Accuracy of remote sensing data versus other sources of

information for estimating Net Primary Production in

Eucalyptus globulus and Pinus pinaster ecosystems in Portugal

Research Article

DOMINGOS M. LOPES*†, JOSÉ T. ARANHA†, NIGEL WALFORD‡, JAMES

O’BRIEN‡, and NEIL LUCAS§

† Forestry Department, UTAD, Apartado 1013 – 5000-911 Vila Real, Portugal

‡ Centre for Earth and Environmental Science Research, School of Earth Sciences and

Geography, Kingston University, Penrhyn Road, Kingston upon Thames, KT1 2EE, Surrey,

UK

§ University of Wales Swansea, Singleton Park Swansea SA2 8PP Wales UK

Correspondence *Corresponding author. Email: dlopes@utad.pt

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Abstract

Net Primary Production (NPP) quantifies vegetation growth. It reflects the impact of biotic

and abiotic factors over an ecosystem and it is an important ecological variable for monitoring

the impact of human activity on ecosystems. Though conceptually simple, NPP figures can be

very difficult to measure accurately. In this paper, different temporal and spatial NPP

products are compared, improving our understanding of the accuracy of these methods for

measuring NPP in small-forested areas of Eucalyptus globulus and Pinus pinaster stands. The

MODIS NPP products were compared with NPP figures obtained from FOREST-BGC and

field measurements. The paper also examines the possibility of estimating the Leaf Area

Index (LAI), a key FOREST-BGC input, using remote sensing techniques. The results

indicate that the most accurate estimates were achieved using FOREST-BGC model, which is

normally applied at the stand level. Since LAI can be estimated from remotely sensed data,

this ecophysiological model may now be regarded as suitable for use at a regional and global

scale. The results also showed that, although average NPP values are similar to fieldwork

measurements, MODIS NPP products are inefficient for identifying extreme NPP values.

Keywords: Net Primary Production; MODIS NPP Products; FOREST-BGC; Accuracy

Résumé

La production primaire nette (NPP) mesure la croissance et reflète l'impact de la biotique et

des facteurs abiotiques étant une variable écologique importante pour surveiller l'impact de

l'activité humaine sur des écosystèmes. Bien que conceptuellement simples, il peut être très

difficiles mesurer exactement des chiffres de NPP. Dans cet article sont comparés des

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différents produits spatiaux et temporels disponibles de NPP; l'amélioration de notre

arrangement de l'exactitude des produits de MODIS pour mesurer NPP dans des petits

secteurs couverts de forêts et comparer ces évaluations à ceux obtenues à partir de FOREST-

BGC et des mesures sur le terrain. En plus l'article exprime l'opportunité d'estimer l'index de

la région de feuille (LAI), une entrée de la clef FOREST-BGC, au moyen de télédétection.

Des mesures sur le terrain NPP ont été faites en utilisant des équations allométriques. Kriging

a été employé pour créer des images continues de NPP pour chaque méthodologie examinée.

Les résultats obtenus indiquent que les évaluations les plus précises sont obtenues à partir du

modèle de FOREST-BGC et ont prouvé que ce modèle, qui est normalement appliqué au

niveau de stand. Puisque LAI peut être estimé à partir des données senties à distance, le

dernier peut maintenant être considéré approprié pour l'usage à un niveau régionale et globale.

Le plus important dispositif des produits de MODIS NPP est la difficulté qu'ils ont

d'identifier des valeurs extrêmes de NPP, bien que les moyennes soient semblables aux

mesures de travaux sur le terrain.

Mots-clés: production primaire nette; produits de MODIS; FOREST-BGC; précision

1 Introduction

Pastor and Post (1988) stated the importance of forested areas as ‘carbon pools’ for increasing

atmospheric carbon dioxide CO2, as they comprise a large proportion of terrestrial vegetal

biomass. Approximately 15% of the atmospheric pool of carbon (C) is annually fixed by

photosynthesis of terrestrial plants (Williams et al., 1997), so any changes in this fixed rate,

resulting from global environmental change, could significantly influence atmospheric CO2

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levels. The 21st Century has brought new challenges for forest management (Phat et al., 2004)

and forest ecosystems, which potentially constitute an extremely important tool for dealing

with climate change, in addition to the ameliorative actions of people. It has been suggested

that trees should be planted expressly to sequester CO2 and thus mitigate the projected further

increase in atmospheric CO2 concentrations (Ford and Teskey, 1991, Olofsson et al., 2007,

Zhou et al., 2007).

Running et al. (1999) suggest that the most fundamental measure of “global change” of

practical interest to humankind with respect to terrestrial biological productivity is probably

annual Net Primary Production (NPP). NPP expresses, on a periodic basis, carbon net fluxes

between atmosphere and terrestrial vegetation through photosynthesis (Goetz and Prince,

1996). Although conceptually straightforward, Field et al. (1995) maintained that the

estimation of NPP could be very difficult to measure accurately in situ. This problem is

exacerbated when attempting to estimate NPP for large areas, which has prompted

investigators to develop practical methodologies for this task. However, relatively few studies

have attempted to compare NPP estimates obtained from ecophysiological models with values

measured in the field.

The Moderate-resolution Imaging Spectroradiometer (MODIS) is the key instrument in

NASA’s Earth Observing Satellite (EOS) series for monitoring the state of land and ocean

surface parameters for global change studies and for understanding the complex interactions

between the atmosphere, oceans, land surface and biosphere. The scientific community has

access to MODIS Gross Primary Production (GPP) and Net Primary Production products,

which provide a powerful set of tools for quantifying NPP across the entire globe. However, it

remains important to investigate the accuracy of the MODIS products for estimating NPP. A

number of validation points are already widely distributed across the globe, but there is still a

lack of information about the accuracy of estimates with respect to smaller study areas. This

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paper reports on research performed with the explicit aim of addressing this deficiency by

focusing on specific forest areas in Northern Portugal, where two of the country’s main forest

ecosystems occur, in order to design a model for NPP and to examine CO2 sequestration at a

local scale in terms of area of occupation and economics.

A series of methods were used to obtain spatial estimates of NPP and this paper will compare

the results of these methods, namely: (1) a Per-plot NPP estimate derived from allometric

equations using field sampling techniques; (2) the FOREST-BGC ecosystem simulation

model; (3) the MODIS NPP images. The first method involves a large amount of field work

and is very time consuming; the FOREST-BGC is a well-known ecophysiological model,

which gives NPP estimations without fieldwork, thus in a more practical way, and can also be

used later for predicting the climate change scenarios impact on NPP; the MODIS NPP

images are costless and cover the entire world, which results in a very useful tool for

monitoring NPP worldwide.

This research seeks to determine whether the methodologies tested offer a practical and

accurate means of estimating NPP in these Portuguese forest ecosystems. If these methods

prove to be useful, then we argue they constitute a powerful tool for quantifying the

production of these forest ecosystems, and thus for monitoring atmospheric CO2 fixation

rates. With this knowledge we can make more informed decisions about managing

environmental and climatic change.

2 Background

Annual Net Primary Production (NPP) represents the net amount of carbon captured by plants

through photosynthesis each year (Melillo et al., 1993; Cao and Woodward, 1998). It

expresses the carbon net fluxes between the atmosphere and terrestrial vegetation through

photosynthesis on a time period basis (Goetz and Prince, 1996), with a minimum of one

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physiological year. In practice, NPP can be defined and measured in terms of either vegetal

biomass production or CO2 exchange (Field et al., 1995). In terms of total biomass, Waring et

al. (1998) define NPP as described by equation 1.

NPP = ∆B + losses (1)

where, ∆B quantifies the difference in plant biomass between a specified time interval and

losses represent the litter produced during that interval (e.g. leaves and dead branches).

A variety of methods has been used to estimate NPP over different temporal and spatial

scales. Estimation for large areas has ranged from simple correlation models to complex

ecophysiological models that couple vegetation-atmosphere exchange energy, mass and

momentum (Goetz, 1997). Models differ markedly in approach, complexity, precision,

accuracy and cost, although they invariably constitute simplified representations of reality

(Lucas and Curran, 1999; Lucas et al., 2000). Nevertheless, simulation modelling constitutes

an essential tool for evaluating ecosystem activity on spatial and temporal scales beyond the

limits of direct measurements (Running, 1994). Ryan et al. (1996). These models are

especially important for understanding the functioning of an ecosystem. More specifically,

they can be used to estimate growth rates and predict the effects of management practices or

attacks by insects and pathogens (Landsberg et al., 1991; Landsberg and Gower, 1997). On a

wider scale, models are capable of forecasting the way ecosystems behave under changing

conditions (Mollicone et al., 2002).

FOREST-BGC is a process level ecosystem model developed by Running and Nemani,

(1988), which calculates canopy interception and evaporation, transpiration, photosynthesis,

growth and maintenance respiration, carbon allocation above and below-ground, litter fall,

decomposition, nitrogen mineralization and mortality. The FOREST-BGC model was

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designed to be particularly sensitive to leaf area index (LAI), which is used as the main

independent variable for calculating the major physiological processes. It is driven ultimately

by remote sensing inputs in the framework of a geographic information system (GIS). LAI

was also chosen as the key variable because previous research has indicated it is strongly

related to physiological processes and successfully estimated from satellite images (e.g.

Running (1989) used a LANDSAT TM; Lucas (1995) used a Airborne Thematic Mapper),

which makes it a key variable in the model’s design. Although this means of LAI estimation

is regarded as relatively unproblematic in homogeneous forests (e.g. arboreal forest - see

Running, 1989; Lucas, 1995), weaker LAI estimates probably occur in the more diverse and

heterogeneous Mediterranean ecosystems.

MODIS is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites.

According to Running et al. (2000), the MODIS Gross Primary Production (GPP) and Net

Primary Production (NPP) products were designed to provide an accurate and regular measure

of growth of terrestrial vegetation, making both products a theoretical and practical utility.

The theoretical use is primarily concerned with defining the seasonally dynamic surface CO2

balance for global cycle studies. The MODIS NPP is based on the concept of radiation use

efficiency (RUE) (Heinsch et al., 2003). In practical terms, GPP/NPP products provide

regular measurements of crop yield and forest production as well as other socially significant

products associated with vegetation growth and are therefore of considerable importance in

economic decision making.

Some results from MODIS NPP validation were already achieved in previous studies (e.g.

Turner et al., 2006; Fensholt et al., 2006), for a reasonable range of ecosystems (boreal forest,

hardwood forest, mixed forest, conifer forest, tropical moist forest, arctic tundra, desert

grassland) in North and South America and in Africa. However, none of these studies have

evaluated MODIS NPP in the context of European Ecosystems, especially within the

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Mediterranean region. The research reported in this paper has attempted to redress this

omission with respect to two Portuguese forest ecosystems.

3 Study area

Pinus pinaster Ait. and Eucalyptus globulus constitute the two most important ecosystems in

Portugal in terms of forested area. Both species are ecologically well adapted, despite

Eucalyptus globulus being an exotic tree, and the case study areas are representative of these

ecosystems in Portugal. The Pinus pinaster Ait. forest is very heterogeneous in canopy

density, has experienced only limited human intervention and covers a wide range of

structures, varying widely in terms of number of trees per hectare, average dimensions and

age groups. The Eucalyptus globulus is much more homogenous and has been more

extensively investigated in order to enable greater timber production which is very valuable

for pulp production.

The Pinus pinaster Ait. study area is a 60 km2 rectangle (10 x 6 km) with extensive stands of

this species, located at North of Vila Real (41º 39’’N; 7º 35’’W, Figure 1C) and the

Eucalyptus globulus study area is a 24 km2 rectangle (4 x 6 km) of extensive stands located at

West Vila Real (41º 02’’N; 7º 43’’W, Figure 1D) as depicted in Figure 1. The land cover

vegetation, accessibility and proximity to the surrounding major towns made them suitable for

field data collection as outlined in the following section.

[Insert Figure 1]

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4 Methodology

In this research, a series of methods were used to obtain spatial estimates of NPP and this

paper will compare the result of these methods, namely:

• Per-plot NPP estimates derived using field sampling techniques. Figures obtained

from fieldwork were then used to produce spatial estimates by means of a

geostatistical interpolation algorithm (i.e. kriging) and will be used as reference

values. Estimates derived by other means were then compared with these reference

maps.

• The FOREST-BGC ecosystem simulation model. This was parameterized for

Portuguese conditions thus improving its efficiency and accuracy and was used to

produce spatial estimates of NPP. Remotely sensed estimates of leaf carbon were used

to drive the model to produce NPP maps. The model was designed to be particularly

sensitive to leaf carbon input values (obtained from the LAI images).

• MODIS NPP images. NPP estimates were derived routinely with data products from

the MODIS programme. These were downloaded and pre-processed to enable

comparison with the NPP maps derived using the methods summarized above.

A more detailed explanation of each methodology follows.

4.1 NPP values from Fieldwork measurement

The first method of determining NPP values followed the traditional forest inventory

approach involving field measurements in the sample plots as outlined generically in Figure 2.

Within each of the study areas, they were marked several 500m2 circular sampling plots (31

for Eucalyptus and 32 Pinus), using a systematic sampling system. A series of biophysical

variables were measured within each sampling plot, in order to collect data for NPP

calculation. Thus, the diameter at breast height (dbh) of all trees within each plot was

measured on two occasions using a beam calliper (1997 and 2001, for the Pinus; and 2000

and 2002 for the Eucalyptus). This dendrometric information was then used to estimate the

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increase of all biomass of individual trees (e.g. the arboreal component of ∆B), using biomass

equations previous presented in literature review. For the Pinus, equations were obtained from

Lopes and Aranha (2004) (equations 2, 3 and 4) and for the Eucalyptus, from Fabião (1986)

(equations 5, 6 and 7).

Allometric equations for the Pinus pinaster partial biomass estimation:

Stem: Log (B) = 3.769+2.706 Log (dbh) R2=98.6 RMSE=0.078 (2)

Crown: Log (B) = 2.911+2.130 Log (dbh) R2=88.8 RMSE=0.152 (3)

Root: Log (B) = 1.972+1.221 Log (dbh) R2=93.7 RMSE=0.164 (4)

Where: B represents the respective fraction of biomass (kg) and dbh is the diameter at the

breast height (m)

Root turnover was considered to constitute 75% of new root production in a single year,

according to Hooker et al. (2000).

Allometric equations for the Eucalyptus globulus partial biomass estimation:

Stem: Ln (B) = -2.612+2.589 Ln (dbh) R2=99.2 RMSE=0.014 (5)

Branches: Ln (B) = -6.989+3.157 Ln (dbh) R2=96.4 RMSE=0.103 (6)

Leaves: Ln (B) = -4.902+2.524 Ln (dbh) R2=97.6 RMSE=0.044 (7)

Where: B represents the respective fraction of biomass (kg) and dbh is the diameter at

the breast height (m)

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Root: According to Fabião (1989) the roots biomass is 12% of the total aboveground biomass.

Literature review does not present allometric equations for root’s biomass estimation. Some

authors (Fabião, 1986; Hooker et al., 2000) state that root’s biomass is a fraction of total

(stem, branches and leaves) aboveground biomass. Because root’s biomass assessment

requires the use of destructive techniques and a large amount of soil removal, we also used a

fraction of total biomass in order to calculate root’s biomass.

Aboveground debris was measured using a 40 x 60 cm litter screen placed at random inside

the plot, according to the methodology proposed by Gower et al. (1997). Litter screens were

deployed in January 2001 and litter was collected in May, July, September and December

2001. This enabled the biomass losses referenced in equation 1 to be evaluated. Understory

aboveground biomass was measured, again following a methodology proposed by Gower et

al. (1997). Thus, in June 2002, 1 x 1m plots were randomly located inside each sample area.

All aboveground vegetation tissue was removed and stored in plastic bags in a cool place.

Vegetation was separated into the main species as soon as possible and weighed to yield the

shrubs component of the change in biomass. Figure 2 depicts a synopsis in order to illustrate

field work for data collection. Using these field measurements NPP was estimated in

accordance with equation 1. The in loco NPP estimations, obtained from fieldwork, were later

used in geostatistical analysis (ex. kriging) in order to get continuous data and, this way, to

obtaining a NPP image for whole study area. The spatial resolution of the final image, from

geostatistical analysis (ex. kriging) was 30m, in order to have the same spatial resolution as

the LANDSAT images, which was subsequently used to estimate LAI for each study area, as

ahead presented in point 5. Because LANDSAT ETM+ image were used to calculate LAI

images, a key variable to run FOREST-BGC, they were used to define the spatial resolution

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for estimation (LAI form LANDSAT ETM+ images, NPP from FOREST-BGC and NPP

from MODIS).

[Insert Figure 2]

4.2 NPP estimation by FOREST-BGC

The simulation model used to estimate NPP was FOREST-BGC, which combines daily and

yearly resolution, in conjunction with MTCLIM (Mountain Microclimate Simulation Model).

Running and Coughlan (1988) explain the reasons for this approach. According to them,

hydrologic balances, plant water availability and canopy gas exchange processes are most

conveniently treated on a daily basis because meteorological data is routinely summarized as

daily averages or totals, and these processes react diurnally to environmental conditions.

However, daily calculation of carbon allocation, litter fall and decomposition processes are

not reliable because the minimum routinely measurable increment of these processes typically

occurs on a monthly basis. This model involves over than 70 variables, covering the three

main groups controlling photosynthesis (plant, soil and climate). Around 60% of the variables

are related to the plant characteristics, with a strong physiology component, 30% with the

climate, and only 10% with the soil properties. After the parameterization of the model,

FOREST-BGC was run using LAI data, from LANDSAT ETM+ images, and climate data

estimation from MTCLIM as the input variables.

Climate data required in order to extrapolate daily climate data to the geographic centres of

the study areas starting from data measured with respect to external stations. Because there

are no meteorological stations within the study areas, it was necessary to appeal for MTCLIM

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estimation (Figure 3). The original available climate data was obtained from two

meteorological stations: the one used for the Pinus study area was located in Vila Real and the

other, for the Eucalyptus, was located in Marão. Both meteorological stations are located less

than 40 km far from each study areas. FOREST-BGC was parameterized using a combination

of figures proposed by Running and Coughlan (1988) and others obtained by measurement in

each study area (Lopes, 2005). FOREST-BGC was run, simulating the NPP for 2001, using

the average input figures for each study area with only leaf carbon content changing in order

to reflect changes in LAI.

[Insert Figure 3]

LAI is an important variable in FOREST-BGC and values were obtained from remotely

sensed data allowing NPP to be estimated at temporal and spatial scales that would otherwise

be impractical. Figure 3 describes the procedure. A practical methodology has been developed

allowing the acquisition of LAI images from remotely sensed data. This was a two stage

procedure involving firstly, the adjustment and validation of models that estimated LAI from

vegetation indices (and described in Lopes, 2005), calculated by means of an ETM+ image;

secondly, these were used to create leaf carbon (LC) images as an input to FOREST-BGC.

The available ETM+ Image was acquired on the 15th of September 2001 at 10:02:13 (UTC).

The image was geometrically and radiometrically corrected at the Autonomous University of

Barcelona (UAB) using MiraMon ("WorldWatcher"). The program allows for the geometric

correction of raster images based on the coordinates of ground control points. Twenty five

control points were collected to allow image correction and eleven control points were used

for its validation. A first degree polynomial correction was chosen for the geometric

correction of this image, using the nearest neighbour option for the resampling process. A

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reliable result was achieved with this geometric correction generating an error margin less

than half a pixel size. The root mean square (RMS) error was 12.34m, representing less than

half of the initial pixel size (30m).

The coordinates of the centre of each plot were recorded using the Global Positioning System

(GPS), with an accuracy of 1m. Locating the plots on the geocorrected images; the reflectance

data was extracted for each waveband and used to calculate a series of vegetation indices. The

original data was divided into two groups in order to parameterize and then validate the

models. In both stand areas six randomly selected plots were used for the validation phase.

This way, 25 and 26 plots were used for the parameterization in the Eucaluptus and Pinus

stands, respectively. The most accurate models were identified by reference to the statistics

obtained from parameterized model and the selected equations were finally adjusted using the

global data (adjustment and validation phases of the methodology).

4.3 Estimating NPP using MODIS NPP products

The MODIS NPP algorithm for estimating NPP is described by Running et al. (2004),

Heinsch et al. (2003), and Running et al. (1999) and is outlined in Figure 4. From these

references we can realize that the essence of the core science in the MODIS NPP algorithm is

an application of the radiation conversion efficiency logic to predictions of daily Gross

Primary Production (GPP), using satellite-derived Fraction of Photosynthetically Active

Radiation (FPAR) and independent estimates of Photosynthetically Active Radiation (PAR)

and other surface meteorological fields, and the subsequent estimation of maintenance and

growth respiration terms that are subtracted from GPP to arrive at annual NPP. The

maintenance respiration (MR) and growth respiration (GR) components are derived from

allometric relationships linking daily biomass and annual growth of plant tissues to satellite

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derived estimates of MODIS LAI. These allometric relationships incorporate the same

parameters used in the Biome-BGC ecosystem process model. The parameters relating APAR

to GPP and the parameters relating LAI to MRGR are estimated separately for each unique

vegetation type in the at-launch MODIS landcover product. The GPP parameters are derived

empirically from the output of Biome-BGC simulations performed over a gridded global

domain using multiple years of gridded global daily meteorological observations. The MR

and GR parameters are taken directly from the Biome-BGC ecophysiological parameter lists,

which are organized by plant functional type.

This way, these products use other MODIS images (e.g. LAI MODIS images) and Biome-

BGC to estimate NPP. This production model is similar to FOREST-BGC, since the same

investigation team adjusted it, but the parameterisation scale is not comparable. Biome-BGC

was parameterized for a global scale and is applicable for a wider range of ecosystems, while

FOREST-BGC was parameterized in this research at a local scale and only applied to the

Pinus pinaster and the Eucalyptus globulus ecosystems.

MODIS products can be directly downloaded from the Internet. The reprojection tool was

used to convert the sinusoidal projection (associated with MODIS hdf images) into a UTM

coordinate system. Then the image was resampled into the Gauss Hayford projection (the

system used in Portugal to produce topographic plans, as reported by Caetano (1999)) to

achieve compatibility with other images used in this research. The interface to MODIS

products allows users to specify the type of resampling (e.g. nearest neighbour, bilinear or

cubic convolution), desired output projection (e.g. Mercator, Geographic, UTM, etc.) and

output pixel size. A UTM output image in hdf format using the nearest neighbour resampling

option was downloaded. The MODIS NPP images have a spatial resolution of 1000m.

[Insert Figure 4]

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4.4 Comparison of the NPP images

The NPP figures obtained by the three methods were assembled for each of the study areas. In

the case of the NPP values obtained from field measurements, used as reference values for

further comparisons, some processing of the data was necessary in order to create continuous

NPP images. The NPP point data and the co-ordinates for the centre of each plot were

assembled in Excel and exported to Surfer 32 (version 6.04 – Golden Software). An ordinary

kriging method was applied using a spherical variogram model, with a lag distance of 200m

(corresponding to the distance between each sampling plot). The followed methodology for

kriging is deeply described by Figueira et al. (2007). The most satisfactory LAI models

identified were used to create the image containing the NPP estimates from FOREST-BGC.

LAI was estimated from vegetation indices (VI) (the Normalized Difference Vegetation Index

(NDVI) for the Eucalyptus and the Ratio Vegetation Index (RVI) for the Pinus) using

LANDSAT ETM+ images. These and other vegetation indices reported in this research are

widely described by literature and were summarized by Lopes (2005). The LAI images were

exported as ASCII files, which were used to run FOREST-BGC in order to get NPP estimates

for each pixel in both study areas. This was achieved using a program developed in the

computer laboratory at Universidade deTrás-os-Montes e Alto Douro (UTAD), which allows

FOREST-BGC to be run in a routine fashion for each pixel, using the climate data file

previously exported for the centre of each study area by MTCLIM. The image created had a

pixel size of 30m.

The NPP images obtained from FOREST-BGC and the MODIS were prepared for direct

comparison. Taking into account that they were not entirely covered by sampling plots and

that kriging has a smoothing effect, the images obtained from other tested methodologies

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were overlain with vector objects representing the sampling plots. Thus, NPP estimates could

be obtained for every plot with respect to each methodology, which were then used to create

new images using ordinary kriging, with a spatial resolution of 30m. This enables each

methodology to be compared with the images created on a consistent basis.

One per plot analysis was carried out in order to compare each of the methodologies

estimation of NPP. A graphical analysis was conducted to visualize the differences between

estimations, followed by an ANOVA and a Duncan New Multiple Range test to identify if

average NPP figures were statistically different between all tested methodologies. It is not

important if the estimation between two or more methodologies is different but more

important that both indicate the same level of productivity and thus the same tendencies. The

class tendency should prevail instead of the direct comparison.

Later, the NPP images obtained from the three methodologies were classified into five

categories to allow further comparison as follows:

0 - non-forested areas;

1 - when NPP was less than 5 ton ha-1year-1

2 - production values between 5 and 10 ton ha-1year-1;

3 - NPP between 10 and 15 ton ha-1year-1;

4 - NPP values higher than 15 ton ha-1year-1.

The categories were defined based on the measured NPP values and the heterogeneity of

production it was able to obtain. With these categories it was possible to separate the stands

into categories of production (a very low, a low, an average and a high productive stand). The

measured NPP figures (and later the measured NPP images) were used as reference values.

The images were compared using the Kappa test statistic and the proportion of agreement

(PA). The Kappa coefficient (K) measures pair wise agreement among a set of code values

making categorical judgements, thus correcting for expected chance of agreement (Carletta,

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1996). For each class, the Kappa statistic (Ki), as described by Stehman (1997) can be

obtained from equation 8.

)P*(PP)P*(PPK

.ii.i.

.ii.ii

−−

=i (8)

where :

Pii – represents the proportion of the entire image in which category i agrees with both

Pi. – represents the proportion of the entire image in which class i is the reference image

P.i – represents the proportion of entire image in which class i is the non-reference image

According to Green (1997) and Rossiter (2004), when there is complete agreement between

two maps K=1, and if the Kappa value is zero the two maps are said to be unrelated. The

overall Kappa value (K), defining the overall proportion of area correctly classified, or in

agreement, is defined by equation 9.

)P*(P1)P*(PPK

.ii.

.ii.ii

−−

= (9)

where :

Pii, Pi and P.i were already defined.

Moss (2004) considers that when Kappa is lower than 0.20 the strength of agreement between

both images is poor; between 0.21 and 0.40 fair; between 0.41 and 0.60 moderate; between

0.61 and 0.80 good; higher than 0.81 very good. According to Green (1997), a Kappa statistic

less than 0.40 indicates a low degree of agreement; between 0.40 and 0.75 a fair to good

degree of agreement; and higher than 0.75 a high degree of agreement. The proportion of

agreement (PA) represents the percentage of pixels that were correctly attributed to a specific

class (equation 10). The best approach would have a PA of 100%, which would mean that

both images were identical.

t

c

NN

PA = (10)

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Where Nc represents the number of pixels where class I is in agreement with the reference

map and Nt is the total number of pixels in each image. The cross tabulation of the images

available was conducted using IDRISI, where the output matrix and the Kappa statistic for

each class are generated automatically. Only the overall Kappa was determined for each

comparison.

The histogram of the NPP images obtained from fieldwork measurements, FOREST_BGC

and MODIS were analysed and the average ( x ) (equation 11) and the standard deviation (sx)

(equation 12) of NPP were estimated from each histogram.

∑=

=n

1iiixpx (11)

( )2n

1iiix xxps ∑

=

−= (12)

where:

pi represents the percentage of the pixels in class i

xi is the central value of each NPP class

x is the average NPP (in terms of class)

A χ2 test was applied (see Snedecor and Cochran, 1989) in order to determine if the

histograms obtained from each methodology were significantly different.

5 Results

The results from LAI estimations are presented first followed by the NPP results and

comparisons made to identify potential uses for each approach. As discussed earlier LAI is a

key input variable for the FOREST-BGC. LAI reflects the amount of biomass capable of

photosynthesis, thus with direct impact on NPP. If LAI is accurately estimated from remotely

sensed data this means the FOREST-BGC, or other models which use LAI as input data, can

be used for large regions, which will drastically change its range and potential of application.

- 19 -

5.1 Eucalyptus LAI estimation from ETM+ images

Table 1 summarizes the main results obtained from undertaking a correlation analysis

between the vegetation indices and the LAI. Apart from the NIR, the strongest correlation was

achieved between the Eucalyptus LAI and the reflectance recorded in each of the individual

satellite wavebands. The vegetation indices that produced the highest correlation were the

NDI(MIR2) and NDVI. NDVI has been widely used to estimate LAI (e.g. Nemani and

Running, 1989; Bouman, 1992; Lucas, 1995; and Franklin et al., 1997) primarily due to its

sensitivity to changes in the recorded NIR reflectance with increases in LAI (Clevers, 1988).

However, Fassnacht et al. (1997) indicate the importance of the relationship between LAI and

reflectance recorded in the MIR wavebands. This supports the high correlation found between

LAI and the NDI(MIR2), since the index combines reflectance data recorded in the MIR and

NIR wavebands.

[Insert Table 1]

On the basis of the results contained in Table 1, it is possible to identify the independent

variables (which means, the best vegetation indices) that are most suitable for adjusting the

LAI prediction model. The most accurate results were obtained via the direct estimation of

LAI using NDVI reflectance as the prediction variable. The selected model used to estimate

LAI for the Eucalyptus stand was described by equation 13.

2

2

1.678NDVI1.325NDVI0.344NDVILAI

+−= (13)

R2=0.838; R2ajust.= 0.826; syx=0.373 m2 m-2; syx (%)=21.9

- 20 -

5.2 Pinus LAI estimation from ETM+ images

The strength of the relationships between the remotely sensed datasets with the main

dendrometric stand parameters for Pinus pinaster were summarized in a general correlation

matrix and the results with respect to LAI are highlighted in Table 1. The correlation values

are generally lower than those found for the Eucalyptus stand. Pinus is a more difficult

species to study from remotely sensed imagery due to (i) differences in management practices

with less human intervention applied in comparison to Eucalyptus and (ii) biophysical

differences, such as its sparse crown and, consequently, high background reflectance. Only

MIR1 presents a statistically significant correlation with LAI. In terms of vegetation indices

the most accurate results were achieved with the TVI1 and RVI vegetation indices. The

results obtained were similar to those presented by Lopes and Aranha (2000). The optimal

results were obtained via the direct estimation of LAI using NDI (MIR1) reflectance as the

prediction variable (equation 14)

LAI=1.840+8.605NDIMIR1-61.455NDIMIR12+163.727NDIMIR13 (14)

R2=0.725 ; R2ajust.= 0.696; syx=0.555 m2m-2; syx (%)=18.5

5.3 NPP for the Eucalyptus area

The NPP images from each methodology are presented in Figure 5.

FOREST-BGC describes a considerable heterogeneity over the area, which implies there is

considerable mixing of production classes across the entire area. In contrast MODIS products

give a more homogeneous impression of this region. Figure 5 summarizes the per plot

- 21 -

comparison between measured NPP and the estimations obtained from MODIS and FOREST-

BGC. The most relevant conclusion is that for both species MODIS tends to simplify reality

and it does not seem able to detect the observed heterogeneity of NPP figures.

[Insert Figure 5]

However, this previous analysis does not give statistical information in order to detect if NPP

estimations are statistically different between them. An ANOVA was implemented and also a

Duncan New Multiple Range test in order to detect if average NPP figures differ from the

tested methodologies.

[Insert Table 2]

[Insert Table 3]

From the analysis of Table 2 and Table 3 it is observed that the differences between NPP

estimations from each of the tested methodologies are not statistically different. Assuming the

measured values as the comparative terms, generally the MODIS products tend to slightly

overestimate the NPP in the Eucalyptus study area while the FOREST-BGC tends to

underestimate it.

As expected the kriging gave a smoother image due to the spatial interpolation. In order to be

able to identify the closeness of the images to the fieldwork measurements, the NPP estimates

derived from each methodology were determined for each sampling plot and kriging was

- 22 -

applied using these values (Figure 6 A1/A2/A3). This allowed images to be produced that

were directly comparable with those previously classified, from which kappa values were

estimated.

From the comparison of the Eucalyptus NPP figures obtained from fieldwork (Figure 6A),

which only takes into account of the sampling plots, from the FOREST-BGC model (Figure

6B) and the MODIS estimates (Figure 6D), it can be observed that there are no areas with an

NPP lower than 5 ton ha-1year-1, and almost the whole Eucalyptus stand presents NPP figures

between 10 and 15 ton ha-1year-1. Again, the MODIS NPP tends to be uniform, whereas the

NDVI NPP image produced the most heterogeneous outcome. Images from Figure 6

A1/A2/A3 were then classified in NPP classes for further comparisons (Figure 6 B1/B2/B3).

[Insert Figure 6]

Taking the two continuous forested areas into consideration, one on the left and the other on

the right of the urban area, the least productive areas are mainly located in the centre of the

left continuous forested area (which coincided with the highest altitude – point X in Figure

6A1/A2/A3)). This is an area where the constraints for the development of the Eucalyptus are

higher due to there being little protection from the wind, thinner soils and a paucity of soil

water. The most productive area is located towards the right lower corner of the study area

and corresponds not only to the oldest stands but also to the base of the small hill where the

stand is located.

Table 4A demonstrates a higher level of agreement between both approaches. However, there

are some areas where the FOREST-BGC tends to underestimate the measured values (mainly

in class 4: >15 ton ha-1 year-1) or to overestimate NPP (mainly in class 3: 10- 15 ton ha-1 year-

- 23 -

1). The overall rate of agreement between both images is 82.6% which indicates that the

FOREST-BGC was able to simulate the production of these stands with a high degree of

accuracy.

[Insert Table 4]

The comparison of the NPP measured from fieldwork with the MODIS NPP estimations

(Table 4B), and excluding the non-forested areas (stratum 0), resulted in a rate of agreement

between both approaches of 54%. The area of overestimation by the MODIS images is similar

in extent to the underestimated area. Once again, the main conclusion to be drawn is that the

MODIS image is not able to detect the least and most productive areas. The MODIS image

tends to simplify reality and homogenize the productive potential of these stands. This

conclusion is not surprising given that the MODIS products are designed for use on a global

scale. The same conclusion is apparent from the analysis of Figure 7. All pixels in the MODIS

image for the Eucalyptus stand fell within the average NPP class (10 to 15 ton ha-1 year-1).

[Insert Figure 7]

Statistical testing of the rate of agreement of these compared images (Table 5) indicates that

FOREST-BGC results were closest to the measured figures. The rate of agreement between

these images is fair to good (Green, 1997 and Moss, 2004). This suggests that FOREST-BGC,

having been parameterized and validated for Portuguese conditions, can reliably simulate

reality. Less accurate results were achieved when NPP was estimated from MODIS, which

tends to simplify reality and obscure details that are present. Thus, Figure 6A presents the

- 24 -

closest representation of the real NPP. In fact, the correlation coefficient between the

FOREST-BGC NPP and the measured NPP was 0.88, similar to the values found by Zhou et

al. (2007) (0.84) and Olofsson et al. (2007) (0.82).

[Insert Table 5]

5.4 NPP for the Pinus area

Figure 8 presents a similar comparison of the Pinus NPP images obtained from each

methodology. Once again, in order to identify the images that were closest to those derived

from field measurements, the NPP estimates from the other methodologies were obtained for

each sampling plot. The comparison between measured NPP and those obtained from the

MODIS products and from the FOREST-BGC can be observed in Figure 5. It shows that

similar result to those obtained for the Eucalyptus, namely that the MODIS tends to simplify

reality in the Pinus study area. Anyway, the analysis of Table 2 and Table 3 allowed

concluding that the differences were not statistically significant. Once again, a deeply

artificial analysis of the results, it can be observed that generally, and in an opposite way of

what happened to the Eucalyptus, the MODIS NPP products tend to underestimate the Pinus

NPP and the FOREST-BGC tends to slightly overestimate it.

Again, kriging was used in each case and the resultant images (Figure 8A1/A2/A3) are

directly comparable. The images in Figure 8A1/A2/A3, representing the real values of NPP,

were reclassified as Figure 8B1/B2/B3 in order to apply the Kappa test and to assess the

pattern of agreement and disagreement between images. Comparison of the pairs of images in

Figure 8A1/A2/A3 and Figure 8B1/B2/B3 reveals a high degree of similarity between NPP

obtained from fieldwork measurements and the estimates from FOREST-BGC (Figure 8A1

- 25 -

and Figure 8B1) and in the case of the classified images Figure 8A2 and Figure 8B2). There

was an agreement in the location of the stands with the highest and the lowest productivity,

even though in the latter case FOREST-BGC tends to overestimate the figures. The MODIS

image (Figure 8A3 and Figure 8B3) fails to identify the least productive areas and

homogenizes the production estimates.

[Figure 8]

Table 5 summarizes the results of applying the Kappa test for the Pinus pinaster area.

Table 6A represents the comparison between NPP estimations from the FOREST-BGC and

the figures from field measurement. The rate of agreement between the images is 91% of the

total pixels. However, there is some tendency for the FOREST-BGC model to overestimate

and to underestimate the measured values almost in a similar proportion. FOREST-BGC

appears most successful at estimating production classes 3 (10 to 15 ton ha-1 year-1) and 4

(>15 ton ha-1 year-1). In Table 6B it is apparent that the MODIS image fails to locate the least

productive areas. In class 2 MODIS presents a very low rate of agreement with a similar rate

for over- and underestimating NPP. This image tends to incorporate those extreme values in

the average class of NPP. Nevertheless, the MODIS image revealed a good rate of

performance in locating the average classes (class 3: 10 to 15 ton ha-1 year-1). Examining

Table 6C we see once again that the MODIS image is capable of locating areas within the

average class of production (between 10 and 25 ton ha-1 year-1). However, these images also

tend to overestimate production, mainly in class 2 (5 to 10 ton ha-1 year-1) and were not

completely able to correctly identify the most productive areas.

- 26 -

[Insert Table 6]

From the analysis of Figure 7 it can be concluded that Pinus pinaster Ait. ecosystem has

different behaviour to Eucalyptus globulus ecosystem. The fieldwork measurements and

FOREST-BGC methodologies present similar behaviour. However, once again MODIS tends

to be distinctive. FOREST-BGC and fieldwork averages are closer but FOREST-BGC and

mainly MODIS tend to simplify reality so the standard deviation is smaller than observed in

fieldwork. Although, in all situations results from χ2 test showed that the structure of the

distributions is always different, even when apparently similar. With 2 degrees of freedom

and a probability of error less than 0.05, the tabled χ2 is 5.99 (Snedecor and Cochran, 1989).

For the Pinus pinaster Ait. ecosystem, the χ2 between the Measured and the FOREST-BGC

NPP maps was 91.9 and between the Measured and the MODIS NPP maps was 47887.5. For

the Eucalyptus globulus ecosystem, the χ2 between the Measured and the FOREST-BGC NPP

maps was 410.3 and between the Measured and the MODIS NPP maps was 45455.9. In all

cases the calculated χ2 is higher than the tabled one, thus the structures are not similar, et all.

Once again, the correlation coefficient between the FOREST-BGC NPP and the measured

NPP was the strongest (0.87), similar to the values found for the Eucalyptus and similar to the

ones presented by Zhou et al. (2007) and Olofsson et al. (2007).

At the local scale, the comparison between the measured NPP and the estimates from the

other methodologies allowed some practical conclusions to be made. Firstly, it is possible to

observe that the FOREST-BGC is the most practical and accurate methodology for obtaining

NPP figures for the Pinus stand. The MODIS products, on the other hand, provided the least

accurate results with an overall Kappa of 0.68. Even if the general pattern of NPP evolution is

found to be in agreement with the measured figures, the MODIS images were not able to

- 27 -

detect the least and the most productive areas. In addition, the average NPP figures from

MODIS were similar to those obtained from fieldwork measurements. The obtained

conclusions are intimately related with the spatial resolution of each data source and the scale

of analysis. It is expected that, when analysing the precision of NPP at a local scale, the

methodologies which present information at a more detailed spatial resolution better describe

the heterogeneity of the variable. This detail is not possible when dealing with a product with

a spatial resolution of 1000m designed for a regional and global scale. Therefore, despite of

all the limitations already noted, MODIS images can be a powerful source of information if

time and money are limited and if there is no need to detect extremes of productivity and/or if

the focus represents the average value of NPP and detailed values are not relevant.

6 Conclusions and final comments

This study has confirmed the importance of scale when investigating NPP, since some of the

phenomena operate at a very small scale (e.g. photosynthesis), while others occur at a global

scale (e.g. climate change). In this research the scale is small (usually referred to as local) and

the study focused exclusively on forested areas. Nevertheless, even though this study was

conducted on a local scale, the relevance of the study and some of the conclusions drawn are

extremely valuable for addressing similar research questions at a global scale. The research

was conducted at a small scale and on these specific forest ecosystems because of the

importance of forests in various types of ecological study (e.g. climate change, urban planning

and stability of ecosystems) and because of the general sparse distribution and heterogeneity

of Portuguese forests. Although Portugal is a relatively small country and its climate and soil

conditions are very favourable to forest growth, there are a number of important lessons to be

- 28 -

learned from local studies of the type reported here that can help to provide a new perspective

on global scale environmental issues.

The extent and intensity of fieldwork required for generating reliable NPP figures using the

traditional approach (forest inventory + allometric equations) is considerable. It takes a

minimum of one year, but preferably three years of fieldwork measurement and more still is

preferable to avoid atypical years. The effort and the costs involved in field data collection are

very high, even when studies are carried out at a local scale, and are virtually untenable on a

regional or global scale. Furthermore, even if such detailed fieldwork was possible at these

scales, the results are only likely to be able to validate the general overview from relatively

few sampling plots. Additionally, allometric equations required for this approach are only

available for a few numbers of species, particularly the most important ones (economically or

in terms of area).

Once again the large amount of work involved in sampling trees needed for data gathering

limits its application. In this particular case allometric equations were already adjusted for the

Eucalyptus stands but not for the Pinus. While not described in this paper the effort required

to adjust them is limiting this method of NPP estimation. It is also important to note that the

other major difficulty of this approach is the quantification of root biomass growth as it is

extremely difficult to collect accurate data for the construction of the allometric models for its

estimation. Even if allometric models for other components of the tree exist for specific

species, often the root component is unavailable. Although, and despite all these difficulties,

the field data collected in this study was invaluable and constituted an indispensable reference

against which to compare estimates obtained via other methodologies.

For both species the methodology that provided the closest results to fieldwork measurements

was the FOREST-BGC. This model, known to be particularly suitable for use at a local scale,

since it is a stand-level ecosystem process model, was successfully adapted to this study Other

- 29 -

recent studies, as well as the present research, have demonstrated that the model can be

extended to regional or global scales by using input variables (e.g. LAI) obtained from

remotely sensed data. Although, as noted earlier, the estimation of LAI from remotely sensed

data may be more problematic for typical Mediterranean ecosystems (e.g. Quercus suber), as

trees are sparsely distributed and the ecosystems are much more heterogeneous.

Therefore, ecophysiological models can, at present, be used for example to simulate climate

change impact on ecosystem production. In this study the estimation of the Pinus LAI was

more difficult than Eucalyptus, because this species has non-flat leaves but foliar clumps.

Additionally Pinus tends to present sparse crowns allowing the understory to make a greater

potential contribution to the reflectance recorded by the satellite. Further studies should be

undertaken to gain a better understanding of these processes and produce more accurate LAI

models for these stands.

The most important conclusion with regard to the MODIS NPP products is their

simplification of reality and their tendency to obscure extreme (high and low) values of NPP.

Nevertheless, the fact that the average values obtained from the MODIS image are very

similar to those obtained from fieldwork measurement is encouraging, even taking into

account the difference of scale between the two approaches (the first designed for a global

application and the second implemented on a local scale). Further research should be carried

out to extend the approach to other forest ecosystems (e.g. Quercus forest and to areas of

shrubland). These ecosystems are also very important and ecologically relevant landcover

types in Portugal and other Mediterranean areas.

Other physiological models could be parameterized and validated for these ecosystems which

could better describe their growth rates. There are a number of ecosystems of varying

complexity and these other physiological models could produce different results from the

ones obtained from the FOREST-BGC.

- 30 -

Finally it would be interesting to analyze of the performance of the MODIS algorithms when

local information is used, such as LAI images, instead of the global MODIS products. The

methodology would be the same but the resolution of the input data would be much higher

which may have a positive impact on the resolution of the output data.

Despite the drawbacks of using the MODIS products for local scale analysis identified by this

research, there is a wider range of situations where their characteristics may make them a

powerful source of information. Without the information derived from remote sensing it

would be impractical to estimate NPP at regional or global scales either directly or indirectly.

7 Acknowledgements

Authors would like to thank Fundação para a Ciência e a Tecnologia and Fundação Calouste

Gulbenkian for their financial support. Authors also thank to Instituto Geográfico do Exército

and to Instituto Geográfico Português for some free data. And finally, authors also thank

CEGE for their financial support for fieldwork validation data.

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Table 1. Correlation coefficient between the LAI and reflectance from each individual band

and the best-correlated vegetation indices for the Eucalyptus and the Pinus stands.

Band/VI Eucalyptus Pinus

B (TM1) -0.702** -0.087 ns

G (TM2) -0.835** -0.238 ns

R (TM3) -0.777** -0.245 ns

NIR (TM4) -0.041ns 0.137 ns

MIR1 (TM5) -0.804** -0.31 ns 9

MIR2 (TM7) -0.797** -0.164 ns

NDI(MIR1) 0.673** 0.332*

NDI(MIR2) 0.718** 0.184 ns

NDTI 0.664** -0.093 ns

NDVI 0.697** 0.278 ns

RVI 0.646** 0.323 ns

VIT1 0.669** 0.312 ns

** - Correlation is significant at the 0.01 level

* - Correlation is significant at the 0.05 level

ns – Not significant at the 0.05 level

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Table 2. ANOVA test for the NPP estimations obtained for each tested methodology.

Eucalyptus

Source df Sum of Squares Mean Square F-Value P-Value

Method 2 15.007 7.503 0.443 0.644

Residual 90 1525.888 16.954

Pinus

Method 2 8.042 4.021 0.228 0.797

Residual 93 1640.119 17.636

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Table 3. Duncan New Multiple Range test for the NPP averages.

Eucalyptus

Methodology Count Mean

FOREST-BGC 31 12.44 a

Measured 31 13.25 a

MODIS 31 13.33 a

Pinus

MODIS 32 13.81 a

Measured 32 14.21 a

FOREST-BGC 32 14.52 a

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Table 4. Cross-classification of the Eucalyptus measured NPP and the FOREST-BGC NPP

images (A); the Eucalyptus measured NPP and the MODIS NPP images (B); the

Eucalyptus FOREST-BGC NPP and MODIS NPP the images (C)

(A) Measured NPP versus FOREST-BGC NPP

MEASURED

0 1 2 3 4 total

0 - 0 0 0 0 0

1 - 0 409 10 0 419

2 - 0 3573 200 0 3773

C

F O R E S T BG

3 - 0 31 1296 60 1387

4 - 0 0 495 857 1352

total - 0 4013 2001 917 6931

(B) Measured NPP versus MODIS NPP

MEASURED

0 1 2 3 4 total

0 - 0 0 0 0 0

1 - 0 0 0 0 0

M O D I S 2 - 0 0 0 0 0

3 - 0 1587 3765 1579 6931

4 - 0 0 0 0 0

total - 0 1587 3765 1579 6931

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(C) FOREST-BGC NPP versus MODIS NPP

FOREST-BGC

0 1 2 3 4 total

0 - 0 0 0 0 0

1 - 0 0 0 0 0

2 - 0 0 0 0 284

3 - 943 1050 2384 2554 35914

4 - 0 0 0 0 5138

total - 943 1040 2384 2554 6931

M O D I S

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Table 5. Overall results from the cross-tabulation of the Eucalyptus and Pinus NPP images

Eucalyptus Pinus

General

Kappa

Proportion of

agreement

General

Kappa

Proportion of

agreement

Measured/FOREST-BGC 0.71 0.61 0.83 0.74

Measured/MODIS 0.63 0.54 0.68 0.51

FOREST-BGC/MODIS 0.51 0.34 0.68 0.51

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Table 6. Cross-classification of the Pinus measured NPP and the FOREST-BGC NPP

images (A); the Pinus measured NPP and the MODIS NPP images (B); the Pinus

FOREST-BGC NPP and MODIS NPP the images (C)

(A) Measured NPP versus FOREST-BGC NPP

MEASURED

0 1 2 3 4 total

0 - 0 0 0 0 0

1 - 0 0 0 0 0

2 - 0 3998 626 0 4624

3 - 0 1146 21472 1223 23841

4 - 0 0 896 11975 12871

total - 0 5144 22994 13198 41336

F O R E S T BCG

(B) Measured NPP versus MODIS NPP

MEASURED

0 1 2 3 4 total

0 - 0 0 0 0 0

1 - 0 0 0 0 0

2 - 0 210 36 54 300

3 - 0 1224 31199 2080 34503

4 - 0 105 943 5485 6533

total - 0 1539 32178 7619 41336

M O D I S

(C) FOREST-BGC NPP versus MODIS NPP

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FOREST-BGC

0 1 2 3 4 total

0 - 0 0 0 0 0

1 - 0 0 0 0 0

2 - 0 212 17 55 284

3 - 0 828 33449 1637 35914

4 - 0 0 969 4169 5138

total - 0 1040 34435 5861 41336

M O D I S

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Figure captions

Figure 1. Study area location, showing the county halls and the major cities which they

contain. (A – General overview of Portugal; B – North of Portugal; C – Pinus study area

location; D – Eucalyptus study area location)

Figure 2. A general overview of the traditional methodological approach.

Figure 3. Methodology used to estimate NPP by FOREST-BGC (Orange boxes are

describing the steps followed to run the FOREST-BGC; Green boxes are describing the

methodology followed for the LAI estimation from remotely sensed data).

Figure 4. Methodology used to obtain NPP estimations from MODIS products. (*) Figure

inside the box was obtained from Heinsch et al. (2003).

Figure 5. Per plot comparisons between measured NPP and MODIS and FOREST-BGC

estimations, for Pinus and Eucalyptus.

Figure 6. Eucalyptus NPP estimations (A) and classes of Eucalyptus NPP estimations

(B)(ton ha-1year-1) from field measurements for a short area (A1/B1), FOREST-BGC

(A2/B2), and MODIS (A3/B3) (Note: the left hand figure in each entry in the legend

represents the NPP class from the first image and the right hand figure signifies the class of

the second image).

Figure 7. Histograms of Eucalyptus and Pinus NPP class images, obtained from fieldwork

data, FOREST-BGC estimations and MODIS NPP products, NPP average and NPP standard

deviation.

Figure 8. Pinus NPP estimations (A) and classes Pinus NPP estimations (B) (ton ha-1year-1)

from field measurements for the entire study area (A1/B1), FOREST-BGC estimations

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(A2/B2), and the MODIS (A3/B3) (Note: the left hand figure in each entry in the legend

represents the NPP class from the first image and the right hand figure signifies the class of

the second image).

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