In this paper we report the results of an experiment with automated landform delineation and classification from digital elevation models (DEMs) using object-based image analysis (OBIA). Archaeologists rely on accurate and detailed geomorphological maps to predict and interpret the location of archaeological sites. However, they have been using high-resolution DEMs primarily for visual interpretation and expert-judgement classification of landform.

We introduce an object-based method to automatically classify topography from SRTM data. The new method relies on the concept of decomposing land-surface complexity into more homogeneous domains. An elevation layer is automatically segmented and classified at three scale levels that represent domains of complexity by using self-adaptive, data-driven techniques. For each domain, scales in the data are detected with the help of local variance and segmentation

In most landform classification studies – either per cell or object-based – the authors have ignored modeling the semantics of landforms explicitly. Thus, landform classification schemes rely on individual knowledge, and are too much tailored to specific areas and/or scales. Integration of structured knowledge models in the classification process has been proposed to overcome the limitations in transferability. We are working towards a general

Increasing availability of high resolution Digital Elevation Models (DEMs) is leading to a paradigm shift regarding scale issues in geomorphometry, prompting new solutions to cope with multi-scale analysis and detection of characteristic scales. We tested the suitability of the local variance (LV) method, originally developed for image analysis, for multi-scale analysis in geomorphometry. The method consists of: 1) up-scaling land-surface parameters

In the last decade landform classification and mapping has developed as one of the most active areas of geomorphometry. However, translation from continuous models of elevation and its derivatives (slope, aspect, and curvatures) to landform divisions (landforms and landform elements) is filtered by two important concepts: scale and object ontology. Although acknowledged as being important, these two issues have received surprisingly little attention.

New deadline: March 15, 2011 http://www.geomorphometry.org/content/late-submission-deadline-geomorphometry-2011-conference

Discrete analysis of the land-surface: Burning issues and upcoming topics in linking geomorphometry with OBIA (workshop at Geomorphometry 2011) September 11, 2011, University of Redlands, California, USA http://www.geomorphometry.org/content/obia-workshop-2011

Geomorphometric approaches in archaeology September 10, 2011, University of Redlands, California, USA http://www.geomorphometry.org/content/archaeology-workshop-2011

Mathematical Geosciences at the Crossroads of Theory and Practice September 5-9, 2011, University of Salzburg, Austria http://www.iamg2011.at/

Relating spatial patterns to ecological processes is one of the central goals of landscape ecology. The patch-corridor-matrix model and landscape metrics have been the predominant approach to describe the spatial arrangement of discrete elements (“patches”) for the last two decades. However, the widely used approach of using landscape metrics for characterizing categorical map patterns is connected with a number of problems. We aim at stimulating