Skip to Main Content
Geospatiotemporal data mining in an early warning system for forest threats in the United StatesAuthor(s): F.M. Hoffman; R.T. Mills; J. Kumar; S.S. Vulli; W.W. Hargrove
Source: In: Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010), July 25-30, 2010, Honolulu, Hawaii, USA.
Publication Series: Scientific Journal (JRNL)
PDF: View PDF (1.58 MB)
DescriptionWe investigate the potential of geospatiotemporal data mining of multi-year land surface phenology data (250 m Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) in this study) for the conterminous United States as part of an early warning system to identify threats to forest ecosystems. Cluster analysis of this massive data set, using high-performance computing, provides a basis for several possible approaches to defining the bounds of “normal” phenological patterns, indicating healthy vegetation in a given geographic location. We demonstrate the applicability of such an approach, using it to identify areas in Colorado, USA, where an ongoing mountain pine beetle outbreak has caused significant tree mortality.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationHoffman, F.M.; Mills, R.T.; Kumar, J.; Vulli, S.S.; Hargrove, W.W. 2010. Geospatiotemporal data mining in an early warning system for forest threats in the United States. In: Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2010), July 25-30, 2010, Honolulu, Hawaii, USA.
- Cluster Analysis-Based Approaches for Geospatiotemporal Data Mining of Massive Data Sets for Identification of Forest Threats
- Identification and visualization of dominant patterns and anomalies in remotely sensed vegetation phenology using a parallel tool for principal components analysis
- Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel
XML: View XML