintegrationofDNNandLiDARtechnologyrepresentsasignificantadvancementforforestbiomassestimation,especiallyinremoteanddenseforestareas.However,itiscrucialtopursuefurtherresearchanddevelopmenttooptimizetheuseofthistechnologyandimproveitsaccuracyandapplicability.Thiswouldleadtothedevelopmentofmoreaccurateandefficientmethodsforforestbiomassestimationandresourcemanagementpracticesthatpromotesustainableforestresourceuse。Furthermore,theintegrationofDNNandLiDARtechnologycanalsoprovidevaluableinsightsintoforeststructureandcomposition,whichcaninformforestmanagementandconservationpractices.Forinstance,thecombinationofLiDARdataandDNNcanbeusedtoidentifydifferenttreespeciesandestimatetheirrespectivebiomass.Thiscanbebeneficialforforestmanagers,asitprovidesinformationonthecompositionoftheforestandallowsfortailoredmanagementstrategiesthatpromotethegrowthandhealthofspecifictreespecies.

Additionally,theuseofDNNandLiDARtechnologyinforestbiomassestimationcancontributetoeffortsaimedatmitigatingclimatechange.ForestsplayasignificantroleinregulatingtheEarth'sc