Browsing by Author "Steinhaeuser, Karsten"

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    A Data Mining Framework for Forest Fire Mapping
    (2012-03-29) Karpatne, Anuj; Chen, Xi; Chamber, Yashu; Mithal, Varun; Lau, Michael; Steinhaeuser, Karsten; Boriah, Shyam; Steinbach, Michael; Kumar, Vipin
    Forests are an important natural resource that support economic activity and play a significant role in regulating the climate and the carbon cycle, yet forest ecosystems are increasingly threatened by fires caused by a range of natural and anthropogenic factors. Mapping these fires, which can range in size from less than an acre to hundreds of thousands of acres, is an important task for supporting climate and carbon cycle studies as well as informing forest management. There are two primary approaches to fire mapping: field and aerial-based surveys, which are costly and limited in their extent; and remote sensing-based approaches, which are more cost-effective but pose several interesting methodological and algorithmic challenges. In this paper, we introduce a new framework for mapping forest fires based on satellite observations. Specifically, we develop spatio-temporal data mining methods for Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate a history of forest fires. A systematic comparison with alternate approaches across diverse geographic regions demonstrates that our algorithmic paradigm is able to overcome some of the limitations in both data and methods employed by other prior efforts.
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    A New Teleconnection : The Australian Southern Oscillation
    (2012-09-21) Kumar, Arjun; Liess, Stefan; Kawale, Jaya; Ormsby, Dominick; Steinhaeuser, Karsten; Kumar, Vipin
    A possibly new teleconnection has been discovered off the east coast of Australia in the region around Tasman sea and Southern Ocean. Found in pressure anomalies using a novel graph based approach called shared reciprocal nearest neighbors, this dipole appears in reanalysis datasets such as NCEP, JRA, ERA and MERRA. The HadSLP2 observation data shows the new dipole, despite of limited observations in the Tasman Sea. Tests are performed in order to understand the uniqueness of the dipole and its relationship to existing well known phenomena. The dipole index is correlated with known dipole indices such as the SO (Southern Oscillation), AAO (Antarctic Oscillation) with which it shares a marginally higher correlation of less than 0.4 and other northern teleconnections with which it is shown to have a poor relationship. We limit further analysis with only the AAO and SO indices as these are spatially close, have a higher correlation with the new index and tend to influence it in one or more seasons. Seasonal analysis is done to look at the variation in strength as well as its influence on other variables such as TAS (Temperature at Surface), OLR (Outgoing Longwave Radiation), Precipitation etc. We also look at composite maps and do significance tests to determine the significant regions in these maps. We also determine regions that are influenced by the new dipole index alone and are not influenced by other dipoles namely the SO and AAO by looking at difference maps. We discover the dipole at different geopotential heights - 700 hPa, 500 hPa and 50 hPa (Sea Level Pressure is 1013 hPa)- and determine if the dipole is a sea surface phenomenon such as the SO or an upper atmospheric phenomenon such as the AAO. Our tests have shown that we may indeed be looking at a new phenomenon and further tests are being conducted to confirm that.
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    Contextual Time Series Change Detection
    (2012-07-23) Chen, Xi; Steinhaeuser, Karsten; Boriah, Shyam; Chatterjee, Snigdhansu; Kumar, Vipin
    Time series are commonly used in a variety of fields, ranging from economics to manufacturing. As a result, time series analysis and modeling has become an active research area in statistics and data mining. In this paper, we focus on a type of change we call contextual time series change (CTC) and propose a novel two-stage algorithm to address it. In contrast to traditional change detection methods, which consider each time series separately, CTC is defined as a change relative to the behavior of a group of related time series. As a result, our proposed method is able to identify novel types of changes not found by other algorithms. We demonstrate the unique capabilities of our approach with several case studies on real-world datasets from the financial and Earth science domains.
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    Supplement for "Change Detection from Temporal Sequences of Class Labels: Application to Land Cover Change Mapping"
    (2013-01-25) Mithal, Varun; Khandelwal, Ankush; Boriah, Shyam; Steinhaeuser, Karsten; Kumar, Vipin
    This is a supplement for paper titled "Change Detection from Temporal Sequences of Class Labels: Application to Land Cover Change Mapping" which is included in proceedings of SIAM International Conference of Data Mining, 2013. This supplement section has enlarged figures mentioned in the main paper and additional experiments on synthetic data.
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    Supplement for "Contextual Time Series Change Detection"
    (2013-01-25) Chen, Xi; Steinhaeuser, Karsten; Boriah, Shyam; Chatterjee, Snigdhansu; Kumar, Vipin
    Time series data are common in a variety of fields ranging from economics to medicine and manufacturing. As a result, time series analysis and modeling has become an active research area in statistics and data mining. In this paper, we focus on a type of change we call contextual time series change (CTC) and propose a novel two-stage algorithm to address it. In contrast to traditional change detection methods, which consider each time series separately, CTC is defined as a change relative to the behavior of a group of related time series. As a result, our proposed method is able to identify novel types of changes not found by other algorithms. We demonstrate the unique capabilities of our approach with several case studies on real-world datasets from the financial and Earth science domains.