Abstract
Characterizing geomorphological patterns based on digital elevation models (DEMs) has become a basic focus of current geomorphology. A new DEM upscaling method based on the high-accuracy surface modelling method (HASM-US method) has been developed to improve the accuracy of current models and the subjectivity of macroscopic geomorphological patterns. The topographic variables of elevation (EL), slope (SL), aspect (AS), relief amplitude (RA), surface incision (SI), surface roughness (SR), and profile curvature (PC) with a spatial resolution of 1 km × 1 km in the Beijing-Tianjin-Hebei (BTH) area of China have been obtained by using the HASM-US method combined with the principal component analysis (PCA) method in terms of the elevation data of the SRTM-4 DEM, meteorological station location information, and field measurements with a GPS receiver. A geomorphological regionalization pattern has been developed to quantitatively classify the geomorphological types in the BTH area by combining the seven topographic factors of EL, SL, AS, RA, SI, SR, and PC that have significant spatial variation. The results show that the upscaling accuracy of elevation (mean difference only −2.32 m) with the HASM-US method is higher than that with the bilinear interpolation method and nearest neighbour interpolation method. The geomorphologic distribution in the BTH area includes 11 types: low plain, low tableland, low hill, low basin, middle plain, middle hill, low mountain with low RA values, low mountain with medium RA values, middle mountain with low RA values, middle mountain with medium RA values, and middle mountain with high RA values. The low plain is the dominant geomorphological type that covers 40.58% of the whole BTH area. The geomorphological distribution shows the different significant characteristics: the elevation rapidly decreases from the Taihang Mountains to the eastern area, gradually decreases from the Yanshan Mountains to the southern area, and first increases and then decreases from the Bashang Plateau to the southeastern area in the whole BTH area.
Highlights
Automatic classification of geomorphological types is currently based mainly on the use of regular statistical windows[2], applying digital elevation models (DEMs) to extract a variety of surface morphological factors for classification, including elevation, slope, relief amplitude (RA), surface incision (SI), surface roughness (SR), profile curvature (PC), and the elevation variance coefficient (VC)[3,4,5,6,7,8,9,10,11]
To verify the advantages and disadvantages of the upscaling algorithm used here combined with sample information and common resampling methods, the methods of cross-validation, bilinear interpolation, nearest neighbour interpolation, and high accuracy surface modelling (HASM)-US were all used to classify geomorphological types within the BTH study area
Multisource elevation data were upscaled in this analysis to obtain DEM grid data with a resolution of 1 km based on the HASM-US method; seven geomorphological parameters, including EL, SL, SA, RA, SI, SR, and PC, were calculated using ArcGIS software
Summary
Automatic classification of geomorphological types is currently based mainly on the use of regular statistical windows[2], applying digital elevation models (DEMs) to extract a variety of surface morphological factors for classification, including elevation, slope, relief amplitude (RA), surface incision (SI), surface roughness (SR), profile curvature (PC), and the elevation variance coefficient (VC)[3,4,5,6,7,8,9,10,11]. A method for high accuracy surface modelling (HASM)[23,24,25,26] has been developed since 1986 to integrate the extrinsic and intrinsic properties and find solutions for the error and multi-scale problems that are prevalent when each type of information is used separately[27]. This approach uses these inputs as its drivers as well as locally accurate information (e.g., ground observation and/or sampling data) as optimum control constraints. Applying the HASM algorithm, a HASM-upscaling (HASM-US) algorithm for DEM upscaling simulation was developed in this study
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
