Formulation of Mathematical Model for Gum Tree Productivity That Subjected to Tree Locust Attack by Using Remotely Sense Data

Ahmed Ismail Ahmed Safi; El Sayed El Bashir Mohamed; Amna Ahmed Hamid

doi:7060323

In this study, a field survey and remote sensing were integrated and carried out for three successive seasons in an area of 28000 feddans (11331 hectares) of Acacia senegal plantation of the Acacia Project (Nawa and Elrahad locations), 37 km south east of El Obeid city. North Kordofan State. Field survey method is time consuming to demonstrate large spatial scale of tree locust problem, but remote sensing provides timely data to tackle the problem immediately. The objective was to develop a model for estimating gum productivity of Acacia senegal (hashab) defoliated by tree locust from satellite imagery using Normalized Difference Vegetation Index (NDVI). 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 (RCBD). Ground Control Points were located and field data was collected. NDVI images were created from Spot imageries (2009) with the help of ERDAS IMAGINE 8.5 Software. Image data is analyzed in conjunction with ancillary spatial data within the geographical information system (using ARC map 9.3) with regards to their spectral reflectance, and then the treatments were assigned to each class of the created NDVI images. NDVI showed that there were four classes: non-, light, moderate and high defoliated Hashab trees. There was a negative relationship between NDVI values and level of tree defoliation due to tree locust attack, but the relation is positive between NDVI and gum production. It can be concluded that, NDVI is a powerful tool for recognition of the different levels of hashab tree defoliation by tree locust attack and it is a useful index to generate an easy and practical method for measuring tree greenness. Results showed that the power function was the best model for estimating gum production of Acacia senegal defoliated by tree locust using NDVI, which can be exemplified by the following equation; Gum production (y) = 275.8x^-2.07 where: 275.8 and 2.07 are empirical coefficients and X stand for NDVI.

[1]

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.

[2]

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. Technische Universit at Dresden.

[3]

Atiyat Abdalla Fadoul Nuri, Amna Ahmed Hamid, El Abbas Doka M. Ali & Eltegani Mohamed Salih (2017) Assessment of Vegetation Cover Degradation Using Remote Sensing and GIS Techniques along Sudanese Red Sea Coast (Suakin to Ashad) Journal of Geography and Geology; Vol. 8, No. 1; 2016. URL: http://dx.doi.org/10.5539/jgg.v8n1p55

[4]

Badi, K. Hassan (1989):“The Natural Forests”. In: the Forests of the Sudan, Khartoum.

[5]

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.

[6]

CCRS, (1999). Fundamental of Remote Sensing by Canada centre of remote sensing http://www.uni-de:8080/castle/ch5/s414p010.htm.

[7]

Chikamai, B. N. (1996). A Review of production, Markets and Quality Control ofGum Arabic inAfrica. Prepared from Mission Reports by: E. Casadei, J. J. W.

[8]

Elbashier, E. M, (1994). The Impact of Defoliation by the Tree locust (Anacridium melanorhdonmelanorhodon Wlk.) on the Gum Arabic Production by Hashab Tree (Acaciasenegal) M.Sc. Thesis, Faculty of Agriculture, University of Khartoum

[9]

Fernandes, R., Butso, C., Leblanc, S. and Latifovic, R. (2003). Landsat-5 TM and Landsat-7 ETM+based accuracy assessment of leaf area index products for Canada derived from Spot-4 vegetation data. Canadian Journal of Remote Sensing, 29, pp. 241–258.

[10]

Franklin, S. E. and Raske, A. G. (1994). Satellite remote sensing of spruce budworm forest defoliation in Western Newfoundland. Canadian Journal of Remote Sensing, 20, pp. 37–48.

[11]

Frantisek Jurecka, Petr Hlavinka, Vojtech Lukas, Miroslav Trnka, Zdenek Zalud, (2016) Crop Yield Estimation in the Field Level Using Vegetation Indices. MENDEL NET 2016 PP 90-49.

[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. Internationl Journal of Science and Research (IJSR). Volume 6, Issue 5, 13 – 20. 2017. DOI. 10.21275/7041713

[14]

Heikkila, J., Nevalainen, S. and Tokola, T. (2002). Estimating defoliation in aboreal coniferous forests by combining Landsat TM aerial photographs and field data. Forest Ecology and Management, 158, pp. 9–23.

[15]

IIES & IES (1990): Gum Arabic Rehabilitation in the Republic of the Sudan. Stage1Report. Volume 2. Main Report and Recommendations for Action. By: International Institute for Environment and Development, London, England and Institute of Environmental Studies, University of Khartoum, Sudan.

[16]

Jensen, John, R. (1986). Introductory Digital Image Processing. Prentice- Hall, New Jersey.

[17]

John W. and David H. (1999). Measuring Vegetation (NDVI & RVI). http://earthobservatory.nasa.gov.accessed on11.02.09.

[18]

Kaufman, Y. J. and Tanre. D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS', in 'Proc. IEEE Int. Geosci. and Remote Sensing Symp. '92, IEEE, New York, 261-270.

[19]

Larry, R. (1997). Creating a Normalized Difference Vegetation Index (NDVI) Image, University of New Hampshire Durham, NH 03824.

[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, pp. 259–275.

[21]

Lillesand, T. M. and Kiefer, R. W. (1994). Remote Sensing and Image Interpretation. John Wiley and Sons Inc., New York.

[22]

Ma B. L., Morrison M. J. and Dwyer L. M. 1996. Canopy light reflectance and field greenness to assess nitrogen fertilization and yield of corn. Agronomy J. 88:915-920.

[23]

Meera Gandhi. G, S. Parthiban, Nagaraj Thummalu Christy. 2015Ndvi: Vegetation change detection using remote sensing and gis – A case study of Vellore District 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015).

[24]

Meng Ji-hua, Wu Bing-fang (2008) Study On The Crop Condition Monitoring Methods With Remote Sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B8. Beijing 2008.

[25]

Mohamed Eltom Elhaja, Elmar Csaplovics, Osman E. A. Abdelkareem, Hassan Elnour Adam, Awad El Karim S. O. Khalifa, Khalid Ahmed Ibrahim, Muneer E. S. Eltahir. Land Use Land Cover Changes Detection in White Nile State, Sudan Using Remote Sensing and GIS Techniques. International Journal of Environmental Monitoring and Protection. Vol. 4, No. 3, 2017, pp. 14-19. Received: April 13, 2017; Accepted: May 4, 2017; Published: July 24, 2017.

[26]

Nelson, R. F. (1983). Detecting forest canopy change due to insect activity using Landsat MSS. Photogrammetric Engineering and Remote Sensing, 49, pp. 1303–1314.

[27]

Radeloff, V. C., Mladenoff, D. J. and Boye, M. S. (1999). Detecting jack pine budworm defoliation using spectral mixture analysis: separating effects from determinants. Remote Sensing of Environment, 69, pp.156–169.

[28]

Russ and John, C. (1995). The Image Processing Handbook. 2nd edition. RC Press, Baca Raton.

[29]

Shanahan J. F., Holland K. H., Schepers J. S., Francis D. D., Schlemmer M. R. and Caldwell R. 2003. Use of a crop canopy reflectance sensor to assess corn leaf chlorophyll content. ASA Special Publication. 66:135-150.

[30]

Shanahan J. F., Schepers J. S., Francis D. D., Varvel G. E., Wilhelm W., Tringe J. M., Schlemmer M. R. and Major D. J. 2001. Use of remote-sensing imagery to estimate corn grain yield. Agronomy J.93:583-589.

[31]

Siddig, S. A. (1991). Evaluation of neem seed and leaf water extracts and powders for the control of insect pests in the Sudan. Tech. Bull. No.6 Shambat Research Station, Khartoum, Sudan.

[32]

Simpson, R. and Coy, D. (1999). An ecological atlas of forest insect defoliation in Canada: 1980–1996. Information Report M-X-206E, Natural Resources Canada, Canadian Forest Service, Fredericton, Canada, 15 pp.

[33]

Solari F., Shanahan J., Ferguson R. B., Schepers J. S. and Gitelson A. A. 2008. Active sensorreflectance measurements of corn nitrogen status and yield potential. Agronomy J.100:571-579.

[34]

Sulieman H. M. (2008) Mapping and Modeling of Vegetation Changes in the Southern Gadarif Region, Sudan Using Remote Sensing. Land-Use Impacts on Biophysical Processes. TUD press.

[35]

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.

[36]

Vikalp Mishra, James F. Cruise, John R. Mecikalski, Christopher R. Hain 3 and Martha C. Anderson (2013) A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields. Remote Sens. 2013, 5, 3331-3356; doi:10.3390/rs5073331

[37]

Yasir A. Qurashi; E. S. Ganawa, A. F. Kheiralla; A. A. Hassaballa. Application of Satellites Imageryin Detecting and Mapping Strigahermonthicain a Sugar Cane Field. Adv. Biores., Vol 8 [2] March2017: 146-152.

[38]

Yichun Xie, Zongyao Sha and Mei Yu (2008) Remote sensing imagery in vegetationmapping: a review. Journal of Plant Ecology, Volume 1, Number 1, Pages 9–23 March 2008 doi:10.1093/jpe/rtm005