Atmospheric Correction Geometric Correction

IV. RESULT AND DISCUSSION

4.1 Digital Image Pre-Processing

4.1.1 Atmospheric Correction

In this study, atmospheric correction was performed on image 2001. Histogram adjustment was used to remove the atmospheric bias that may occur in the imagery in each band. The procedure made use of the Scattergram Cut-off Atmospheric Correction Technique. By principal, this technique makes use of the cut-off information that is determined from the bivariate scattergram. A line of best fit drawn through the distribution between the two bands will intercept the shorter wavelength axis at a DN approximating the scattered component. Each cut-off value was used to readjust the minimum value in the histogram. This was accomplished by employing the simple formula “INPUT1 – cut-off value” on the formula editor of the software. The result of histogram adjustment for radiometric correction is presented in Table 4.1 and the histogram performance before and after radiometric correction can be seen in Figure 4.1. DN value of original data has increased the minimum brightness all band. Generally, after performed histogram adjustment the minimum brightness value will be zero. 40 Table 4.1. Comparing the DN Value Before and After Performed Histogram Adjustment of Image 2001. BandChannel DN Value of Original Data Histogram Adjustment 1 56 – 255 0 – 199 2 40 – 255 0 – 215 3 28 – 255 0 – 227 4 29 – 211 0 – 182 5 23 – 255 0 – 232 7 13 – 255 0 – 242 The lowest digital number of 2001 imagery was zero, even when no objects in the scene truly have a reflectance of zero.

4.1.2 Geometric Correction

To determine control point in geometric correction and image rectification easier, it is needed to make a composite color image. The purpose of making composite color image is to find general illustration about data that will be processed further that is with manipulate visual appearance of the earth’s surface object. Composite color image that made is band combination-542. By using this composite color, the objects in the image to determining GCP’s is easier to be recognized and should be taken from exact and no change objects like small islands, intersection of road or river. In this research, to conform the pixel grids and remove any geometric distortions in the Landsat imagery, the Landsat-7 ETM+ image, December 22, 2001 has been registered to the UTM Zone South 48, WGS 84 coordinate system. the Landsat-7 ETM + were registered to Then Topographic map utilizing similar sets of ground control points GCP’s. 41 In selecting the GCP’s have to be careful, not only one should check that the object selected on the two images is similar, but also must be sure that the two have the same location on each image. The result of corrected image and collection of GCP’s can be seen on Figure 4.1. a Row Landsat imagery with Ground Control Point b Rectified imagery: Registered to the UTM zone 48, WGS 84 coordinate system Figure. 4.1. Rectification Of Landsat 7 ETM + Imagery PathRow 122065, Image Date: December 22, 2001 With Composite Band 542 Even the smallest amount of RMS error has the potential to introduce some degradation to the change detection accuracy. This degradation has the potential to affect the boundaries of the landuse classes incorporated in the study as spurious differences can be detected because the land surface properties at wrong locations are evaluated instead of the real changes at the same location between one time and another. The spatial resolution of the imagery becomes an important factor in this assessment Bottomley 1998. 42 Landsat-7 ETM+ image, December 22, 2001 were registered to an RMS error of less than 0.5 pixels or exactly the average of RMS error is 0.19 pixels Appendix 4. This Root Mean’s Square error was deemed acceptable at the time of registration. After geometric correction have already finished, it is better to subset of image that cover study area, because in some cases, Landsat 7 ETM + scenes are much larger than a research study area. In these instances it is beneficial to reduce the size of the image file to include only the area of interest Figure 4.2. This not only eliminates the extraneous data in the file, but it speeds up processing due to the smaller amount of data to process. This is important when utilizing multi- band data such as Landsat 7 ETM + imagery. This reduction of data is known as subset cropping. This process has been done for the both images. The image cut by using vector data from vector of west java which was produced by Bakorsurtanal. Table 4.2 shows the geographical coordinates of the cropping area. Table 4.2. The Geographical Coordinates of Ciliwung Watershed. Geo-position Top Left Bottom Right Latitude 6 o 24’24.20”N 6 o 46’06.91”S Longitude 104 o 47’41.37”E 107 o 00’01.59”E Easting 421305.00E 460875.00E Northing 143235.00N 121635.00N 43 a Landsat imagery pathrow 122065 b Ciliwung Watershed Figure 4.2. Subset of Remotely Sensed Data to Focus Study Area.

4.1.3 Topographic Correction