Urban Drainage Network Generation with Geographic Information Systems Using Remotely Sensed Data

Abstract

In Sri Lanka most of the existing urban drainage systems are not functioning properly. Establishing a proper drainage system in an urban area.is extremely important since it could avoid floods and inundation which causes damage to property and inconvenience to city dwellers; improve health conditions of the urban community by avoiding stagnant water; give an aesthetically pleasing environment, etc. Therefore, in Sri Lanka, establishing a proper drainage system and maintaining the same has become a major challenge in urban areas. Urban drainage network identification is a primary requirement for developing a proper drainage system. Urban drainage network identification is a tedious task, which is usually done through engineering surveys. The computer aided Geographical Information Systems (GIS) can be utilized to generate drainage network from the terrain data. The main objective of this work is the study of stream network generation in GIS using data extracted from aerial photographs, carryout accuracy comparison with surveyed drainage network and then to identify the parameters that affect the accuracy of generating streamlines. In this study, two urban watersheds from Colombo and one watershed from Moratuwa were studied to identify the stream network generation and associated terrain indicators using data extracted from aerial photographs. The data extraction for the study was done using three methods. Existing drainage network and watershed boundary were digitized from hard copy maps of Sri Lanka Land Reclamation and Development Corporation (SLLRDC). Contours, spot heights, buildings and road data in digital form were extracted from the digital archives of the National Survey Department (NSD). Some of the existing streamlines and culvert locations were surveyed specifically using GPS. Then the generation of the Triangulated Irregular Network (TIN) was carried out using the extracted contours and spot heights. Using the TIN, Digital Elevation Models (DEM) for spatial resolutions of 2m, 5m, 10m, 20m & 50m were generated. For each DEM, flow direction grids and flow accumulation grids were generated as components of stream network generation. Once the streamlines are generated from the flow accumulation grid, it is necessary to give a threshold value to separate the stream network. After comparing several vector and grid based methods, it was identified that Grid Based Comparative Squares (GBCS) method could be effectively used for comparing the accuracy of generated and extracted streamlines. In the GBCS method the streamlines generated and observed are matched for fitting only within identified square areas. In the squares, comparison is done using zones of distance from observed streamlines created using buffering capability of GIS, and giving an error code for the generated lines which fall into buffers that represent the deviation. Therefore, once the squares are selected spatially, then the degree of fitting of the computed and the observed streamline are compared with a system of buffers drawn to each observed streamline within a square. The squares and a buffer zoning of the extracted streams enable the identification of deviations of the generated streams from those of engineering survey sheets. Deviations were compared using RMSE (Root Mean Square Error) as the numerical indicator. The extracted stream network of catchments indicated a strong human influence in deviating the stream network and hence buildings were combined to the DEM to identify whether the results would show a difference. The same accuracy assessment method for five spatial resolutions was used for comparison of stream network. Also Flatness, Stream Order and Surface Slope based on streamline network of each watershed were computed for comparison. The study also identified that the accuracy levels were lower in the areas of lower stream order. This shows that in the upper reaches the streamlines would vary significantly probably due to less contributing cells and also as a result of human interventions. The stream network identification to a reasonable accuracy using remotely sensed data of 0.4m accurate contours and additional spot heights is not a straightforward operation in the case of selected watersheds. The significant flat terrain poses problems in identifying the flow directions to find a unique stream network.