Unlocking Digitalization in Forest Operations with Viewshed Analysis to Improve GNSS Positioning Accuracy

This study evaluated the positioning accuracy of moving forest harvesters using global navigation satellite system (GNSS) signals under a canopy, and developed approaches for forecasting mature spruce canopy cover. Real-time kinematic with Trimble R12 receiver on top harvester achieved high accuracy, 86% observations meeting maximum precision 8 mm. However, presence cover hampered GNSS’s performance as there were fewer satellites available, leading to an increased number inaccurate positions larger values dilution in geometry (GDOP), position (PDOP), vertical (VDOP) horizontal directions (HDOP). The estimated from viewshed analysis digital surface model (DSM) was found be significant predictor deviation true position. These findings suggest that provides more precise results than mere percentage evaluating impact harvester, despite its computational demands. Developing intelligent algorithms can facilitate autonomous harvesting forwarding, allowing implementation digitalization operations.