[1]
Ahmed Ismail Ahmed Safi, Institute of Gum Arabic Research and Desertification Studies, University of Kordofan, Elobied, Sudan.
[2]
El Sayed El Bashir Mohamed, Crop Protection Department, Faculty of Agriculture, University of Khartoum, Khartoum, Sudan.
[3]
Amna Ahmed Hamid, Remote Sensing Authority, University of Khartoum, Khartoum, Sudan.
[4]
Mohamed Magoub Elzubeir, Faculty of Natural Resources and Environmental Studies, University of Kordofan, Elobeid, Sudan.
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.
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