Evaluation of Graph-based Analysis for Forest Fire Detections
Spatial outliers in remotely sensed imageries represent
observed quantities showing unusual values compared to their
neighbor pixel values. There have been various methods to detect the
spatial outliers based on spatial autocorrelations in statistics and data
mining. These methods may be applied in detecting forest fire pixels
in the MODIS imageries from NASA-s AQUA satellite. This is
because the forest fire detection can be referred to as finding spatial
outliers using spatial variation of brightness temperature. This point is
what distinguishes our approach from the traditional fire detection
methods. In this paper, we propose a graph-based forest fire detection
algorithm which is based on spatial outlier detection methods, and test
the proposed algorithm to evaluate its applicability. For this the
ordinary scatter plot and Moran-s scatter plot were used. In order to
evaluate the proposed algorithm, the results were compared with the
MODIS fire product provided by the NASA MODIS Science Team,
which showed the possibility of the proposed algorithm in detecting
the fire pixels.
[1] S. Shekhar, C.T. Lu, and P. Zhang. "A unified approach to detecting
spatial outliers," GeoInformatica, Vol.7, No.2, 2003, pp.139-166.
[2] S. Shekhar, C.T. Lu, and P. Zhang. "Detecting graph-based spatial
outliers: algorithms and application(a summary of results)." In Proc. the
ACM SIGKDD international conference on Knowledge discovery and
data mining, , San Francisco, CA, USA , 2001, pp. 371-376.
[3] V.Barnett and T.Lewis, Outliers in Statistical Data. 3rd edition, John
Wiley: New York, 1994.
[4] A.S.Forheringham, C.Brunsdon and M.Chatlton, Quantitative
Geography : Perspectives on Spatial Data Analysis, London, UK: SAGE
Publications, 2000, pp. 203-211.
[5] R. Haining, Spatial Data Analysis : Theory and Practice, Cambridge,
UK: Cambridge Univ. Press, 2003, pp. 242-243.
[6] D, O-Sullivan and D.J.Unwin, Geographic Information Analysis,
Hoboken, New Jersey: John Wiley & Sons, Inc., 2003, pp.196-201.
[7] J.Dozier, "A method for satellite identification of surface temperature
fields of subpixel resolution," Remote Sensing of Environment, Vol.11,
1981, pp. 221-229.
[8] Ying Li, V. Anthony, R.L.Kremens, O. Ambrose and T. Chunqiang , "A
Hybrid Contextual Approach to Wildland Fire Detection Using
Multispectral Imagery," IEEE Tran. Geoscience and remote sensing,
vol.43, No.9 September, 2005, pp. 2115-2126.
[9] Z. Li, Y.J.Kaufman, C.Ichoku, R.Fraser, A.Trishchenko, L.Giglio, J.Jin
and X.Yu. (2000, Sep.), A Review of AVHRR-based Active Fire
Detection Algorithms: Principles, Limitations, and Recommendations,
Available : http://www.fao.org/gtos/gofc-gold/other.html
[10] L.Giglio, J.Descloitresa, C.O.Justicec and Y.J.Kaufman, "An Enhanced
Contextual Fire Detection Algorithm for MODIS," Remote Sensing of
Environment, vol. 87, 2003, pp. 273-282.
[11] R. LASAPONARA, V. CUOMO, M.F. MACCHIATO and T.
SIMONIELLO, "A self-adaptive algorithm based on AVHRR
multitemporal data analysis for small active fire detection," INT.J.
Remote Sensing, Vol.24, No.8, 2003, pp.1723-1749.
[12] MODIS Science Team. (1998, Nov., 10 ), Algorithm Technical
Background Document ver2.2 Available:
http://modis.gsfc.nasa.gov/data/atbd/atbd_mod14.pdf
[13] L.Giglioa, J.Descloitresa, C.O.Justicec and Y.J.Kaufman, "Evaluation of
global fire detection algorithms using simulated AVHRR infrared data"
International journal of Remote Sensing, 1998.
[14] J.R. Jensen Remote sensing of the environment : An earth resource
perspective, Upper Saddle River, New Jersey: Prentice Hall, 2000, pp.
243-284.
[15] C.A.Seielstad, J.P.Riddering, S.R.Brown, L.P.Queen, and W.M.Hao,
"Testing the Sensitivity of a MODIS-Like Daytime Active Fire
Detection Model in Alaska Using NOAA/AVHRR Infrared Data,"
Photogrammetric Engineering & Remote Sensing, Vol.68, No.8, 2002,
pp.831-838.
[16] C.O.Justice, L. Giglio, S. Korontzi, J. Owens, J.T. Morisette, D. Roy, J.
Descloitres, S. Alleaume,F. Petitcolin and Y. Kaufman, "The MODIS
fire products," Remote Sensing of Environment, Vol. 83, 2002, pp. 244-
262.
[17] Korea Forest Service http://www.foa.go.kr/
[1] S. Shekhar, C.T. Lu, and P. Zhang. "A unified approach to detecting
spatial outliers," GeoInformatica, Vol.7, No.2, 2003, pp.139-166.
[2] S. Shekhar, C.T. Lu, and P. Zhang. "Detecting graph-based spatial
outliers: algorithms and application(a summary of results)." In Proc. the
ACM SIGKDD international conference on Knowledge discovery and
data mining, , San Francisco, CA, USA , 2001, pp. 371-376.
[3] V.Barnett and T.Lewis, Outliers in Statistical Data. 3rd edition, John
Wiley: New York, 1994.
[4] A.S.Forheringham, C.Brunsdon and M.Chatlton, Quantitative
Geography : Perspectives on Spatial Data Analysis, London, UK: SAGE
Publications, 2000, pp. 203-211.
[5] R. Haining, Spatial Data Analysis : Theory and Practice, Cambridge,
UK: Cambridge Univ. Press, 2003, pp. 242-243.
[6] D, O-Sullivan and D.J.Unwin, Geographic Information Analysis,
Hoboken, New Jersey: John Wiley & Sons, Inc., 2003, pp.196-201.
[7] J.Dozier, "A method for satellite identification of surface temperature
fields of subpixel resolution," Remote Sensing of Environment, Vol.11,
1981, pp. 221-229.
[8] Ying Li, V. Anthony, R.L.Kremens, O. Ambrose and T. Chunqiang , "A
Hybrid Contextual Approach to Wildland Fire Detection Using
Multispectral Imagery," IEEE Tran. Geoscience and remote sensing,
vol.43, No.9 September, 2005, pp. 2115-2126.
[9] Z. Li, Y.J.Kaufman, C.Ichoku, R.Fraser, A.Trishchenko, L.Giglio, J.Jin
and X.Yu. (2000, Sep.), A Review of AVHRR-based Active Fire
Detection Algorithms: Principles, Limitations, and Recommendations,
Available : http://www.fao.org/gtos/gofc-gold/other.html
[10] L.Giglio, J.Descloitresa, C.O.Justicec and Y.J.Kaufman, "An Enhanced
Contextual Fire Detection Algorithm for MODIS," Remote Sensing of
Environment, vol. 87, 2003, pp. 273-282.
[11] R. LASAPONARA, V. CUOMO, M.F. MACCHIATO and T.
SIMONIELLO, "A self-adaptive algorithm based on AVHRR
multitemporal data analysis for small active fire detection," INT.J.
Remote Sensing, Vol.24, No.8, 2003, pp.1723-1749.
[12] MODIS Science Team. (1998, Nov., 10 ), Algorithm Technical
Background Document ver2.2 Available:
http://modis.gsfc.nasa.gov/data/atbd/atbd_mod14.pdf
[13] L.Giglioa, J.Descloitresa, C.O.Justicec and Y.J.Kaufman, "Evaluation of
global fire detection algorithms using simulated AVHRR infrared data"
International journal of Remote Sensing, 1998.
[14] J.R. Jensen Remote sensing of the environment : An earth resource
perspective, Upper Saddle River, New Jersey: Prentice Hall, 2000, pp.
243-284.
[15] C.A.Seielstad, J.P.Riddering, S.R.Brown, L.P.Queen, and W.M.Hao,
"Testing the Sensitivity of a MODIS-Like Daytime Active Fire
Detection Model in Alaska Using NOAA/AVHRR Infrared Data,"
Photogrammetric Engineering & Remote Sensing, Vol.68, No.8, 2002,
pp.831-838.
[16] C.O.Justice, L. Giglio, S. Korontzi, J. Owens, J.T. Morisette, D. Roy, J.
Descloitres, S. Alleaume,F. Petitcolin and Y. Kaufman, "The MODIS
fire products," Remote Sensing of Environment, Vol. 83, 2002, pp. 244-
262.
[17] Korea Forest Service http://www.foa.go.kr/
@article{"International Journal of Information, Control and Computer Sciences:64841", author = "Young Gi Byun and Yong Huh and Kiyun Yu and Yong Il Kim", title = "Evaluation of Graph-based Analysis for Forest Fire Detections", abstract = "Spatial outliers in remotely sensed imageries represent
observed quantities showing unusual values compared to their
neighbor pixel values. There have been various methods to detect the
spatial outliers based on spatial autocorrelations in statistics and data
mining. These methods may be applied in detecting forest fire pixels
in the MODIS imageries from NASA-s AQUA satellite. This is
because the forest fire detection can be referred to as finding spatial
outliers using spatial variation of brightness temperature. This point is
what distinguishes our approach from the traditional fire detection
methods. In this paper, we propose a graph-based forest fire detection
algorithm which is based on spatial outlier detection methods, and test
the proposed algorithm to evaluate its applicability. For this the
ordinary scatter plot and Moran-s scatter plot were used. In order to
evaluate the proposed algorithm, the results were compared with the
MODIS fire product provided by the NASA MODIS Science Team,
which showed the possibility of the proposed algorithm in detecting
the fire pixels.", keywords = "Spatial Outlier Detection, MODIS, Forest Fire", volume = "1", number = "10", pages = "3332-6", }