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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


� 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



3. RESULTS

4. DISCUSSION

5. CONCLUSION


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


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


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


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



3. RESULTS

4. DISCUSSION

5. CONCLUSION


RESULTS

11

� A problem with cluster plots validation � height was predict according to an allometric relation with diameter ...


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


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


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


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


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).


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.


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 …).


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 – [email protected]


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