GIS-based landform classification of Bronze Age archaeological sites on Crete Island

Various physical attributes of the Earth's surface are factors that influence local topography and indirectly influence human behaviour in terms of habitation locations. The determination of geomorphological setting plays an important role in archaeological landscape research. Several landform types can be distinguished by characteristic geomorphic attributes that portray the landscape surrounding a settlement and influence its ability to sustain a population. Geomorphometric landform information, derived from digital elevation models (DEMs), such as the ASTER Global DEM, can provide useful insights into the processes shaping landscapes. This work examines the influence of landform classification on the settlement locations of Bronze Age (Minoan) Crete, focusing on the districts of Phaistos, Kavousi and Vrokastro. The landform classification was based on the topographic position index (TPI) and deviation from mean elevation (DEV) analysis to highlight slope steepness of various landform classes, characterizing the surrounding landscape environment of the settlements locations. The outcomes indicate no interrelationship between the settlement locations and topography during the Early Minoan period, but a significant interrelationship exists during the later Minoan periods with the presence of more organised societies. The landform classification can provide insights into factors favouring human habitation and can contribute to archaeological predictive modelling

Tools for Semi-automated Landform Classification: A Comparison in the Basilicata Region (Southern Italy)

Recent advances in spatial methods of digital elevation model (DEMs) analysis have addressed many research topics on the assessment of morphometric parameters of the landscape. Development of computer algorithms for calculating the geomorphometric properties of the Earth’s surface has allowed for expanding of some methods in the semi-automatic recognition and classification of landscape features. In such a way, several papers have been produced, documenting the applicability of the landform classification based on map algebra. The Topographic Position Index (TPI) is one of the most widely used parameters for semi-automated landform classification using GIS software. The aim was to apply the TPI classes for landform classification in the Basilicata Region (Southern Italy). The Basilicata Region is characterized by an extremely heterogeneous landscape and geological features. The automated landform extraction, starting from two different resolution DEMs at 20 and 5 m-grids, has been carried out by using three different GIS software: Arcview, Arcmap, and SAGA. Comparison of the landform maps resulting from each software at a different scale has been realized, furnishing at the end the best landform map and consequently a discussion over which is the best software implementation of the TPI method

カンボジア・トンレサップ湖の湖岸北西地域における完新世中期以降の地形発達史

報告番号: ; 学位授与年月日: 2008-03-24 ; 学位の種別: 修士 ; 学位の種類: 修士(環境学) ; 学位記番号: 修創域第2596号 ; 研究科・専攻: 新領域創成科学研究科自然環境学専

An Ontology for Submarine Feature Representation on Charts

A landform is a subjective individuation of a part of a terrain. Landform recognition is a difficult task because its definition usually relies on a qualitative and fuzzy description. Achieving automatic recognition of landforms requires a formal definition of the landforms properties and their modelling. In the maritime domain, the International Hydrographic Organisation published a standard terminology of undersea feature names which formalises a set of definition mainly for naming and communication purpose. This terminology is here used as a starting point for the definition of an ontology of undersea features and their automatic classification from a terrain model. First, an ontology of undersea features is built. The ontology is composed of an application domain ontology describing the main properties and relationships between features and a representation ontology deals with representation on a chart where features are portrayed by soundings and isobaths. A database model was generated from the ontology. Geometrical properties describing the feature shape are computed from soundings and isobaths and are used for feature classification. An example of automatic classification on a nautical chart is presented and results and on-going research are discussed

Map the distribution of glaciofluvial deposits and associated glacial landforms

There are no author-identified significant results in this report

Andrean examples of mega-geomorphology themes

Geomorphic (or physiographic) provinces have been a well known and useful method of regional landform classification for a century. Every earth scientist will recognize a phrase such as Appalachian Plateau or Southern Rocky Mountains as defining a discrete region of consistent geologic structure that has experienced a similar interval of erosion by a similar process or set of processes. The geomorphic provinces formalized in the United States by Fenneman in the 1920's continue to be highly satisfactory even though some boundaries were only vaguely drawn. Mosaics of LANDSAT images illustrate better than any earlier maps the validity and coherence of Fenneman's provinces. The concept of geomorphic provinces has been used subconsciously or intuitively, to describe the relief of the ocean floor and the topography of the Moon and other planets

Comparison of Terrain Indices and Landform Classification Procedures in Low-Relief Agricultural Fields

Landforms control the spatial distribution of numerous factors associated with agronomy and water quality. Although curvature and slope are the fundamental surface derivatives used in landform classification procedures, methodologies for landform classifications have been performed with other terrain indices including the topographic position index (TPI) and the convergence index (CI). The objectives of this study are to compare plan curvature, the convergence index, profile curvature, and the topographic position index at various scales to determine which better identifies the spatial variability of soil phosphorus (P) within three low relief agricultural fields in central Illinois and to compare how two methods of landform classification, e.g. Pennock et al. (1987) and a modified approach to the TPI method (Weiss 2001, Jenness 2006), capture the variability of spatial soil P within an agricultural field. Soil sampling was performed on a 0.4 ha grid within three agricultural fields located near Decatur, IL and samples were analyzed for Mehlich-3 phosphorus. A 10-m DEM of the three fields was also generated from a survey performed with a real time kinematic global positioning system. The DEM was used to generate rasters of profile curvature, plan curvature, topographic position index, and convergence index in each of the three fields at scales ranging from 10 m to 150 m radii. In two of the three study sites, the TPI (r ≥ -0.42) was better correlated to soil P than profile curvature (r ≤ 0.41), while the CI (r ≥ -0.52) was better correlated to soil P than plan curvature (r ≥ -0.45) in all three sites. Although the Pennock method of landform classification failed to identify footslopes and shoulders, which are clearly part of these fields’ topographic framework, the Pennock method (R² = 0.29) and TPI method (R² = 0.30) classified landforms that captured similar amounts of soil P spatial variability in two of the three study sites. The TPI and CI should be further explored when performing terrain analysis at the agricultural field scale to create solutions for precision management objectives

A comparative Digital Soil Mapping (DSM) study using a non-supervised clustering analysis and an expert knowledge based model - A case study from Ahuachapán, El Salvador

DSM is the inference of spatial and temporal soil property variations using mathematical models based on quantitative relationships between environmental information and soil measurements. The quality of DSM information depends on the method and environmental covariates used for its estimations. We compared two DSM methods to predict soil properties such as Organic Matter “MO” (%), Sand (%), Clay (%), pH (H2O), Phosphorus (mg/kg), Effective Cationic Exchange Capacity “CICE” (cmol/L), Potassium (cmol/L) and Water Holding Capacity (mm/m) for the department of Ahuachapán in El Salvador to support the activities of the Agriculture Landscape Restoration Initiative (ALRI) in the countr