Izvorni znanstveni članak

https://doi.org/10.31298/sl.142.11-12.1

A novel automated method for the improvement of photogrammetric DTM accuracy in forests

Mateo Gašparović ; University of Zagreb, Faculty of Geodesy
Anita Simic Milas ; Bowling Green State University, School of Earth, Environment and Society, USA
Ante Seletković ; Faculty of Forestry University of Zagreb
Ivan Balenović orcid.org/0000-0001-7422-753X ; Faculty of Forestry University of Zagreb

Sažetak

Accuracy of a Digital Terrain Model (DTM) in a complex forest environment is critical and yet challenging for accurate forest inventory and management, disaster risk analysis, and timber utilization. Reducing elevation errors in photogrammetric DTM (DTM_PHM), which present the national standard in many countries worldwide, is critical, especially for forested areas. In this paper, a novel automated method to detect the errors and to improve the accuracy of DTM_PHM for the lowland forest has been presented and evaluated. This study was conducted in the lowland pedunculate oak forest (Pokupsko Basin, Croatia). The DTM_PHM was created from three-dimensional (3D) vector data collected by aerial stereo-photogrammetry in combination with data collected from existing maps and field surveys. These data still present the national standard for DTM generation in many countries, including Croatia. By combining slope and tangential curvature values of raster DTM_PHM, the proposed method developed in open source Grass GIS software automatically detected 91 outliers or 3.2% of the total number of source points within the study area. Comparison with a highly accurate LiDAR DTM confirmed the method efficiency. This was especially evident in two out of three observed subset areas where the root mean square error (RMSE) values decreased for 8% in one and 50% in another area after errors elimination. The method could be of great importance to other similar studies for forested areas in countries where the LiDAR data are not available.

Ključne riječi

digital terrain model (DTM); vertical accuracy; LiDAR; lowland forest

Hrčak ID:

212745

URI

https://hrcak.srce.hr/212745

Datum izdavanja:

31.12.2018.

Podaci na drugim jezicima: hrvatski

Posjeta: 2.483 *