On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data

Simple item page
dc.contributor.authorBrooks, Evan B.en
dc.contributor.authorWynne, Randolph H.en
dc.contributor.authorThomas, Valerie A.en
dc.contributor.authorBlinn, Christine E.en
dc.contributor.authorCoulston, John W.en
dc.contributor.departmentCenter for Environmental Applications of Remote Sensing (CEARS)en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2014-09-29T14:40:45Zen
dc.date.available2014-09-29T14:40:45Zen
dc.date.issued2014-06en
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
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
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
dc.identifier.doihttps://doi.org/10.1109/TGRS.2013.2272545en
dc.identifier.issn0196-2892en
dc.identifier.urihttp://hdl.handle.net/10919/50544en
dc.language.isoen_USen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.isreferencedbyhttp://hdl.handle.net/10919/50543en
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.en
dc.subjectStatistical process controlen
dc.subjectThinningen
dc.subjectTrajectoryen
dc.subjectDegradationen
dc.subjectstatistical process controlen
dc.subjectthinningen
dc.subjecttrajectoryen
dc.titleOn-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Dataen
dc.title.serialIEEE Transactions on Geoscience and Remote Sensingen
dc.typeArticle - Refereeden

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
On the fly massively multitemporal change detection - 06573358.pdf
Size:
3.51 MB
Format:
Adobe Portable Document Format
Description:
Reference Article
Download
License bundle
Now showing 1 - 1 of 1
Name:
license.txt
Size:
1.5 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Scholarly Works, Forest Resources and Environmental Conservation
Scholarly Works, Center for Environmental Applications of Remote Sensing (CEARS)