Incorporating natural variation into time series-based land cover change identification

Varun Mithal, Ashish Garg, Ivan Brugere, Shyam Boriah, Vipin Kumar, Michael S Steinbach, Christopher Potter, Steven Klooster

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Scopus citations

Abstract

The ability to monitor forest related change events like forest fires, deforestation for agriculture intensification, and logging is critical for effective forest management. Time series remote sensing data sets such as MODIS Enhanced Vegetation Index (EVI) can be used to identify these changes. Most existing approaches work on small data sets spanning over a specific geographic region of a homogeneous vegetation type. Also, most of these need training samples or require setting of parameters for each geographic region individually. These limitations make the algorithms unscalable and restrict their global applicability. In this paper, we present a scalable time series based change detection framework that overcomes these limitations of the existing methods. We introduce the concept of natural variation in EVI for a given of location and incorporate it into the change detection paradigm. We evaluate the change events identified by our approach using forest fire validation data in California and Canada. The results of this study demonstrate that the inclusion of a measure of natural variability improves detection accuracy, and makes the paradigm more robust across vegetation types and regions.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 Conference on Intelligent Data Understanding, CIDU 2011
Pages45-59
Number of pages15
StatePublished - Dec 1 2011
EventNASA Conference on Intelligent Data Understanding, CIDU 2011 - Mountain View, CA, United States
Duration: Oct 19 2011Oct 21 2011

Other

OtherNASA Conference on Intelligent Data Understanding, CIDU 2011
CountryUnited States
CityMountain View, CA
Period10/19/1110/21/11

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