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FORESEE Workshop - Forestry applications of remote sensing technologies8-10 October 2014 - INRA Champenoux - France
Using empirical modelisation for dendrometric
prediction at stand level.
A. Munoz, P. Miller
C. Riond, J. Fay, J. Bock
(ONF)
SUMMARY
1. INTRODUCTION : ISSUES
2. MATERIALS AND METHODS
3. RESULTS
4. DISCUSSION
5. CONCLUSION
2FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
INTRODUCTION
3FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
� Foresters need to optimize inventories for management purposes
(compartment descriptions)
� Complet stand inventories are precise but too much time consuming
� Statistic inventories with relascope are suspect to bee operator dependante and
also time consuming (1 pts /ha) for large forest area (>500 ha).
� Statistic inventories with fixed area are precise at forest scale but there is a lack
of description between plots (1 plot for 3 or 7 ha).
� Lidar data was available just before forest inventories in some forest
IS LIDAR AND ALTERNATIVE AND/OR AN COMPLEMENTARY
TOOL FOR FOREST DESCRIPTIONS ?
WHAT ARE THE ISSUES ?
SUMMARY
1. INTRODUCTION : ISSUES
2. MATERIALS AND METHODS
3. RESULTS
4. DISCUSSION
5. CONCLUSION
4FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
5
Combe d’Aillon Brotonne Méaudre
Site Aillon Méaudre Brotonne
forest area 620 ha 1800 ha 10600 ha
data CG 73 IRSTEA Newfor GIP Seine aval
region mountain mountain plaine
structure Uneven-aged Uneven-agedEven-aged
species S.P / EPC / HET S.P / EPC / HET HET / P.Semiting pulse 12 pts/m² 12 pts/m² 4.5 pts/m²
2. MATERIALS AND METHODS - Sites
3 CASE STUDIES
FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
2. MATERIALS AND METHODS - principe
SAMPLE PLOTS FOR CALIBRATION AND VALIDATION
� Plots with fixed area to calibrate a predictive model of dendrometrical
parameters� Random sampling stratified according to forest types.
� Be aware with harvest probabilty between lidar survey and inventory campagne
� Good accurracy of XY plots (GNSS, tree mapping, CHM lidar ...)
� Independante validation plots to assess accuraccy of predictive model� Instal new plots according to lidar explicative variables :
� Random sample / stratified according to forest parameters or predictive maps
� To assess accuracy at stand level, instal cluster of plots (>1ha)
6
SiteCalibration Validation plots Validation for 1ha
G V D0 G V D0 G V D0
Aillon 54 54 - - - 8 - 8
Méaudre 113 150 113 - 52 - 22 22 22
Brotonne 60 - 60 58 - 58 14 - 14
FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
2. MATERIALS AND METHODS - modeling
CALIBRATION OF PREDICTION MODEL WITH THE PLOT DATA
7
� Reference field data
� Trees (diameter > 17.5 cm)
� Basal area G, Stem density,
Dominant diameter D0
� Metrics calculation
� Height distribution
� Tree level metrics
� Regression model calibration
� Stepwise metrics selection (three
dependent variables)
� Validation of linear model assumptions
� Assessment of effects of external
factors
� 3-fold cross-validation
FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
88
� LiDAR points cloud metrics
� Calculated from the pointscloud using Lasmanager
software (© ONF)• Points distribution (number of points, density …)
• Heights distribution (quantile, mean, max, min, sd…)
� metrics are computed using all points or only first return,
last return …
� Tree level metrics
� Calculated from the detected apices• Apices distribution (sum of apices)
• Height distribution (sum, quantile, mean, sd …)
• Canopy volume distribution (sum, quantile, mean, sd …)
• Crown surface distribution (sum, quantile, mean, sd …)
� metrics are computed using all apices or only those
above given thresholds (h > 15 m, surf > 12.5 m² …)
Detected apex from CHM LiDAR
using an ONF method(ArcGis watershed method)
LiDAR points cloud
2. MATERIALS AND METHODS - modeling
METRICS CALCULATION
FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
9
2. MATERIALS AND METHODS - Validation
INVENTORIES AT STAND LEVEL TO ASSESS GROUND TRUTH
� Stand inventories on 1 ha surfaces� Compare measured value vs mean of prediction values
� Precision of predictive model = RMSE of measured value vs mean of prediction values
� Bias of prediction = mean of difference between measured value and mean of
prediction values
� Statistic inventories on cluster plots� Compare mean of measured values to mean of prediction values
FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand
level.
Stand inventories Statistic inventories
SUMMARY
1. INTRODUCTION : ISSUES
2. MATERIALS AND METHODS
3. RESULTS
4. DISCUSSION
5. CONCLUSION
10FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
RESULTS
11
� A problem with cluster plots validation � height was predict according to an allometric relation with diameter ...
FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
VOLUME PREDICTION WITH AN ERROR OF 23-29% : EXP MÉAUDRE
R² = 0,2097
0
100
200
300
400
500
600
700
800
900
0 200 400 600 800
Vo
lum
e e
stim
é (
en
m3
/ha
)
Volume estimé (en m3/ha)
RMSE = 118 m3/ha (29%)
Biais = -7 m3/ha (ns)
N=150 N=52 N=22
RMSE = 102m3/ha (23%) RMSE = 81m3/ha (23%)
Biais = 0.3 m3/ha (ns)
Calibration Independante validation
with sample plots in FC
Independante validation
with cluster plots
Site G_feuillu Nha pts/m² pente alti VolMéaudre ns ns ns ns ns ns
� Sensitivity analysis : Model seems to be valid every where
R²=0.76 R²=0.76
RESULTS
12FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
BASAL AREA PREDICTION WITH AN ERROR AROUND 15 % : EXP. BROTONNE
RMSE = 3.5 m²/ha (17%) RMSE = 5.9 m²/ha (22%)
Biais = 1.8 m²/ha (*)
R²=0.84 R²=0.47
N=60N=58
N=14
� Except in Brotonne where there is a problem with validation plot positions� Plot position as a random error of 5m which explain poor relation
Calibration Independante validation
with sample plots in FC
Independante validation
with cluster plots
RESULTS
13FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
R² = 0,7884
40
45
50
55
60
65
40 45 50 55 60 65
D0
me
suré
e (
en
cm
)
Do estimé (en cm)
RMSE = 3.63 cm (7%)
Biais = -1.64 cm
RMSE = 3.5 cm (6%)
R²=0.88N=54 N=8
DOMINANTE DBH PREDICTION WITH AN ERROR LESS THAN 10 % : EXP. AILLON
� Some problem with definition of D0 in heterogenous stand� In Brotonne when plot have less than 100 dominante stems / ha, error increase
dramatically because D0 is an average of dominante trees and understory trees.
� The mean of Do by plot differt from Do calcultate according to complet
inventories
RESULTS
SUMMARY OF MAIN RESULTS
14
* a negative value means understimation
Site Study variableExplicative variables
R2Plot level Stand level
RMSE (CV) RMSE (CV)Biais
(mean)
Méaudre Volume (m3/ha) 4 0.76 110 (25 %) 118 (29%) -7
Méaudre Basal area (m²/ha) 3 0.76 4.3 (14%) 4.1 (14%) -1
Méaudre D0 (cm) 3 0.75 3.7 (8 %) 3.3 (7%) -1.5
Aillon Basal area (m²/ha) 4 0.79 6.5 (14 %) 4.3 (11%) -0.7
Aillon D0 (cm) 3 0.88 3.5 (6%) 3.6 (7%) -1.6
Brotonne Basal area (m²/ha) 2 0.84 3.5 (17 %) 3.8 (14%) 1.3
Brotonne D0 (cm) 3 0.93 3.4 (9%) 8 (15%) 2.8
FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
RESULTS
15
FROM MODEL TO MAPS (GHA MODEL IN MÉAUDRE)
� 92 % of the 36760 predictive plots are in the domain of validity of G model
FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
n=115 49 65
25
26
27
28
29
30
31
32
33
34
Vercors Tot Vercors_ne Vercors_no
G e
n m
²/h
a
Estimation pperm
Estimation Lidar
Axe 1 (58 %)
Axe
2 (
25
%)
Out of validity
DISCUSSION
16
LIDAR RESULTS COMPARE TO CLASSICAL INVENTORIES
� 95 % of G / V estimation from complet inventories are between [-15 %, + 10 %]
of real value, ie a bias pf -2.5% (Duplat & Perrotte, 1981).
FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
R&D gestion
optimiste1 pessimiste2 optimiste3 pessimiste4
Measured Random error
DBH0.25 0.6 1.8 15.00
Measured Random error
Htot0.7 0.85 2.00 3.00
Simulate random Error G0.02 0.1 1 -
Simulated random error V1.4 2.7 10.7 27.9
1 : U-B Brändli et al., 2001
2 : quality control RENECOFOR (J. Bock 2014)
3 : quality control Méaudre (J. Bock, 2014
4 : hypotesis
DISCUSSION
17
LIDAR RESULTS COMPARE TO RELASCOPE INVENTORIES
� Lidar estimation at stand level is some times as precise than relascopique
inventories, and … many times more precise.
FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
Error and bias in 4 lidar case studies 202 control plots in 7 Rhône Alpine forests
CONCLUSION
18
ERROR AT FOREST LEVEL
� Accuracy of forest quantification by lidar is equivalent to those of complet
inventories at forest and stand level.
� Lidar is more accurate than relascopic inventories
� In bonus, you have maps, compartment description and MNT, but ..
� Not for free !
� We need to calibrate models in each forests, survey, stands …
� Some problem of dendrometric definition should be investigate to enhance
knowledges of heterogenous forests caracterisation (Ho, Do …).
FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
LIDAR APPEARS TO BE A COMPLEMENTARY TOOL FOR FOREST MANAGEMENT AND
DESCRIPTION, PARTICULARLY WHEN IT IS ASSOCIATED WITH PERMANENT PLOTS
INVENTORIES
FIN
Contact :
BOCK Jérôme & Alain MUNOZ
SIG – Géomatique – Télédétection
Office National des Forêts
Pôle R&D de Chambéry
42 quai Charles Roissard
73000 CHAMBERY
04 79 69 96 37 – jerome.bock@onf.fr
19FORESEE Workshop - 8 au 10/10/2014 - Using empirical modelisation for dendrometric prediction at stand level.
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