Application of the topographic position index to heterogeneous landscapes (original) (raw)
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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
This work presents the potential for high-resolution remote sensing data (LiDAR digital terrain models) to determine the spatial heterogeneity of terraced landscapes. The study objective is achieved through the identification of a new parameter that distinguishes this unique landscape form from more natural land formations. The morphological indicator proposed is called the Slope Local Length of Auto-Correlation (SLLAC), and it is derived from the local analysis of slope self-similarity. The SLACC is obtained over two steps: (i) calculating the correlation between a slope patch and a defined surrounding area and (ii) identifying the characteristic length of correlation for each neighbourhood. The SLLAC map texture can be measured using a surface metrology metric called the second derivative of peaks, or Spc. For the present study, we tested the algorithm for two types of landscapes: a Mediterranean and an Alpine one. The research method involved an examination of both real LiDAR DTMs and simulated ones, in which it was possible to control terrace shapes and the percentage of area covered by terraces. The results indicate that SLLAC maps exhibit a random aspect for natural surfaces. In contrast, terraced landscapes demonstrate a higher degree of order, and this behaviour is independent of the morphological context and terracing system. The outcomes of this work also prove that Spc values decrease as the area of terraced surfaces increases within the investigated region: the Spc for terraced areas is significantly different from the Spc of a natural landscape. In areas of smooth natural morphology, the Spc identifies terraced areas with a 20% minimum height range covered in terraces. In contrast, in areas of steep morphologies and vertical cliffs, the algorithm performs well when terraces cover at least 50% of the investigated surface. Given the increasing importance of terraced landscapes, the proposed procedure offers a significant and promising tool for the exploration of spatial heterogeneity in terraced sites.
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.
Comparative Study of Landform Mapping Using Terrain Attributes and Topographic Position Index (TPI): a Case Study in Al-Alamien, 2018
The aim of this study is to compare the classification of the landscape of the area located between Al-Alamien and Ras El-Hekma to landform classes using two different classification methods; the terrain attributes method and the topographic position index (TPI) method. Terrain attributes classification method derived from a digital elevation model and were overlaid using cell statistics to generate a landform map with seven classes. The landform classes were: (1) Lower coastal plain (17.86%), (2) Upper coastal plain (43.61%), (3) Piedmont plain (2.33%), (4) Dissected escarpment (2.64%), (5) Plateau (26.79%), (6) Ridges and coastal bars (3.62%), and (7) Inland dunes (3.15%). The topographic position index (TPI) method which generate the landform classes uses only the digital elevation model. The generated landform classes were: (1) Valleys (2.1%), (2) Midslopes drainages and shallow valleys (1.1%), (3) Plains (92.77%), (4) Hills in plains (0.34%), (5) Mesas (2.44%), and (6) Ridges (1.25%). The comparison of the two methods showed that using the terrain attributes method was more detailed.
ArcEvolve: A Suite of GIS Tools for Assessing Landform Evolution
This paper describes the tools developed to link the SIBERIA model with a GIS. SIBERIA simulates the evolution of landforms within a catchment using digital elevation data. Integration of SIBERIA with a GIS provides easier access to the model for non-specialist users, and also extends model functionality. The complexity of the model means that integrating the two technologies using an 'embedded' approach is not feasible. However, by using a 'tightly-coupled' approach the model and the GIS retain separate executables and memory space but share the database and provides a single integrated interface to the user. In addition, this approach enables the processing and analytical capabilities of the GIS for the analysis of SIBERIA output. The suite of tools developed to link SIBERIA with the ArcView® GIS package have been assembled into an ArcView® extension named 'ArcEvolve'. The functionality ArcEvolve adds to the GIS graphical user interface is grouped into two parts. The first group contains components relating to the user interface including; (i) import/export utilities for the different SIBERIA native format files; (ii) access through dialog boxes to the creation and management of a SIBERIA parameter database; and (iii) running the model. The second group includes functionality for the geomorphometric analysis of digital elevation data, the primary output of SIBERIA. The geomorphic statistics adapted for ArcEvolve include the width function, hypsometric curve, cumulative area distribution, area-slope relationship, denudation rate and volumetric change. These descriptors are important measures of catchment geomorphology and hydrology and have been successfully used to quantify and compare SIBERIA derived landscapes with natural landscapes.
Modern relief of lowland areas covered by Pleistocene glaciations was formed by accumulation, erosion and deformation action of ice-sheets, because of denudation processes in periglacial environment and interglacial (postglacial) and Holocene action of rivers and wind. The relief created this way distinguishes considerable variety of landforms but small diversity in relative heights. Commonly used for upland areas landforms classification methods can not be uncritically imported. The aim of this research is to implement selected application GIS Free Open Source Software G: Grass, R, and TAS to automatic and semiautomatic classifications of landforms on lowland areas and to compare results applying in older geomorphologic-cartographic studies. Classification have been made on digital elevation model of area 42 x 25 km with resolution of 5 metres for raster cell. The surface of the trend of the drainage network beginning was determined using the Local Polynomial Regression Fitting pro...
Digital elevation models in geomorphology
Hydro-Geomorphology - Models and Trends, 2017
This chapter presents place of geomorphometry in contemporary geomorphology. The focus is on discussing digital elevation models (DEMs) that are the primary data source for the analysis. One has described the genesis and definition, main types, data sources and available free global DEMs. Then we focus on landform parameters, starting with primary morphometric parameters, then morphometric indices and at last examples of morpho-metric tools available in geographic information system (GIS) packages. The last section briefly discusses the landform classification systems which have arisen in recent years.
Geomorphology, 2014
A morphometric analysis of drainage networks and relief using geomorphic indices and geostatistical analyses of topographical data are useful tools for discussing the morphoevolution of a given area. Among the geomorphic indices, the stream length-gradient (SL) index represents a practical tool to highlight anomalous changes in river gradients. Perturbations of SL are usually indicative of (1) differences in the resistance of outcropping lithological units to erosion, (2) sub-surface processes, such as active faulting, and (3) slope failures that directly reach the stream channels, particularly in small catchments. In this work, the SL index was calculated for the upstream sector of the Gállego River basin in the central Spanish Pyrenees to test its accuracy and sensitivity for detecting the imprints of different surface processes. A geostatistical procedure is proposed to obtain SL index maps through the interpolation and filtering of the values estimated along the drainage network. This method allows computing of SL, validation and assessing of its spatial distribution with robust statistical accuracy, and objectively defining the anomalies in SL. The anomalies in the SL map of the study area, which coincide with knickpoints and knickzones, were analyzed in detail. The results indicate (1) perturbation in the drainage network caused by differences in the resistance to erosion of outcropping lithological units and (2) hillslopes affected by large landslides, earth flows, and rock falls directly reaching the stream bed. This study indicates that the SL index has strong potential to solve geomorphological problems in different geological settings, especially in detecting the role of active, large-scale features that influence landscape evolution.
Geomorphology, 2006
This paper presents a comprehensive and flexible new geomorphological combination legend that expands the possibilities of current geomorphological mapping concepts. The new legend is presented here at scale of 1:10,000 and it combines symbols for hydrography, morphometry/morphography, lithology and structure with colour variations for process/genesis and geologic age. The piece-by-piece legend forms a "geomorphological alphabet" that offers a high diversity of geomorphological information and a possibility for numerous combinations of information. This results in a scientific map that is rich in data and which is more informative than most previous maps but is based on a simple legend. The system is developed to also be used as a basis for applications in GIS. The symbol-based information in the geomorphological maps can be digitally stored as a powerful database with thematic layers and attribute tables. By combining and further developing aspects of different classical mapping systems and techniques into expanded data combinations, new possibilities of presentation and storage are developed and thus a strong scientific tool is provided for landscape configuration and the reconstruction of its development; in turn the combination paves the way for specific thematic applications. The new system is illustrated for two contrasting landscape types: the first is located on the border of Vorarlberg, western Austria, and Liechtenstein in a glacially influenced, high altitude alpine setting that is strongly modified by various degradation processes; the second area represents a formerly glaciated region in Dalarna, central Sweden near Mora, an area that is characterized by a variety of aeolian, fluvial, glaciofluvial and lacustrine depositional and erosional landforms and also reflects isostatic uplift. The new method functions well for both areas and results in detailed scientific outlines of both landscape types.
Study of the Landforms of the Ibicuí River Basin with Use of Topographic Position Index
Revista Brasileira de Geomorfologia
The Topographic Position Index (TPI) is an algorithm which calculates the diff erence of the elevation between a central pixel (and the mean of the elevation of its surroundings (Z) surroundings defi ned by of a radius determined by the user. The study was developed in the Ibicuí Basin, located in the west of Rio Grande do Sul, with a surface area of 46,602.58 km², with a perimeter of 1,268.76 km and an 8th order hierarchy. The TPI analysis associated with inclination determined 08 landforms elements in the BHRI: fl at area elements, top elements identifi ed as fl at and wavy, springs, slopes and the footslopes and channel elements that can be closed or opened. Resumo: O Índice de Posição Topográfi ca (IPT) é um algoritmo que calcula a diferença de elevação entre um pixel central (e a média dos ambientes de elevação do ambiente (Z) defi nidos por um raio determinado pelo usuário. O estudo foi desenvolvido na Bacia hidrográfi ca do rio Ibicuí, localizado no oeste do Rio Grande do Sul, com uma área de 46.602,58 km², com um perímetro de 1.268,76 km e uma hierarquia da 8ª ordem. A análise IPT associada à inclinação determinou 08 elementos de relevo no BHRI: elementos de área plana, elementos de topo identifi cados como planos e ondulados, nascentes, encostas, base da encosta e elementos de canal que podem ser fechados ou abertos.