Extensive improvement to srtm dem for the application of hydrology

Dadiyorto Wendi, Shie-Yui Liong, Yabin Sun, Chi Dung Doan

Thursday 2 july 2015

17:00 - 17:15h at Central America (level 0)

Themes: (T) Water resources and hydro informatics (WRHI), (ST) Catchment hydrology

Parallel session: 13H. Water resources - Catchment

Digital elevation model (DEM) is commonly used in hydrological study; it provides topographical information to depict hydrological characteristics of a catchment, such as upstream and downstream area, slope, depression or ponds, and can be used to derive catchment boundary or area, flow path and length, etc to be used as input parameters to set up hydrological model. In addition, DEM is used directly as input in spatially distributed hydrological and flood model, such as MIKE SHE, MIKE FLOOD, HEC-HMS (MOD-CLARK Model), etc. Shuttle Radar Topography Mission (SRTM) is a publicly available DEM and an essential dataset when no reliable DEM is available due to budget constraints to purchase very costly high resolution satellite data or to conduct labour intensive and time-consuming ground survey data. However, SRTM with horizontal resolution of 90m outside US, its vertical accuracy is known to be rather limited with approx. 10m in general and severely worsens at the area predominated by canopy such as forest and urban features like buildings. This can result in a rather unreliable hydrological representation such as incorrect flow path or loss of information of the depressions or ponds, and etc and therefore unreliable hydrological and flood model. This paper considers both forested and urbanized area catchment to present proof of concept of an approach to extensively improve the SRTM dataset. The approach makes full use of (1) the introduction of Landsat 8 data, of a resolution of 30m, into SRTM data; and (2) the Artificial Neural Networks to flex its known strengths in pattern recognition. The study shows a series of significant improvements of the DEM, for examples: (1) a reduction of more than 50% in RMSE in elevation; (2) their spatial patterns; and (4) a much clearer delineation of drainage network. The proposed approach has been demonstrated to be very promising to improve the low accuracy DEM, directly obtained from publicly accessible SRTM data, by cleverly amending SRTM data with Landsat 8 data and then use the strengths of pattern recognition power of ANN.