This paper tends to depict the accuracy assessment of the area of Acacia senegal defoliated by tree locust in the study area. Multi-temporal satellite imagery covering the study area includes: Landsat7 (ETM+) 2007 and Spot5 (2008, 2009) were acquired. Radiometric and geometric correction, image enhancement and supervised classification were done with the help of ERDAS 9.3. Accuracy assessment was calculated based on the confusion matrix and Kappa coefficient. The results of the area of A. senegal defoliated by tree locust for Landsat7 (ETM+) 2007 showed overall classification accuracy 80%, the producer accuracy was 100, 100, 35 and 100% for non-defoliated, light defoliated moderate defoliated and high defoliated A. senegal respectively, the user accuracy was 100, 100, 55, and 95% for non-defoliated, light defoliated moderate defoliated and high defoliated A. senegal respectively. The overall Kappa Statistics = 0.75. The same accuracy assessment was also scrod for supervised classification of the area of A. senegal defoliated by tree locust for Spot5 (2008 and 2009). The results revealed, the overall classification accuracy 86.67%, the producer accuracy was 70, 100, 100 and 65%for non-defoliated, light defoliated moderate defoliated and high defoliated A. senegal respectively, and the user accuracy was 100, 90, 100, 100% for non-defoliated, light defoliated moderate defoliated and high defoliated A. senegal respectively. The overall Kappa Statistics = 0.82. However the results of accuracy assessment of supervised classification of A. senegal defoliated by tree locust classes in all years showed excellent classification for the majority of classes. The study concluded that aaccuracy assessment is one of the most important tools for quantifying how accurate the classification product is, more over confusion error matrix and Kappa coefficient were very efficient in the calculation of accuracy assessment.
Abdulla, N. A. (2006). Effect of Tillage on Soil Moisture Storage of Three Gardud Soils of North Kordofan State. Ph.D. Thesis, University of Kordofan- Sudan, September 2006.
Abineh Tilahun, Bogale Teferie, (2015) Accuracy Assessment of Land Use Land Cover Classification using Google Earth, American Journal of Environmental Protection. Vol. 4, No. 4, 2015, pp. 193-198. doi: 10.11648/j.ajep.20150404.
Adam, H. E., (2011). Integration of Remote Sensing and GIS in Studying Vegetation Trends and Conditions in the Gum Arabic Belt in North Kordofan, Sudan, PhD thesis, TU Dresden, Germany.
Bolstad, P. (2005) GIS Fundamentals: A First Text on Geographic Information Systems. 2nd Edition, Eider Press, White Bear Lake, Minnesota.
Conglton, R. G. 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 37 (l): 35-46.
Conglton, R. G. and GREEN, K. 1999. Assessing the accuracy of remotelysensed data: Principles and practices. Lewis Publishers, Boca Raton, FL. 160 p.
Conglton, R. G., Oderwald, and MEAD, R. A. 1983. Assessing Landsat classification accuracy using discrete multivariate analysis statisticaltechniques. Photogramm. Eng. Remote Sens. 49 (12): 1671-167.
De Fries, R. S., Townshend J. R. G, and M. C. Hansen (1999). Continuous field of vegetation characteristics at the global scale at 1 km resolution. Journal of Geophysical Research, 104: 16911-16925.
Ewing B., Hillier L., Wendl M. C., and Green P., (2017). Base-Calling of Automated Sequencer Traces Using Phred. I. Accuracy Assessment, Downloaded from genome.cshlp.org on July 10, 2017 - Published by Cold Spring Harbor Laboratory Press ISSN 1054-9803/98.
FAO (1997). Food and Agriculture Organization. Prevention and Disposal of Obsolete pesticides In: Annu. Rev. Entomol. 2001. 46: 671 Downloaded from arjouenals.annualreviews.org by CIRAD- DIST- UNIT BIBLIOTHEQUE.
Foody, G. M. (2002). Status of land cover classification accuracy assessment, Remote Sensing of Environment 80 (2002) 185-201.
HAY, A. M. (1979). Sampling designs to test land-use map accuracy. Photogramm. Eng. Remote Sens. 45 (4): 529-533.
Hord, R. M., and W. Brooner. 1976 Land-Use Map Accuracy Criteria, Photogrammetric Engineering and Remote Sensing, Vol. 42, pp. 671-677, A2 (5): 611-611.
KAY, ITAKIRE, F., C. FARCY, AND P. DEFOURNY. 2002. IKONOS-2 imagery potential for forest stands mapping. P. 1-11 in Proc. of Forest SAT symposium. Heriot Wyatt University, Edinburgh, Scotland.
Landis J, Koch G (1977). The measurement of observer agreement for categorical data. Biometrics 33: 159-74.
Mather, P. M. (2009). Computer processing of remotely-Sensed images; an introduction. (4rd ed) The University of Nottingham. John Wiley and Sons Ltd.
Osman Esaid Adlan Abdelkareem, Hatim Mohamed Ahmed Elamin, Muneer Elyas Siddigltahir, Hassan Elnour Adam, Mohamed Eltom Elhaja, Abualgasim Majdeldin Rahamtalla, Sunmadewa Babatunde, Csaplovics Elmar (2017). Accuracy Assessment of Land Use Land Cover in Umabdalla Natural Reserved Forest, South Kordofan, Sudan. International Journal of Agricultural and Environmental Sciences. Vol. 3, No. 1, 2017, pp. 5-9.
Pontius, R. G. (2000). Quantification error versus location error in comparison of categorical maps. Photogrametric Engineering and Remote Sensing, 66 (8): 1011-1016.
Rao K. V. G., P. Chand P., Murthy M. V. R., (2011). Image classification using content based image Retrieval system, International Journal of Image Processing and Applications, 2 (1), 2011, pp. 85-91.
Skidmore, A. K. (1999). Accuracy assessment of spatial information. In: Stein, A. van der Meer, F. and Gorte. B. (ed.). Spatial statistics for remote sensing Dordrecht, Kluwer Academic Publishers, Netherlands.
Skirvin, S. M., W. G. Kepner, S. E. Marsh, S. E. Drake, J. K. Maingi, C. M. Edmonds, C. J. Watts, and D. R. Williams. 2004. Assessing the accuracy of satellite-derived land-cover classificadon using historicalaerial photography, digital orthophoto quadrangles, and airborne videodata. P. 115-131 in Remote Sensing and GIS Accuracy Assessment, Lunetta, R., and J. G. Lyon (eds.). GRG Press, New York.
Stehman, S. V., and R. L. Czaplewski, (1998). Design and analysis forthematic map accuracy assessment: Fundamental principles. RemoteSens. Environ. 64: 331-344.
Story, M. and Congalton, R. G. (1986). Accuracy assessment: a user’s perspective. Photogrammetric Engineering and Remote Sensing, 52 (3): 397-399.
Taha, M. E. (2006). The Socio-economic Role of Acacia senegal in Sustainable Development of Rural Areas in the Gum Belt of the Sudan. ISBN3-9809816- 4-9. Dresden University of Technology, Germany Institute: Institute of International Forestry and Forest Products, Tharandt, Germany.
Wilkie, D. S. and Finn, J. T. (1996). Remote sensing imagery for natural resources Monitoring. A guide for first-time users. Methods and cases in conservation science series. New York, Chichester: Columbia University Press.
Zimmerman, P. L., W. Housman, C. H. Perry, R. A. Chastain, J. B. Webb, and M. V. Fingo, (2013). An accuracy assessment of forest disturbance mapping in the westetn Great Lakes. Remote Sens. Environ. 128: 176-185.