TY - GEN
T1 - A new data mining framework for forest fire mapping
AU - Chen, Xi C.
AU - Karpatne, Anuj
AU - Chamber, Yashu
AU - Mithal, Varun
AU - Lau, Michael
AU - Steinhaeuser, Karsten
AU - Boriah, Shyam
AU - Steinbach, Michael S
AU - Kumar, Vipin
AU - Potter, Christopher S.
AU - Klooster, Steven A.
AU - Abraham, Teji
AU - Stanley, J. D.
AU - Castilla-Rubio, Juan Carlos
PY - 2012/12/1
Y1 - 2012/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84872375270&partnerID=8YFLogxK
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U2 - 10.1109/CIDU.2012.6382190
DO - 10.1109/CIDU.2012.6382190
M3 - Conference contribution
AN - SCOPUS:84872375270
SN - 9781467346252
T3 - Proceedings - 2012 Conference on Intelligent Data Understanding, CIDU 2012
SP - 104
EP - 111
BT - Proceedings - 2012 Conference on Intelligent Data Understanding, CIDU 2012
T2 - 2012 Conference on Intelligent Data Understanding, CIDU 2012
Y2 - 24 October 2012 through 26 October 2012
ER -