Comparative Study of Landform Mapping Using Terrain Attributes and Topographic Position Index (TPI): a Case Study in Al-Alamien -Ras El-Hekma Region, Egypt (original) (raw)
Related papers
Landform Classification From a Digital Elevation Model and Satellite Imagery
Geomorphology, 2008
Abstract The Iranian Soil and Water Research Institute has been involved in mapping the soils of Iran and classifying landforms for the last 60 years. However, the accuracy of traditional landform maps is very low (about 55%). To date, aerial photographs and topographic maps have been used for landform classification studies. The principal objective of this research is to propose a quantitative approach for landform classification based on a 10-m resolution digital elevation model (DEM) and some use of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image. In order to extract and identify the various landforms, slope, elevation range, and stream network pattern were used as basic identifying parameters. These are extractable from a DEM. Further, ASTER images were required to identify the general outline shape of a landform type and the presence or absence of gravel. This study encompassed a relatively large watershed of 451 183 ha with a total elevation difference of 2445 m and a variety of landforms from flat River Alluvial Plains to steep mountains. Classification accuracy ranged from 91.8 to 99.6% with an average of 96.7% based upon extensive ground-truthing. Since similar digital and ASTER image information is available for Iran, an accurate landform map can now be produced for the whole country. The main advantages of this approach are accuracy, lower demands on time and funds for field work and ready availability of required data for many regions of the world. Keywords: Landform identification; Digital elevation model (DEM); Elevation range; Stream network; Golestan Dam watershed; Geographic information systems (GIS) Article Outline 1. Introduction 2. Materials and methods 2.1. The study area 2.2. Preprocessing of existing data and DEM generation 2.3. Defining parameters required for the process of creating landform maps 2.3.1. Extracting stream network pattern, watershed points and watershed polygons 2.3.2. Extracting the slope map 2.3.3. Determining elevation range 2.4. Extracting and identifying landform types 2.4.1. River Alluvial Plains (RP) 2.4.2. Piedmont Plains (PD) and Gravelly Talus Fans (GFc) and Gravelly River Fans (GFr) 2.4.3. Plateaux and Upper Terraces (TR) 2.4.4. Hills (H) 2.4.5. Mountains (M) 3. Evaluating accuracy of the landform map 4. Results and discussion 5. Conclusions and recommendations Acknowledgements References
Environment and Ecology Research, 2023
This study aimed to carry out a classification of landforms in Wadi Araba, southwest Jordan, using the topographic Position Index (TPI) by GIS. TPI was used at different spatial scales (mean, 50 m, 75 m, 100 m, 200 m, 500 m, 1000 m, 1500 m, and 2000 m) for such classification. Landform classification goes by ten levels: Valleys Bottoms, Midslope Drainage, Upland Drainages, Gentle Valleys, Plains, Open Slopes, Upper Slopes, Local Ridges, Midslope Ridges, and High Ridges. This requires defined classes using the TPI for all scales. The analysis showed that there were ten types of landforms; most of them were ridges, valleys, and steep slopes. In addition, the proportions of these types varied according to the variation of indicator values in the scale. Landform map according to the Neighborhood Values mean (m) showed that areas with upland drainages were about 14%, while areas with Gentle Valleys represented about 5.52%, of the total area of the study area. Finally, it was found possible to rely on the classification maps of the landforms for the purpose of spatial planning, as this included determining the land uses that are appropriate for each landform, as well as determining the levels of risk for landslides and floods.
This paper presents LANDFORM, a customized GIS application for semi-automated classification of landform elements, based on landscape parameters. Using custom commands, topographic attributes like curvature or elevation percentile were derived from a Digital Elevation Model (DEM) and used as thresholds for the classification of Crests, Flats, Depressions and Simple Slopes. With a new method, Simple Slopes were further subdivided in Upper, Mid and Lower Slopes at significant breakpoints along slope profiles. The paper discusses the results of a fuzzy set algorithm that was used to compare the similarity between the map generated by LANDFORM and the visual photo-interpretation conducted by a soil expert over the same area. The classification results can be used in applications related to precision agriculture, land degradation studies, and spatial modelling applications where landform is identified as an influential factor in the processes under study.
Advances in Remote Sensing, 2013
Land cover map for a part of North Sinai was produced using the FAO-Land Cover Classification System (LCCS) of 2004. The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approach also has the advantage of facilitating the integration of Sinai land cover mapping products to be included with the regional and global land cover datasets. The total study area is 7450 km 2 (1,773,842) feddans. The landscape classification was performed on SPOT4 data acquired in 2011 using combined multi-spectral bands of 20 meter spatial resolution. Geographic Information System (GIS) was used to edit the classification result in order to reach the maximum possible accuracy. GIS was also used to include all necessary information. The identified vegetative land cover classes of the study area are irrigated herbaceous crops, irrigated tree crops and rain fed tree crops. The non-vegetated land covers in the study area include: bare rock, bare soil, bare soil stony, bare soil very stony, bare soil salt crusts, loose and shifting sands and sand dunes. The water bodies were classified as artificial perennial water bodies (fish ponds and irrigated canals) and natural perennial water bodies as lakes (standing) and rivers (flowing). Artificial surfaces in the study area include linear and non-linear. The produced maps and the statistics of the different land covers are included in the following subsections .
Landform Characterization with Geographic Information Systems
The ability to analyze and quantify morphology of the surface of the Earth in terms of landform characteristics is essential for understanding of the physical, chemical, and biological processes that occur within the landscape. However, because of the complexity of taxonomic schema for landforms which include their provenance, composition, and function, these features are difficult to map and quantify using automated methods. The author suggests geographic information systems (GIS) based methods for mapping and classification of the landscape suqface into what can be understood as fourth-order-of-relief features and include convex areas and their crests, concave areas and their troughs, open concavities and enclosed basins, and horizontal and sloping flats. The features can then be analyzed statistically, aggregated into higher-order-of-relief forms, and correlated with other aspects of the environment to aid fuller classification of landforms.
IOP Conference Series: Earth and Environmental Science, 2020
Mostly, the researchers have been carried out landform using survey method and manual delineation. Integrating remote sensing data and GIS technique can automate identifying of landform using Topographic Position Index data as one of remote sensing data. The aim of paper is to identify and categorize landform element using Topographic Position Index in the Gede Watershed derived by DEMNAS with 30 m resolution and output scale of map is 1:25.000. The method was carried by algorithm of slope and TPI automation with Geographic Information System technique. To obtain the appropriate TPI with the real condition of landform, we used radius circle 5m, 10m, 25m, 50m and 100m neighborhood size cell for TPI. The combination of slope and TPI shows the best result which are appropriate with the real condition of landform is radius circles 5m neighborhood size cells. Based on the result shows five heterogeneous of landscape i.e peak (0,6%), upper slope (10%), middle slope (9,49%), lower slope (3...
2019
The main target of this study is to classify the landforms of wadi Al-Mujib Canyon, as considered one of the main wadi draining towards the Dead Sea. Based on Topographic Position Index (TPI), and depend upon Shuttle Radar Topography Mission (SRTM) data for preparing digital elevation model (DEM) which is available with 30*30 m ground resolution. By using the TPI, the landforms were classified into both Slope Position index and landform types. Landform categories were generated by combined two TPI grids at different scales (neighborhoods: (a 1km radius and 2km radius). TPI defended as: algorithms of Weiss and Jenness used to measure the topographic slope positions and automated landform classifications. TPI values are depended upon the cell size, type, elevation, and the standard deviation (SD) of TPI. By using TPI, the study area classified into slope position index with 6 classes; Valley, Lower slope, Flat slope, Model Slope, Upper Slope, Ridge, and the Landform categories with 10...
Geographic Mapping and Analysis Using GIS of Study Areas in Bahariya Oasis, Egypt
The 18th World Congress of Soil Science, 2006
Geographic database techniques offer powerful capabilities to manage and integrate vast amounts of environmental data. In this research, ArcView GIS system is used to perform the geographic and physiographic maps. Egypt has directed major efforts to explore the natural resources in the Western Desert Oases. In this accord, the main aim of this study is to produce geomorphologic geometric maps that are expressing the landscape conditions and different landforms available in the selected study areas in Bahariya Oasis. These maps can be used as the basic geo-referenced documentations for the land evaluation decision support system. Concerning the thematic maps, database and climatic data were available. Based on the digitized aerial photo interpretation and field check of the plot study areas, the workable physiographic legend was formulated. In this legend, a geomorphologic approach was applied up to the level of landform features available in the area. Four different landscapes have been recognized in the study area including Hilland, Plateau, Peneplain and Plain that were divided into 6 repeated relief types, each of which were further subdivided based on lithology/origin and finally 27 landforms were distinguished in the area.
Updated soil surveys are considered quite helpful for planning, developing monitoring and for the sustainable management of the limited agricultural soils available. Information about soil properties and behavior over tracts of land is vital for making decisions on proper land use and management, environmental protection, and land use planning. This has been the motivation for systematic soil surveys, soil survey interpretations, and maps of soil properties required by empirical or process models. Egypt has directed major efforts to explore the natural resources in the Western Desert. Thus, storing data files in a digital geographically correlated format is considered of prime importance for the successful management of the natural resources in the study area of Western Desert and for a better land use planning.
ISPRS International Journal of Geo-Information
An accurate geomorphometric description of the Iranian loess plateau landscape will further enhance our understanding of recent and past geomorphological processes in this strongly dissected landscape. Therefore, four different input datasets for four landform classification methods were used in order to derive the most accurate results in comparison to ground-truth data from a geomorphological field survey. The input datasets in 5 m and 10 m pixel resolution were derived from Pléiades stereo satellite imagery and the "Shuttle Radar Topography Mission" (SRTM), and "Advanced Spaceborne Thermal Emission and Reflection Radiometer" (ASTER GDEM) datasets with a spatial resolution of 30 m were additionally applied. The four classification approaches tested with this data include the stepwise approach after Dikau, the geomorphons, the topographical position index (TPI) and the object based approach. The results show that input datasets with higher spatial resolutions produced overall accuracies of greater than 70% for the TPI and geomorphons and greater than 60% for the other approaches. For the lower resolution datasets, only accuracies of about 40% were derived, 20-30% lower than for data derived from higher spatial resolutions. The results of the topographic position index and the geomorphons approach worked best for all selected input datasets.