Statistics applied to forest modelling Module 1. Summary Introduction, objectives and scope...
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Transcript of Statistics applied to forest modelling Module 1. Summary Introduction, objectives and scope...
Statistics applied to forest modelling
Module 1
Summary
Introduction, objectives and scope Definitions/terminology related to forest modelling
Initialization and projection
Allometry in tree and stand variables
Growth functions Empirical versus biologically based growth functions
Simultaneous modelling of growth of several individuals0 Expressing the parameters as a function of stand and
environmental variables0 Self-referencing functions: algebraic difference and
generalized algebraic difference approach
Definitions/terminology related to forest modelling
Forest model
A dynamic representation of the forest and its behaviour, at whatever level of complexity, based on a set of (sub-) models or modules that together determine the behaviour of the forest as defined by the values of a set of state variables as well as the forest responses to changes in the driving variables
State variables and driving variables
State variables (at stand and/or tree level) characterize the forest at a given moment and whose evolution in time is the result (output) of the model: Principal variables if they are part of the growth modules
Derived variables if they are indirectly predicted based on the values of the principal variables
Driving variables are not part of the forest but influence its behaviour: Environmental variables (e.g. climate, soil)
Human induced variables/processes (e.g. silvicultural treatments)
Risks (e.g. pests and diseases, storms, fire)
Modules and components
Model module Set of equations and/or procedures that led to the
prediction of the future value of a state variable
Algorithms that implement driving variables (e.g. silvicultural treatments, impact of pests and diseases)
Module component Equation or procedure that is part of a model module
Modules types
Modules can be briefly classified as Initialization modules
Growth modules
Prediction modules
Modules for silvicultural treatments
Modules for hazards
Forest simulator
Computer tool that, based on a set of forest models and using pre-defined forest management alternative(s), makes long term predictions of the status of a forest under a certain scenario of climate, forest policy or management
Forest simulators usually predict, at each point in time, wood and non-wood products from the forest
There are forest simulators for application at different spatial scales: stand, management area/watershed, region, country or even continent
Forest simulators at different spatial scales
Stand simulator Simulation of a stand
Landscape simulator Simulation, on a stand basis, of all the stands included in
a certain well defined region in which the stands are spatially described in a GIS
It allows for the testing of the effect of spatial restrictions such as maximum or minimum harvested areas or maximization of edges
Forest simulators at different spatial scales
Regional/National simulator – not spatialized Simulation of all the stands inside a region, without
individualizing each stand, stands are not connected to a GIS
Regional/National simulator – spatialized through a grid Simulation of all the stands inside a region, without
individualization of each stand, stands are not exactly located but can be placed in relation to a grid
Forest management alternatives and scenarios
Forest management alternative (prescription) Sequence of silvicultural operations that are applied to a
stand during the projection period
Scenario Conditions (climate, forest policy measures, forest
management alternatives, etc) present during the projection period
Decision support system
Simulator that includes optimization algorithms that point out for a solution – list of forest management alternatives for each stand: Multi-criteria decision models
Artificial neural networks
Knowledge based systems
Initialization and projection
Initialization and projection
Initialization modules provide the values of state variables from the driving variables such as Site index and/or site characteristics
Silvicultural decisions (e.g. trees at planting)
Growth modules predict the evolution of the state variables
The need for initialization modules
When do we need initialization modules?
When forest inventory does not measure all the state variables (concept of minimum input)
In the simulation of new plantations
For the simulation of regeneration
In landscape and regional simulators after a clear cut
Compatibilidade entre produção e crescimento
Embora o crescimento e a produção estejam biológica e matematicamente relacionados, nem sempre esta relação foi tida em conta nos estudos de produção florestal
É contudo essencial que os modelos, ao serem construidos, tenham esta propriedade: Se eu estimar o crescimento anual em 10 anos e somar
os crescimentos, o valor obtido tem que ser igual àquele que se obtém se eu estimar directamente a produção aos 10 anos
Compatibilidade entre produção e crescimento
Sendo Y a produção (crescimento acumulado) e t o tempo (idade), o crescimento será representado por
tfdt
dY
r A produção acumulada até à idade t será
,ctFY
onde c é determinado a partir da produção Y0 no instante t0 (condição inicial)
Allometry in tree and stand variables
As relações alométricas
Diz-se que existe uma relação de alometria linear, ou relação alométrica, entre dois elementos dimensionais (L e C) de um indivíduo ou população (no nosso caso, povoamento florestal), quando a relação entre eles se pode expressar na forma
CabLCbL a lnlnln
a constante alométrica, caracteriza o indivíduo num dado ambiente
b depende das condições iniciais e das unidades de L e C
As relações alométricas
A relação alométrica resulta da hipótese de que, em indivíduos normais, as taxas relativas de crescimento de L e C são proporcionais
dt
dC
Ca
dt
dL
L
11
ClnakLlndt
dC
C
1a
dt
dL
L
1
As relações alométricas
O conhecimento da existência de relações alométricas entre as componentes de um indivíduo ou povoamento é bastante importante para a modelação do crescimento de árvores e povoamentos
É uma das hipóteses biológicas que podemos utilizar na formulação dos modelos
Growth functions
Growth functions
The selection of functions – growth functions - appropriate to model tree and stand growth is an essencial stage in the development of growth models.
Two types of functions have been used to model growth:
Empirical growth functions0 Relationship between the dependent variable – the one we want to
model – and the regressors according to some mathemeatical function – e.g. linear, parabolic
Analitical or functional growth functions0 Functions that are derived from logical propositions about the
relationship between the variables, usually according to tree growth principles
Which should we prefer?
Growth functions
Growth functions must have a shape that is in accordance with the principles of biological growth:
The curve is limited by yield 0 at the start (t=0 ou t=t0) and by a
maximum yield at an advanced age (existence of assymptote)
the relative growth rate (variation of the x variable per unit of time and unit of x) presents a maximum at a very earcly stage, decreasing afterwards; in most cases, the maximum occurs very early so that we can use decreasing functions to model relative growth rate
The slope of the curve increases in the initial stage and decreases after a certain point in time (existence of an inflexion point)
Schumacher function
The model proposed by Schumacher is based on the hypothesis that the relative growth rate has a linear relationshiop with the inverse of time (which means that it decreases nonlinearly with time):
t
1kddY
Y
1
t
1k
dt
dY
Y
12
Schumacher function
In integral form:
t
1k
eAy
where the A parameter is the assymptote and (t0,Y0) is the initial
value
the k parameter is inversely related with the growth rate
0t/k0eYA
Lundqvist-Korf function
Lundqvist-Korf is a generalization of Schumacher function with the following differential forms:
nn tkddY
Yt
nk
dt
dY
Y
111)1(
Lundqvist-Korf function
The corresponding integral form is:
The A parameter is the assymptote
The k and n parameters are shape parameters: k is inversely related with the growth rate
n influences the age at which the inflexion point occurs
nt
1k
eAY
Lundqvist-Korf function
D - assímptota e n variável
0
10
20
30
40
50
60
0 10 20 30 40
idade
Y
70-0.45 70-0.5 90-0.45 90-0.5
B - k variável
0
10
20
30
40
50
60
70
80
0 10 20 30 40
idade
Y
1.00 3.00 5.00
C - n variável
0
10
20
30
40
50
60
70
80
90
0 10 20 30 40
idade
Y
1.00 0.50 0.10
A - assímptota variável
0
10
20
30
40
50
60
0 10 20 30 40
idade
Y
90 70 50
Lundqvist-Korf function
D - assímptota e n variável
0
10
20
30
40
50
60
0 10 20 30 40
idade
Y
70-0.45 70-0.5 90-0.45 90-0.5
B - k variável
0
10
20
30
40
50
60
70
80
0 10 20 30 40
idade
Y
1.00 3.00 5.00
C - n variável
0
10
20
30
40
50
60
70
80
90
0 10 20 30 40
idade
Y
1.00 0.50 0.10
A - assímptota variável
0
10
20
30
40
50
60
0 10 20 30 40
idade
Y
90 70 50
Lundqvist-Korf function
k variável
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0 1 2 3 4 5 6
va lor do pa râ m e tro k
ida
de
a q
ue
oc
orr
e o
p.i
n= 0.1 n= 0.5 n= 1
n variável
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0.0 0.4 0.8 1.2 1.6
va lor do pa râ m e tro n
ida
de
a q
ue
oc
orr
e o
p.i
k= 0.5 k= 2 k= 5
Lundqvist-Korf function
A variável
-2
0
2
4
6
8
10
12
14
16
18
20
20 40 60 80 100
va lor do pa râ m e tro A
Y q
ua
nd
o o
co
rre
o p
.i
n=0.1 n=0.5 n=1
n variável
0
2
4
6
8
10
12
14
16
18
20
0.0 0.4 0.8 1.2 1.6
va lor do pa râ m e tro n
Y q
ua
nd
o o
co
rre
o p
.i
A =90 A =70 A =50
Monomolecular function
Assumes that the absolute growth rate is proportional to the difference between the maximum yield (asymptote) and the current yield:
YAkdt
dY
Monomolecular function
The corresponding integral form:
tkec1AY
A
Y1ec 0tk 0
A- assymptote; k – shape parameter, expressing growth speed
Logistic function
The logistic function is based on the hypothesis that the relative growth rate is the result of the biotic potential k reduced by the current yield or size nY (environmental resistence):
nYkdt
dY
Y
1
Relative growth rate is therefore a decreasing linear function of the current yield
Gompertz function
This function assumes that the relative growth rate is inversely proportional to the logarithm of the proportion of current yield to the maximum yield:
The integral form:
A
YlogkYlogAlogk
dt
dY
Y
1
tkec
eAY
0tk
0 eYlogAlogc
Richards function
The absolute growth rate of biomass (or volume) is modeled as: the anabolic rate (construction metabolism)
0 proportional to the photossintethicaly active area (expressed as an allometric relationship with biomass)
the catabolic ratea (destruction metabolism)0 proportional to biomass
Anabolic rateCatabolic ratePotential growth rateGrowth rate
S – photossintethically active biomass ; Y – biomass; m – alometric coefficient;c0,c1,c2,c3 – proportionality coefficients; c4 – eficacy coefficient
taxa anabólica c1S=c1 (c0Ym) =c2Y
m taxa catabólica c3Y taxa potencial de crescimento c2Y
m - c3Y taxa de crescimento c4 (c2Y
m - c3Y),
taxa anabólica c1S=c1 (c0Ym) =c2Y
m taxa catabólica c3Y taxa potencial de crescimento c2Y
m - c3Y taxa de crescimento c4 (c2Y
m - c3Y),
Richards function
The differential form of the Richards function follows:
YYdt
dY m
Richards function
By integration and using the initial condition y(t0)=0, the
integral form of the Richards function is obtained:
,ce1AY m1
1tk
with parameters m, c, k and A where:
m1k
eec 00 tk
tm1
m1
1
A
Richards function
D - assímptota e k variável
0
10
20
30
40
50
60
70
80
0 10 20 30 40
idade
Y
70-0.05 70-0.045 90-0.05 90-0.045
B - k variável
0
10
20
30
40
50
60
70
80
90
0 10 20 30 40
idade
Y
0.03 0.05 0.07
C - m variável
0
10
20
30
40
50
60
70
80
0 10 20 30 40
idade
Y
-0.2 0.2 0.4
A - assímptota variável
0
10
20
30
40
50
60
70
80
0 10 20 30 40
idade
Y
90 70 50
Richards function
D - assímptota e k variável
0
10
20
30
40
50
60
70
80
0 10 20 30 40
idade
Y
70-0.05 70-0.045 90-0.05 90-0.045
B - k variável
0
10
20
30
40
50
60
70
80
90
0 10 20 30 40
idade
Y
0.03 0.05 0.07
C - m variável
0
10
20
30
40
50
60
70
80
0 10 20 30 40
idade
Y
-0.2 0.2 0.4
A - assímptota variável
0
10
20
30
40
50
60
70
80
0 10 20 30 40
idade
Y
90 70 50
Richards function
m variável
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
-0.1 0.0 0.1 0.2 0.3 0.4 0.5
valor do parâmetro m
ida
de
a q
ue
oc
orr
e o
p.i
k=0.1 k=0.3 k=0.55
k variável
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
0.0 0.2 0.4 0.6 0.8
valor do parâmetro k
ida
de
a q
ue
oc
orr
e o
p.i
m=0.05 m=0.2 m=0.4
Richards function
A variável
-2
0
2
4
6
8
10
12
14
16
18
20
20 40 60 80 100
valor do parâmetro A
Y q
ua
nd
o o
co
rre
o p
.i
m=0.05 m=0.15 m=0.30
m variável
0
2
4
6
8
10
12
14
16
18
20
0.0 0.1 0.2 0.3 0.4
valor do parâmetro m
Y q
ua
nd
o o
co
rre
o p
.i
A=90 A=70 A=50
Generalization of Richards and Lundqvist-Korf functions
Lundqvist function Schumacher’s function is a specific case of Lundqvist
function for n=1
Richards function Monomolecular, logistic and Gompertz are specific cases of
Richards function dor the m parameter equal to 0, 2, 1
Using growth functions as age-independent formulations
In many applications age is not known, e.g. in trees that do not exhibit easy to measure growth rings or in uneven aged stands
For these cases it is useful to derive formulations of growth functions in which age is not explicit
The derivation of these formulations is obtained by expressing t as a function of the variable and the parameters and substituting it in the growth function writtem for t+a (Tomé et al. 2006)
Using growth functions as age-independent formulations
Example with the Lundqvist function
mt
1k
t eAY
m1
t Ayln
kt
mat
1k
at eAY
mam
1
Atyln
k
1k
at eAY
Families of growth functions
Families of growth functions
The fitting of a growth function to data from a permanent plot is starightforward
Example: Fitting the Lundqvist function to basal area and doiminant
height growth data from a permanent plot
nt
keAY
1A - asymptotek, n – shape parameters
Growth functions
0.0
10.0
20.0
30.0
40.0
50.0
0 5 10 15 20 25 30 35 40
Idade (anos)
Áre
a b
asa
l (m
2ha
-1)
0.0
10.0
20.0
30.0
40.0
50.0
0 5 10 15 20 25 30 35 40
Idade (anos)
Altura
dom
inante
(m
)
Basal areaA = 58.46, k = 5.13, n = 0.81Modelling efficiency = 0.995
Dominant heightA = 48.75, k = 4.30, n = 0.75Modelling efficiency = 0.960
But how to model the growth of a series of plots? This is our objective when developing
FG&Y models…
Those plots represent “families” of curves
Using growth functions formulated as difference equations - ADA
Algebraic difference approach (ADA)
When formulating a growth function as a difference equation, it is assumed that the curves belonging to the same “family” differ just by one parameter - the free parameter
A growth function with 3 parameters allows for 3 different formulations, usually denoted by the free parameter
For example for the Richards function:Richards-A (model with site specific asymptote)
Richards-k (model with common asymptote)
Ricjards-m (model with common asymptote)
Using growth functions formulated as difference equations - ADA
Example with the Lundqvist function, formulation with common asymptote and common n parameter, k as free parameter (Kundqvist-k):
A specific curve of the familty is defined by the value of the free parameter
n
tt
AY
AY
2
1
12
In practice, the fee parameter is a function of an initial condition (Y0,t0)
nt
k
eAY 1
1
1
nt
k
eAY 2
1
2
nt
k
eAY 1
1
1
nt
k
eAY 2
1
2
Using growth functions formulated as difference equations - GADA
Generalized algebraic difference approach (GADA)
One of the problems with ADA is the fact that it originates formulations that differ just by one parameter
With GADA it is possible to obtain formulations that have more than one site-specific parameter
In GADA parameters are assumed to be function of an unobservable set of varibales (denoted by X) that expresse site differences
The equations is then solved by X, which, for a particular site, is substituted in the original equation (X0)
Using growth functions formulated as difference equations - GADA
Example with the Schumacher function
Suppose that =X and =X, then
By substituting X0 into the previous expression, we get
t
Yln
t1
YlnX
t
XXYln
0
00 t1
YlnX
0
00 tt
ttYlnYln
Using growth functions formulated as difference equations - GADA
Another example with the Schumacher function
Suppose now that =X and =X, then
and
Solving for X:
Finnally, substituting X0 in the previous expression
t
XYln
1t
YlntX
0
00
0
tYln
t1t
t1t
tYln
t
XYln
t
X
tXYln2
1t
YlntX
0
000
Expressing parameters as a function of tree/stand variables
Example with the Lundqvist function fit to basal area growth of eucalyptus (GLOBULUS 2.1 model) :
1000
Nn)Iqeln(nnnIqeAA
A
GAG i
gngQ0ggi2
gQgt
t
g
1g2
2gn2
1gn1
gn
g t
1k
g eAG
fek1000
NkIqekkk gf
plgnpgQ0gg
1000
Nn)Iqeln(nnnIqeAA
A
GAG i
gngQ0ggi2
gQgt
t
g
1g2
2gn2
1gn1
Using mixed-models
Mixed-models (linear and non-linear) “split” the model error according to different sources of variance, such as: Region
Stand
Plots
…
When using a model fitted with mixed-models theory it is possible to calibrate the parameters with random components by measuring a small sample of individuals
This means that it is possible to use specific parameters for a particular tree/stand
Which is the best method to model “families” of growth functions?
There is no best method to model “families” of growth functions
If appropriate the three methods can be combined in order to obtain more flexible growth models
FIM !!!