Using landscape ecology to evaluate an alternative management
scenario in abandoned Mediterranean mountain areas
Teodoro Lasantaa*, José C. González-Hidalgob, Sergio M. Vicente-Serranoc, Emna Sferia
Pirenaico de Ecología (CSIC). Apdo. 202. E-50080 – Zaragoza. Spain
de Geografía. Universidad de Zaragoza. C/ Pedro Cerbuna 12. E-50009, Zaragoza, Spain.
cCentre d'Etudes Spatiales de la Biosphère (CESBIO). 18 avenue. Edouard Belin, bpi 2801, 31401 Toulouse cedex 9,
France
* corresponding author. e-mail: fm@ipe.csic.es
aInstituto
bDepartamento
Abstract
The main characteristic of landscape evolution in the Pyrenees during the
twentieth century is the increased presence of shrubs in old fields. Revegetation is
mainly recorded on medium and low slopes because of land abandonment. The increase
in shrubs causes the loss of grazing resources during winter, landscape homogenization
and an increase in fire risk. The objective of this paper was to create a scenario for land
use change by combining Geographical Information Systems (GIS) and Landscape
Ecology Analysis (LEA). To improve the present seasonal imbalance in pastoral
resources, the scenario proposes to convert shrubland areas to grasslands without an
excessive increase in soil erosion or landscape fragmentation. The results obtained
indicate that the strategy produces greater landscape diversity and an increase in annual
pasture resources (16.7 %), which exceeds 60 % of total in winter. The effects of
landscape fragmentation on biodiversity, fire risk control and the imbalance of pastures
are discussed. The scenario would improve extensive livestock farming, which is the
most important economic activity in this area and other Mediterranean mountains.
Key words: Land use scenario, Land Management, Grazing resources, Mediterranean landscape, Pyrenees.
1. Introduction
Mediterranean mountainous landscapes have a complex structure that is the result
of traditional land management (Puigdefábregas and Fillat 1986; Grove and Rackham
2000). For centuries, man has exploited natural resources to a maximum in order to
guarantee food for the population and a large number of animals. Land management
was in the context of a self-sufficient economy in which exchanges with the outside
world were scarce (ovine transhumance and few agricultural imports). This situation led
to a highly humanised landscape with great ecological and cultural diversity (Crowling
1
et al. 1996; García-Ruiz and Valero 1998; Vos and Meekes 1999; Taillefumier and
Piégay 2003).
During the second half of the twentieth century, the mountainous areas in the
Mediterranean were abandoned for agricultural productions and suffered high
population emigration, abandonment, decrease in livestock and limited use of forested
areas (Lasanta 1988; MacDonald et al. 2000; Metailié and Paegelow 2004). The cause
of this marginalisation was the intensification of agricultural activity in flat areas, due to
an increased productivity caused by mechanisation, use of chemical fertilisers and
irrigation. The agricultural production in mountainous areas could not compete with that
of lowlands. The production costs are higher in the former because of small plot sizes,
highly divided property structure, shortage of flat lands and low soil fertility. Thus, the
intensification of agricultural land use in one area caused marginalization in the
mountain areas.
In the mountain areas, these changes in land use during the last 50 years have led
to extensive revegetation with increment of shrubs and forests, which has considerably
simplified landscape structure (González-Bernáldez 1991; Poyatos et al. 2003; Torta
2004). This caused a homogenisation of the landscape, with a loss of flora and fauna
(Ruiz and Ruiz 1989; Farina 1995 and 1997; Preiss et al. 1997; Caraveli 2000; Olson
et al. 2000; Kozak 2003; Laiolo et al. 2004). In addition, the increase in shrubs has
caused a decrease in grazing resources, which are crucial for the maintenance of the
livestock. In the Pyrenees, the shortage of these resources during winter is, at present,
the main problem for livestock development.
Consequently, new land use scenarios are required to revitalise the economy and to
maintain the landscape diversity.
2
Geographical Information Systems (GIS) and Landscape Ecology Analysis (LEA)
provide one of the most powerful analytical methods for creating a spatial and temporal
analysis framework. There are numerous examples of the use of this approach (GISLEA) for species conservation (Nikolakaki 2004), biological diversity and landscape
structure (Papadimitrou 2002), land use cover evolution and its effects on landscape
structure (Hietala-Koivu 1999; Nagendra and Utkarsh 2003; Southworth et al. 2004)
and landscape planning and management (Geoghegan et al. 1997). Also it could be an
useful approach for creating future scenarios for environmental planning and landscape
management (see the example of Herzog and Lausch 2001, and the review of Nagendra
et al. 2004), particularly in areas where human pressure is intensifying or where rural
exodus from the mid twentieth century has allowed revegetation through natural
processes. The Mediterranean mountainous areas of Europe provide such an example,
with some areas abandoned during the twentieth century, and other areas still at present
subjected to human pressure. Nevertheless, these mountain lands are human-dominated
landscapes, where true natural habitats are scarce, and where species and natural
landscape functions could be preserved in a network of patches that not only allowed
sustained biological diversity, but also natural and socio-economic functions (Verboom
et al. 2001).
Here we combine GIS-LEA to propose an alternative land use scenario adapted to
the Mediterranean mountains of Spain: the Central Pyrenees. We analysed the
feasibility of the proposed scenario to increase grassland areas and avoid landscape
degradation. Therefore, we searched for shrubland areas (mainly old abandoned field)
with favourable topographical conditions, which could be converted to grasslands
without risk of increasing erosion. Besides, the reduction of shrub land could contribute
to diminish fire risk. This type of analysis is highly relevant since one of the current
3
problems faced by extensive livestock farming in the Pyrenees is the shortage of
grasslands during winter (García-Ruiz and Lasanta 1990); a problem that was
traditionally solved by transhumance to flat areas located between 150 and 200 km from
the Pyrenees (Puigdefábregas and Fillat 1986).
2. The study area
The area selected was the basin of the Aragón Subordán river (Spanish Central
Pyrenees), an area of 307.7 km2, with three valleys of N-S direction (Figure 1). In 1900
this area registered a population of 3626 inhabitants, in 1950 2537 and by 2001 the
population had dropped to 1249, distributed in 6 towns, resulting in a population density
of only 4.96 inhab/km2.
Two relief units can be identified (Sierras Interiores and Flysch areas). The first,
which is of calcareous and gritty calcareous soil, shows the highest elevations: Bisaurín
(2668 m), Castillo de Acher (2390 m), Mesa de los Tres Reyes (2434 m), covered by
pastures. The Flysch area is located immediately to the south, with rolling hillsides,
slopes between 20 and 40%, and altitudes from 600 to 2000 m a.s.l. (Lorente et al.
2002). It is here that we find the main areas of forests and vast extensions of shrubs,
which cover old fields. The cultivated areas are now located in the valley bottoms.
The dominant climate has Atlantic influences with frequent snowfalls in winter,
although the lowest hillsides show sub-Mediterranean climatic characteristics (López
Moreno and García-Ruiz 2004). At the observatory of Hecho (860 m), the mean annual
precipitation is 1227 mm and the temperature 10.3 ºC. Autumn and summer register the
highest and lowest precipitation, respectively. Only during July, there is a negative
water balance in the southern areas of the basin. Frost occurs from October to June in
4
the high elevated areas and between November and May in the southern sectors (Del
Barrio et al. 1990).
The study area was traditionally exploited by extensive grazing, which used the
pastures above the timberline during summer. During winter, the livestock was taken to
the centre of the Ebro valley, a flat area of semi-arid characteristics, located between
150 and 200 km from the Pyrenees, where the sheep grazed on the steppes, fallows and
cereal fields (García-Ruiz and Lasanta, 1993). The local statistics indicate that in the
early twenty century the livestock comprised 47345 sheep and 1450 head of cattle. In
1961, these figures had fallen to 20674 and 1373, respectively. In 2004, livestock
farming comprised 1237 sheep and 1878 head of cattle. The dramatic decrease in the
number of sheep was a result of the crisis in the transhumance system (Balcells 1986).
During the first decades of the twentieth century the valley bottoms and the low
hillsides were cultivated with cereals. The agricultural areas covered 7025 ha., but
between 1940 and 1981 5546 ha. (78.9% of the original total) were abandoned. The
remaining lands were cultivated for cereals and grasslands (García-Ruiz and Lasanta
1990). The abandonment of agricultural lands and the low livestock pressure caused a
significant increase in shrubs, mainly on low hillsides, where Genista scorpius covers
most of the abandoned fields and creates a highly homogeneous landscape (Lasanta et
al. 2005), which is at great risk of fire (Valderrábano and Torrano 2000).
3. Methods
3.1. Land cover mapping
Considering that land cover pattern can be used as proxy data for landscape
analysis, the complexity of landscape is determined by the number of patches, their
5
characteristics, size and shape. Their connectivity and structural landscape complexity
should then be measured from land cover maps (Papadimitrou 2002).
Land cover was mapped using Landsat images (Smiatek 1995; Honnay et al.
2003; Sommer et al. 1998). The vegetation in the Hecho valley has large spatial and
temporal variability because of the wide diversity of land cover. Seasonal differences
between vegetation types are considerable. For this reason, variations in the spectral
response of vegetation cover are crucial for determining the distinct classes of
vegetation cover. Therefore, three images taken in distinct seasons were used to avoid
errors in classification caused by differences in the phenological cycles (Dennison and
Roberts 2003; Serra et al. 2003).
Two Landsat 5-TM images (August of 1991 and June of 1993) and one Landsat 7ETM+ image (October of 1999) were used. Although it is recommendable to use
images from the same year, the frequent cloud cover made it necessary to use images
from different years. However, the use of distinct years does not affect the land cover
classification in the study area. In the Hecho valley, extensive landscape
transformations were recorded in the twentieth century, but in the last 15 years human
activities have been scarce and the main process has been natural revegetation and
succession in old fields, in which the advance of vegetation is slow. In the study area,
more than 50 years are required for complete substitution of shrubs by forests (Molinillo
et al. 1997). Consequently, the eight year difference between the satellite images used in
this study is not enough to record significant changes in the vegetation cover that may
alter the classification.
The images were geometrically corrected using control points and a digital
elevation model (30 meters of resolution) following the method described by Palá and
Pons (1995). The digital elevation model was developed by interpolating elevation
6
isolines (1:50000) obtained from the National Geographic Institute (Spain) at a spatial
resolution of 30 meters. The use of the elevation model minimises geometric errors. The
Root Mean Square Error (RMSE) was less than 30 meters in each image (RMSE = 18.7,
RMSE = 20.9 and RMSE = 18.3 for the images of 1991, 1993 and 1999, respectively).
The radiometric disturbances caused by atmosphere and relief especially affect
mountainous areas (Riaño et al. 2003). Therefore, the images were corrected following
the method of Gilabert et al. (1994) and the radiative model of Bird and Riordan (1986),
modified for mountainous areas by Beguería (2003).
Classified distinct land cover classes on the basis of the vegetation of the valley:
coniferous forests, leafy forests, pastures above timberline, meadows, mixed forests, bare
rock and alpine pastures, shrublands, bare soil, urban areas, rivers, and fluvial bars.
Using field-work, we identified several areas that were representative of each of the
land cover classes selected. Land cover classification was obtained using the spectral
bands of the three images, with a hybrid method that combines the ISODATA and
CLSMIX modules of MiraMon software (view details in Serra et al. 2003). After
automatic classification, we identified three conflictive assignations: i) between bare rock
and bare soil, ii) non-irrigated grasslands and pastures above the timberline and iii)
between shrubs and non-irrigated grasslands. The two first assignations were solved
using topographic criteria. Above 1600 m, bare soil was reclassified as bare rock and
alpine pastures. Below this altitude, the pixels classified as bare rock were classified as
bare soil. A similar criterion was used to differentiate between pastures above the
timberline (> 1500 m) and grasslands (< 1500 m). The main difficulty was to
discriminate between some grassland and shrub areas because of similar spectral
characteristics between them. In this case, the altitudinal range of both land use classes
7
coincided. Therefore, we used present-day aerial photographs (1998) to map the
grasslands. The spatial scale of the aerial photographs is very detailed (1:5.000) and the
geometric quality good (MAPA, 1998). Photographs were interpreted visually and
grassland areas were digitized manually. The other grasslands pixels obtained in the
classification of Landsat images were assigned to shrubs.
Finally, we checked the validity of the final land cover map by comparing the land
cover type recorded randomly at 150 points by field-work with the cover obtained by means
of supervised land cover classification. For this purpose, we used a confusion matrix
(Chuvieco 2003). The comparison between the classification obtained from remote
sensing and the points collected in the field is shown in Table 1, which indicates the high
reliability of the final classification obtained (79.8%).
3.2. Development of scenario
The land cover map was useful to identify marginal areas where action could be taken
to improve livestock management. These areas include abandoned agricultural lands
that are now covered by shrubs or have sparse vegetation cover and are located on low
hillsides. Highly productive grasslands can be obtained by shrub clearance given that,
under shrubs, these lands have a very dense herbaceous cover composed of gramineae
and leguminous plants, which are of great interest for extensive livestock raising
(Molinillo et al. 1997).
The main problem for the development of an alternative land cover scenario was to
select those shrubland and bare areas that could be converted to grasses and those which
were more suitable for revegetation. For this purpose, we combined several
environmental, landscape and economic criteria.
8
A first priority was not to increase soil erosion, as soil is a very scarce and unstable
resource in the Pyrenees (García-Ruiz et al. 2005). Ruiz Flaño et al. (1992) show the
significant roles that slope and human management have on triggering different erosion
processes with variable erosion rates. We therefore decided to make the change on
slopes under 35 %. The slope threshold of 35% was selected on the basis of the
information from the “Experimental Station Valle de Aísa” (ESVA- see location in
Figure 1), where hydrological behaviour and soil erosion in several land covers is
studied (García-Ruiz et al. 1995). The ESVA is located at 1150 m a.s.l., 5 km from the
study area, on a hillside with a slope of 35 %. The area was cultivated with cereal crops
until the 1960s. After abandonment, the land was colonised by shrubs of Genista
scorpius and Rosa sp. In 1991, it was converted to nine land uses, including dense shrub
and grassland. Each plot had an automatic system to measure continuously runoff and
sediment transport. Between 1992 and 2003, the average runoff coefficient was 4.5%
for the shrub plot and 6.8% for the grassland. In the same period, the annual rates of soil
erosion were 10.9 gm-2 and 15.9 gm-2, respectively. The erosion on these land covers
was inferior to that recorded for fallow (93.2 gm-2) and shifting agriculture (137 gm-2)
(García-Ruiz et al. 2004). The substitution of shrubs for grasslands causes a limited
increase in soil erosion that can be considered acceptable if the change improves forage
production and landscape structure.
A second criterion was chosen to avoid excessive dispersion of the new grasslands,
which could produce excessive landscape fragmentation with negative environmental
effects (Jongman 2002). In our strategy, new grassland patches with a surface less than
1 ha were removed. Also, this measure facilitates access of livestock to the new
grassland.
9
3.3. Landscape analysis
Analyses of the present landscape and under scenario were performed using the
FRAGSTAT v2 environment. We used the Number of Patches (NP), Patch Density
(PD), Largest Patch Index (LPI), and Mean Patch Size (AREA) as patch area metrics.
To quantify the occurrence of ecotones, we have calculated Total Edges (TE) and Edge
Density (ED). The NP, LPI, and AREA correspond to metric area and together with PD,
ED and TE provide indications of the degree of fragmentation. The LPI provides the
area of the largest patch in each class (or landscape) expressed as a percentage of total
landscape area. The NP is a measure of fragmentation of a given class within a
landscape since the landscape size is constant (Southworth et al. 2004), as in our study.
Landscape configuration metrics were evaluated by the Interspersion and
Juxtaposition Index (IJI). As a global estimator of landscape structure, we calculated
Shanon’s (SDI) diversity index (see Geoghegan et al. 1997; Nagendra 2002; Herzog and
Lausch 2001). Finally, the Cohesion index (CO) was applied to verify isolation.
Details of calculus, formulae and justification can be found in numerous
references (Geoghegan et al. 1997; Chust et al. 2004; Verboom et al. 2001; Cook 2002;
Southworth et al. 2004; Hietala-Koivu 1999; Honnay et al. 2003; Bogaert et al. 2000;
Herzog
and
Lausch
2001),
and
in
web
of
FRAGSTAT
(http://www.umass.edu/landeco/research/fragstats/documents/, date 6/01/2005).
3.4. Evaluation of grazing resources
To evaluate the differences in grazing resources between the present land cover and
the one proposed in our strategy, we used the values on forage productivity in each land
cover. These data were obtained from different studies on this topic carried out in the
study area (García-González et al. 1990; Molinillo et al. 1997; Aldezábal et al. 1998;
10
Marinas et al. 2003). Table 2 summarizes the annual productivity of each land cover
(Mj/ha/year) and the percentage of productivity during each season. During summer, the
total productivity is located on the alpine and subalpine pastures whilst in winter it is
concentrated on grassland areas. In spring and autumn, the grasslands, shrubs and
some woodlands are the main source of livestock grazing.
4. Results
4.1. Present land cover distribution and distribution in the proposed scenario
Figure 2 and Table 3 show the total surface and spatial distribution of the present
land cover and that in the proposed scenario. The area covered by grassland is also
shown. Forests currently predominate (62.1% of the total surface), and are mainly
coniferous (29.1%). The pastures above the timberline and the alpine pastures used for
livestock feeding during the summer also cover a large percentage of surface (20.4%)
Shrubs cover 12.7% of the valley, and are located on agricultural lands that were
abandoned during the twentieth century (Lasanta 1988; García-Ruiz and Lasanta 1990),
whilst the meadows (used for livestock feeding during the winter and spring) account
only for 3.2% of total surface. The present land use scheme causes a significant
imbalance in forage availability between warm and cold seasons.
Topography determines the land cover distribution. Bare rock and alpine pastures
are located at mean altitudes of 2060 and 1850 m a.s.l., respectively. Forests are located
on average at between 1100 and 1200 meters. Meadows are found in the valley bottoms
at around 885 m, and the shrubs and soils with sparse vegetation are at between 900 and
950 m on average.
Topography defines shrub distribution. This vegetation cover appears in the south
of the study area, on the lowest slopes. In these areas the climate is dry and the
11
succession of vegetation types is very slow after abandonment. Abandonment at greater
altitude has originated forests during the same period of time (Vicente-Serrano et al.
2005) because of more humid conditions.
In the proposed land cover scenario, land cover modifications are rare because the
scenario only affects shrubs, grasslands and areas with little vegetation. At present,
982.5 ha are occupied by meadows. In our scenario, meadow cover reaches 2044.3 ha,
and the most marked changes are located in the southern areas, where shrubs cover a
high percentage of the area, thereby generating a landscape of low diversity.
4.2. Landscape analysis
On a global scale (landscape analysis), the main difference between the present state
and our scenario is the increase in landscape fragmentation (Table 4). The number of
new patches increases by 419 in the scenario. This controlled fragmentation produces a
reduction in the average area of patches (from 4.4 to 4.1), an increase in patch density
and also in edge length. The scenario does not modify the overall landscape diversity in
the valley, or cohesion, although the IJI index increases, which indicates a higher level
of diversity in the contact between patches.
A more detailed analysis at land cover level is shown in Table 5. The landscape scenario
is dominated by fragmentation, although this differs depending on the land cover type.
In shrublands, the increase is caused by the fragmentation of present patches, while in
meadows it is produced by the development of new patches. In shrubs, the scenario
increases over the number of present patches by 38% (2.4 PD versus 3.3 PD), and the
number of patches in meadows by 78% (0.6 PD versus 1.0). Moreover, the shrub
patches with the largest area are highly fragmented and reduce the LPI, while in
grasslands the scenario produces a small increment. This observation indicates that the
12
scenario does not homogenize the landscape but increase diversity. In general, the
average area of shrub patch decreases and grassland cover increase.
As a result of controlled landscape fragmentation, the length of the edges in these two
land cover types is modified. The results show a remarkable increase in edge density in
the grasslands and a minimal increment in shrubland areas.
Finally, the spatial structure of the two land cover types also changes (IJI and CO).
Cohesion decreases more intensely in shrubland areas, while the diversity of contacts
(IJI) increases slightly in the shrubs and decreases in grassland.
Shrublands and meadows show a predominance of patches of less than 2 ha (77.5% in
shrublands and 79.2% in meadows) (Table 6). In the former, the transformation affects
original patches of distinct sizes. In grasslands the changes are more significant and an
increase in the number of patches over 1 ha is observed. At present, there are 112
patches of less 1 ha, and in the scenario the number decreases to 82. The most
spectacular increment is recorded in the number of patches between 1-2 ha (25 at
present, and 94 in scenario).
Thus, our scenario is characterized by controlled landscape fragmentation caused by
transformation of shrublands to meadows.
4.3. Comparison of the grazing resources between the present state and the scenario
The comparative analysis of grazing resources between the present state and our
scenario indicates an annual increase of 16.7% %, from 18486.2 x 103 Mj/year to
21538.6 x 103 Mj/year (Figure 3). However, more important than the annual increase is
the seasonal distribution of these resources (Figure 4). Changes occur during winter, the
season in which grazing resources are scarce and limit the maintenance of livestock.
During this season, the grazing resources in the strategy are doubled.
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5. Discussion and conclusions
Extensive livestock production could provide a basic agricultural activity which
could reactivate the economy of marginal mountainous areas such as the Pyrenees and,
in general, all mountainous areas in the Mediterranean. These areas comprise large
zones covered by communal pastures, which have been used for centuries for extensive
livestock grazing, thereby creating highly diverse landscapes. These landscapes can
only be maintained by the permanent presence of extensive livestock (Ruiz and Ruiz
1984; Baudry 1991; Canals and Sebastià 2000; Magda and Gonnet 2001). Nevertheless,
during the twentieth century, livestock farming in the Pyrenean valleys decreased
between 60-70 % as a consequence of the crisis in the transhumance system (Manrique
et al. 1996) and also because of the scarce grazing resources during winter. According
to Molinillo et al. (1997), the grazing resources in summer are three times greater than
in winter. The seasonal imbalance of pastures is due to the wide extension of shrubs on
low and medium slopes, which extended across abandoned fields during the twentieth
century (Lasanta et al. 2005). As a consequence, such landscapes in the Pyrenees
mountain are endangered if no management practices are implemented.
After abandonment shrubs cover has increased fire risk and decreasing livestock
resources. Here we describe how shrubland areas can be converted to grasslands by
means of clearing vegetation, a measure that has shown good results in other
Mediterranean areas (Stagliano et al. 1999; Lécrivain and Beylier 2004; Etienne and
Rigolot 2004; Delgado et al. 2004). Our scenario would increase the annual availability
of grazing resources by 16.7 %, mainly during the winter. This increase would reduce
the current imbalance in forage availability between warm and cold seasons, thereby
favouring the development of larger livestock exploitations that are more profitable and
14
have more guarantee of continuity (Manrique et al. 1999). Moreover, the increase in
livestock would allow a better use of summer pastures, which are currently suffering
intense degradation because of low livestock pressure, which in turn, causes the advance
of shrubs and forests (Camarero and Gutiérrez 2002) and the loss of forage.
Among the negative effects of our scenario is a possible increase in soil erosion
rates. Nevertheless, the results obtained from the experimental plots in the Aisa valley
allow us to conclude that the substitution of shrubs for grasslands in areas of less than
35% slope does not significantly increase erosion rates because a dense herbaceous
cover is enough to control soil erosion. Ruiz Flaño et al. (1992), García-Ruiz et al.
(1995, 1996 and 2005) have investigated in detail erosion processes in abandoned fields
and the influence of land use changes in hydrological dynamics and erosion rates These
studies demonstrate that a dense grass cover slows down aggressive erosion processes.
Our scenario also considers the changes in the landscape structure and causes a
higher fragmentation of land cover. Fragmentation usually is associated with negative
effects on movement and persistence of organisms, and the redistributing matter and
nutrients (Turner 1989). Furthermore, fragmentation may have socio-economic
consequences because the increase in diversity as a result of fragmentation can result in
more edges between potential conflicting habitats and hence in opportunities for
external elements to affect neighbours in a positive or negative way. Land use returns
and incentives can also be affected by new spatial arrangements (Nagendra et al. 2004).
Under human pressure, landscape is usually fragmented, and then not only habitat
heterogeneity but also isolation between suitable habitats and the shape of the habitat
patch determines biological diversity and species viability (Honnay et al. 2003;
Nikolakaki 2004).
15
Nevertheless, the effects of fragmentation can be observed from another point of
view. Agricultural abandonment in several mountainous Mediterranean areas has caused
the colonisation of old fields by highly inflammable species, which represent an
increased risk of fire (Papio and Trabaud 1991; Moreira et al. 2001; Romero and Perry
2004) and may have considerable implications for geomorphic and hydrological
processes (Kutiel et al. 1995; Cerdà 1998; Inbar et al. 1998; Moody et al. 2001).
Vicente-Serrano et al. (2000) indicated that in the Borau valley, between 1957 and
2000, fire risk increased in 27.1% of the total area because of agricultural abandonment
and lack of livestock pasturage. Management practices to reduce fire risk centre on
causing controlled fires (which is at present widely criticised), or reducing the spatial
continuity of shrub patches to mitigate fire propagation risk. In this case, the
fragmentation of shrubs, as in our scenario, could help to control fire risk in the area
In relation to the feasibility of the land cover modifications proposed, in nearby
areas such as the Pre-Pyrenees (20 km from the study area), the practice of clearing
vegetation has been applied for 30 years. Also, in the Iberian System (200 km to the
South of the study area), a mountainous area of similar characteristics to those of the
study area, the Regional Government has applied a policy of clearing the vegetation in
shrubland areas for livestock use for the last 10 years. In both cases, the results are
positive because of the increase in grazing resources and the decrease in fire risk.
Given that overall land management practices aim to combine the conservation of
resources and economic development, the conservation of shrubland areas is highly
debatable because this land cover causes landscape homogenisation and increased fire
risk (Lasanta et al. 2005). The Pyrenees have a lack large pasturage during winter and
spring. This deficit has risen over the last twenty years because of the increase in tourist
urbanisation of grasslands (Laguna and Lasanta 2003). Consequently, it is more logical
16
to selectively clear the slopes covered by shrubs to improve landscape structure, reduce
the risk of forest fires and increase forage to maintain more livestock.
Acknowledgements
This work has been supported by the projects: “Efectos erosivos del fuego a lo largo de
un gradiente climático: Aportaciones para la gestión de áreas quemadas” (REN200200133/GLO) and “Variabilidad climática y dinámica forestal en ecosistemas de ecotono”
(REN2003-07453) financed by the Spanish Comission of Science and Technology
(CICYT) and FEDER and “Programa de grupos de investigación consolidados”
(BOA 147 of 18-12-2002), financed by the Aragón Government. Research of the third
author was supported by a postdoctoral fellowship by the Ministerio de Educación y
Ciencia (Spain).
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Table 1: Confusion matrix. Vertical: field observations, Horizontal: obtained from remote
sensing classification. 1: Coniferous forests, 2: Leafy forests, 3: Pastures above timberline, 4:
Meadows, 5: Mixed forests, 6: Bare rock and alpine pastures, 7: Shrublands, 8: Bare soil, 9:
Urban areas, 10: Rivers, 11: Fluvial bars
Table 2: Pasture production in land covers (Mj/ha/año) and its seasonal distribution in the
Hecho valley.
Table 3: Area of various land use categories for present and proposed scenario
Table 4: Landscape index: Present situations and proposed scenario
Table 5: Changes in shrub and meadow indices by land cover type
Table 6: Patch size distribution for present situation and proposed scenario. Total number and
%.
Figure 1: Location and topography of the study area.
Figure 2: Present land use (1) and proposed land use scenario (2). Images 3 and 4 show the
grassland areas in their present state (3) and in the proposed scenario (4).
Figure 3: Spatial distribution of pastoral production (Mj/Year)
Field observations
Figure 4: Seasonal differences in present pastoral resources and in the proposed scenario.
Obtained by image classification
1 2 3 4 5 6 7 8 9 10 11 Omission
error (%)
1 35 5
3
2
22 1 1 5
3
3 16
4
1
3
5
4
15
6
1
9
1
18.6
24.1
15.8
25.0
21.1
18.2
Commission Mapping
error (%)
accuracy
(%)
4.7
77.8
31.0
57.9
26.3
66.7
25.0
60.0
42.1
55.6
0.0
81.8
25
7
8
9
10
11
2
1
14 1
1
22.2
5.6
73.7
0.0
0.0
100.0
Table 1.
26
Land cover
Coniferous forests
Leafy forests
Pastures above timberline
Meadows
Mj/Ha/
Año
0
0
13766
32700
Mixed forests
Bare rock and alpine pastures
Shrublands
Bare soil
Urban areas
16133
6727
4201
0
0
Winter
(%)
0
43.3
Spring
(%)
0
12
25.
3
11
19
35
Summer
(%)
78
0
60
100
22
Fall
(%)
10
31.
4
29
24
Table 2.
27
Land uses
Coniferous forests
Present state Scenario proposed
8957.2
8957.2 (29.1%)
(29.1%)
Leafy forests
6047.4
6047.4 (19.7%)
(19.7%)
Pastures above timberline
3979.5
3979.5 (12.9%)
(12.9%)
Meadows
982.5 (3.2%)
2044.3 (6.6%)
Mixed forests
4091 (13.3%)
4091 (13.3%)
Bare rock and alpine pastures 2308.3 (7.5%)
2308.3 (7.5%)
Shrublands
3909.4
2910.7 (9.4%)
(12.7%)
Bare soil
118.1 (0.4%)
54.99 (0.2%)
Urban areas
43.4 (0.1%)
43.4 (0.1%)
Rivers
313.8 (1.0%)
313.8 (1.0%)
Fluvial bars
17.2 (0.1%)
17.2 (0.1%)
Total
30767.9 30767.9 (100%)
(100%)
Table 3.
28
Area
Edge
Diversity
Configuration
Patch Number
Patch Density
Mean area (Patch)
Total Edges
Edges Density
Shannon Index
Interspersion and Justaposition
Index
Cohesion
Current Scenario
NP
7000
7419
PD
22.7
24.1
AREA (ha)
4.4
4.1
TE (km)
3489
3651
2
ED (km/km ) 113.4
118.7
SDI
1.859
1.881
IJI
CO
58.906
97.510
61.33
97.31
Table 4.
29
Shrublands
Area
Edges
Configuratio
n
Index
Current
Scenario
NP
PD
LPI
AREA_M
N
733
2.4
2.1
1017
3.3
0.4
5.3
2.8
TE
894.3
910.0
ED
29.1
IJI
CO
Meadows
Curre
Scenario
nt
173
309
0.6
1.0
0.8
0.9
5.7
6.6
29.6
189.
0
6.1
16.6
58.3
61.4
73.1
68.8
96.4
92.5
96.1
95.0
513
Table 5.
30
Shrubs
Present
Scenario
Present (%)
Scenario (%)
Meadows
Present
Scenario
Present (%)
Scenario (%)
Area Patches
3909.4
733
2910.7
1017
Area
982.5
2044.3
Patches
173
309
<1ha
455
670
62.1
65.9
<1ha
112
82
64.7
26.5
1-2
113
136
15.4
13.4
1-2
25
94
14.5
30.4
2-3
37
58
5.0
5.7
2-3
12
37
6.9
12
3-4
29
38
4.0
3.7
3-4
3
25
1.7
8.1
4-5
18
20
2.5
2.0
4-5
2
12
1.2
3.9
5-10 10-25 >25
34
22
25
41
31
23
4.6
3.0 3.4
4.0
3.0 2.3
5-10 10-25 >25
9
3
7
29
19
11
5.2
1.7 4.0
9.4
6.1 3.6
Table 6.
31
Figure 1.
32
Figure 2.
33
1
2
Figure 3.
3
35
1200
1000
PRESENT STATE
PROPOSED SCENARIO
MJ (x 103)
800
600
400
200
0
1
2
3
winter
spring
summer
fall
Figure 4.
36