Characterizing major agricultural land change trends in the Western Corn Belt

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dc.contributor.authorShao, Yangen
dc.contributor.authorTaff, Gregory N.en
dc.contributor.authorRen, Jieen
dc.contributor.authorCampbell, James B. Jr.en
dc.contributor.departmentGeographyen
dc.date.accessioned2018-02-04T19:18:11Zen
dc.date.available2018-02-04T19:18:11Zen
dc.date.issued2016-12-01en
dc.description.abstractIn this study we developed annual corn/soybean maps for the Western Corn Belt within the United States using multi-temporal MODIS NDVI products from 2001 to 2015 to support long-term cropland change analysis. Based on the availability of training data (cropland data layer from the USDA-NASS), we designed a cross-validation scheme for 2006–2015 MODIS data to examine the spatial generalization capability of a neural network classifier. Training data points were derived from a three-state subregion consisting of North Dakota, Nebraska, and Iowa. Trained neural networks were applied to the testing sub-region (South Dakota, Kansas, Minnesota, and Missouri) to generate corn/soybean maps. Using a default threshold value (neural network output signalP0.5), the neural networks performed well for South Dakota and Minnesota. Overall accuracy was higher than 80% (kappa > 0.55) for all testing years from 2006 to 2015. However, we observed high variation of classification performance for Kansas (overall accuracy: 0.71–0.82) and Missouri (overall accuracy: 0.65–0.77) for various testing years. We developed a threshold-moving method that decreases/increases threshold values of neural network output signals to match MODIS-derived corn/soybean acreage with the NASS acreage statistics. Over 70% of testing states and years showed improved classification performance compared to the use of a default 0.5 threshold. The largest improvement of kappa value was about 0.08. This threshold-moving method was used to generate MODIS-based annual corn/soybean map products for 2001–2015. A non-parametric Mann-Kendall test was then used to identify areas that showed significant (p < 0.05) upward/downward trends. Areas showing fast increase of corn/soybean intensities were mainly located in North Dakota, South Dakota, and the west portion of Minnesota. The highest annual increase rate for a 5-km moving window was about 6.8%.en
dc.description.versionPublished versionen
dc.format.extent116 - 125 (10) page(s)en
dc.identifier.doihttps://doi.org/10.1016/j.isprsjprs.2016.10.009en
dc.identifier.eissn1872-8235en
dc.identifier.issn0924-2716en
dc.identifier.orcidCampbell, JB [0000-0002-2617-7272]en
dc.identifier.urihttp://hdl.handle.net/10919/82006en
dc.identifier.volume122en
dc.languageEnglishen
dc.publisherElsevieren
dc.relation.urihttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000390719600009&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=930d57c9ac61a043676db62af60056c1en
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectTechnologyen
dc.subjectGeography, Physicalen
dc.subjectGeosciences, Multidisciplinaryen
dc.subjectRemote Sensingen
dc.subjectImaging Science & Photographic Technologyen
dc.subjectPhysical Geographyen
dc.subjectGeologyen
dc.subjectCropland mappingen
dc.subjectNeural networken
dc.subjectThreshold-movingen
dc.subjectMODISen
dc.subjectTIME-SERIES DATAen
dc.subjectCENTRAL GREAT-PLAINSen
dc.subjectNEURAL-NETWORKen
dc.subjectUNITED-STATESen
dc.subjectNDVI DATAen
dc.subjectCROP CLASSIFICATIONen
dc.subjectAREA ESTIMATIONen
dc.subjectMODIS DATAen
dc.subjectALGORITHMSen
dc.subjectPERFORMANCEen
dc.titleCharacterizing major agricultural land change trends in the Western Corn Belten
dc.title.serialISPRS Journal of Photogrammetry And Remote Sensingen
dc.typeArticle - Refereeden
dc.type.otherArticleen
dc.type.otherJournalen
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/All T&R Facultyen
pubs.organisational-group/Virginia Tech/Natural Resources & Environmenten
pubs.organisational-group/Virginia Tech/Natural Resources & Environment/CNRE T&R Facultyen
pubs.organisational-group/Virginia Tech/Natural Resources & Environment/Geographyen
pubs.organisational-group/Virginia Tech/Natural Resources & Environment/Geography/Geography T&R facultyen

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