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Title: Spatial-temporal event detection in climate parameter imagery.

Abstract

Previously developed techniques that comprise statistical parametric mapping, with applications focused on human brain imaging, are examined and tested here for new applications in anomaly detection within remotely-sensed imagery. Two approaches to analysis are developed: online, regression-based anomaly detection and conditional differences. These approaches are applied to two example spatial-temporal data sets: data simulated with a Gaussian field deformation approach and weekly NDVI images derived from global satellite coverage. Results indicate that anomalies can be identified in spatial temporal data with the regression-based approach. Additionally, la Nina and el Nino climatic conditions are used as different stimuli applied to the earth and this comparison shows that el Nino conditions lead to significant decreases in NDVI in both the Amazon Basin and in Southern India.

Authors:
;
Publication Date:
Research Org.:
Sandia National Laboratories
Sponsoring Org.:
USDOE
OSTI Identifier:
1029771
Report Number(s):
SAND2011-6876
TRN: US201201%%181
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; CLIMATES; DEFORMATION; IMAGES; REMOTE SENSING; DETECTION; SATELLITES; SOUTHERN OSCILLATION

Citation Formats

McKenna, Sean Andrew, and Gutierrez, Karen A. Spatial-temporal event detection in climate parameter imagery.. United States: N. p., 2011. Web. doi:10.2172/1029771.
McKenna, Sean Andrew, & Gutierrez, Karen A. Spatial-temporal event detection in climate parameter imagery.. United States. doi:10.2172/1029771.
McKenna, Sean Andrew, and Gutierrez, Karen A. Sat . "Spatial-temporal event detection in climate parameter imagery.". United States. doi:10.2172/1029771. https://www.osti.gov/servlets/purl/1029771.
@article{osti_1029771,
title = {Spatial-temporal event detection in climate parameter imagery.},
author = {McKenna, Sean Andrew and Gutierrez, Karen A.},
abstractNote = {Previously developed techniques that comprise statistical parametric mapping, with applications focused on human brain imaging, are examined and tested here for new applications in anomaly detection within remotely-sensed imagery. Two approaches to analysis are developed: online, regression-based anomaly detection and conditional differences. These approaches are applied to two example spatial-temporal data sets: data simulated with a Gaussian field deformation approach and weekly NDVI images derived from global satellite coverage. Results indicate that anomalies can be identified in spatial temporal data with the regression-based approach. Additionally, la Nina and el Nino climatic conditions are used as different stimuli applied to the earth and this comparison shows that el Nino conditions lead to significant decreases in NDVI in both the Amazon Basin and in Southern India.},
doi = {10.2172/1029771},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Oct 01 00:00:00 EDT 2011},
month = {Sat Oct 01 00:00:00 EDT 2011}
}

Technical Report:

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