Leaf Water Potential in a Mixed Mediterranean Forest from Machine Learning and Unmanned Aerial Vehicle (UAV)-Based Hyperspectral Imaging

Keyphrases

• Spectral Indices 100%
• Leaf Water Potential 100%
• Machine Learning 100%
• UAV 100%
• Hyperspectral Imaging 100%
• Mediterranean Forest 100%
• Vehicle-based 100%
• Hyperspectral 66%
• Support Vector Machine 66%
• Pistacia Lentiscus 66%
• Mixed Forest 66%
• Cupressus Sempervirens 66%
• Normalized Difference Spectral Index 66%
• Ceratonia Siliqua 33%
• Pinus Halepensis 33%
• Spatial Coverage 33%
• Plant Water Status 33%
• Remote Sensing 33%
• Coarse Resolution 33%
• Leaf Model 33%
• Satellite Data 33%
• Machine Learning Algorithms 33%
• Machine Learning Models 33%
• Woody Species 33%
• Three-machine 33%
• Extreme Gradient Boosting(XGBoost) 33%
• Multispecies 33%
• Quercus Calliprinos 33%
• Vegetation Water Status 33%
• Interspecies Variability 33%
• Leaf Measurements 33%
• Seasonal Variance 33%

Agricultural and Biological Sciences

• Leaf Water Potential 100%
• Unmanned Aerial Vehicle 100%
• Learning System 100%
• Machine Learning 100%
• Hyperspectral Image 100%
• Pistacia lentiscus 40%
• Support Vector Machine 40%
• Mixed Forest 40%
• Cupressus sempervirens 40%
• Ceratonia siliqua 20%
• Pinus halepensis 20%
• Quercus 20%
• Linear Models 20%
• Hyperspectral Imagery 20%
• Remote Sensing 20%
• Xgboost 20%