Scholarly Works, Center for Environmental Applications of Remote Sensing (CEARS)

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https://hdl.handle.net/10919/73612
Research articles, presentations, and other scholarship

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Browsing Scholarly Works, Center for Environmental Applications of Remote Sensing (CEARS) by Author "Blinn, Christine E."

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    Crowds for Clouds: Using an Internet Workforce to Interpret Satellite Images
    Yu, Ling; Ball, Sheryl B.; Blinn, Christine E.; Moeltner, Klaus; Peery, Seth; Thomas, Valerie A.; Wynne, Randolph H. (2014)
    A chronologically ordered sequence of satellite images can be used to learn how natural features of the landscape change over time. For example, we can learn how forests react to human interventions or climate change. Before these satellite images can be used for this purpose, they need to be examined for clouds and cloud shadow that may hide important features of the landscape and would lead to misinterpretation of forest conditions. Once clouds and their shadow have been identified, researchers can then look for other images that include the feature of interest, taken a bit earlier or later in time, to fill in the "missing information" for the original image. Therefore, the task of identifying clouds and their shadow is extremely important for the correct and efficient use of each image. Computer algorithms are only imperfectly suited for this task. The aim of this project is to outsource the cloud interpretation task to a global internet community of "turkers" -workers recruited via amazon.com's online job market known as "Mechanical Turk."
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    On-the-Fly Massively Multitemporal Change Detection Using Statistical Quality Control Charts and Landsat Data
    Brooks, Evan B.; Wynne, Randolph H.; Thomas, Valerie A.; Blinn, Christine E.; Coulston, John W. (Institute of Electrical and Electronics Engineers (IEEE), 2014-06)
    One 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.