An Evaluation of Remotely Sensed Imagery as a Data Source For Hydrological Information

Abstract

In the field of hydrology, with increasing opportunities afforded by remote sensing for assessing and monitoring various components of the hydrological cycle it is important to recognise the current capabilities and appropriate applications of remotely sensed data. This thesis examines the use of Landsat MSS, Landsat TM and Panchromatic Aerial Photography at 1:50000 scale for their accuracy in delineating, mapping and measuring, morphometric parameters and landcover.

A review of morphometric parameters is presented, including the application of remotely sensed imagery for their assessment. The use of remotely sensed data in hydrological modelling is discussed. Technical details of each type of imagery are given. A sequence of image enhancement techniques is applied to both the Landsat TM and Landsat MSS data to evaluate optimal image enhancements for drainage network delineation and landcover discrimination.

Supervised and unsupervised automatic classification procedures are applied to the digital data for landcover classification. Different visual interpretation techniques are compared for drainage network and landcover interpretation.

Landsat TM is seen to exhibit considerable advantage over Landsat MSS for the interpretation of drainage networks. High correlation is found between Landsat TM catchment area interpretations and those derived from a 1:50000 scale map of the area. Basin length and mainstream length parameters also show correlation. Similar results are obtained for the aerial photography. A minimum catchment size of 30km² is suggested for Landsat TM imagery, and a minimum lake size of 0.6ha. Broad landcover classifications using visual and automatic classification methods, and also by different interpreters, are seen to be in general agreement.

The important role of Geographic Information Systems and Digital Elevation Models(DEM’s) in hydrology is discussed. A DEM is created for the research area and a classified Landsat TM image is vectorised and stored together with other data in a GIS.

Date of Award	Jun 1991
Original language	English
Awarding Institution	Aston University