Automatic Classification of Landforms (ACL) with two models of Terrain Attributes (TA) and Topographic Position Index (TPA) in the Northeast Slopes of Natanz and Kashan Karkas Heights
Document Type : Original Article
Authors
1
Hakim Sabzevari University
2
hakim sabzevari university
10.22034/gmpj.2023.380996.1410
Abstract
Automatic Classification of Landforms (ACL) with two models of Terrain Attributes (TA) and Topographic Position Index (TPA) in the Northeast Slopes of Natanz and Kashan Karkas Heights
Introduction
Geomorphology studies the forms and processes of earth's surface reliefs and their changes over time. Obtaining information about landforms and mapping of them re considered not only as a basis for different types of geomorphological research, but also for landscape evaluation, suitability studies, erosion studies, hazard prediction and various fields of landscape and regional planning or land system inventories is essential. recognition and extracting of landforms using traditional methods is time-consuming, costly, and affected by opaque and often unrepeatable decisions of the interpreter. Consequently, to accurately describe the topographical structure, new spatial analysis procedures and models need to be developed. Accessibility of digital elevation models, software development and increasing computational power of computers provide geomorphologists with tools and opportunities which may revolutionize their discipline. Nowadays, the automatic recognition of landforms is regarded as one of the most important procedures to classify landforms and deepen the understanding on the morphology of the earth. The main purpose of the study is Automatic Classification of Landforms and separation of the landscape of the Northeast Slopes of Natanz and Kashan Karkas Heights into landform classes using two methods of classification of Terrain Attributes (TA) and Topographic Position Index (TPA).
Methodology
- Case Study
The Northeast Slopes of Natanz and Kashan Karkas Heights was selected as the case area in the current study. The geocoordinates of the area are between E 33° 25′ 51′′ to E 34° 11′ 16′′ and N 50° 54′ 19′′ to N 52° 9′ 49′′ based on the World Geodetic System 1984 (WGS84), with a total area of 4,739 km2.
- Landform classification process using Terrain Attributes
The purpose of many models for the recognition and classification of landforms is to determine the froms of the hillslope. Terrain attributes is also one of these models. Chabala et al. (2013) used this model for the first time to Landform classification for digital soil mapping in the Chongwe-Rufunsa area, Zambia. The selected attributes were elevation, slope, relief intensity, and curvature. Terrain attributes derived from a digital elevation model were overlaid using cell statistics to generate a landform map with five classes: (1) Hills (Summit), (2) Upper Terraces (Shoulder), (3) Plateau (Back Slope), (4) Foot Slope and (5) Lowlands (Toe Slope).
- Landform classification process using Topographic Position Index
TPI is only one of a vast array of morphometric properties based on neighboring areas that can be useful in topographic and DEM analysis. The classification using TPI is the difference between elevation value on pixel and the average elevation of the neighboring pixels. Positive values mean that the analyzed pixel has values greater than the surrounding values, while negative indicates that it is smaller. TPI values near zero are either flat areas (where the slope is near zero) or areas of constant slope (where the slope of the point is significantly greater than zero). Using topographic position index (TPI), a landform classification map of the study area was generated. The classification has six classes: (1) Valleys, (2) Lower Slopes, (3) Gentle Slopes, (4) Steep Slopes, (5) Upper Slopes and (6) Ridges.
Results and Discussion
- Landform Generation using Terrain Attributes
The landform map was generated by overlaying the reclassified grids representing relief intensity, curvature, elevation and slope. This was done using the cell statistics tool in ArcMap with the mean set as the overlay statistic. The results of the landform classification are shown The landform of the Upper Terraces (Shoulder) with an area of 1,810 km2., which covers about 38% of the studied area, is the dominant landform of the landscape of the study area.
- Landform Generation using Topographic Position Index
Land Facet Corridor Extension introduced for ArcMap software was used to classify landform elements with TPI model. The Valleys landform with an area of 1872 square kilometers, equivalent to about 40% of the study area, is considered as the dominant landform of the study area.
Conclusion
This research aims to Landform Classification and Mapping of the Northeast Slopes of Natanz and Kashan Karkas Heights Using Terrain Attributes (TA) and Topographic Position Index (TPI) which Both methods depend on digital elevation models (DEMs). Considering that the Terrain Attributes model uses the four parameters of Height above mean sea level, Topographic Slop, Curvature and Relief Intensity as input for processing and classifying landforms, it can potentially have higher accuracy than the TPI model that only uses DEM to identify features.
Keywords
landform recognition, Automatic Classification of Landform, Terrain Attributes Model, Topographic Position Index, Karkas Heights.
Introduction
Geomorphology studies the forms and processes of earth's surface reliefs and their changes over time. Obtaining information about landforms and mapping of them re considered not only as a basis for different types of geomorphological research, but also for landscape evaluation, suitability studies, erosion studies, hazard prediction and various fields of landscape and regional planning or land system inventories is essential. recognition and extracting of landforms using traditional methods is time-consuming, costly, and affected by opaque and often unrepeatable decisions of the interpreter. Consequently, to accurately describe the topographical structure, new spatial analysis procedures and models need to be developed. Accessibility of digital elevation models, software development and increasing computational power of computers provide geomorphologists with tools and opportunities which may revolutionize their discipline. Nowadays, the automatic recognition of landforms is regarded as one of the most important procedures to classify landforms and deepen the understanding on the morphology of the earth. The main purpose of the study is Automatic Classification of Landforms and separation of the landscape of the Northeast Slopes of Natanz and Kashan Karkas Heights into landform classes using two methods of classification of Terrain Attributes (TA) and Topographic Position Index (TPA).
Methodology
- Case Study
The Northeast Slopes of Natanz and Kashan Karkas Heights was selected as the case area in the current study. The geocoordinates of the area are between E 33° 25′ 51′′ to E 34° 11′ 16′′ and N 50° 54′ 19′′ to N 52° 9′ 49′′ based on the World Geodetic System 1984 (WGS84), with a total area of 4,739 km2.
- Landform classification process using Terrain Attributes
The purpose of many models for the recognition and classification of landforms is to determine the froms of the hillslope. Terrain attributes is also one of these models. Chabala et al. (2013) used this model for the first time to Landform classification for digital soil mapping in the Chongwe-Rufunsa area, Zambia. The selected attributes were elevation, slope, relief intensity, and curvature. Terrain attributes derived from a digital elevation model were overlaid using cell statistics to generate a landform map with five classes: (1) Hills (Summit), (2) Upper Terraces (Shoulder), (3) Plateau (Back Slope), (4) Foot Slope and (5) Lowlands (Toe Slope).
- Landform classification process using Topographic Position Index
TPI is only one of a vast array of morphometric properties based on neighboring areas that can be useful in topographic and DEM analysis. The classification using TPI is the difference between elevation value on pixel and the average elevation of the neighboring pixels. Positive values mean that the analyzed pixel has values greater than the surrounding values, while negative indicates that it is smaller. TPI values near zero are either flat areas (where the slope is near zero) or areas of constant slope (where the slope of the point is significantly greater than zero). Using topographic position index (TPI), a landform classification map of the study area was generated. The classification has six classes: (1) Valleys, (2) Lower Slopes, (3) Gentle Slopes, (4) Steep Slopes, (5) Upper Slopes and (6) Ridges.
Results and Discussion
- Landform Generation using Terrain Attributes
The landform map was generated by overlaying the reclassified grids representing relief intensity, curvature, elevation and slope. This was done using the cell statistics tool in ArcMap with the mean set as the overlay statistic. The results of the landform classification are shown The landform of the Upper Terraces (Shoulder) with an area of 1,810 km2., which covers about 38% of the studied area, is the dominant landform of the landscape of the study area.
- Landform Generation using Topographic Position Index
Land Facet Corridor Extension introduced for ArcMap software was used to classify landform elements with TPI model. The Valleys landform with an area of 1872 square kilometers, equivalent to about 40% of the study area, is considered as the dominant landform of the study area.
Conclusion
This research aims to Landform Classification and Mapping of the Northeast Slopes of Natanz and Kashan Karkas Heights Using Terrain Attributes (TA) and Topographic Position Index (TPI) which Both methods depend on digital elevation models (DEMs). Considering that the Terrain Attributes model uses the four parameters of Height above mean sea level, Topographic Slop, Curvature and Relief Intensity as input for processing and classifying landforms, it can potentially have higher accuracy than the TPI model that only uses DEM to identify features.
Keywords
landform recognition, Automatic Classification of Landform, Terrain Attributes Model, Topographic Position Index, Karkas Heights.
Keywords
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