Steven A. Klooster

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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(More)
A simulation model based on satellite observations of monthly vegetation cover was used to estimate monthly carbon fluxes in terrestrial ecosystems from 1982 to 1998. The NASA – CASA model was driven by vegetation properties derived from the Advanced Very High Resolution Radiometer (AVHRR) and radiative transfer algorithms that were developed for Moderate(More)
This paper presents preliminary work in using data mining techniques to find interesting spatio-temporal patterns from Earth Science data. The data consists of time series measurements for various Earth science and climate variables (e.g. soil moisture, temperature, and precipitation), along with additional data from existing ecosystem models (e.g. Net(More)
The content of this work does not necessarily reflect the position or policy of the government and no official endorsement should be inferred. Access to computing facilities was provided by the AHPCRC and the Minnesota Supercomputing Institute. ABSTRACT Ocean climate indices (OCIs), which are time series that summarize the behavior of selected areas of the(More)
[1] We have applied association analysis to 17 years of climate index observations and predicted net ecosystem production on land to infer short-term (monthly to yearly) teleconnections between atmosphere-ocean climate forcing and terrestrial carbon cycles. The analysis suggests that on a global level, climate indices can be significantly correlated to net(More)
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(More)
The content of this work does not necessarily reflect the position or policy of the government and no official endorsement should be inferred. Access to computing facilities was provided by AHPCRC and the Minnesota Supercomputing Institute. ABSTRACT This paper reports on recent work applying data mining to the task of finding interesting patterns in earth(More)
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 mul-tivariate time series data by employing a kernel matrix alignment method to capture the(More)
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(More)
This paper presents preliminary work in using data mining techniques to find interesting spatio-temporal patterns from Earth Science data. The data consists of time series measurements for various Earth Science variables (e.g. soil moisture, temperature, and precipitation), along with additional data from existing ecosystem models (e.g. Net Primary(More)