Testing ALS Visualisation Methods for Detecting Kiln Remains in a Densely Vegetated Area in Japan (original) (raw)

2021

Abstract

In May 2018, Lidar data was acquired in a densely vegetated test area in southern Japan with the aim of detecting Sue kiln sites, that date back in the 9 th century. In the region including the test area, several surveys as well as an excavation recorded evidence of such sites in recent years. As the densely forested, mountainous topography complicates any ground based archaeological investigations, airborne Lidar was considered the method of choice for surveying the area. This is the first study of this kind in Japan. The company that acquired the Lidar data also provided lists of ground points. Initial interpolations and visualisations relied on these lists; later the program SCOP++ was applied for generating digital terrain models based on alternative ground point selection strategies. The results of several approaches for visualising and analysing the Lidar data sets will be presented and discussed. For visualisations, mainly low-cost or free software was applied, including GIS software, the Relief Visualisation Toolbox (RVT), and planlauf/TERRAIN for 3D virtual flights. Additional GIS approaches for analysing the data are presented: (1) contour maps that assist navigation in the field, (2) a density map of ground points allows assessing the reliability of the Lidar visualisations, (3) cross sections are useful for validating the features recognized and measuring their depth or height, and (4) slope maps support delimiting manmade terraces, identifying platforms and are an important input for deductive predictive modelling of Sue kiln sites. The work of mapping probable kiln locations detected in the LiDAR data and verifying these sites by traditional prospection methods is still in progress. Only after reliably identifying a large number of Sue kilns in the test area, approaches such as predictive modelling or machine learning may be applied for locating additional kilns in this region.

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