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dc.contributor.authorBrooks, Evan B,
dc.contributor.authorWynne, Randolph H.
dc.contributor.authorThomas, Valerie A.
dc.contributor.authorBlinn, Christine E.
dc.contributor.authorCoulston, John
dc.date.accessioned2014-09-29T14:40:45Z
dc.date.available2014-09-29T14:40:45Z
dc.date.issued2014-06
dc.identifier.citationBrooks, E. B., Wynne, R. H., Thomas, V. A., Blinn, C. E., and Coulston, J. W. (2014) “On-the-fly massively multitemporal change detection using statistical quality control charts and Landsat data.” IEEE Transactions on Geosciences and Remote Sensing, 52(6), 3316-3332.en_US
dc.identifier.issn0196-2892
dc.identifier.urihttp://hdl.handle.net/10919/50544
dc.description.abstractOne challenge to implementing spectral change detection algorithms using multitemporal Landsat data is that key dates and periods are often missing from the record due to weather disturbances and lapses in continuous coverage. This paper presents a method that utilizes residuals from harmonic regression over years of Landsat data, in conjunction with statistical quality control charts, to signal subtle disturbances in vegetative cover. These charts are able to detect changes from both deforestation and subtler forest degradation and thinning. First, harmonic regression residuals are computed after fitting models to interannual training data. These residual time series are then subjected to Shewhart X-bar control charts and exponentially weighted moving average charts. The Shewhart X-bar charts are also utilized in the algorithm to generate a data-driven cloud filter, effectively removing clouds and cloud shadows on a location-specific basis. Disturbed pixels are indicated when the charts signal a deviation from data-driven control limits. The methods are applied to a collection of loblolly pine (Pinus taeda) stands in Alabama, USA. The results are compared with stands for which known thinning has occurred at known times. The method yielded an overall accuracy of 85%, with the particular result that it provided afforestation/deforestation maps on a per-image basis, producing new maps with each successive incorporated image. These maps matched very well with observed changes in aerial photography over the test period. Accordingly, the method is highly recommended for on-the-fly change detection, for changes in both land use and land management within a given land use.en_US
dc.description.sponsorshipThis work was supported in part by the U.S. Department of Agriculture Forest Service Cooperative Agreement with Virginia Polytechnic Institute and State University (Virginia Tech) under Grant 10-CA-11330145-158, by the Landsat Science Team under U.S. Geological Survey Contract G12PC00073, by the Pine Integrated Network: Education, Mitigation, and Adaptation Project (PINEMAP, Coordinated Agricultural Project funded in 2011 by the USDA National Institute of Food and Agriculture), by theMcIntire-Stennis Cooperative Forestry Research Program through the USDA CSREES under Project VA-136614, and by the Department of Forest Resources and Environmental Conservation at Virginia Tech.en_US
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isreferencedbyhttp://hdl.handle.net/10919/50543
dc.rights© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectStatistical process control
dc.subjectThinning
dc.subjectTrajectory
dc.subjectDegradationen_US
dc.subjectstatistical process controlen_US
dc.subjectthinningen_US
dc.subjecttrajectoryen_US
dc.titleOn-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Dataen_US
dc.typeArticle - Refereeden_US
dc.title.serialIEEE Transactions on Geoscience and Remote Sensing
dc.identifier.doihttps://doi.org/10.1109/TGRS.2013.2272545


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