基于深層神經(jīng)網(wǎng)絡的激光雷達位置校準與生物量估測

基于深層神經(jīng)網(wǎng)絡的激光雷達位置校準與生物量估測摘要：本文結(jié)合深層神經(jīng)網(wǎng)絡技術，研究了激光雷達位置校準與生物量估測問題。首先，針對激光雷達安裝位置不確定性和激光雷達點云數(shù)據(jù)噪聲干擾問題，提出了基于最小二乘法與梯度下降算法相結(jié)合的位置校準方法。該方法可以高效地解決位置校準問題，提高激光雷達測量數(shù)據(jù)的精度與可信度。其次，針對生物量估測中林下植被的自動分割、分類問題，提出了基于深度卷積神經(jīng)網(wǎng)絡的方法，可以準確地識別林下植被的類型，為生物量估測提供了可靠的數(shù)據(jù)。最后，提出了基于深度逆卷積神經(jīng)網(wǎng)絡的生物量估測模型，該模型可以對林下植被進行分類、識別和預測，進一步提高生物量估測的準確性和可靠性。實驗結(jié)果表明，本文提出的方法可以有效地解決激光雷達位置校準與生物量估測問題，具有良好的應用前景。

關鍵詞：激光雷達；深層神經(jīng)網(wǎng)絡；位置校準；生物量估測；自動分割；分類；逆卷積

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