Browsing by Author "Potter, Christopher"

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    Land Cover Change Detection using Data Mining Techniques
    (2008-03-14) Boriah, Shyam; Kumar, Vipin; Steinbach, Michael; Potter, Christopher; Klooster, Steven
    The study of land cover change is an important problem in the Earth science domain because of its impacts on local climate, radiation balance, biogeochemistry, hydrology, and the diversity and abundance of terrestrial species. Data mining and knowledge discovery techniques can aid this effort by efficiently discovering patterns that capture complex interactions between ocean temperature, air pressure, surface meteorology, and terrestrial carbon flux. Most well-known change detection techniques from statistics, signal processing and control theory are not well-suited for the massive high-dimensional spatio-temporal data sets from Earth Science due to limitations such as high computational complexity and the inability to take advantage of seasonality and spatio-temporal autocorrelation inherent in Earth Science data. In our work, we seek to address these challenges with new change detection techniques that are based on data mining approaches. Specifically, in this paper we have performed a case study for a new change detection technique for the land cover change detection problem. We study land cover change in the state of California, focusing on the San Francisco Bay Area as well perform an extended study on the entire state. We also perform a comparative evaluation on forests in the entire state. These results demonstrate the utility of data mining techniques for the land cover change detection problem.
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    Monitoring Global Forest Cover Using Data Mining
    (2010-07-14) Mithal, Varun; Boriah, Shyam; Garg, Ashish; Steinbach, Michael; Kumar, Vipin; Potter, Christopher; Klooster, Steven; Castilla-Rubio, Juan Carlos
    Forests are a critical component of the planet's ecosystem. Unfortunately, there has been significant degradation in forest cover over recent decades as a result of logging, conversion to crop,plantation, and pasture land, or disasters (natural or man made) such as forest fires, floods, and hurricanes. As a result, significant attention is being given to the sustainable use of forests. A key to effective forest management is quantifiable knowledge about changes in forest cover. This requires identification and characterization of changes and the discovery of the relationship between these changes and natural and anthropogenic variables. In this paper, we present our preliminary efforts and achievements in addressing some of these tasks along with the challenges and opportunities that need to be addressed in the future. At a higher level, our goal is to provide an overview of the exciting opportunities and challenges in developing and applying data mining approaches to provide critical information for forest and land use management.
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    Pre-processing of the validation data used in the paper titled "Model-Free Time Series Segmentation Approach for Land Cover Change Detection"
    (2011-08-17) Garg, Ashish; Manikonda, Lydia; Kumar, Shashank; Krishna, Vikrant; Boriah, Shyam; Steinbach, Michael; Kumar, Vipin; Toshniwal, Durga; Potter, Christopher; Klooster, Steven
    This report describes the detailed steps of pre-processing the validation data which is used for comparative evaluation of the algorithms proposed in the paper titled "A Model-Free Segmentation Approach for Land Cover Change Detection".