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Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany

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  • Mohamed Ali Mohamed

    (Department of Geography, Humboldt University of Berlin, 10099 Berlin, Germany)

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 km 2 . 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.

Suggested Citation

  • Mohamed Ali Mohamed, 2020. "Classification of Landforms for Digital Soil Mapping in Urban Areas Using LiDAR Data Derived Terrain Attributes: A Case Study from Berlin, Germany," Land, MDPI, vol. 9(9), pages 1-26, September.
    • Handle: RePEc:gam:jlands:v:9:y:2020:i:9:p:319-:d:411136
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    References listed on IDEAS

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    1. de Groot, Rudolf S. & Wilson, Matthew A. & Boumans, Roelof M. J., 2002. "A typology for the classification, description and valuation of ecosystem functions, goods and services," Ecological Economics, Elsevier, vol. 41(3), pages 393-408, June.
    2. Annamária Laborczi & Gábor Szatmári & Katalin Takács & László Pásztor, 2016. "Mapping of topsoil texture in Hungary using classification trees," Journal of Maps, Taylor & Francis Journals, vol. 12(5), pages 999-1009, October.
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    Cited by:

    1. Changda Liu & Jie Li & Qiuhua Tang & Jiawei Qi & Xinghua Zhou, 2022. "Classifying the Nunivak Island Coastline Using the Random Forest Integration of the Sentinel-2 and ICESat-2 Data," Land, MDPI, vol. 11(2), pages 1-15, February.
    2. Mohamed Ali Mohamed, 2021. "An Assessment of Forest Cover Change and Its Driving Forces in the Syrian Coastal Region during a Period of Conflict, 2010 to 2020," Land, MDPI, vol. 10(2), pages 1-25, February.

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