A simple method to automatically remove artificial terrain from airborne LiDAR DTMs in plain areas

Abstract

Alluvial plains are highly vulnerable to floods and ground disasters. Recent rapid urbanization and climate change have heightened disaster risks in urban areas. Geomorphological maps, crucial for estimating disaster risks, delineate landform boundaries based on patterns of concave breaks of slope and micro-landforms. However, enhancing the accuracy of such maps requires data extraction methods capable of capturing these features with greater precision. Traditional topographic measurements derived from adjacent elevation points in airborne light detection and ranging (LiDAR) digital terrain models (DTMs) fail to accurately represent slope variations on low ground surfaces due to the inclusion of numerous noise-like artificial terrain features. Consequently, analyzing natural terrain becomes challenging. To address this issue, our study devised a method to automatically identify and eliminate noise-like artificial terrain from LiDAR DTMs. We achieved this by removing major artificial terrain features from land-use vector data, creating an edge-preserving smooth DTM, and selectively removing and interpolating only those areas where were large differences between the smooth DTM and the LiDAR DTM. This method minimizes the interpolation of artificial terrain and quotes the LiDAR DTM for other areas, thereby minimizing the data quality loss. It is possible to identify and demarcate topographic boundaries in plains with a longitudinal gradient of approximately 1% or less at a high resolution, which can be used to investigate the relationship between flood and ground characteristics and landform volume in the plains. This method can be easily processed using only QGIS and free open data. This approach enhances the precision of disaster risk estimation and facilitates more effective urban planning in vulnerable areas.