Comparison of Individual Tree Height Estimated from LiDAR and Digital Aerial Photogrammetry in Young Forests

Keyphrases

• Acceptable Error 10%
• Analysis of Variance (ANOVA) 10%
• ANOVA Test 10%
• Aspen Plus 10%
• Biomass Stocks 10%
• Birch 30%
• Carbon Stock 10%
• Comparable Accuracy 10%
• Correlation Value 10%
• Correlation-based 10%
• Digital Aerial Photogrammetry 100%
• Error Accuracy 10%
• Estimated Height 10%
• Finland 10%
• Forest Biomass 10%
• Forest Carbon 10%
• Forest Inventory 20%
• Fossil Fuel Consumption 10%
• Height Estimation 10%
• Height Measurement 20%
• Individual Tree Level 100%
• Light Detection 100%
• Long-term Sustainability 10%
• Measurement Accuracy 10%
• Measurement Approach 10%
• Minimum Error 10%
• Norway Spruce 10%
• Paired T-test 20%
• Private Forests 10%
• Range Estimation 10%
• Remote Sensing 10%
• Remote Sensing Methods 30%
• Scots pine 10%
• Sensing Field 10%
• Silver Birch 10%
• Silver(II) 10%
• Spruce 20%
• Statistical Correlation 10%
• Stock Monitoring 10%
• Strong Correlation 10%
• Temporal Difference 10%
• Traditional Fields 30%
• Tree Height 20%
• Tree Height Estimation 10%
• Young Forest 100%

INIS

• accuracy 30%
• aspens 10%
• biomass 20%
• carbon 10%
• comparative evaluations 100%
• correlations 50%
• detection 100%
• errors 20%
• finland 10%
• forests 100%
• fossil fuels 10%
• fuel consumption 10%
• height 100%
• inventories 20%
• levels 10%
• monitoring 10%
• norway 10%
• output 10%
• pines 50%
• reduction 10%
• remote sensing 40%
• silver 10%
• spruces 30%
• stocks 10%
• sustainability 10%
• values 10%

Agricultural and Biological Sciences

• Analysis of Variance 10%
• Betula 30%
• Betula pendula 10%
• Carbon Sink 10%
• Forest Inventory 20%
• Fossil Fuel 10%
• Photogrammetry 100%
• Picea Abies 10%
• Pinus 50%
• Populus 10%
• Remote Sensing 40%