Remote sensing and modeling in forestry
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Remote sensing and modeling in forestry Lecture 11 Models. Dario Papale Contributi: Vern Vanderbilt, TA- Quinn Hart, M. Meroni, CCRS. What is affecting canopy reflectance.

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Dario Papale Contributi: Vern Vanderbilt, TA- Quinn Hart, M. Meroni, CCRS

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Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Remote sensing and modeling in forestry

Lecture 11

Models

Dario Papale

Contributi: Vern Vanderbilt, TA- Quinn Hart, M. Meroni, CCRS


What is affecting canopy reflectance

What is affecting canopy reflectance

1. Absorption and reflection of the canopy elements at different wavelengths (leaves, branches, trunks, flowers, fruits, understory, soil etc.)

2.Canopy architecture (overstory biomass, LAI, leaves distribution, degree of coverage, etc.)

3. Remote sensing system (geometry Sun-target-sensor, atmosphere interaction etc.)


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Δ

A

β

Vegetation indexes

For this reason the vegetation indexes are based in particular on reflectance in red and NIR


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Intrinsic indexes

SR (Simple Ratio)

It is the easiest VI. It can have values between 0 and infinite, in particular between 0 and 1 for soils and between 6 and 10 for green vegetation.

NDVI (Normalized Difference Vegetation Index)

The most used and common. Values between -1 and 1, in particular less than 0 for water, just above 0 for bare soils and between 0.4 and 0.7 for vegetation. Only very dense vegetations reach NDVI = 0.8.


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Use of vegetation indexes for spatialization

Satellite images

Variable of interest measured with

geographic position (GPS)

Georeferencing

VIs calculation at the ground

Measurement points

Regression VIs/variable

Best VI selection

VI calculated on the full image

Regression application

Map ofthe variable


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Vegetation Index

LAI, biomass, …

Saturation

One of the most important limits of the vegetation indexes is that above a certain limit they saturate. For this reason they are more indicated for sparse vegetation

The alternative to the VIs are the models, in particular the models based on the physics of the reflection processes of leaves and canopy


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Models

Models are simplifies representations of the real world (Wainwright and Mulligan 2004)

Models don’t include all the characteristics of the process that want to reproduce and try to include all the most important and characteristic.

Models in remote sensing, differently from vegetation indexes, are often based on some knowledge ofthe process of interest or of the relation between reflectance and chemical/physical properties of the object, trying to maximize the use of data and knowledge.


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Models

All the models are however based on some assumptions because there are aspects of the real world unknown or difficult to replicate and that needs to be simplified.

In the models there are three main entities

Constants: values that don’t change with the system that we want to model (e.g. the Solar constant or the speed of light in vacuum)

Parameters: elements that can change their value but are fix in the single application or system (e.g. ratio between height and diameter of a specific forest analyzed)

Variables: are the model input and a free to change in the simulations (e.g. diameter value used to calculate the heights using a H-Diam function)


Empirical and process models

Empirical and process models

Empirical or statistical

  • Relations between input and output are defined using the data without imposing them and also without knowing the process

  • Very efficient and quite easy

  • Problems in the extrapolation or where the data used don’t represent all the most important conditions

    Process-oriented

  • The system under study is re-built in order to simulate its behavior

  • Based on scientific knowledge of the processes and system

  • In general complex

  • Can be applied in extrapolation

Input

Output

Black box

Input

Output

Glass box


Remote sensing and carbon balance

Remote sensing and carbon balance

The epsilon-models or radiation use efficiency models

GPP = g (Qa)

Qa = absorbed radiation

Qa = Qi x %ass

= FAPAR

Qi = incirent radiation

%ass = absorbed percentage


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Remote sensing and carbon balance

The epsilon-models or radiation use efficiency models

FAPAR can be estimated for remote sensing, generally using models but it is also strongly correlated with vegetation indexes. For example in 1982 Kumar and Monteith proposed for AVHRR data the equation:

FAPAR = 1.08 x NDVI – 0.08

So our equation become

GPP = g (PAR x FAPAR)


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Remote sensing and carbon balance

The epsilon-models or radiation use efficiency models

It has been demonstrated (e.g. Monteith 1972) that the g function is almost linear so:

ε, known also as RUE, is the radiation use efficiency:

GPP = ε x PAR x FAPAR


Remote sensing and carbon balance1

Remote sensing and carbon balance

The epsilon-models or radiation use efficiency models

GPP = ε x PAR x FAPAR

What is influencing the value of epsilon?

ε = εmax x f1 x f2 x f3 x …

Linear reduction factors (between 0 and 1) linked to the environmental conditions: Ta, Ts, VPD, SWC, …

Maximum potential efficiency in optimal conditions


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Absorbed

Photosynthetically Active

Radiation

Radiation

Use

Efficiency

GPP = (Rnet*0.45*Fpar) * {max* [mTmin] [mvpd] }

Mod15

(remote sensing)

NEP=GPP – Reco

L’esempio del MOD17+

GPP = APAR * 


Model data fusion validation

Additional independent measured true data

New model

&

parameters

Validation

Others data (e.g. at different spatial resolution

Model-data-fusion-validation

Model

(Structure,

Assumptions)

Measured true

data

Model improvement

parameterization


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Importance of parameterization and validation in different conditions

Wellwateredconditions

0.2

Eddy cov.

(a)

(b)

0.18

Sap flow

0.16

Modelled

0.14

0.12

H2O flux [mm/h]

0.1

0.08

0.06

0.04

0.02

0

Eddy cov.

(c)

(d)

12

Modelled

10

8

6

CO2 flux of GPP [µmol m-2 s-1]

4

2

0

-2

0

4

8

12

16

20

24

0

4

8

12

16

20

24

Local time [hr]

Local time [hr]

Droughtstressedconditions

Reichstein, Tenhunen et al., Global Change Biology, 2002


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Target region

The multiple constrain

Reichstein et al. 2003, JGR


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Example of the RUEmax parameterization using measuremetns in the MOD17 model

1.8

1.6

1.4

1.2

1

RUE [gC / MJ APAR]

0.8

0.6

0.4

0.2

0

ENF

EBF

DBF

MF

Sav

Oshrub

Crop


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Leaf reflectance

Radiative transfer models

Based on models that can reproduce the spectral reflectance of a single leaf and a canopy

Leaf reflectance model- LRM

Simulate spectral reflectance, absorbance and transmittance of a single leaf starting from its chemical and physical characteristics

  • Leaf characteristics

  • Clorophyll

  • Water

  • Structure

LRM


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Leaf reflectance

Canopy reflectance

Radiative transfer models

Based on models that can reproduce the spectral reflectance of a single leaf and a canopy

Canopy reflectance models - CRM

Simulate the spectral reflectance of the canopy starting from the leaves characteristics, canopy structure, soil spectral reflectance of the soil, light conditions, data acquisition system etc.

CRM

  • Data acquisition

  • Light

  • Geometry

  • Canopy characteristics

  • Structure

  • Soil

  • LAI


Dario papale contributi vern vanderbilt ta quinn hart m meroni ccrs

Leaf reflectance

Canopy reflectance

Models inversion

  • Leaf characteristics

  • Clorophyll

  • Water

  • Structure

  • Canopy characteristics

  • Structure

  • Soil

  • LAI

  • Data acquisition

  • Light

  • Geometry

LRM

Join models

Inverted models

CRM


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