Large Scale Landform Mapping Using Lidar DEM


Thesis Type: Postgraduate

Institution Of The Thesis: Yildiz Technical University, Faculty of Civil Engineering, Geomatic Engineering, Turkey

Approval Date: 2015

Thesis Language: English

Student: Moustafa Baker

Consultant: Türkay Gökgöz

Abstract:

Landform is often the most important factor in distinguishing between regions and an important element of geographic classification, typification, and regionalization. One of the best-known classifications was developed by the American geographer Edwin H. Hammond (1954), who classified the landforms of the United States in great detail. It has since been modified into a deterministic analysis which can be computed using elevation data and performed in a GIS. More recently, Morgan and Lesh (2005) has developed pixel-based analysis approach using GIS. All of these approaches have been used to obtain landform maps at medium or small scales using coarse resolution DEM such as USGS NED, ASTER GDEM and SRTM so far .

In this study, using LIDAR DEM data, a landform map was firstly obtained in accordance with Morgan and Lesh's workflow. In this stage, radius of the search window in neighborhood operator was determined as 50 pixel by trial and error. Futhermore, an interface was developed so that some of the parameters in the model could be changed by the user besides the automation of Morgan and Lesh's workflow. In order to make the slope threshold a model parameter in the interface a logical tool (Greater Than) was used instead of slope reclassification tool in Morgan and Lesh's model. Lastly, this landform map was generalized using focal statistics (Majority) considering minimum area condition in cartographic generalization in order to obtain landform maps at 1:1,000 and 1:5,000 scales.

Both the primary and the generalized landform maps were verified with hillshaded DEM and orthophoto visually. As a result, all these maps show the landforms satisfactorily. Moreover, in order to show the effect of generalization, area of each landform in both the primary and the generalized maps was computed. Consequently, landform maps at large scales could be obtained with the proposed method including generalization using LIDAR DEM.