Satellite Data Classification Accuracy Assessment Based from Reference Dataset
In order to develop forest management strategies in
tropical forest in Malaysia, surveying the forest resources and
monitoring the forest area affected by logging activities is essential.
There are tremendous effort has been done in classification of land
cover related to forest resource management in this country as it is a
priority in all aspects of forest mapping using remote sensing and
related technology such as GIS. In fact classification process is a
compulsory step in any remote sensing research. Therefore, the main
objective of this paper is to assess classification accuracy of
classified forest map on Landsat TM data from difference number of
reference data (200 and 388 reference data). This comparison was
made through observation (200 reference data), and interpretation
and observation approaches (388 reference data). Five land cover
classes namely primary forest, logged over forest, water bodies, bare
land and agricultural crop/mixed horticultural can be identified by
the differences in spectral wavelength. Result showed that an overall
accuracy from 200 reference data was 83.5 % (kappa value
0.7502459; kappa variance 0.002871), which was considered
acceptable or good for optical data. However, when 200 reference
data was increased to 388 in the confusion matrix, the accuracy
slightly improved from 83.5% to 89.17%, with Kappa statistic
increased from 0.7502459 to 0.8026135, respectively. The accuracy
in this classification suggested that this strategy for the selection of
training area, interpretation approaches and number of reference data
used were importance to perform better classification result.
[1] Achard, F. and Estreguil, C. 1995. Forest classification of Southeast
Asia using NOAA AVHRR data. Remote Sensing of Environment.
54(3):198-208.
[2] Fitzgerald,R.W and Lees,B.G. 1994. Assessing the classification
accuracy of multi-sources remote sensing data. Remote Sensing of
Environment, 47: 362-368.
[3] Foody,G.M. and Hill,R.A. 1996. Classification of tropical forest classes
from Landsat TM data. International Journal of Remote
Sensing,17(12):2353-2368.
[4] Franklin, J. 1993. Discrimination of tropical vegetation using SPOT MS
data. Geocarto International. 8: 57-63.
[5] Franklin, S.E. 2001. Remote sensing for sustainable forest management.
Lewis Publisher,New York,U.S.A;407p.
[6] Hartson,G.S. 1980. Neotropical forest dynamic. Biotopica, 12: 23-30.
[7] ]Hudson,W.D and Ramm,C.W. 1987. Correct formulation of the kappa
coefficient of agreement. Photogrammetry Engineering and Remote
Sensing, 53, (4): 421-422.
[8] Janssen, L. F. J., and F. J. M. Van Der Wel. 1994. Accuracy assessment
of satellite derived land cover data: A review. Photogrammetric
Engineering and Remote Sensing 60:419-426.
[9] Jensen,J.R. 1996. Introductory digital image processing. A remote
sensing perspective.2nd Edition, Prentice Hall, Englewood, NJ.
[10] Kamaruzaman.J and Abdul Haye.S. 1993. Detection of logging
disturbance using satellite imagery. Paper presented at The South East
Asian Regional Conference on Education and Research in Remote
Sensing, 28-30 June 19993, University Teknologi Malaysia, Skudai,
Johor, Malaysia.15p.
[11] Kamaruzaman.j and Souza, G.D. 1997. Use of satellite remote sensing in
Malaysia forestry and its potential. International Journal of Remote
Sensing, 18, (1): 57-70.
[12] Khali,A.H. 2001. Remote sensing, GIS and GPS as a tool to support
precision forestry practices in Malaysia. Paper presented at the 22nd
Asian Conference on Remote Sensing, 5-9 November 2001,
Singapore.5p.
[13] Landis,J.R. and Koch,G.C. 1987. The measurement of observer
agreement for categorical data. Biometric, 33: 159-179.
[14] Lowell, K. 1994. A fuzzy surface cartographic representation for
forestry based on voronoi diagram area stealing, Canadian Journal of
Forest Resources, 24: 1970-1980
[15] Mohd Hasmadi, I and Kamaruzaman,J. 1999. Use of satellite remote
sensing in forest resource management in Malaysia. Paper presented at
Second Malaysian Remote Sensing and GIS Conference, 16-18 March,
1999, ITM Resort & Convention Centre, Shah Alam, Selangor,
Malaysia. 24p.
[16] Mohd Hasmadi,I. 2000. Forest cover change detection and water yield
relationship in Semenyih and Langat watershed using remote sensing
technique. M. Sc. (Thesis), Universiti Putra Malaysia, Serdang,
Selangor. 174p.
[17] Monserud,R.A.1990. Method for comparing global vegetation map. WP-
90-40 II ASA,Laxenburg.
[18] Paul,M. 1991. Computer processing of remotely sensed images: An
introduction, Biddley Limited Publication.352p.
[19] Rosenfield, G.H and Fitzpatrick, L.K.1986. A coefficient of agreement
as a measure of thematic classification. Photogrammetry Engineering
and Remote Sensing, 52, (2): 223-227.
[20] Sodowski ,F.G. and Danjoy,W.A. 1980. Some observation on the utility
of remote sensors for humid tropical forest. Paper presented at the 14th
International Symposium on Remote Sensing of Environment, April
1980,Costa Rica, Brazil.3p.
[21] Stehman,S.V. 1997. Selecting and interpreting measures of thematic
classification accuracy. Remote Sensing of Environment, 62:77-89.
[22] ]Story,M. and Congalton,R.G. 1986). Accuracy assessment: A users
perspective. Photogrammetry Engineering and Remote Sensing,
52(3):397-399.
[23] Wilson,E.H. and Sader,S.A. 2002. Detection of forest harvest type using
multiple dates of Landsat TM imagery. Remote Sensing of Environment,
80: 385-396
[24] Zailani, K. 2000. Timber inventory and volume estimation in Gunung
Stong Forest Reserve using remote sensing technique. M.Sc. (Thesis),
Universiti Putra Malaysia. 138p.
[1] Achard, F. and Estreguil, C. 1995. Forest classification of Southeast
Asia using NOAA AVHRR data. Remote Sensing of Environment.
54(3):198-208.
[2] Fitzgerald,R.W and Lees,B.G. 1994. Assessing the classification
accuracy of multi-sources remote sensing data. Remote Sensing of
Environment, 47: 362-368.
[3] Foody,G.M. and Hill,R.A. 1996. Classification of tropical forest classes
from Landsat TM data. International Journal of Remote
Sensing,17(12):2353-2368.
[4] Franklin, J. 1993. Discrimination of tropical vegetation using SPOT MS
data. Geocarto International. 8: 57-63.
[5] Franklin, S.E. 2001. Remote sensing for sustainable forest management.
Lewis Publisher,New York,U.S.A;407p.
[6] Hartson,G.S. 1980. Neotropical forest dynamic. Biotopica, 12: 23-30.
[7] ]Hudson,W.D and Ramm,C.W. 1987. Correct formulation of the kappa
coefficient of agreement. Photogrammetry Engineering and Remote
Sensing, 53, (4): 421-422.
[8] Janssen, L. F. J., and F. J. M. Van Der Wel. 1994. Accuracy assessment
of satellite derived land cover data: A review. Photogrammetric
Engineering and Remote Sensing 60:419-426.
[9] Jensen,J.R. 1996. Introductory digital image processing. A remote
sensing perspective.2nd Edition, Prentice Hall, Englewood, NJ.
[10] Kamaruzaman.J and Abdul Haye.S. 1993. Detection of logging
disturbance using satellite imagery. Paper presented at The South East
Asian Regional Conference on Education and Research in Remote
Sensing, 28-30 June 19993, University Teknologi Malaysia, Skudai,
Johor, Malaysia.15p.
[11] Kamaruzaman.j and Souza, G.D. 1997. Use of satellite remote sensing in
Malaysia forestry and its potential. International Journal of Remote
Sensing, 18, (1): 57-70.
[12] Khali,A.H. 2001. Remote sensing, GIS and GPS as a tool to support
precision forestry practices in Malaysia. Paper presented at the 22nd
Asian Conference on Remote Sensing, 5-9 November 2001,
Singapore.5p.
[13] Landis,J.R. and Koch,G.C. 1987. The measurement of observer
agreement for categorical data. Biometric, 33: 159-179.
[14] Lowell, K. 1994. A fuzzy surface cartographic representation for
forestry based on voronoi diagram area stealing, Canadian Journal of
Forest Resources, 24: 1970-1980
[15] Mohd Hasmadi, I and Kamaruzaman,J. 1999. Use of satellite remote
sensing in forest resource management in Malaysia. Paper presented at
Second Malaysian Remote Sensing and GIS Conference, 16-18 March,
1999, ITM Resort & Convention Centre, Shah Alam, Selangor,
Malaysia. 24p.
[16] Mohd Hasmadi,I. 2000. Forest cover change detection and water yield
relationship in Semenyih and Langat watershed using remote sensing
technique. M. Sc. (Thesis), Universiti Putra Malaysia, Serdang,
Selangor. 174p.
[17] Monserud,R.A.1990. Method for comparing global vegetation map. WP-
90-40 II ASA,Laxenburg.
[18] Paul,M. 1991. Computer processing of remotely sensed images: An
introduction, Biddley Limited Publication.352p.
[19] Rosenfield, G.H and Fitzpatrick, L.K.1986. A coefficient of agreement
as a measure of thematic classification. Photogrammetry Engineering
and Remote Sensing, 52, (2): 223-227.
[20] Sodowski ,F.G. and Danjoy,W.A. 1980. Some observation on the utility
of remote sensors for humid tropical forest. Paper presented at the 14th
International Symposium on Remote Sensing of Environment, April
1980,Costa Rica, Brazil.3p.
[21] Stehman,S.V. 1997. Selecting and interpreting measures of thematic
classification accuracy. Remote Sensing of Environment, 62:77-89.
[22] ]Story,M. and Congalton,R.G. 1986). Accuracy assessment: A users
perspective. Photogrammetry Engineering and Remote Sensing,
52(3):397-399.
[23] Wilson,E.H. and Sader,S.A. 2002. Detection of forest harvest type using
multiple dates of Landsat TM imagery. Remote Sensing of Environment,
80: 385-396
[24] Zailani, K. 2000. Timber inventory and volume estimation in Gunung
Stong Forest Reserve using remote sensing technique. M.Sc. (Thesis),
Universiti Putra Malaysia. 138p.
@article{"International Journal of Earth, Energy and Environmental Sciences:56750", author = "Mohd Hasmadi Ismail and Kamaruzaman Jusoff", title = "Satellite Data Classification Accuracy Assessment Based from Reference Dataset", abstract = "In order to develop forest management strategies in
tropical forest in Malaysia, surveying the forest resources and
monitoring the forest area affected by logging activities is essential.
There are tremendous effort has been done in classification of land
cover related to forest resource management in this country as it is a
priority in all aspects of forest mapping using remote sensing and
related technology such as GIS. In fact classification process is a
compulsory step in any remote sensing research. Therefore, the main
objective of this paper is to assess classification accuracy of
classified forest map on Landsat TM data from difference number of
reference data (200 and 388 reference data). This comparison was
made through observation (200 reference data), and interpretation
and observation approaches (388 reference data). Five land cover
classes namely primary forest, logged over forest, water bodies, bare
land and agricultural crop/mixed horticultural can be identified by
the differences in spectral wavelength. Result showed that an overall
accuracy from 200 reference data was 83.5 % (kappa value
0.7502459; kappa variance 0.002871), which was considered
acceptable or good for optical data. However, when 200 reference
data was increased to 388 in the confusion matrix, the accuracy
slightly improved from 83.5% to 89.17%, with Kappa statistic
increased from 0.7502459 to 0.8026135, respectively. The accuracy
in this classification suggested that this strategy for the selection of
training area, interpretation approaches and number of reference data
used were importance to perform better classification result.", keywords = "Image Classification, Reference Data, Accuracy
Assessment, Kappa Statistic, Forest Land Cover", volume = "2", number = "3", pages = "23-7", }