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Keyphrases
Central Chile
100%
Random Forest
100%
Generalized Linear Model
100%
Model Forest
100%
Natural Forest
100%
LiDAR Data
100%
Vascular Plant Species
100%
Error Distribution
66%
Species Richness
50%
LiDAR
50%
Biodiversity
33%
Non-axisymmetric
33%
Symmetric Errors
33%
Count Data
33%
Normalized Root-mean-square Error (NRMSE)
33%
Monitoring System
16%
Ecosystem Services
16%
Native Forest
16%
Challenging Tasks
16%
Changing World
16%
Ecological Perspective
16%
Heteroscedasticity
16%
Riverside
16%
Second-growth
16%
High Performance
16%
Feature Selection Methods
16%
Earth System
16%
Local Plants
16%
Information Type
16%
Negative Binomial
16%
Evergreen Species
16%
Sub-Mediterranean
16%
Canopy Height
16%
Overlapping Distribution
16%
Low Elevation
16%
Structural Information
16%
Plant Species Richness
16%
Four-layer
16%
Thermopile
16%
Vegetation Composition
16%
Biodiversity Conservation
16%
Nothofagus Obliqua
16%
Topographic Information
16%
Discrete Return LiDAR
16%
Species number
16%
Altitude above Sea Level
16%
Derived Variables
16%
Precise Monitoring
16%
INIS
chile
100%
data
100%
forests
100%
randomness
100%
lidar
100%
distribution
55%
errors
44%
biodiversity
33%
shrubs
33%
herbs
33%
values
22%
information
22%
layers
22%
capture
22%
symmetry
22%
canopies
22%
comparative evaluations
11%
ecosystems
11%
height
11%
applications
11%
water
11%
capacity
11%
performance
11%
growth
11%
monitoring
11%
mapping
11%
vegetation
11%
elevation
11%
altitude
11%
asymmetry
11%
sea level
11%
thermopiles
11%
Earth and Planetary Sciences
Chile
100%
Species Richness
100%
Plant Specie
100%
Vascular Plant
100%
Optical Radar
100%
Natural Forest
100%
Standard Deviation
16%
Ecosystem Service
16%
Monitoring System
16%
Conservation of Biodiversity
16%
Sea Level
16%
Thermopile
16%
Agricultural and Biological Sciences
Linear Models
100%
Vascular Plant
100%
Random Forest
100%
Species Richness
100%
Herbs
50%
Biodiversity
50%
Nothofagus Obliqua
16%
Ecological Perspective
16%
Sea Level
16%
Second Growth
16%
Ecosystem Service
16%
Feature Selection
16%