DETECTION of SINGLE TREE STEMS in FORESTED AREAS from HIGH DENSITY ALS POINT CLOUDS USING 3D SHAPE DESCRIPTORS

Keyphrases

• High Density 100%
• Airborne Laser Scanning 100%
• 3D Shape Descriptor 100%
• Airborne Laser Scanning Point Cloud 100%
• Point Density 66%
• Tree Stem 66%
• Planimetric 33%
• Austria 33%
• Classification Accuracy 33%
• Visual Interpretation 33%
• High Probability 33%
• Geometric Features 33%
• Spatial Orientation 33%
• Point Cloud 33%
• Segment-based 33%
• Object-level 33%
• Vertically Aligned 33%
• Laser Scanning Method 33%
• Forest Canopy 33%
• Hierarchical Clustering 33%
• Airborne Laser Scanning Data 33%
• Point Distribution 33%
• 3D Point Cloud 33%
• Single Tree 33%
• Forest Mapping 33%
• Overstory 33%
• Neighboring Points 33%
• Short Stem 33%
• Stem Segment 33%
• Recall Value 33%
• Orthogonal Distance Regression 33%
• Flight Height 33%
• Forest Management 33%
• Radiometric Characteristics 33%
• Laser Reflection 33%
• Segment-level 33%
• Management Purposes 33%
• Least Square Approach 33%
• Tree Position 33%

Agricultural and Biological Sciences

• Probability 100%
• Forest Canopy 100%
• Overstory 100%
• Flight 100%
• Likelihood 100%
• Least Squares Method 100%

Earth and Planetary Sciences

• Airborne Laser Scanning 100%
• Attitude (Inclination) 20%
• Overstory 20%
• Forest Canopy 20%
• Austria 20%
• Probability Theory 20%