چكيده لاتين :
Describing geomorphological environments according to identification and extraction of landform elements is essential in landscape analyses and modeling. In fact, identification of landform elements is the key element of the geomorphological analyses. This process leads to the classification of landforms on a large scale, and extracting patterns and elements, which is the first step towards identification of landforms. Information about landforms is obtained through various models that incorporate visual analysis and quantitative techniques such as geoecosystem techniques. In fact, all these models and techniques are based on finding key elements of the landscape benefitting from geomorphometric science. Identification of landforms on a large scale requires a method for extracting patterns and elements. Because pattern recognition is the first essential step in identifying landforms. Landform classification and extraction extentended their application of DEM in the 1990s. In this study, a novel method for the extraction of landform elements from a DEM based on the principle of pattern recognition is introduced and discussed in detail. At the core of the method is the concept of geomorphon (geomorphologic phonotypes). A general-purpose geomorphometric map — an interpreted map of topography — obtained by generalizing all geomorphon to a small number of the most common landform elements.
Method
In order to examine the practical application of the introduced geomorphon method, it was used to generate a geomorphometric map of Halberd watershed, located in the south of Alborz mountain between latitudes 35-57-22 N and longitudes 83-8-53 E positioned between Semnan and Tehran provinces. A DEM ALOSPOL SAR 12.5-2010-as input and landsat8 image -29-06-2019 were used respectively and observations were filed as the following step. This process is applied in SAGA7.5 and ArcGIS10.5 software and Google Earth. The results state that using geomorphon to map landscapes has some desirable properties. First, it must calculate differential geometry-based terrain. Second, the method can identify specific landforms having different sizes and it establishes a finite, absolute set of possible landforms so no landform is too rare to be found. Finally, geomorphon is calculated using a scale-flexible procedure. This map was obtained through generalizing all geomorphon to the most common landform elements. The pattern arises from a comparison of a focus pixel with its eight neighbors. Starting from the one located to the east and continuing counterclockwise. For example, a tuple [+,-,-,-, 0, +, +, +] describes one possible pattern of relative measures, {higher, lower, lower, lower, equal, higher, higher, higher} for pixels surrounding the focus pixel. It is important to stress that the neighboring entities are not immediate neighbors of the focus pixel in the grid. But, pixels are determined from the line-of-sight principle along the eight principal directions. The results are defined according to the values of two parameters: search radius (L) and relief threshold (d). The search radius is also defined as the allowable distance for the calculation of zenith and nadir study. Subsequently, the resulting geomorphon map was adapted to the photos taken from the field.
Results and discussion
Geomorphon map includes the 10 most common landform elements namely: peak, ridge, shoulder, spur, slope, hollow, foot slope, valley, pit, and flat obtained from 498 patterns. In the geomorphon map, a pattern of various landscapes has been created. Due to the distribution of landforms, Hablehroud catchment has different features such as steep valleys and narrow flood plains in the northern, central and southern parts. According to the results, the greatest percentage of extraction of landform element was related to the slope and the least percentage to the flat. In the next step, the compliance of the geomorphon map extracted from the images of the conducted field study. The results showed a match between landform elements and the surface. The location of a pit within the valley and the density of hollows on the slope have also shown a good adaptation.
Conclusion
Geomorphon was introduced and examined as a novel perspective on how to approach quantitative terrain analysis. The method grew from our desire to develop a robust and efficient tool for identification and extraction of landform elements from DEMs. Investigation of geomorphometric variables in Hableroud watershed showed that Geomorphon identifies both earth properties and landforms with a single scan of the DEM. The results of this study revealed the landform and the nature of the processes that has been present or are currently active in this area. It was shown that the evolution of unevenness in Shoulder, Spur, Hollow on the slope are more advanced on Ridge and valley. In addition, in geomorphon comprising complex shapes, the evolution of the elements was determined to be more. Thus, the automatic extraction of landform elements lead to a pattern of roughness. Furthermore, the identification of their elements, in turn, have expressed the differences, similarities and instabilities of the roughness.
