NDLI: A new data mining framework for forest fire mapping

Content Provider	IEEE Xplore Digital Library
Author	Chen, X.C. Karpatne, A. Chamber, Y. Mithal, V. Lau, M. Steinhaeuser, K. Boriah, S. Steinbach, M. Kumar, V. Potter, C.S. Klooster, S.A. Abraham, T. Stanley, J.D. Castilla-Rubio, J.C.
Copyright Year	2012
Description	Author affiliation: Planetary Skin Institute, USA (Abraham, T.; Stanley, J.D.; Castilla-Rubio, J.C.) \|\| Dept. of Computer Science & Engineering, University of Minnesota, USA (Chen, X.C.; Karpatne, A.; Chamber, Y.; Mithal, V.; Lau, M.; Steinhaeuser, K.; Boriah, S.; Steinbach, M.; Kumar, V.) \|\| NASA Ames Research Center, USA (Potter, C.S.; Klooster, S.A.)
Abstract	Forests are an important natural resource that support economic activity and play a significant role in regulating the climate and the carbon cycle, yet forest ecosystems are increasingly threatened by fires caused by a range of natural and anthropogenic factors. Mapping these fires, which can range in size from less than an acre to hundreds of thousands of acres, is an important task for supporting climate and carbon cycle studies as well as informing forest management. Currently, there are two primary approaches to fire mapping: field- and aerial-based surveys, which are costly and limited in their extent; and remote sensing-based approaches, which are more cost-effective but pose several interesting methodological and algorithmic challenges. In this paper, we introduce a new framework for mapping forest fires based on satellite observations. Specifically, we develop unsupervised spatio-temporal data mining methods for Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate a history of forest fires. A systematic comparison with alternate approaches in two diverse geographic regions demonstrates that our algorithmic paradigm is able to overcome some of the limitations in both data and methods employed by prior efforts.
Starting Page	104
Ending Page	111
File Size	2282918
Page Count	8
File Format	PDF
ISBN	9781467346252
e-ISBN	9781467346276
DOI	10.1109/CIDU.2012.6382190
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Barium Time series analysis Noise Fires Vegetation mapping Robustness MODIS
Content Type	Text
Resource Type	Article

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