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Research Article Open access CC BY 4.0

Enhancing Terrain Analysis from Digital Elevation Models Using 2-D Kalman Filtering Technique

Hart, Lawrence, Marcus, Hilda Celestine

Journal of Geography, Environment and Earth Science International · pp. 40–51 · Published 10 Jul 2024

10.9734/jgeesi/2024/v28i7788

Abstract

Three-dimensional spatial information, particularly elevation, is crucial for understanding terrain characteristics essential for meaningful development, often expressed as a Digital Elevation Model (DEM). To achieve reliable and accurate DEM values for terrain analysis, modeling uncertainties is necessary. The primary objective of this study is to determine improved terrain variables from the Digital Elevation Model of the study area. The recursive 2-D Kalman filtering technique was applied four times at different orientations to 121 elevation values extracted from a 30-meter resolution ALOS DEM of the study area using QGIS Desktop 3.22.7 software of an area covering approximately 10.80 Hectares using QGIS, the process involved 144 iterations. MATLAB was used for the computations. The terrain variables (elevation, first partial derivatives along the X and Y axes) of the central point of the DEM were obtained as a linear combination of the four filtering results. The final estimated values for the central point were 26.5589m for elevation, 0.0002m and 0.0011m for partial derivatives along the X and Y directions, with standard errors of ±0.0001m, ±0.0005m, and ±0.0007m, respectively. A 3-D plot of the terrain surface of the study area using Surfer10 software showed that the recursive 2-D Kalman filtering significantly improved the quality of the terrain surface when applied over the DEM. Therefore, the adopted recursive 2-D Kalman filter is well-suited for terrain surface modeling using grid DEMs. Its use is encouraged for determining improved values of terrain topographic variables, leading to more accurate terrain interpretation. In addition, when compared with ground survey data confirmed the technique's efficiency in reducing DEM noise. These results are promising as they are necessary information for flood route modelling, land use allocation and enhance functionality of the urban space domain of the study area. 

Digital elevation model Kalman filtering topographic terrain spatial analysis

References (5)

  1. 1 Causes and consequences of error in digital elevation models [DOI]
  2. 2 Accuracy and resolution of shuttle radar topography mission data [DOI]
  3. 3 Review and critical analysis on digital elevation models [DOI]
  4. 4 A method for Sentinel-1 DEM outlier removal using 2-D Kalman filter [DOI]
  5. 5 Full 4D Change Analysis of Topographic Point Cloud Time Series using Kalman Filtering [DOI]

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