GOPHER (Global Observation of Planetary Health and Ecosystem Resources) is a collection of data mining algorithms for detecting global land use and land cover change that builds on a decade of research on spatio temporal data mining at the University of Minnesota; the GOPHER approach to analyzing remote sensing imagery provides such a solution by providing rapid, inexpensive, robust, scalable, and precise detection of land use change. GOPHER algorithms are able to detect historical changes with high precision, and more recent changes with reasonable precision in as little as 8 weeks after changes occur (this is due to a combination of remote sensing data availability and modeling needs). This paper outlines the GOPHER approach and provides some illustrative results to demonstrate its utility.

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 article, 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.

Massive degradation in forest cover over recent decades caused by natural and human activities has made ability to detect changes in forest cover of critical importance. This paper provides a brief overview of our research on identifying changes in forest cover.

Anomaly detection in multivariate time series is an important data mining task with applications to ecosystem modeling, network traffic monitoring, medical diagnosis, and other domains. This paper presents a robust algorithm for detecting anomalies in noisy multivariate time series data by employing a kernel matrix alignment method to capture the dependence relationships among variables in the time series. Anomalies are found by performing a random walk traversal on the graph induced by the aligned kernel matrix. We show that the algorithm is flexible enough to handle different types of time series anomalies including subsequence-based and local anomalies. Our framework can also be used to characterize the anomalies found in a target time series in terms of the anomalies present in other time series. We have performed extensive experiments to empirically demonstrate the effectiveness of our algorithm. A case study is also presented to illustrate the ability of the algorithm to detect ecosystem disturbances in Earth science data.

Remote sensing data from Earth observation satellites offer unprecedented opportunity for predicting and understanding the behavior of the Earth’s ecosystem. However, because of their massive volume, extracting interesting patterns such as ecosystem disturbances from the data is a challenging task. In this paper, we present a case study on the application of data mining to the disturbance event detection problem. We describe two approaches—moving average and random walk—for detecting ecosystem disturbances. We then illustrate how clustering can be used to identify locations with similar incidents of ecosystem disturbance events. Finally, we develop a clustering-based framework to aid the visual exploration and detection of ecosystem disturbances from high resolution vegetation cover data.

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. 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 and 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.

To analyze the effect of the oceans and atmosphere on land climate, Earth Scientists have developed climate indices, which are time series that summarize the behavior of selected regions of the Earth’s oceans and atmosphere. In the past, Earth scientists have used observation and, more recently, eigenvalue analysis techniques, such as principal components analysis (PCA) and singular value decomposition (SVD), to discover climate indices. However, eigenvalue techniques are only useful for finding a few of the strongest signals. Furthermore, they impose a condition that all discovered signals must be orthogonal to each other, making it difficult to attach a physical interpretation to them. This paper presents an alternative clustering-based methodology for the discovery of climate indices that overcomes these limitations and is based on clusters that represent regions with relatively homogeneous behavior. The centroids of these clusters are time series that summarize the behavior of the ocean or atmosphere in those regions. Some of these centroids correspond to known climate indices and provide a validation of our methodology; other centroids are variants of known indices that may provide better predictive power for some land areas; and still other indices may represent potentially new Earth science phenomena. Finally, we show that cluster based indices generally outperform SVD derived indices, both in terms of area weighted correlation and direct correlation with the known indices.