Abstract
Digital terrain models (DTMs) are important for a variety of applications in geosciences as a valuable information source in forest management planning, forest inventory, hydrology, etc. Despite their value, a DTM in a forest area is typically lower quality due to inaccessibility and limited data sources that can be used in the forest environment. In this paper, we assessed the accuracy of close-range remote sensing techniques for DTM data collection. In total, four data sources were examined, i.e., handheld personal laser scanning (PLShh, GeoSLAM Horizon), terrestrial laser scanning (TLS, FARO S70), unmanned aerial vehicle (UAV) photogrammetry (UAVimage), and UAV laser scanning (ULS, LS Nano M8). Data were collected within six sample plots located in a lowland pedunculate oak forest. The reference data were of the highest quality available, i.e., total station measurements. After normality and outliers testing, both robust and non-robust statistics were calculated for all close-range remote sensing data sources. The results indicate that close-range remote sensing techniques are capable of achieving higher accuracy (root mean square error < 15 cm; normalized median absolute deviation < 10 cm) than airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) data that are generally understood to be the best data sources for DTM on a large scale.
Highlights
Accepted: 21 May 2021A digital terrain model (DTM) is a generalization of the Earth’s surface that does not contain vegetation and manmade objects
A few higher discrepancies indicate that outliers might be present in some datasets
After calculating non-robust and robust statistics, outliers were filtered out based on three times the root mean square error (RMSE) value [29]
Summary
A digital terrain model (DTM) is a generalization of the Earth’s surface that does not contain vegetation and manmade objects It is a valuable source of information for a variety of environmental disciplines such as hydrology, geology, agronomy, archaeology and forestry. It provides crucial information for water management [1], hydro-geomorphological modelling and landslide monitoring [2] It is used as supplementary data for soil property assessment in agriculture [3], whereas in archaeology it is used for detailed landscape investigation [4]. DTM finds many applications in the forestry practice [5], e.g., for forest management and planning, forest inventory, planning cuts and transport, etc In forest science, it is often used in forest structural attributes estimation [6,7], where it serves as a base for the estimation of various forest and tree attributes. Recent advances in the miniaturization of remote sensing technology are creating potential applications for high-resolution remote sensing such as post-harvest skid trail identification and assessment of land displacement [9]
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