Unit Hydrograph of Watershed

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3.2.4 Hydrological Dataset

Daily series of discharge data of Palu River were collected from observation station of Ministry of Water Resources PU-SDA during the field- work. There are two of river discharge stations available; one is located near to the estuary precisely located under Palu II Bridge, and one is located on the upper side of Palu River. Both of these discharge stations are recording the river discharge once per day. The collected discharge data were used to validate the model. Figure 3.6 Daily discharges recorded at Palu River observation station. Palu Catchment consist of more than 50 river which is can be grouped into five major group; detail information about its stream network served in figure 3.7 as below: Figure 3.7. List of existing river in Palu Catchment Draft of disaster management plan RPB of Palu City, 2009. 100 200 300 400 500 2002 2003 2004 2006 2007 Q m 3 s 23 23

3.3 Research Method

There are two main processes of this research; first is image processing to produce land cover maps and the second is hydrological modeling to evaluate the hydrological response to the land cover changes. Image processing procedure contain for some analysis executed during the research such as image classification, change detection, and land cover change prediction in the future. While the hydrological modeling procedure was apply some scenarios to simulate hydrograph response. Data Collection Secondary Data Field Data Hydrological Dataset Meteorological dataset Topographic Data Remote Sensed Data Land Cover Information for Ground truth Image Classification Change Detection Analysis of Statistic Report of Change Detection DEM Hydro Processing Watershed Deleniation Watershed Parameters Hydrological Model Land Cover Map Analysis of Hydrological Model Results Figure 3.8. Study workflow. Remote sensing processes were started by collecting the land cover information through secondary data and field work. Secondary data obtained from Geospatial Information Bureau, while filed work is mostly a ground check process to take the coordinate and land cover information as well by using GPS. All of these data was use for accuracy assessment to the classified images. Hydrological modeling process was started from deriving the watershed, calculating curve number grid, and executing hydrological scenarios. The main concern of this hydrological modeling was to evaluate the river discharge by adjusting the curve number based on the land cover changes on each sub-basin. 24 24

3.3.1 Image Processing

Three different time series images of Landsat were used to identifying the land cover changes. Those images are Landsat ETM 5 image on 1990, Landsat ETM+ on 2001, and 2009 of years respectively. The provided images covered completely all of Palu Catchment area. To ensure that all the images conform to each other, co-referencing process was done while the 2001 Landsat of year image was used as baseline image. Co-referencing process is image-to-image geo- referencing of the images, where one of the images is used as base to geo-correct other images. Using image geo-referencing method, the maximum allowed of root mean square error RMSE is 0.5 of pixel resolution was achieved, and all of the images was projected into UTM zone 51S with WGS 1984 datum. The Landsat images used in this study was acquired from different season, the atmosphere condition have highly effect to the quality of the images. To prevent the bad effect of the atmospheric condition, radiometric correction was done to the all images. The radiometric correction has been done by subtract each band on the image by its minimum digital number value. Beside both image co registration and radiometric correction, image enhancement process was done in this research. Image enhancement is the improvement process of digital quality on an image. Image enhancement process was done to get the good image to make easier to identify the object on the Landsat satellite imageries.

3.3.1.1 Image Classification

Classification is a process to grouping all pixels in an image into certain classes. Thus, every class can represent an entity with specific properties. Four time series of Landsat images were used to get the information about land cover information on the study area. To obtain good accuracy of the land cover classes, the image was classified through visual interpretation; the land cover classification flowchart for each Landsat images is shown in Figure 7. Visual interpretation is utilizing several band combinations to obtain clear images. Images with Red, Green, and Blue RGB combination 542 and 741 are commonly used to classifying Landsat imagery; these combinations produce good 25 25 RGB images. Visual interpretation procedure is semi-automatic method using on screen digitizing. Landsat 1990 Landsat 2001 Landsat 2009 Landsat 2001 Images Correction Geometric and Atmospheric Images Classification Accuracy Assessment Land Cover Maps Change Detection Change Detection Analysis Ground Truth Data No Figure 3.9 Image classification processes; four multi temporal images was used to produce land use and land cover classes. Visual interpretation was done by observing the pattern of visible object on the imagery; the object such as river, settlement, and road network are very helpful to assist us to map the vegetation or land cover. The vegetation mapping is performed by delineating the outer boundary of pixels that have same pattern, then it was classified by using an support maps such as land cover maps, topographic, concessions, and vegetation as a reference maps. Based on the existing condition of land cover type in study area, the Landsat images were classed into 6 major classes. The classes are: 1. Forest Land: Area with high density of trees which include primary dry land forest, secondary dry land forest, swamp forest, mangrove, and plantation forest.