Effective Identification of Terrain Positions from Gridded DEM Data Using Multimodal Classification Integration

Abstract

Terrain positions are widely used to describe the Earth’s topographic features and play an important role in the studies of landform evolution, soil erosion and hydrological modeling. This work develops a new multimodal classification system with enhanced classification performance by integrating different approaches for terrain position identification. The adopted classification approaches include local terrain attribute (LA)-based and regional terrain attribute (RA)-based, rule-based and supervised, and pixel-based and object-oriented methods. Firstly, a double-level definition scheme is presented for terrain positions. Then, utilizing a hierarchical framework, a multimodal approach is developed by integrating different classification techniques. Finally, an assessment method is established to evaluate the new classification system from different aspects. The experimental results, obtained at a Loess Plateau region in northern China on a 5 m digital elevation model (DEM), show reasonably positional relationship, and larger inter-class and smaller intra-class variances. This indicates that identified terrain positions are consistent with the actual topography from both overall and local perspectives, and have relatively good integrity and rationality. This study demonstrates that the current multimodal classification system, developed by taking advantage of various classification methods, can reflect the geographic meanings and topographic features of terrain positions from different levels.

Parameters Derived from and/or Used with Digital Elevation Models (DEMs) for Landslide Susceptibility Mapping and Landslide Risk Assessment: A Review

“…A detailed method was developed by [78] for the calculation of TPI in their study of land facet corridor design and has been added in ArcGIS software as an extension [79]. Recently, [80] published their research about the effective identification of terrain positions from gridded DEM, which includes local and regional terrain attributes based classification methods. TPI was used in their study to identify terrain positions, ridge, shoulder, lower flat, valley, hillock, and side slope, etc.…”

“…The topographic wetness index (TWI) is dependent on slope largely and is generally exploited in studies of hydrological processes. It is a commonly used tool to forecast the amount of soil moisture at the catchment scale and allows the analysis of topographic control on hydrologic response of a watershed [78][79][80]. Topography is a factor for landslide hazards, and therefore indices related with topography have always been employed for data analysis performed for various applications [55].…”

High-Resolution Landslide Susceptibility Map Generation using Machine Learning (Case Study in Pacitan, Indonesia)

“…TRI was calculated using "DOCELL" command in ArcGIS, which calculates the sum change in elevation between a grid cell and its eight-neighbor grid cell. The last topographic factor, TWI, is commonly used to quantify topographic control on hydrological processes [31]. TWI can examine the pattern of potential soil moisture in the field and can also detect the changes in soil texture caused by an erosion process.…”

A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping

“…The TWI is considered the most critical parameter measuring topographic controls of basic hydrological processes [50]. The TWI map was created using the altitude map by applying Equation (5) [51,52].…”