Speaker
Description
Accurate terrain modelling is essential for effective planning and management in oil palm plantations, particularly in regions with complex topography. Digital Elevation Models (DEMs) are critical for guiding decisions on drainage, road construction, and planting. Although Light Detection and Ranging (LiDAR) derived DEMs offer high precision, their cost limits widespread use in the oil palm sector. To address these limitations, the present study evaluates the vertical accuracy of various open-source DEMs including National Aeronautics and Space Administration (NASA) DEM, Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Digital Elevation Model (ASTER), Copernicus DEM, Forest And Buildings removed Copernicus DEM (FABDEM), Advanced Land Observing Satellite (ALOS), TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurements), DEMs from ground surveys using GNSS GPS RTK and drone ortho photogrammetry against a high-resolution LiDAR drone-derived DEM. This study uses a methodology that combines quantitative accuracy assessments using ground control points (GCPs) with machine learning and Geographically Weighted Regression (GWR). Machine learning models analyze spatial variations and predict vertical discrepancies across DEMs, while GWR examines how topographical variations impact DEM accuracy. This integrated approach provides a locally adaptive assessment, enhancing feasibility evaluations for large-scale plantation management. The study’s implications are significant for the oil palm sector, as it identifies cost-effective DEM alternatives that support reliable terrain analysis without the financial burden of LiDAR. By clarifying the accuracy and applicability of these DEMs, this research provides plantation managers with insights for informed decision-making, promoting efficiency and sustainability in plantation operations.
