عنوان مقاله [English]
Geomorphologists use different models to illustrate topography and geomorphic features. One of these common models is to use hillshading, as an effective tool to detect and represent morphological shape of the terrain. This model applys a light source to make a contrast between bright sections and the parts that fall in the shadows. Many researchers have worked on the hillshade modelling. Some of them work on the azimuthal and zenithal angle of light source illumination on the earth surfaces. Others focussed on the direction and the gradient of the earth and their effect on the quality of the shadows and bright areas representations. In this research new concepts called optical morphology is introduced which is considerred as a set of methods, models and technics for representing geomorphologic and topographic features more accurate and visible. We have employed 14 terrain curvature models and also 6 models for azimuthal and zenithal angle adjustmernt. For running these models, Digital Surface Model (DSM) extracted from ALOS satellite data was used on Iran diverse geomorphological landforms. Then, these models were scripted by python and Graphical User Interface (GUI)using Python Tkinter library. A GIS-Based toolkit named Optical Morphology was prepared to calculate all introduced models in the form of raster file format. Finally, numerical analysis, including statistical, morphological, directional and contrast analysis, were run for all the model outputs. The performances and some general applications of these models are described in the field of geomorphology.
Materials and methods
In this research, Digital Surface Model published by Japan Aerospace Exploration Agency (JAXA), with a spatial resolution, near 23m, has been used for the purpose. The data were obtained from ALOS satellite image. The database is based on the global 3-D topographical DSM, which is currently the most accurate elevation data on the global scale. Several hill-shade modeling is used to enhance terrain feature’s representation. For this purpose, Python programming is used to prepare all these models.
The main local terrain descriptors such as slope and aspect have also been used to enhance terrain morphology appearance. The 6 models were run based on changes of azimuthal and zenithal angles of light source position. The models for azimuthal and zenithal analysis are including Aspect Frequency Distribution Analysis (AFDA), Un-weighted Multi-Directional Light Source (UMDLS), Weighed Multi-Directional Light Source (WMDLS), Vertical Light Source Illumination (VLSI), Slope Shading Model (SSM), and Sinusoidal Light Source Fluctuation (SLSF). The 14 models run according to the terrain curvatures are including Profile Curvature Shading Model (PCSM), Tangential Curvature Shading Model (TCSM), Plan Curvature Shading Model (PCSM), Un-sphericity Curvature Shading Model (UCSM), Mean Curvature Shading Model (MCSM), Differential Curvature Shading Model (DCSM), Maximal Curvature Shading Model (MaCSM), Minimal Curvature Shading Model (MiCSM), Horizontal Excess Curvature Shading Model (HECSM), Vertical Excess Curvature Shading Model (VECSM), Total Gaussian Curvature Shading Model (TGCSM), Total Accumulation Curvature Shading Model (TACSM), Flowlines Curvature Shading Model (FCSM), and Total Ring Curvature Shading Model (TRCSM). All these models are programmed using python (V.2.7 and Tkinter for GUI programming).
Results and discussion
In this research, optical morphology of terrain has been performmed using basic geographic information system concepts. The python programming has been used to execute different hillshade models. Some topographical factors such as terrain slope and aspect have been considered with regards to light source directions (Azimuthal and zenithal directions). In general, 20 different shading models have been programmed for calculating optical morphology and prepared as GIS toolkit named Optical Morphology. This tool is able to uses Digital Elevation Model as an input to analyze its raster structure and then store results as an ASCII file format. Finally, we have explained results, applications, advantages and disadvantages of these models.
Light source direction modeling combined with the geomorphological attributes is a powerful tool to more accurately recognize and detect landforms and could help geomorphologist in different field of studies. In this research, optical morphology modeling was done using Python programming language to enhance representation of the geomorphological terrain features. The results of these efforts are abstracted in the GIS-based toolkit which is applicable in the quantitative geomorphology area. These models have different approaches against local topographic properties, local conditions of each place and shading properties. Some geomorphological factors such as slope and aspect, topographic characteristics, terrain curvatures and, pixel distribution are effective and suitable in running and performing and adjusting the models.