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Large-Scale Machine Learning in the Earth Sciences Chapman & Hall/CRC Data Mining and Knowledge Discovery Series Series Editor: Vipin Kumar UniversityofMinnesota DepartmentofComputerScienceandEngineering Minneapolis,Minnesota,U.S.A. AIMSANDSCOPE Thisseriesaimstocapturenewdevelopmentsandapplicationsindataminingandknowledge discovery,whilesummarizingthecomputationaltoolsandtechniquesusefulindataanalysis. Thisseriesencouragestheintegrationofmathematical,statistical,andcomputationalmethods andtechniquesthroughthepublicationofabroadrangeoftextbooks,referenceworks,and handbooks.Theinclusionofconcreteexamplesandapplicationsishighlyencouraged.The scopeoftheseriesincludes,butisnotlimitedto,titlesintheareasofdataminingand knowledgediscoverymethodsandapplications,modeling,algorithms,theoryandfoundations, dataandknowledgevisualization,dataminingsystemsandtools,andprivacyandsecurity issues. PUBLISHEDTITLES AcceleratingDiscovery:MiningUnstructuredInformationforHypothesisGeneration ScottSpangler AdvancesinMachineLearningandDataMiningforAstronomy MichaelJ.Way,JeffreyD.Scargle,KamalM.Ali,andAshokN.Srivastava BiologicalDataMining JakeY.ChenandStefanoLonardi ComputationalBusinessAnalytics SubrataDas ComputationalIntelligentDataAnalysisforSustainableDevelopment TingYu,NiteshV.Chawla,andSimeonSimoff ComputationalMethodsofFeatureSelection HuanLiuandHiroshiMotoda ConstrainedClustering:AdvancesinAlgorithms,Theory,andApplications SugatoBasu,IanDavidson,andKiriL.Wagstaff ContrastDataMining:Concepts,Algorithms,andApplications GuozhuDongandJamesBailey DataClassification:AlgorithmsandApplications CharuC.Aggarawal DataClustering:AlgorithmsandApplications CharuC.AggarawalandChandanK.Reddy DataClusteringinC++:AnObject-OrientedApproach GuojunGan DataMining:ATutorial-BasedPrimer,SecondEdition RichardJ.Roiger DataMiningforDesignandMarketing YukioOhsawaandKatsutoshiYada DataMiningwithR:LearningwithCaseStudies,SecondEdition LuísTorgo DataScienceandAnalyticswithPython JesusRogel-Salazar EventMining:AlgorithmsandApplications TaoLi FoundationsofPredictiveAnalytics JamesWuandStephenCoggeshall GeographicDataMiningandKnowledgeDiscovery,SecondEdition HarveyJ.MillerandJiaweiHan Graph-BasedSocialMediaAnalysis IoannisPitas HandbookofEducationalDataMining CristóbalRomero,SebastianVentura,MykolaPechenizkiy,andRyanS.J.d.Baker HealthcareDataAnalytics ChandanK.ReddyandCharuC.Aggarwal InformationDiscoveryonElectronicHealthRecords VagelisHristidis IntelligentTechnologiesforWebApplications PritiSrinivasSajjaandRajendraAkerkar IntroductiontoPrivacy-PreservingDataPublishing:ConceptsandTechniques BenjaminC.M.Fung,KeWang,AdaWai-CheeFu,andPhilipS.Yu KnowledgeDiscoveryforCounterterrorismandLawEnforcement DavidSkillicorn KnowledgeDiscoveryfromDataStreams JoãoGama Large-ScaleMachineLearninginTheEarthSciences AshokN.Srivastava,RamakrishnaNemani,andKarstenSteinhaeuser MachineLearningandKnowledgeDiscoveryforEngineeringSystemsHealthManagement AshokN.SrivastavaandJiaweiHan MiningSoftwareSpecifications:MethodologiesandApplications DavidLo,Siau-ChengKhoo,JiaweiHan,andChaoLiu MultimediaDataMining:ASystematicIntroductiontoConceptsandTheory ZhongfeiZhangandRuofeiZhang MusicDataMining TaoLi,MitsunoriOgihara,andGeorgeTzanetakis NextGenerationofDataMining HillolKargupta,JiaweiHan,PhilipS.Yu,RajeevMotwani,andVipinKumar Rapidminer:DataMiningUseCasesandBusinessAnalyticsApplications MarkusHofmannandRalfKlinkenberg RelationalDataClustering:Models,Algorithms,andApplications BoLong,ZhongfeiZhang,andPhilipS.Yu Service-OrientedDistributedKnowledgeDiscovery DomenicoTaliaandPaoloTrunfio SpectralFeatureSelectionForDataMining ZhengAlanZhaoandHuanLiu StatisticalDataMiningUsingSASApplications,SecondEdition GeorgeFernandez SupportVectorMachines:OptimizationBasedTheory,Algorithms,andExtensions NaiyangDeng,YingjieTian,andChunhuaZhang TemporalDataMining TheophanoMitsa TextMining:Classification,Clustering,andApplications AshokN.SrivastavaandMehranSahami TextMiningandVisualization:CaseStudiesUsingOpen-SourceTools MarkusHofmannandAndrewChisholm TheTopTenAlgorithmsinDataMining XindongWuandVipinKumar UnderstandingComplexDatasets:DataMiningwithMatrixDecompositions DavidSkillicorn Large-Scale Machine Learning in the Earth Sciences Editedby Ashok N. Srivastava Ramakrishna Nemani Karsten Steinhaeuser MATLABⓇ is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracyofthetextorexercisesinthisbook.Thisbook’suseordiscussionofMATLABⓇsoftwareorrelatedproductsdoesnot constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLABⓇsoftware. CRCPress Taylor&FrancisGroup 6000BrokenSoundParkwayNW,Suite300 BocaRaton,FL33487-2742 ©2017byTaylor&FrancisGroup,LLC CRCPressisanimprintofTaylor&FrancisGroup,anInformabusiness NoclaimtooriginalU.S.Governmentworks Printedonacid-freepaper InternationalStandardBookNumber-13:978-1-4987-0387-1(Hardback) Thisbookcontainsinformationobtainedfromauthenticandhighlyregardedsources.Reasonableeffortshavebeenmadeto publishreliabledataandinformation,buttheauthorandpublishercannotassumeresponsibilityforthevalidityofallmaterials ortheconsequencesoftheiruse.Theauthorsandpublishershaveattemptedtotracethecopyrightholdersofallmaterial reproducedinthispublicationandapologizetocopyrightholdersifpermissiontopublishinthisformhasnotbeenobtained.If anycopyrightmaterialhasnotbeenacknowledgedpleasewriteandletusknowsowemayrectifyinanyfuturereprint. ExceptaspermittedunderU.S.CopyrightLaw,nopartofthisbookmaybereprinted,reproduced,transmitted,orutilizedinany formbyanyelectronic,mechanical,orothermeans,nowknownorhereafterinvented,includingphotocopying,microfilming, andrecording,orinanyinformationstorageorretrievalsystem,withoutwrittenpermissionfromthepublishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/)orcontacttheCopyrightClearanceCenter,Inc.(CCC),222RosewoodDrive,Danvers,MA01923, 978-750-8400.CCCisanot-for-profitorganizationthatprovideslicensesandregistrationforavarietyofusers.Fororganizations thathavebeengrantedaphotocopylicensebytheCCC,aseparatesystemofpaymenthasbeenarranged. TrademarkNotice:Productorcorporatenamesmaybetrademarksorregisteredtrademarks,andareusedonlyforidentifica- tionandexplanationwithoutintenttoinfringe. LibraryofCongressCataloging-in-PublicationData Names:Srivastava,AshokN.(AshokNarain),1969-editor.|Nemani, Ramakrishna,editor.|Steinhaeuser,Karsten,editor. Title:Large-scalemachinelearningintheearthsciences/[editedby]Ashok N.Srivastava,Dr.RamakrishnaNemani,KarstenSteinhaeuser. Description:BocaRaton:Taylor&Francis,2017.|Series:Chapman& Hall/CRCdatamining&knowledgediscoveryseries;42|“ACRCtitle, partoftheTaylor&Francisimprint,amemberoftheTaylor&Francis Group,theacademicdivisionofT&FInformaplc.” Identifiers:LCCN2017006160|ISBN9781498703871(hardback:alk.paper) Subjects:LCSH:Earthsciences–Computernetworkresources.|Earth sciences–Dataprocessing. Classification:LCCQE48.87.L372017|DDC550.285/6312–dc23 LCrecordavailableathttps://lccn.loc.gov/2017006160 VisittheTaylor&FrancisWebsiteat http://www.taylorandfrancis.com andtheCRCPressWebsiteat http://www.crcpress.com Contents Foreword.............................................................................................................. ix Editors................................................................................................................. xi Contributors........................................................................................................xiii Introduction .........................................................................................................xv 1 NetworkSciencePerspectivesonEngineeringAdaptationtoClimateChangeand WeatherExtremes ........................................................................................... 1 UditBhatiaandAuroopR.Ganguly 2 StructuredEstimationinHighDimensions:ApplicationsinClimate.....................13 AndréRGoncalves,ArindamBanerjee,VidyashankarSivakumar,andSoumyadeepChatterjee 3 SpatiotemporalGlobalClimateModelTracking .................................................33 ScottMcQuadeandClaireMonteleoni 4 StatisticalDownscalinginClimatewithState-of-the-ArtScalableMachine Learning........................................................................................................55 ThomasVandal,UditBhatia,andAuroopR.Ganguly 5 Large-ScaleMachineLearningforSpeciesDistributions......................................73 ReidA.Johnson,JasonD.K.Dzurisin,andNiteshV.Chawla 6 UsingLarge-ScaleMachineLearningtoImproveOurUnderstandingofthe FormationofTornadoes..................................................................................95 AmyMcGovern,CoreyPotvin,andRodgerA.Brown 7 DeepLearningforVeryHigh-ResolutionImageryClassification........................113 SangramGanguly,SaikatBasu,RamakrishnaNemani,SupratikMukhopadhyay,Andrew Michaelis,PetrVotava,CristinaMilesi,andUttamKumar vii viii Contents 8 UnmixingAlgorithms:AReviewofTechniquesforSpectralDetectionand ClassificationofLandCoverfromMixedPixelsonNASAEarthExchange ..........131 UttamKumar,CristinaMilesi,S.KumarRaja,RamakrishnaNemani,SangramGanguly, WeileWang,PetrVotava,AndrewMichaelis,andSaikatBasu 9 SemanticInteroperabilityofLong-TailGeoscienceResourcesovertheWeb.........175 MostafaM.Elag,PraveenKumar,LuigiMarini,ScottD.Peckham,andRuiLiu Index ......................................................................................................201 Foreword The climate and Earth sciences have recently undergone a rapid transformation from a data-poor to a data-richenvironment.Inparticular,massiveamountsofclimateandecosystemdataarenowavailable fromsatelliteandground-basedsensors,andphysics-basedclimatemodelsimulations.Theseinformation- richdatasetsofferhugepotentialformonitoring,understanding,andpredictingthebehavioroftheEarth’s ecosystemandforadvancingthescienceofglobalchange. Whilelarge-scalemachinelearninganddatamininghavegreatlyimpactedarangeofcommercialappli- cations, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava,RamakrishnaNemani,andKarstenSteinhaeuser,servesasanoutstandingresourceforany- oneinterestedintheopportunitiesandchallengesforthemachinelearningcommunityinanalyzingthese datasetstoanswerquestionsofurgentsocietalinterest. ThisbookisacompilationofrecentresearchintheapplicationofmachinelearninginthefieldofEarth sciences.Itdiscussesanumberofapplicationsthatexemplifysomeofthemostimportantquestionsfaced bytheclimateandecosystemscientiststodayandtherolethedataminingcommunitycanplayinanswer- ingthem.Chaptersarewrittenbyexpertswhoareworkingattheintersectionofthetwofields.Topics covered include modeling of weather and climate extremes, evaluation of climate models, and the use of remote sensing data to quantify land-cover change dynamics. Collectively, they provide an excellent cross-sectionofresearchbeingdoneinthisemergingfieldofgreatsocietalimportance. Ihopethatthisbookwillinspiremorecomputerscientiststofocusonenvironmentalapplications,and Earthscientiststoseekcollaborationswithresearchersinmachinelearninganddataminingtoadvance thefrontiersinEarthsciences. VipinKumar,PhD DepartmentofComputerScienceandEngineering UniversityofMinnesota Minneapolis,MN ix

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From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an ou
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Large-Scale Machine Learning in the Earth Sciences Chapman & Hall/CRC Data Mining and Knowledge Discovery Series Series Editor: Vipin Kumar UniversityofMinnesota DepartmentofComputerScienceandEngineering Minneapolis,Minnesota,U.S.A. AIMSANDSCOPE Thisseriesaimstocapturenewdevelopmentsandapplicationsindataminingandknowledge discovery,whilesummarizingthecomputationaltoolsandtechniquesusefulindataanalysis. Thisseriesencouragestheintegrationofmathematical,statistical,andcomputationalmethods andtechniquesthroughthepublicationofabroadrangeoftextbooks,referenceworks,and handbooks.Theinclusionofconcreteexamplesandapplicationsishighlyencouraged.The scopeoftheseriesincludes,butisnotlimitedto,titlesintheareasofdataminingand knowledgediscoverymethodsandapplications,modeling,algorithms,theoryandfoundations, dataandknowledgevisualization,dataminingsystemsandtools,andprivacyandsecurity issues. PUBLISHEDTITLES AcceleratingDiscovery:MiningUnstructuredInformationforHypothesisGeneration ScottSpangler AdvancesinMachineLearningandDataMiningforAstronomy MichaelJ.Way,JeffreyD.Scargle,KamalM.Ali,andAshokN.Srivastava BiologicalDataMining JakeY.ChenandStefanoLonardi ComputationalBusinessAnalytics SubrataDas ComputationalIntelligentDataAnalysisforSustainableDevelopment TingYu,NiteshV.Chawla,andSimeonSimoff ComputationalMethodsofFeatureSelection HuanLiuandHiroshiMotoda ConstrainedClustering:AdvancesinAlgorithms,Theory,andApplications SugatoBasu,IanDavidson,andKiriL.Wagstaff ContrastDataMining:Concepts,Algorithms,andApplications GuozhuDongandJamesBailey DataClassification:AlgorithmsandApplications CharuC.Aggarawal DataClustering:AlgorithmsandApplications CharuC.AggarawalandChandanK.Reddy DataClusteringinC++:AnObject-OrientedApproach GuojunGan DataMining:ATutorial-BasedPrimer,SecondEdition RichardJ.Roiger DataMiningforDesignandMarketing YukioOhsawaandKatsutoshiYada DataMiningwithR:LearningwithCaseStudies,SecondEdition LuísTorgo DataScienceandAnalyticswithPython JesusRogel-Salazar EventMining:AlgorithmsandApplications TaoLi FoundationsofPredictiveAnalytics JamesWuandStephenCoggeshall GeographicDataMiningandKnowledgeDiscovery,SecondEdition HarveyJ.MillerandJiaweiHan Graph-BasedSocialMediaAnalysis IoannisPitas HandbookofEducationalDataMining CristóbalRomero,SebastianVentura,MykolaPechenizkiy,andRyanS.J.d.Baker HealthcareDataAnalytics ChandanK.ReddyandCharuC.Aggarwal InformationDiscoveryonElectronicHealthRecords VagelisHristidis IntelligentTechnologiesforWebApplications PritiSrinivasSajjaandRajendraAkerkar IntroductiontoPrivacy-PreservingDataPublishing:ConceptsandTechniques BenjaminC.M.Fung,KeWang,AdaWai-CheeFu,andPhilipS.Yu KnowledgeDiscoveryforCounterterrorismandLawEnforcement DavidSkillicorn KnowledgeDiscoveryfromDataStreams JoãoGama Large-ScaleMachineLearninginTheEarthSciences AshokN.Srivastava,RamakrishnaNemani,andKarstenSteinhaeuser MachineLearningandKnowledgeDiscoveryforEngineeringSystemsHealthManagement AshokN.SrivastavaandJiaweiHan MiningSoftwareSpecifications:MethodologiesandApplications DavidLo,Siau-ChengKhoo,JiaweiHan,andChaoLiu MultimediaDataMining:ASystematicIntroductiontoConceptsandTheory ZhongfeiZhangandRuofeiZhang MusicDataMining TaoLi,MitsunoriOgihara,andGeorgeTzanetakis NextGenerationofDataMining HillolKargupta,JiaweiHan,PhilipS.Yu,RajeevMotwani,andVipinKumar Rapidminer:DataMiningUseCasesandBusinessAnalyticsApplications MarkusHofmannandRalfKlinkenberg RelationalDataClustering:Models,Algorithms,andApplications BoLong,ZhongfeiZhang,andPhilipS.Yu Service-OrientedDistributedKnowledgeDiscovery DomenicoTaliaandPaoloTrunfio SpectralFeatureSelectionForDataMining ZhengAlanZhaoandHuanLiu StatisticalDataMiningUsingSASApplications,SecondEdition GeorgeFernandez SupportVectorMachines:OptimizationBasedTheory,Algorithms,andExtensions NaiyangDeng,YingjieTian,andChunhuaZhang TemporalDataMining TheophanoMitsa TextMining:Classification,Clustering,andApplications AshokN.SrivastavaandMehranSahami TextMiningandVisualization:CaseStudiesUsingOpen-SourceTools MarkusHofmannandAndrewChisholm TheTopTenAlgorithmsinDataMining XindongWuandVipinKumar UnderstandingComplexDatasets:DataMiningwithMatrixDecompositions DavidSkillicorn Large-Scale Machine Learning in the Earth Sciences Editedby Ashok N. Srivastava Ramakrishna Nemani Karsten Steinhaeuser MATLABⓇ is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracyofthetextorexercisesinthisbook.Thisbook’suseordiscussionofMATLABⓇsoftwareorrelatedproductsdoesnot constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or particular use of the MATLABⓇsoftware. CRCPress Taylor&FrancisGroup 6000BrokenSoundParkwayNW,Suite300 BocaRaton,FL33487-2742 ©2017byTaylor&FrancisGroup,LLC CRCPressisanimprintofTaylor&FrancisGroup,anInformabusiness NoclaimtooriginalU.S.Governmentworks Printedonacid-freepaper InternationalStandardBookNumber-13:978-1-4987-0387-1(Hardback) Thisbookcontainsinformationobtainedfromauthenticandhighlyregardedsources.Reasonableeffortshavebeenmadeto publishreliabledataandinformation,buttheauthorandpublishercannotassumeresponsibilityforthevalidityofallmaterials ortheconsequencesoftheiruse.Theauthorsandpublishershaveattemptedtotracethecopyrightholdersofallmaterial reproducedinthispublicationandapologizetocopyrightholdersifpermissiontopublishinthisformhasnotbeenobtained.If anycopyrightmaterialhasnotbeenacknowledgedpleasewriteandletusknowsowemayrectifyinanyfuturereprint. ExceptaspermittedunderU.S.CopyrightLaw,nopartofthisbookmaybereprinted,reproduced,transmitted,orutilizedinany formbyanyelectronic,mechanical,orothermeans,nowknownorhereafterinvented,includingphotocopying,microfilming, andrecording,orinanyinformationstorageorretrievalsystem,withoutwrittenpermissionfromthepublishers. For permission to photocopy or use material electronically from this work, please access www.copyright.com (http://www.copyright.com/)orcontacttheCopyrightClearanceCenter,Inc.(CCC),222RosewoodDrive,Danvers,MA01923, 978-750-8400.CCCisanot-for-profitorganizationthatprovideslicensesandregistrationforavarietyofusers.Fororganizations thathavebeengrantedaphotocopylicensebytheCCC,aseparatesystemofpaymenthasbeenarranged. TrademarkNotice:Productorcorporatenamesmaybetrademarksorregisteredtrademarks,andareusedonlyforidentifica- tionandexplanationwithoutintenttoinfringe. LibraryofCongressCataloging-in-PublicationData Names:Srivastava,AshokN.(AshokNarain),1969-editor.|Nemani, Ramakrishna,editor.|Steinhaeuser,Karsten,editor. Title:Large-scalemachinelearningintheearthsciences/[editedby]Ashok N.Srivastava,Dr.RamakrishnaNemani,KarstenSteinhaeuser. Description:BocaRaton:Taylor&Francis,2017.|Series:Chapman& Hall/CRCdatamining&knowledgediscoveryseries;42|“ACRCtitle, partoftheTaylor&Francisimprint,amemberoftheTaylor&Francis Group,theacademicdivisionofT&FInformaplc.” Identifiers:LCCN2017006160|ISBN9781498703871(hardback:alk.paper) Subjects:LCSH:Earthsciences–Computernetworkresources.|Earth sciences–Dataprocessing. Classification:LCCQE48.87.L372017|DDC550.285/6312–dc23 LCrecordavailableathttps://lccn.loc.gov/2017006160 VisittheTaylor&FrancisWebsiteat http://www.taylorandfrancis.com andtheCRCPressWebsiteat http://www.crcpress.com Contents Foreword.............................................................................................................. ix Editors................................................................................................................. xi Contributors........................................................................................................xiii Introduction .........................................................................................................xv 1 NetworkSciencePerspectivesonEngineeringAdaptationtoClimateChangeand WeatherExtremes ........................................................................................... 1 UditBhatiaandAuroopR.Ganguly 2 StructuredEstimationinHighDimensions:ApplicationsinClimate.....................13 AndréRGoncalves,ArindamBanerjee,VidyashankarSivakumar,andSoumyadeepChatterjee 3 SpatiotemporalGlobalClimateModelTracking .................................................33 ScottMcQuadeandClaireMonteleoni 4 StatisticalDownscalinginClimatewithState-of-the-ArtScalableMachine Learning........................................................................................................55 ThomasVandal,UditBhatia,andAuroopR.Ganguly 5 Large-ScaleMachineLearningforSpeciesDistributions......................................73 ReidA.Johnson,JasonD.K.Dzurisin,andNiteshV.Chawla 6 UsingLarge-ScaleMachineLearningtoImproveOurUnderstandingofthe FormationofTornadoes..................................................................................95 AmyMcGovern,CoreyPotvin,andRodgerA.Brown 7 DeepLearningforVeryHigh-ResolutionImageryClassification........................113 SangramGanguly,SaikatBasu,RamakrishnaNemani,SupratikMukhopadhyay,Andrew Michaelis,PetrVotava,CristinaMilesi,andUttamKumar vii viii Contents 8 UnmixingAlgorithms:AReviewofTechniquesforSpectralDetectionand ClassificationofLandCoverfromMixedPixelsonNASAEarthExchange ..........131 UttamKumar,CristinaMilesi,S.KumarRaja,RamakrishnaNemani,SangramGanguly, WeileWang,PetrVotava,AndrewMichaelis,andSaikatBasu 9 SemanticInteroperabilityofLong-TailGeoscienceResourcesovertheWeb.........175 MostafaM.Elag,PraveenKumar,LuigiMarini,ScottD.Peckham,andRuiLiu Index ......................................................................................................201 Foreword The climate and Earth sciences have recently undergone a rapid transformation from a data-poor to a data-richenvironment.Inparticular,massiveamountsofclimateandecosystemdataarenowavailable fromsatelliteandground-basedsensors,andphysics-basedclimatemodelsimulations.Theseinformation- richdatasetsofferhugepotentialformonitoring,understanding,andpredictingthebehavioroftheEarth’s ecosystemandforadvancingthescienceofglobalchange. Whilelarge-scalemachinelearninganddatamininghavegreatlyimpactedarangeofcommercialappli- cations, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava,RamakrishnaNemani,andKarstenSteinhaeuser,servesasanoutstandingresourceforany- oneinterestedintheopportunitiesandchallengesforthemachinelearningcommunityinanalyzingthese datasetstoanswerquestionsofurgentsocietalinterest. ThisbookisacompilationofrecentresearchintheapplicationofmachinelearninginthefieldofEarth sciences.Itdiscussesanumberofapplicationsthatexemplifysomeofthemostimportantquestionsfaced bytheclimateandecosystemscientiststodayandtherolethedataminingcommunitycanplayinanswer- ingthem.Chaptersarewrittenbyexpertswhoareworkingattheintersectionofthetwofields.Topics covered include modeling of weather and climate extremes, evaluation of climate models, and the use of remote sensing data to quantify land-cover change dynamics. Collectively, they provide an excellent cross-sectionofresearchbeingdoneinthisemergingfieldofgreatsocietalimportance. Ihopethatthisbookwillinspiremorecomputerscientiststofocusonenvironmentalapplications,and Earthscientiststoseekcollaborationswithresearchersinmachinelearninganddataminingtoadvance thefrontiersinEarthsciences. VipinKumar,PhD DepartmentofComputerScienceandEngineering UniversityofMinnesota Minneapolis,MN ix

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From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an ou
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