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Spatial Distribution of Tree Locust (Anacridium M. Melanorhodon), Using Remote Sensing in Acacia senegal Scheme
Current Issue
Volume 5, 2018
Issue 4 (December)
Pages: 58-63   |   Vol. 5, No. 4, December 2018   |   Follow on         
Paper in PDF Downloads: 30   Since Feb. 12, 2019 Views: 965   Since Feb. 12, 2019
Ahmed Ismail Ahmed Safi, Institute of Gum Arabic Research and Desertification Studies, University of Kordofan, Elobeid, Sudan.
El Sayed El Bashir Mohamed, Crop Protection Department, Faculty of Agriculture, University of Khartoum, Khartoum, Sudan.
Amna Ahmed Hamid, Remote Sensing Authority, University of Khartoum, Khartoum, Sudan.
The present study was conducted in two locations in Acacia Senegal Scheme (Nawa and Elrahad locations) for three successive seasons; 2007/2008, 2008/2009 and 2009/2010 to investigate; the capability of remote sensing in monitoring and recognizing the spatial distribution pattern and population dynamic of tree locust in Acacia senegal. To provide tree level estimates of tree locust across a large area, this will assist entomologists to determine appropriate management strategies that minimize tree losses and reduce the risk of future infestations. Such a monitoring system provides information essential for determining the status of the infestation, monitoring the tree locust impact of the infestation on forest structure, assessing the efficacy of mitigation measures, and reducing the future risk of tree locust attack. In terrestrial survey at early morning without any disturbance, coordinates of tree locust population density were recorded using global positioning system, also visual counting of the population was done, in addition the trees greenness on which the tree locusts roasted were determined using remote sensing software. Remotely sensed data covering the study area were acquired using Spot imageries (2009) with spatial resolution 2.5*2.5m. Remotely sensed data were pre-processed, classified and assessed. With the help of terrestrial survey spectral signature of tree locust population density was extracted and used in image classification and mapping. The final findings of the study of both field survey and remote sensing methods were integrated with Arc GIS methods to produce maps. Results of the study showed that tree locust population is found in low density (50/tree) on trees of poor greenness (0.2NDVI) and high density (200/tree) on highly green trees (0.6NDVI).
Acacia Senegal, Gum Arabic, Tree Locust and Remote Sensing
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