[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.
The present study was conducted in two locations in Acacia Agricultural Company (Nawa and Elrahad locations) for three successive seasons; 2007/2008, 2008/2009 and 2009/2010), 37 km south east of ElObeid city. Field survey method is tedious and laborious to address large geographical area of tree locust problem, but remote sensing provides timely data to handle the problem immediately. The objective is to evaluate the role of remote sensing in identifying areas of defoliated Hashab tree by tree locust. In this context four treatments (non- defoliated Hashab trees, light, moderate and high defoliated Hashab trees by tree locust) were arranged in a randomized complete block design. These treatments are referred to as training areas (information classes). Ground Control Points were located and field data was collected. Supervised classification of Spot images (2009) was done based on predefined classes and on the analyst's familiarity with the geographical area and knowledge with the actual defoliation levels present in the image, then the numerical information in all spectral bands for the pixels comprising these areas are used to train the computer to recognize spectrally similar areas for each class. The computer uses a special program or algorithm to determine the numerical signatures for each training class with the help of Erdas Imagine 8.5 software. Accuracy assessment was done and ARC map 9.3 was used for map production. Results showed that the supervised classification recognized five major classes; non-defoliated, light, moderate, high defoliated Hashab trees and areas of tree locusts warms.
[1]
Badi, K. Hassan (1989):“The Natural Forests”. In: ‘the Forests of the Sudan’. Khartoum–Sudan, 1989.
[2]
Ballal, M. E. (2002). Yield trends of gum Arabic from Acacia Senegal as related to some environmental and managerial factors. Ph.D. Thesis, University of Khartoum.
[3]
Bastin, L. (1997). Comparison of fuzzy c-means classification, linear mixture modelling and MLC probabilities as tools for unmixing coarse pixels, Int. J. Remote Sensing, 1997, vol.18, no.17, 3629-3648.
[4]
Chikamai, B. N. (1996). A Review of production, Markets and Quality Control of Gum Arabic in Africa. Prepared from Mission Reports by: E. Casadei, J. J. W. Coppen, H. O.
[5]
DeFries, R. S., J. R. G, Townshend 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.
[6]
Doka, A. M. and Hamid, A. A. (2013). Assessment of land resources potentiality for agricultural development at Wadi Al-Mogadam area, Sudan. Sudan Journal of Desertification Research, 5 (1):1-18.
[7]
Elaalem, M. M., Ezlit Y. D., Elfghi, A., Abushnaf, F. (2013). Performance of Supervised Classification for Mapping Land Cover and Land Use in Jeffara Plain of Libya 2013 International Conference on Food and Agricultural Sciences IPCBEE vol. 55 (2013)© (2013) IACSIT Press, Singapore DOI: 10.7763 / IPCBEE. 2013. V55.7.
[8]
Elbashier, E. M, (1994). The Impact of Defoliation by the Tree locust (Anacridium melanorhdon melanorhodon Wlk.) on the Gum Arabic Production by Hashab Tree (Acacia senegal) M.Sc. Thesis, Faculty of Agriculture, University of Khartoum.
[9]
Eltom, M., Dafalla, M. and Hamid, A. (2014) Detection of changes in Vegetation Cover Caused by Desert Locust in Sudan. SPIE Proceedings 2014.
[10]
Erdas (2003). Erdas Field Guide™, Seventh Edition. Leica Geosystems GIS & Mapping, LLC.
[11]
Franklin, S. E. and Raske, A. G. (1994). Satellite remote sensing of spruce budworm forest defoliation in Western New found land. Canadian Journal of Remote Sensing, 20, pp. 37-48.
[12]
Hall, J. P. and Moody, B. H. (2003). Forest depletions caused by insects and diseases in Canada, 1982-1987. Forest Insect and Disease Survey Information Report ST-X-8, Natural Resources Canada, Canadian Forest Service, Ottawa, Canada, 14 pp.
[13]
Hamid, A. A., Doka, A. M., and Adam, T. O., (2017). An Integrated Approach towards Natural Resources Management and Prevention of Resource-Based Conflicts in Sudan. International Journal of science and Research. Volume 6, Issue 5, 13-20.2017.
[14]
Hansen, M. C. (2012). Classification of trees and mixed pixel training data. C. Giri (Ed.). Remote sensing of land cover: Principles and applications.
[15]
Hansen, M. C. Egorov, A. Potapov, P. V. Stehman, S. V. Tyukavina, A. (2014). Monitoring conterminous United States (CONUS) land cover change with Web- Enabled Landsat Data (WELD). Remote Sensing of Environment, 140 (2014), pp. 466-484.
[16]
Hansen, M. C, and Loveland, T. R. (2012). A review of large area monitoring of land cover change using Landsat data. Remote Sensing of Environment, 2012 (2012), pp.66-74.
[17]
Hansen, P. V., Potapov, R., Moore, M., Hancher, S. A., and Turubanova, A. (2013). High-resolution global maps of 21st century forest cover change. Science, 342 (6160) (2013), pp.850-853.
[18]
Heikkila, J., Nevalainen, S. and Tokola, T. (2002). Estimating defoliation in arboreal coniferous forests by combining Landsat TM aerial photographs and field data. Forest Ecology and Management, 158, pp.9-23.
[19]
IIES & IES (1990): Gum Arabic Rehabilitation in the Republic of the Sudan (1990). Stage1 Report. Volume2. Main Report and Recommendations for Action. By: International Institute for Environment and Development, London, England and Institute of Environmental Studies, University of Khartoum, Sudan.
[20]
Leckie, D. G. and Ostaff, D. P. (1988). Classification of airborne multispectral scanner data for mapping current defoliation caused by the spruce budworm. Forest Science, 34, p.259-275.
[21]
Mather, P. M. (2004). Computer processing of remotely-sensed images. An introduction (3rded.), Wiley, Chichester, United Kingdom (2004) (324pp.).
[22]
Nelson, R. F. (1983). Detecting forest canopy change due to insect activity using Landsat MSS. Photogrammetric Engineering and Remote Sensing, 49, pp.1303-1314.
[23]
Nuri, A. A. F., Hamid, A. A., Doka, A. M., and Salih, E. M. (2016). Assessment of Vegetation Cover Degradation Using Remote Sensing and GIS Techniques along Sudanese Red Sea Coast (From Suakin to Ashad). Journal of Geography and Geology (JGG) Vol. 8, No. 1; 2016. Published by Canadian Center of Science and Education.
[24]
Simpson, R. and Coy, D. (1999). An ecological atlas of forest insect defoliation in Canada: 1980-1996. Information Report M-X-206 E, Natural Resources Canada, Canadian Forest Service, Fredericton, Canada, 15 pp.
[25]
Taha, M. E. (2006). The Socio-economic Role of Acacia Senegal in Sustainable Development of Rural Areas in the Gum Belt of the Sudan. ISBN 3-9809816-4-9. Dresden University of Technology, Germany Institute: Institute of International Forestry and Forest Products, Tharandt, Germany.
[26]
Wickham, J. D. Wickham, S. V. Stehman, L. Gass, J. Dewitz, J. A. Fry, T. G. Wade. (2013). Accuracy assessment of NLCD 2006 land cover and impervious surface. Remote Sensing of Environment, 130 (2013), pp. 294-304.
[27]
Yan, L. and Roy, D. P. (2015). Improved time series land cover classification by missing –observation –adaptive non linear dimensionality reduction. Remote Sensing of Environment, 158(2015), pp. 478-491.
[28]
Yasir, A. Qurashi; E. S. Ganawa; A. F. Kheiralla; A. A. Hassaballa. Application of Satellites Imagery in Detecting and Mapping Strigaher monthicaina Sugar Cane Field. Received 01/11/2016 Revised 12/01/2017 Accepted 19/02/2017. Adv. Biores., Vol 8 March 2017:146-152.
