# American-Eurasian Journal of Sustainable Agriculture

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3 12 Ahmed Hassanein, 2015 / American-Eurasian Journal of Sustainable Agriculture 9(3), March, Pages: rest of this paper is divided into three parts. The first deals with using Matlab to convert an image of the elevation profile in GE to a plot. The second part deals with fitting the data in the plot to an equation that would transform it to a plot of the elevation data. The third part concludes the results. In this section, names and variables are put inside inverted commas, while Matlab commands are put inside angle brackets. Transform Image to Plot: Fig. 2: An image copied from GE containing a plot of the altitude data. [1] Fig. 3: A chart showing the steps undertaken to extract elevation values from GE. From GE, one starts with drawing a path over the place understudy which is the Nile River in this paper. As discussed before, an Elevation Profile opens in the lower part of the 3D Viewer. To copy an image from GE, one goes to Edit and then selects Copy Image. [1] A copy of the 3D viewer can then be pasted and saved as an image file as shown in fig. 2. The image used in this study is captured on 4th of

4 13 Ahmed Hassanein, 2015 / American-Eurasian Journal of Sustainable Agriculture 9(3), March, Pages: October 2013 and copied from GE on 8th of August Matlab is then initiated to be used to perform the following steps as shown in fig. 3. The Matlab command [9]: <image_i = imread( nile.jpg )> is used. nile.jpg is an example of a possible name of an image file saved from GE. The command reads the image which is shown in fig. 2 in a three dimensional array MxNx3. The first vector M represents the positions of pixels along the x-axis. The second vector N represents positions of pixels along the y-axis. The third vector represents color of pixels in RGB system of colors. The system defines the red, green and blue color components for each individual pixel. The output image is saved in an array called image_i. Fig. 4: A selected part of the image in fig. 2 containing the plot of the altitude data. Fig. 5: A binary image of the image in fig. 4 which contains a plot of the altitude data. The command [9]: <rgb = image_i(k,l)> is then used. K is an array that is a subset of the array N. L is an array that is a subset of the array N. The command is used to cut a part of an image and save it. It is used to select the lower part containing the Elevation Profile of the image which is shown in fig. 2. K specifies the range of pixels along the y-axis containing the elevation profile in the image image_i. L specifies the range of pixels along the x-axis containing the Elevation Profile. In this example, K is defined to be the range 575:750 and L is defined to be the range 289:1227. The selected part is shown in fig. 4. The output image is saved in an array called rgb. The command [9]: <BW = roicolor(rgb,v)> is then used. First, it transfers the image from the RGB system to a gray scale image. Second, it selects a region of interest V within an intensity image and returns a binary image. The gray scale is divided into 256 degrees. In this example, V is defined to be the range 0:125. For each pixel in the image of fig. 4 (rgb), if the value of its gray color matches the values of V, the command replaces it with 255 (white) but if the value of its gray color is outside the values of V, the command replaces it with 0 (black). The output image is named BW and is shown in fig. 5. Artifacts are seen in the shown image. They are erroneous pixels which don t belong to the line representing the elevation profile. An example of those pixels is shown in fig. 5 with a circle around them. Next, we try to remove these artifacts. Fig. 6: A refined image of the binary image shown in fig. 5 where erroneous pixels are removed.

5 14 Ahmed Hassanein, 2015 / American-Eurasian Journal of Sustainable Agriculture 9(3), March, Pages: Fig. 7: A final image of the altitude data showing the plotted graph. Following that, the command [9]: <BW2 = bwareaopen(bw,p)> is used. It removes from the binary image BW all connected artifacts that have fewer than P pixels. In this example, P is defined to be 4. The output binary image is saved in the image BW2. BW2 is shown in fig. 6. No artifacts are seen in this image. Following that, the command [9]: <[row,col] = find(bw2)> is used. It finds out the location of all nonzero (white) pixels in the input image of fig. 6 ( BW2 ). The indices of the nonzero pixels along the y and x- axis in the BW2 image are the output row and col arrays respectively. A plot of row versus col is shown in fig. 7. The row array represents the order of the nonzero pixels and not the value of elevation at each point in the col array. An equation is required to transfer the order of each pixel row to its elevation value for each point in the col array. Next, mapping the order of each pixel to a matching value of elevation is discussed. Fit and Interpolate: The commands [9]: <min>, <mean> and <max> are then used to find the minimum, mean and maximum values respectively of the row array. The outputs are saved in an array called xx. From the nile.jpg image, we can find the minimum, mean and maximum values of the elevation values as these are written in the upper left corner of the profile. The three values are stored in an array called yy. The length of the whole path is also given in the image. Fig. 8: A plot of the altitude values extracted from fig. 6. An equation is needed to match the data in the xx array which contains the minimum, mean and maximum of the order of the pixels to the data in yy array which contains the minimum, mean and maximum of the values of elevation. The command [9]: <coef = polyfit(xx,yy,2)> is then used. It finds the coefficients of a polynomial f(xx) of degree 2 that fits the data xx to yy using the least squares. The coefficients are stored in the array coef. The polynomial is then used to find an

6 15 Ahmed Hassanein, 2015 / American-Eurasian Journal of Sustainable Agriculture 9(3), March, Pages: elevation value for each order of pixel in the whole row array. The command [9]: <elev = polyval(coef,row)> is then used. This function uses a polynomial with the coefficients coef to calculate the output elev for the input row. The elev array should contain the elevation values for each value in the row array. A plot of elev versus the length of the path Distance is shown in fig. 8. Discussion and Conclusion: A technique is successfully used to extract elevation values from the elevation profile which is shown in GE. The technique is based on applying a serial of Matlab commands. Two main parts are explained in this paper. The first is concerned about transforming an image of a plot to a graph. The second is concerned with fitting the pixels numbering in the image to a matching elevation values. The two parts resulted in a plot of the elevation values for a part of the Nile River. The technique can be used successfully for any GE image. 7. Noradila, R. and M.M. Rafee, Digital Elevation Model (DEM) Extraction From Google Earth: A Study in Sungai Muar Watershed: Geoinformation Catalyst for planning, development and good governance, Applied Geoinformatics for Society and Environment (AGSE); Solomon, V., K.D. Nagesh and J. Indu, Extraction of Drainage Pattern from ASTER and SRTM Data for a River Basin using GIS Tools: International Conference on Environment, Energy and Biotechnology, Singapore, MATLAB version (R2009b). Natick, Massachusetts: The MathWorks Inc., References 1. "Earth Help." Earth Help. N.p., n.d. Web. 20 Sept Nagi Zomrawi, M., G. Ahmed and M. Hussam Eldin, Positional Accuracy Testing of Google Earth: International Journal Of Multidisciplinary Sciences And Engineering, 4(6). 3. Qiong, H., W. Wenbin, X. Tian, Y. Qiangyi, Y. Peng, L. Zhengguo and S. Qian, Exploring the Use of Google Earth Imagery and Object- Based Methods in Land Use/Cover Mapping: Remote Sens, 5: Bharati, L. and P. Jayakody, Hydrology of The Upper Ganga River: International Water Management Institute. 5. M.J., V.O. and R.A.M., Using Google Earth to map gully extent in the West Gippsland region (Victoria, Australia); 19th International Congress on Modelling and Simulation, Perth, Australia, December. 6. Shivangi, S., K.P., S.N.B. and K.T.S., Water Turbidity Assessment in Part of Gomti River Using High Resolution Google Earth s Quickbird satellite data: Geospatial World Forum, Hyderabad, India; January.

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