Abstract

In this study, a knowledge-based fuzzy classification method was used to classify possible soil-landforms in urban areas based on analysis of morphometric parameters (terrain attributes) derived from digital elevation models (DEMs). A case study in the city area of Berlin was used to compare two different resolution DEMs in terms of their potential to find a specific relationship between landforms, soil types and the suitability of these DEMs for soil mapping. Almost all the topographic parameters were obtained from high-resolution light detection and ranging (LiDAR)-DEM (1 m) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-DEM (30 m), which were used as thresholds for the classification of landforms in the selected study area with a total area of about 39.40 km2. The accuracy of both classifications was evaluated by comparing ground point samples as ground truth data with the classification results. The LiDAR-DEM based classification has shown promising results for classification of landforms into geomorphological (sub)categories in urban areas. This is indicated by an acceptable overall accuracy of 93%. While the classification based on ASTER-DEM showed an accuracy of 70%. The coarser ASTER-DEM based classification requires additional and more detailed information directly related to soil-forming factors to extract geomorphological parameters. The importance of using LiDAR-DEM classification was particularly evident when classifying landforms that have narrow spatial extent such as embankments and channel banks or when determining the general accuracy of landform boundaries such as crests and flat lands. However, this LiDAR-DEM classification has shown that there are categories of landforms that received a large proportion of the misclassifications such as terraced land and steep embankments in other parts of the study area due to the increased distance from the major rivers and the complex nature of these landforms. In contrast, the results of the ASTER-DEM based classification have shown that the ASTER-DEM cannot deal with small-scale spatial variation of soil and landforms due to the increasing human impacts on landscapes in urban areas. The application of the approach used to extract terrain parameters from the LiDAR-DEM and their use in classification of landforms has shown that it can support soil surveys that require a lot of time and resources for traditional soil mapping.

Highlights

  • In urban areas like the Berlin metropolitan area, soils have a variety of functions; they are a scarce property, which are not increasable

  • Since geomorphological maps with sufficient accuracy for the study area are not available as reference data to assess the accuracy of the landforms classification, the ground truth data collected from field observations in the study area was used

  • The knowledge-based fuzzy classification was used to extract the topographic parameters from high-resolution digital terrain model derived from light detection and ranging (LiDAR)-digital elevation models (DEMs) in order to classify landform elements in the city urban areas

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Summary

Introduction

In urban areas like the Berlin metropolitan area, soils have a variety of functions; they are a scarce property, which are not increasable. The conservation of soil structure and functions is essential for sustainable spatial and regional management as well as for maintaining the ecological functioning of an ecosystem in these areas [1,2,3]. This is true for suburban areas where soil. Digital mapping of the soil based on remote sensing data is an important and major source of information for the protection of soil functions and the management of land uses in urban areas [4]

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