Spatial-temporal event detection in climate parameter imagery.

McKenna, Sean Andrew ; Gutierrez, Karen A.

Sandia National Laboratories

关键词: 99 General And Miscellaneous//Mathematics, Computing, And Information Science;    Remote Sensing;    Detection;    Southern Oscillation;    54 Environmental Sciences;   

DOI  :  10.2172/1029771
RP-ID  :  SAND2011-6876
RP-ID  :  AC04-94AL85000
RP-ID  :  1029771

美国|英语

来源: UNT Digital Library

【 摘 要 】

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.