Comparison of Srtm and Aster Derived Elevation Models Along Ali Ethiopia - Deghamedo and Abala - Irepti Road Corridors

The earthwork estimation has a huge impact on the construction cost of the road. Therefore, the initial road design relies on precise terrain modeling. In this context, a low cost data offered by Digital Elevation Models (DEMs) are introduced. This study assessed the performances of three freely available DEMs; ALOS, SRTM and ASTER in flat, rolling and mountainous terrain for road design. Digital Elevation Models (DEMs) are helpful for estimating cut-and-fill volumes in a road construction project. Not being accurate can cost too much. This study aims to examine the accuracy of DEMs by comparing them with field survey data and their suitability for earthwork estimation in various terrain conditions. The study used statistical methods for error Analysis like Root mean square error calculations. Elevation profile and percentage error for comparison between DEM data versus reference ground survey data. The cut-and-fill volumes were estimated using TIN surfaces created from each DEM and the actual surveyed estimates were compared. The study found that the most accurate DEM was from ALOS with RMSE value for flat terrain, rolling terrain and mountainous terrain being 4.89 m, 5.14 m and 27.96 m respectively. The heights recorded by SRTM were relatively better with RMSE of 5.02 m, 5.47 m and 29.75 m whereas those from ASTER were the worst with RMSE of 9.15 m, 8.51 m and 30.51 m. The percentage errors in earthwork estimations for cut volumes recorded using ALOS were the lowest 21.28% (flat), 15.64% (rolling) and 47.93% (mountainous) while those of ASTER were 42.45%, 47.33% and 55.20% respectively and STRM were 31.28%, 36.26% and 47.43%. The study found that ALOS produces the best elevation and earthwork estimates, compared to SRTM and ASTER. ALOS is a very accurate sensor with very high accuracy on flat and rolling terrains and reduced accuracy on all models in mountainous terrain. ALOS is so effective that using them could help improve the cost estimation significantly and lessen the uncertainty of money in the road construction. In the future, scientists should work on dealing ALOS data with other sets of geospatial data. Also, a cost-benefit analysis of ALOS-based models in large-scale infrastructure projects can help us understand its application better.