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Combination of structural and compositional factors for describing forest types using national forest inventory data. Monitoring and indicators of forest biodiversity in Europe: from ideas to operationality

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For the first time in Portugal, simple variables describing the vertical structure and the composition of forests on the Portuguese mainland were included in the 2258 sample plots of the National Forest Inventory (DGF 2001). The vertical forest structure was assessed by percentage cover of seven height classes and the composition of the different layers was described using plant species, or groups of plant species, easily identifiable in the field. Cluster analysis, in particular K-means statistics, was performed using combinations of vertical structure and compositional data, resulting in ten main natural groups or forest types: 1) Quercus pyrenaica forests; 2) Other deciduous oak forests; 3) Arbutus unedo forests; 4) Cistus shrubs; 5) Cytisus shrubs; 6) Acacia forests; 7) Quercus suber forests; 8) Pinus pinaster forests; 9) Eucalyptus forests; and 10) Other forests. The last four groups were further subdivided according to the vertical structure resulting in twenty final forest types. The geographical distribution of these forests types and the implications for biodiversity and other forest issues are presented and discussed.
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Marco Marchetti (ed.)
Monitoring and Indicators of Forest Biodiversity in Europe – From Ideas to Operationality
EFI Proceedings No. 51, 2004
Combination of Structural and Compositional Factors
for Describing Forest Types Using National Forest
Inventory Data
F. Rego1, P. Godinho-Ferreira2, J. S. Uva3 and J. Cunha 4
1CEABN – Centro de Ecologia Aplicada Baeta Neves, Instituto Superior de Agronomia,
Lisbon, Portugal
2EFN – Estação Florestal Nacional, Instituto Nacional de Investigação Agrária e das Pescas,
Lisbon, Portugal
3DGF – Direcção Geral das Florestas, Lisbon, Portugal
4FORESTIS – Associação Florestal de Portugal, Porto, Portugal
Abstract
For the first time in Portugal, simple variables describing the vertical structure and the
composition of forests on the Portuguese mainland were included in the 2258 sample plots of
the National Forest Inventory (DGF 2001). The vertical forest structure was assessed by
percentage cover of seven height classes and the composition of the different layers was
described using plant species, or groups of plant species, easily identifiable in the field.
Cluster analysis, in particular K-means statistics, was performed using combinations of
vertical structure and compositional data, resulting in ten main natural groups or forest types:
1) Quercus pyrenaica forests; 2) Other deciduous oak forests; 3) Arbutus unedo forests; 4)
Cistus shrubs; 5) Cytisus shrubs; 6) Acacia forests; 7) Quercus suber forests; 8) Pinus
pinaster forests; 9) Eucalyptus forests; and 10) Other forests. The last four groups were
further subdivided according to the vertical structure resulting in twenty final forest types.
The geographical distribution of these forests types and the implications for biodiversity and
other forest issues are presented and discussed.
Keywords: national forest inventory, composition, vertical structure, forest types,
biodiversity.
154 Monitoring and Indicators of Forest Biodiversity in Europe – From Ideas to Operationality
1. Introduction
Biological diversity in forests depends on their composition and vertical structure
(Puumalainen 2001) and efforts have been made all over the world to include them in the
criteria and indicators of forest biodiversity (Larson et al. 2001; Stork et al. 1997). The top to
bottom description of forest composition, from tall to smaller trees, shrubs, grasses and other
plants characterise its vertical structure. Among other features, a forest with a more diverse
composition and vertical structure provides more habitats for animals where they can find
food and cover, maintains moderate temperatures by reducing convective and radiative heat
loss, thus providing climatic buffered areas, and has greater aesthetic and recreational value.
The aim of the traditional National Forest Inventory (NFI) was to describe the main
features of Portuguese forests in terms of size, condition and change. But it was more
concerned with their productive features than an extensive description of the forests. The last
revision of the NFI (DGF 2001) aimed to go further, building on the information on
productivity to describe the forests more completely. Despite adopting a simple key based on
the cover of the dominant forest tree species it assessed, for the first time in Portugal, the
cover of other plant species according to height classes. This information provided a useful
tool to better understand our forests in their composition and vertical structure. Adopting a
simple methodology, we obtained a new key of Portuguese forest types based on the vertical
distribution of the forest plant species and a “tri-dimensional” view of our forest areas.
2. Methodology
To describe forest composition and vertical structure together, and to better understand the
combined effect on the diversity of vertical forest structure, the percentage cover of forest
species according to seven height classes are used as variables. These height classes were
defined from ground level to 0.5 m, from 0.5 m to 1.0 m, from 1.0 m to 2.0 m, from 2.0 m to
4.0 m, from 4.0 m to 8.0 m, from 8.0 m to 16.0 m, and over 16.0 metres. The composition of
the different layers was described by plant species, or groups of plant species, easily
identifiable by the NFI team responsible for data collection on the ground.
The measurements were assessed from 2258 sample plots randomly distributed through the
forest area and identified by their geographical coordinates. In every sample plot the
percentage cover of the first three dominant plant species was assessed in decreasing order of
importance (Figure1). The percentage cover of plant species, or groups of plant species, was
assembled in a table in which each species according to height class was considered as
pseudo-species. The connection established between the plant species and its respective layer,
called a pseudo-species, allowed for an estimate of the proportion of cover that each plant
species contributes to a specific height class.
Applying a K-means analysis (SPSS 2003) to the matrix of the pseudo-species percentage
cover, natural groups based on the forest composition and vertical structure similarities were
obtained. Following a sequence of attempts to determine the number of natural groups that better
distributed the 2258 sample plots, ten groups were defined that effectively characterised
Portuguese forests, with each natural group corresponding to a particular forest type. The result
of the K-means analysis was the average percentage cover of plant species according to height
class for each natural group, thus outlining the profiles of resulting forest types.
Once the number and the description of the forest types were determined, they were
mapped by the Thiessen polygons method (Soares 2000) using the geographical coordinates
of the 2258 sample plots.
Combination of Structural and Compositional Factors for Describing Forest Types Using... 155
3. Results and discussion
According to the NFI (DGF 2001), of the total area of the Portuguese mainland (8 879 862
ha), forest constitutes the main land use, occupying 38% of the territory, followed by 33% of
agriculture land and 23% of uncultivated land including shrubs, natural meadows and
abandoned land. The traditional forest area key is described as: maritime pine (31%), cork
oak (22%), eucalyptus (21%), holm oak (14%), other oaks (4%), umbrella pine (3%),
chestnut trees (1%), other broadleaves (3%) and other conifers (1%).
Based on the combination of composition and vertical structure, ten major forest types
were identified: Quercus pyrenaica forest (0.4%), other oak forests (0.2%), Arbutus unedo
forest (0.4%), Cistus shrubs (6.6%), Cytisus shrubs (4.5%), Acacia forest (0.8%), Quercus
suber forest (8.5%), Pinus pinaster forest (29.5%), Eucalyptus forest (18.4%) and other
forests (30.7%). Although the occupation areas of both keys are quite different, the forest
types obtained by the K-means analysis give us complementary information.
Quercus pyrenaica forests
Quercus pyrenaica forests, distributed mainly in the north and the interior centre of Portugal,
represent 0.4% of the Portuguese mainland forest area. They are very dense forests that
occupy all the height classes. The Quercus pyrenaica has a very significant percentage of
cover from the ground to 16 metres. The chestnut trees form, together with the Quercus
pyrenaica some mixed stands where Cytisus spp,Erica spp,Lavandula spp and other groups
of species occupy the understory up to 2 metres (Table 1).
Figure 1. Percentage cover of the first three dominant plant species.
156 Monitoring and Indicators of Forest Biodiversity in Europe – From Ideas to Operationality
Other oak forests
The other oak forests are distributed in three main small areas in the north, the central north
and the centre of the Portuguese mainland. They represent only 0.2% of Portuguese forest
area and are mainly dominated by Quercus faginea. It is a very dense forest of oak from the
ground to 16 metres. In some stands, holm oak is present from 1 to 8 metres. Rubus spp, a
typical climber, is abundant in these forests. The understory is also composed of Cytisus spp
and other plant species (Table 1).
Arbutus unedo forests
These forests are distributed in small areas in the interior north and centre of Portugal but
toward the south, these formations are larger. This forest type represents 0.4% of the
Portuguese forest area. Its vertical structure shows that these formations are lower forests
with a dense cover of Arbutus unedo from the ground to 4 metres, sometimes reaching 8
metres. The understory is also defined by significant amounts of Ulex spp,Erica spp,Cistus
ladanifer, and other plants such as Quercus coccifera,Juniperus spp and Pterospartum
tridentatum. The overstory is mainly composed of cork oak formations, sometimes forming
mixed stands with maritime pine (Table 1).
Cistus shrubs
The Cistus shrubs are distributed in the interior north and centre of the Portuguese mainland
but it is more abundant in the south, increasing from the coast to the interior. They represent
6.6% of the forest area. Although it is considered a forest land use class, it is important to
notice that the most important cover does not exceed the 2–4 metres height class. It is
composed mainly of Cistus ladanifer,Lavandula spp and other understory plants. The
overstory is characterised by open formations of “montados” of cork oak and holm oak and
this is the reason why they are considered forest areas. From our point of view, this forest
type should be considered a shrub formation (Table 1).
Cytisus shrubs
This forest type is mainly distributed in the interior north and centre of the country and a
significant area of Cytisus shrubs exists on the west coast of Portugal. It represents 4.5 % of the
forest area. But, like Cistus shrubs, the most important cover does not exceed the 2-4 metres
height class and is composed mainly by Cytisus spp,Cistus ladanifer, and other understory
plants. The overstory is characterised by very open and tall formations of maritime pine and
Quercus pyrenaica. We could also consider this forest type a shrub formation (Table 1).
Acacia forests
Even if we can find some significant acacia stands on the coast near Lisboa and the southwest
coast of Portugal, this forest type is mostly distributed along the central and northwestern
coasts of Portugal, in some regions reaching the interior north and centre of the country.
Acacia forests represent 0.8% of the forest area. This forest type shows a dense cover from
Combination of Structural and Compositional Factors for Describing Forest Types Using... 157
Table 1. Average percentage cover of plant species, or groups of plant species, according to height class (C1 to C7) of forest types: Quercus pyrenaica forests, other
oak forests, Arbutus unedo forests, Cistus shrubs, Cytisus shrubs and Acacia forests.
Forest type Quercus pyrenaica Other oaks forests Arbutus unedo forests Cistus shrubs Cytisus shrubs Acacia forests
Forests
Plant species
composition
according to
height class
C1 (>16m) 3.3 0.3 0.5 2.8 1.5 0.8 1.8
C2 (8–16m) 10.8 0.9 17.5 3.4 1.5 3.0 1.3 2.6 2.2 1.0 2.0 4.8 4.7 2.1 9.4 0.3
C3 (4–8m) 26.9 1.4 47.5 1.5 8.3 1.8 1.0 8.5 6.5 4.2 3.4 2.5 0.6 6.1 6.6 5.6 2.4 18.5 0.3
C4 (2–4m) 36.7 2.8 0.1 46.3 1.5 6.3 2.0 7.5 2.0 0.3 0.3 0.5 3.9 5.7 4.5 4.1 3.0 2.8 6.4 5.8 2.2 3.5 1.8 25.9 0.6
C5 (1–2m) 42.2 3.9 0.7 0.7 37.5 0.5 8.8 5.0 4.0 26.3 3.5 1.3 1.2 3.2 1.4 2.4 17.1 0.1 5.2 2.7 2.2 22.0 4.9 1.5 2.9 0.6 26.5 0.4
C6 (0.5–1m) 34.7 3.9 2.1 2.3 30.0 15.0 2.5 13.8 2.5 29.0 7.0 2.5 1.5 10.5 0.4 1.5 30.8 1.9 8.2 2.1 1.9 38.1 7.7 1.2 0.6 26.5 3.1
C7 (0–0.5m) 35.6 4.4 4.1 6.7 27.5 16.3 3.8 21.3 30.5 6.0 4.0 4.5 8.0 0.5 1.4 37.3 5.1 12.5 1.8 1.8 42.8 11.2 1.8 26.0 9.5
Quercus pyrenaica
Castanea sativa
Cytisus spp
Others
Quercus spp
Quercus rotundifolia
Rubus spp
Cytisus spp
Others
Quercus suber
Arbutus unedo
Ulex spp
Erica spp
Cistus ladanifer
Others
Quercus suber
Quercus rotundifolia
Cistus ladanifer
Lavandula spp
Others
Pinus pinaster
Quercus pyrenaica
Cytisus spp
Others
Pinus pinaster
Pinus pinea
Eucalyptus spp
Acacia spp
Others
158 Monitoring and Indicators of Forest Biodiversity in Europe – From Ideas to Operationality
the ground level to 16 metres and, as an invader species, it spreads in open and tall maritime
pine, umbrella pine, and eucalyptus stands (Table 1).
The forest types Quercus suber forests, Pinus pinaster forests, Eucalyptus forests and
Other forests reveal a diverse vertical structure in their geographical distribution. To better
characterise these forest types they were subdivided into four different groups according their
vertical structure: open and tall formations, open and low formations, dense and tall
formations, and dense and low formations.
Quercus suber forests
The Quercus suber forests are distributed mainly in the south of Portugal and they represent
8.5% of the forest area. The open formations of Quercus suber, representing 20% of this
major forest type, are “montados” where cork oaks never reach 16 metres and the sparse
understory is never higher than 1 metre, and composed of Cistus salvifolius and Ulex spp
(Figure 2).
Dense formations reveal an overstory composed of cork oak sometimes mixed with
Quercus rotundifolia and Pinus pinea. Dense and tall formations comprise 28% of this forest
type and show an understory composed mainly of Cistus salvifolius,Ulex spp and Erica spp.
Dense and low formations are more abundant, corresponding to 52% of the Quercus suber
forests area; they have a denser understory composed of Cistus salvifolius and Ulex spp.
Cistus ladanifer and Lavandula spp are also significant (Figure 3).
Figure 2. Vertical structure of open formations of Quercus suber forests.
Figure 3. Vertical structure of dense formations of Quercus suber forests.
010203040506070
% o f C o v e r
C7
C6
C5
C4
C3
C2
C1
Op en & Tall Quercus suber Forest
Quercu s sube r Oth e rs
0 10203040506070
% of Cover
C7
C5
C3
C1
Open & Low Quercus suber Forest
Quercus suber Ot hers
01020 3040506070
% of cover
C7
C6
C5
C4
C3
C2
C1
Dens e & Tall Quercus suber Forest
Quercus suber Cist u s sa lv if olius Ot h e rs
01020 3040506070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Dense & Low Quercus suber Forest
Quercus suber Cist us sa lvif o lius Ulex spp Ot he r s
Combination of Structural and Compositional Factors for Describing Forest Types Using... 159
0 10203040506070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Open & Tall Pinus pinaster Forest
Pinus pinaster Others
0 10203040506070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Open & Low Pinus pinaster Forest
Pinus pinaster Erica spp Non representative species Others
Pinus pinaster forests
Open and tall formations occupy 10% of this forest type and they are distributed in large patches
mainly in the centre of Portugal from the coast to the interior. A significant large patch can be
observed south of Lisboa at the Península de Setúbal. Some of these formations are mixed stands
of maritime pine and eucalyptus. The understory is mainly composed of Erica spp and Ulex spp.
Open and low formations represent only 4% of this forest type. They are distributed in the
interior north and centre of the country, and south of the River Tejo with a large stand. The
maritime pine overstory sometimes contains eucalyptus and the understory is composed mainly
of Erica spp,Ulex spp,Pterospartum tridentatum and other plants (Figure 4).
Dense and tall formations of Pinus pinaster represent 32% of this forest type, distributed
mainly in the north and centre, from the coast to the interior, and on the southwest coast of the
country. Stands show a dense overstory of maritime pine, sometimes mixed with eucalyptus
and other oaks. Ulex spp,Erica spp,Cytisus spp,Pterospartum tridentatum and other plants
compose the understory. Dense and low formations characterise 54% of this forest type,
mainly distributed in the north and centre, from the coast to the interior, spreading to the
south of Portugal. The overstory of these stands is composed of maritime pine sometimes
mixed with eucalyptus and cork oak. The dense understory is composed of Erica spp,Ulex
spp,Pterospartum tridentatum and other plants (Figure 5).
Figure 4. Vertical structure of open formations of Pinus pinaster forests.
Figure 5. Vertical structure of dense formations of Pinus pinaster forests.
0 10203040506070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Dense & Tall Pinus pinaster Forest
Pinus pina ster Eucalyptus spp
Ulex spp Erica spp
Non r epresenta tive sp ecies Others
0 10203040506070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Dense & Low Pinus pinaster Forest
Pinus pina ster Ulex spp
Pterospartum tridentatum Erica spp
Non r epresent ative sp ecies Others
160 Monitoring and Indicators of Forest Biodiversity in Europe – From Ideas to Operationality
0 10203040506070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Open & Tall Eucalyptus Forest
Eucalyptus spp Others
0 10203040506070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Open & Low Eucalyptus Forest
Eucalyptus spp Others
0 10203040506070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Dense & Tall Eucalyptus Forest
Eucalyptus spp Pinus pinaster Ulex spp Others
0 10203040506070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Dense & Low Eucalyptus Forest
Eucalyptus spp Pinus pinaster Ulex spp Others
Eucalyptus forests
All the Eucalyptus forest formations follow the same geographical distribution pattern: from
north to south generally near the coast, spreading to the interior in the centre of the country.
Open formations, representing 22% of this forest type, show an overstory mainly composed
of Eucalyptus spp and an understory of Erica spp and Ulex spp (Figure 6).
Dense formations represent 76% of Eucalyptus forests. Both tall and low formations show
an overstory mainly composed of Eucalyptus spp, sometimes mixed with maritime pine. The
tall formation understory is composed of Ulex spp,Erica spp,Pterospartum tridentatum and
other plants. The low formation understory is denser and composed of Ulex spp,Erica spp,
Pterospartum tridentatum,Cistus ladanifer,Cistus salvifolius and other plants (Figure 6).
Other forests
This main forest type covers a variety of situations representing a total of 30.7% of
Portuguese forest. Open and tall formations, representing 25% of this forest type, are open
stands of Quercus suber and Quercus rotundifolia, called “montados de sobro e azinho”,
sometimes mixed with eucalyptus and maritime pine, with an understory mainly composed of
Ulex spp. Open and low formations represent 50% of the Other forests. They are stands of
Quercus rotundifolia, called “montados de azinho”, sometimes mixed with eucalyptus and
maritime pine. The understory is mainly composed of Ulex spp,Cytisus spp and Cistus spp
(Figure 7). Open formations are distributed in the south of Portugal.
Figure 6. Vertical structure of Eucalyptus forests.
Combination of Structural and Compositional Factors for Describing Forest Types Using... 161
Dense and tall formations correspond to 4% of this forest type. They are stands of Pinus
pinea,Castanea sativa and Pinus sylvestris, showing high diversity when mixed with
maritime pine, eucalyptus and oaks. Their understory is composed mainly of Rubus spp,Ulex
spp and other plants. Dense and low formations represent 21% of this forest type. They are
very diverse stands with an overstory mainly composed of maritime pine, umbrella pine,
eucalyptus, cork oak, holm oak and other trees. In the dense and diverse understory mainly
composed of Ulex spp, Rubus spp, Cistus salvifolius, important species of Quercus coccifera
and Cytisus spp can also be found (Figure 8). Dense formations are distributed from north to
south of the country.
4. Conclusions
The map produced according to the forest types is a powerful tool to better understand the
spatial distribution of vegetation structural diversity. The relationships between compositional
Figure 7. Vertical structure of open formations of Other Forests type: “montados de sobro e azinho”
and “montados de azinho”.
Figure 8. Vertical structure of dense formations of Other Forests type: umbrella pine and chestnut
woods, and the very diverse Portuguese forest.
0 10203040506070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Open & Tall Other Forests
Quercus suber Quercus rotundifolia
Eucalyptus spp Others
0 10203040506070
% of Cov er
C7
C6
C5
C4
C3
C2
C1
Open & Low Other Forests
Quercus rotundifolia Others
010 20304050 6070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Dense & Tall Other Forests
Pinus pinea Pi n u s sy l v e st r i s
Castanea sat iva Rub u s sp p
Non representat iv e species Ot h e r s
010203040506070
% of Cover
C7
C6
C5
C4
C3
C2
C1
Dense & Low Other Forests
Pi nus pi nast er Pi nus pi nea Eucalyptus spp
Querc us r ot undi f ol i a Quer cus r obur Quercus suber
Ulex spp Cistus salvifolius Rubus spp
Non repr esent ati ve species Others
162 Monitoring and Indicators of Forest Biodiversity in Europe – From Ideas to Operationality
and vertical structure of forest formations and the vertebrate and invertebrate fauna diversity
can be studied. Research on the influence of these forest types on the distribution of reptile
and amphibian richness in the Portuguese mainland is in progress. The good correlations
obtained by this work suggest how sensitive this tool is. It can be applied to other biodiversity
subjects and forest issues such as the evaluation of above-ground biomass, CO2 sequestration,
fuel loads and fire hazards.
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... On the large geographical scale, e.g. the country, gaining data on small-scale structural elements within habitats is only possible in case of closed schemes, e.g. forest inventory database (Rego et al. 2004). This kind of data can be very useful for ecological research and conservation purposes on the national and transnational level (Stachura-Skierczyn´ska et al. 2009;Skierczyn´ski et al. 2013, Stachura-Skierczyn´ska and Kosin´ski 2014) but have not been sufficiently employed in the species distribution model (SDM) of density. ...
... In order to avoid multicollinearity among environmental variables, principal components' analysis (PCA) was performed with Varimax normalized rotation, separately for each of the two environmental datasets, i.e. climate and habitat (Quinn and Keough 2002). Principal components' axes with eigenvalues >1 were retained as predictor variables in the analyses. ...
... km 2 ) we used data from the forest inventory database. The primary aim of the database was to describe the main features of forests in terms of size, condition and change of growing trees, focusing mainly on productive aspects, namely wood resources (Rego et al., 2004). Nevertheless, the forest stand description can be very useful for modelling species' occurrence (Stachura-Skierczyn´ska et al. 2009; Stachura-Skierczyn´ska and Kosin´ski 2014), implementing monitoring schemes , and as shown by our study on species distribution modelling of density specialized species. ...
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Species distribution models should identify ecological requirements of species and predict their spatial density. However, data from remote sensing sources are often used alone as predictors in modelling distributions. Such data will only produce accurate models if features that are distinguishable by remote sensing are a good match to the environmental traits that delineate habitat requirements. Both the Goldcrest Regulus regulus and the Firecrest Regulus ignicapilla respond to complex features of habitats that are not described by simple remote sensing data. We tested the usefulness of remote sensing data as a predictor for two Regulus species according to data from 970 study plots sized 1 × 1 km. Predictors were aggregated using the PCAs and related to the Hayne estimator of species density using GAMs. The models based on both remote sensing data and detailed environmental data proved to be better than the model based only on remote sensing data and/or detailed forest structure data. The Goldcrest reached the highest density in areas with a high share of old spruce-dominated forests with a substantial share of the fir, avoiding the pine, and it preferred forests with a low number of tree species. In turn, the Firecrest favoured old forests, dominated by the spruce and the beech, with an admixture of single old fir and larch trees, avoiding the pine, and preferring forests with a high number of tree species. We suggest using not only free data sources, but also more detailed data containing thorough information on forest inventory derived from ground measurements.
... A composição e estrutura vertical foram descritas em conjunto pela percentagem de cobertura por espécies florestais, ou grupos de espécies, facilmente identificáveis no solo, de acordo com sete classes de altura definidas a partir do nível do solo até 0,5m, de 0,5m a 1,0m, de 1,0m a 2,0m, de 2,0m a 4,0m, de 4,0m a 8,0m, de 8,0m a 16,0m, e acima de 16,0 metros (DGF; 1999). Os tipos de floresta foram então definidos aplicando uma análise K-means aos dados recolhidos, a partir de 2258 locais de amostragem distribuídos aleatoriamente pela área florestal, e identificados pelas suas coordenadas geográficas (DGF, 2001;Rego et al, 2004). Numa primeira abordagem, foram identificados dez tipos principais de floresta: floresta de Pinus pinaster (PPF), floresta de ...
... De entre os dez tipos principais de floresta, a de Pinus pinaster, a de Eucalyptus, a de Quercus suber e os "Outros Tipos de Floresta" são os mais representativos em Portugal Continental e apresentam uma estrutura vertical mais diversa, como publicado em Rego (2004). ...
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Portugal, com o seu clima Mediterrânico, sofre frequentes fogos florestais durante o Verão. O conhecimento da composição e da configuração de manchas florestais representativas da matriz florestal (Godinho-Ferreira et al., in press) é importante para compreender os processos que nela ocorrem, incluindo a probabilidade de ocorrência de fogos florestais. A estrutura vertical das florestas descrita na terceira revisão do Inventário Florestal Nacional (IFN) mostra diferentes níveis compostos por diferentes tipos de espécies que ocupam percentagens diferentes de cobertura, identificando 10 tipos principais de floresta e 22 tipos estruturais (Godinho-Ferreira et al., 2005). Uma primeira análise das parcelas de amostragem do IFN ardidas entre 1999 e 2005 indica que manchas grandes e muito grandes de floresta de pinheiro-bravo e eucalipto apresentam as maiores taxas de áreas ardidas. Esta apresentação é parte integrante de uma primeira abordagem genérica com o objectivo de desenvolver metodologias mais precisas no âmbito do projecto “PHOENIX – Reconversão florestal em áreas queimadas” (POCI/AGR/58896/2004), particularmente na “Tarefa 1 – Que tipos de floresta têm menor probabilidade de arder?”.
... Linking sample data on biodiversity indicators to ecologically meaningful forest type units brings substantial advantages for forest biodiversity assessment: (i) it allows improved understanding, interpretation and communication of data on biodiversity variables by enabling comparison of ecologically similar forests; (ii) it enables a more detailed and richer analysis of biodiversity indicators in a specific forest habitat such as the relationship between the vertical structure of forest habitat and vertebrate and invertebrate fauna diversity (Rego et al., 2004); and (iii) it provides a basis for stratified sampling, thus ensuring that different forest habitats are adequately represented in the plots (Winter et al., 2011, submitted for publication). ...
... To this end, several forest typological approaches have been devised in Europe in the framework of NFIs (e.g. Rego et al., 2004) and sustainable forest management regional strategies (e.g. Corona et al., 2004a), or to cross-link NFI sample data to units of European Table 1 Forest biodiversity features and related indicators that can be estimated using European NFI data (Chirici et al., 2011 Barbati et al., 2007Barbati et al., , 2011. ...
Article
Statistically-designed inventories and biodiversity monitoring programs are gaining relevance for biological conservation and natural resources management. Mandated periodic surveys provide unique opportunities to identify and satisfy natural resources management information needs. However, this is not an end in itself but rather is the beginning of a process that should lead to sound decision-making in biodiversity conservation. Forest inventories are currently evolving towards multipurpose resource surveys and are broadening their scope in several directions: (i) expansion of the target population to include non-traditional attributes such as trees outside the forest and urban forests; (ii) forest carbon pools and carbon sequestration estimation; (iii) assessment of forest health; and (iv) inclusion of additional variables such as biodiversity attributes that are not directly related to timber assessment and wood harvesting. There is an on-going debate regarding the role of forest inventories in biodiversity assessment and monitoring. This paper presents a review on the topic that aims at providing updated knowledge on the current contribution of forest inventories to the assessment and monitoring of forest biodiversity conditions on a large scale. Specific objectives are fourfold: (i) to highlight the types of forest biodiversity indicators that can be estimated from data collected in the framework of standard forest inventories and the implications of different sampling methods on the estimation of the indicators; (ii) to outline current possibilities for harmonized estimation of biodiversity indicators in Europe from National Forest Inventory data; (iii) to show the added value for forest biodiversity monitoring of framing biodiversity indicators into ecologically meaningful forest type units; and (iv) to examine the potential of forest inventory sample data for estimating landscape biodiversity metrics.
... The result of the K-means analysis produced the average percentage coverage of species according to height class (pseudo-species) for each cluster, thus outlining the profiles of resulting forest types. The establishment of forest types based on the composition of the stands was already explored by other authors in Portugal [45,46]. These authors took the information from the Fourth Portuguese National Forest Inventory (PTNFI4), that presented, for the first time, the coverage of species according to height classes. ...
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National Forest Inventories (NFIs) collect and provide a large amount of information regarding the forest volume, carbon stocks, vitality, biodiversity, non-wood forest products and their changes. Forest stands variables data are paramount to understanding their composition, especially on those related with understory characteristics and the coverage of species according to canopy layers; they are essential to assess biodiversity and to support forest management. At the same time, these inventories allow the development of harmonized forest descriptions beyond the national scale. This study aims to develop a homogeneous characterization of the Iberian Peninsula’s forests, in order to classify and identify the forest types. For this purpose, harmonized data from NFIs of Portugal and Spain were used to assess the composition of species, dominance and the percentage of cover for each species in a vertical space defined by seven canopy layers. Using the “K-means” clustering algorithm, a set of clusters was identified and georeferenced using forest polygons from land use and cover maps of both countries. The interpretation and description of the clusters lead to the establishment of 28 forest types that characterize all of the Iberian Peninsula forests. Each forest area has been described through one of the forest types and their relation with other ecological characteristics of the stands was analyzed. Shrubs formations are generally widely distributed in the forest area of the Iberian Peninsula, however their abundance in terms of cover is lower in comparison with tree species. Around 71% of the forest types are dominated by trees, mainly species from the genera Pinus and Quercus, and 21% are dominated by shrub formations with species of Ulex spp., Cytisus spp., and Cistus spp. The Quercus ilex s.l. L. and Pinus pinaster Aiton are the common species of importance for both NFIs. The results represent a powerful and homogenous multi-use tool describing the Iberian Peninsula’s forestlands with applications on landscape analysis, forest management and conservation. This information can be used for comparisons at larger scales, allowing cross-border analysis in relation to various aspects, such as hazards and wildfires, as well as management and conservation of forest biodiversity. The developed method is adaptable to an updated dataset from more recent NFIs and to other study areas.
... About twenty years later, the progress in the development of forest inventory and monitoring methods was initiated in the USA (Stott 1947) followed by the works in Switzerland (Schmid 1963). While at the beginning, forest inventory was primarily aimed at gathering the information about the production parameters of stands (Rego et al. 2005), in the 80s of the last century their ecological functions started to become more important in the developed part of the world, due to which the *Corresponding author. Ján Merganič,phone: +421 45 5206 292 inventoried information spectrum has expanded (Söderberg & Fridman 1998). ...
Article
Sample plots are basic units of statistical forest inventories. The choice of their shape and size, and sampling methods have changed over time due to economic constraints, efficiency and changes in human demands on data about forests. In the presented study we analysed the impact of three different sampling units: fixed-area plots, fixed-different-area plots, and nested concentric plots, on the estimates of tree level production and diversity parameters. These sampling units were measured during the regional inventory at the University Forest Enterprise of Technical University Zvolen, Slovakia, which was repeated four times (1986, 1992, 1998, 2012). Within each inventory plot, all positions of trees were repeatedly and independently measured three times (1986, 1998, 2012) by different operators using different tools. From these data we quantified the error of tree position resulting from human and technological factors and analysed its impact on the estimates of tree level diversity and production parameters. The selected parameters were: number of trees, stand basal area, standing volume per hectare, number of tree species and number of vertical tree layers. The results indicate that the plot design primarily affects ecological characteristics of forests. Fixed-area plots seem to be the most suitable sampling unit from the point of multi-criteria evaluation of forest status and forest change.
... Structural characteristics of forest stands are relatively easy to assess: measurements of forest structure that can contribute to indicators include canopy cover, vertical structure of the canopy and size or age and spatial distribution of trees. A relevant example of combination of structural and compositional factors for describing forest types using NFI data is reported by Rego et al. (2004). ...
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A summary and discussion of selected published results on the current and potential role of forest inventories (with particular reference to the national ones) are presented in the light of the challenges posed by society and policy decisions in the environmental sector. The analysis concentrates mainly on the ecological and socio-economic aspects of the question and on forest inventories' potential contribution to achieving sustainable forest management.
... Deste modo definiramse vinte e dois tipos florestais (Quadro 2). Para cada tipo florestal obteve-se a composição média do seu perfil e, recorrendo aos Sistemas de Informação Geográfica, a sua distribuição espacial para todo o continente (REGO et al., 2004). Salienta-se que a análise de K-means segregou grupos como a Floresta de carvalho negral, a Floresta de outros carvalhos de folha caduca, o Medronhal e o Acacial que, pela sua reduzida mas expressiva distribuição e, simultaneamente, importante composição, se revelam áreas de especial interesse de estudo. ...
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Sumário. Quando da elaboração da 3ª Revisão do Inventário Florestal Nacional (DGF, 2001) foram desenvolvidas metodologias que permitiram definir parâmetros para caracterizar os povoamentos florestais quanto à sua biodiversidade. Assim, em cada parcela de amostragem foi avaliada a percentagem da composição de espécies, ou grupos de espécies, em sete classes de altura. A análise estatística dos dados (cluster analysis) pelo método do K-means, combinando a estrutura vertical com a composição, permitiu identificar na floresta portuguesa do continente dez grandes grupos, ou tipos florestais, e elaborar a Carta da Tipologia Florestal de Portugal Conti-nental. Palavras-chave: biodiversidade; Inventário Florestal Nacional; composição; estrutura vertical; tipos florestais Map of Forest Types of the Portuguese Mainland
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The evaluation of landscape patterns is necessary to explain the relationships between ecological processes and spatial patterns and between the processes and patterns and the factors that control them or that they control. For decades, landscape metrics have been used to measure and abstract landscape patterns. Since the emergence of FRAGSTATS in 1993, the measures and methods incorporated in this software have become widely used and are now a de facto standard tool for calculating landscape metrics. However, there are no special metrics unique to forest landscapes. The selection of metrics depends on the purpose of the study rather than on the land use or cover type. However, some metrics are more often used for forested landscapes (e.g., core area metrics). Forest landscape patterns are changing fast due to both natural and human disturbances. Remote sensing offers a rapid method of acquiring up-to-date information over a large geographical area and is therefore widely used as a source of the data needed for pattern assessment and the calculation of landscape metrics. However, to obtain meaningful results, correct preparation of the data is essential. In this chapter, we review the various metrics used to measure forest landscapes for different purposes. We deal with five main issues from the perspective of forest landscape patterns: (1) data preparation before the calculation of metrics (e.g., vector vs. raster data, scale, classification) and the associated uncertainties, (2) measurements of a landscape’s configuration and composition using metrics, (3) interpretation of the results, (4) possible uses of the outcomes, and (5) future perspectives (e.g., 3D and 4D landscape metrics).
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Under the current energy scenario, the development of alternatives to fossil fuels, like bioethanol from lignocellulosic materials, is highly relevant. Therefore it is important to search and study new raw materials and to optimize the different steps that lead to bioethanol production. In this work, acid diluted pretreatment was optimized considering the release of sugars. Under the optimal conditions, the reducing sugars yield was of 293.4 mg/g of dry biomass in liquid fraction. The tested pretreated samples of Pterospartum tridentatum that presented a higher glucose yield in enzymatic saccharification where those that were subject to a pretreatment at 180 °C for 75 min with 2.75% (w/w) of sulfuric acid when using a biomass/liquid ratio of 2.25 g/10 mL leading to a maximum yield of glucose that was 92% of the theoretical maximum. From the fermentation of filtrates it was possible to obtain a maximum ethanol yield of 0.26 g ethanol/g total sugars, without previous detoxification.
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Summary The need for new criteria and indicators for the assessment of biodiversity conservation as part of sustainable forest management of tropical forests has been identified as a priority by many international organisations. Those biodiversity criteria and indicators which formed part of a much broader initial assessment by the Center for International Forestry Research (CIFOR) (Prabhu et al. 1996) were found to be deficient. This Working Paper contains specific proposals for biodiversity criteria and indicators. These proposals originated from a workshop of experts, and are intended to be adapted and refined for use in specific situations. Criteria and indicators need to be applied at the forest management unit level and those for biodiversity are just one part of a package that includes socio-economic and other categories. Biodiversity is an extraordi- narily broad concept and, given the huge diversity of life in tropical forests, it is impossible to make rapid direct assessments of biodiversity in forests in anything other than a superficial manner. It is likely that there will be limited skilled human resources and time for biodiversity assessment in any system of criteria and indicators, so it is important that we design tools that do not require expert application and interpretation. The usefulness of Òindicator groupsÓ, ÒkeystoneÓ species and other concepts is still argued among biolo- gists and their utility is questionable. This paper suggests that, in contrast to more traditional approaches to assessing taxonomic diversity, it may be possible to assess the effects of management practices on biodiversity by examining the state of those processes that generate or maintain biodiversity. The indicators and verifiers that we have suggested examine the state of these processes. We recommend that for each indicator, quick and easy verifiers, which we designate ÒPrimaryÓ verifiers are used first, and more sophisticated (ÒSecondaryÓ) verifiers are used only if clear results are not obtained from Primary verifiers. This paper is merely a first step in creating a suitable framework for applying a proposed a set of forest biodiversity indicators and verifiers. The framework and the indicators and verifiers require field testing, and we fully expect there to be changes resulting from the field trials, which will be reflected in major improvements in their effectiveness. For the sake of brevity we have not discussed the advantages and disadvantages of the verifiers in full. While changes are expected, the approach taken is powerful in that it recognises the relation- ship between interventions and consequences, and it demonstrates that some indicators are more widely valu- able than others.
Structural, compositional and functional aspects of forest biodiversity in Europe. Geneva Timber and Forest Discussion Papers. ECE/TIM/DP/22. United Nations
  • J Puumalainen
Puumalainen, J. 2001. Structural, compositional and functional aspects of forest biodiversity in Europe. Geneva Timber and Forest Discussion Papers. ECE/TIM/DP/22. United Nations. New York and Geneva.
Geoestatística para as ciências da terra e do ambiente
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Soares, A. 2000. Geoestatística para as ciências da terra e do ambiente. IST Press, Instituto Superior Técnico, Lisboa, Portugal. 206 p. SPSS 2003. SPSS 12.0 for Windows. SPSS Inc.