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R.A. Diaz-Varela et al. 2010. Assessment of conservation status in managed chestnut forest
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indicators of the conservation status of chestnut woodlands. Then using statistic methods, we
explored the predictor value of several datasets potentially related to forest structure and
composition, like patch morphology metrics (Saura and Carballal 2004) or satellite image
texture (Kayitakire et al. 2006). As these potential predictor features are prone to be influenced
by topography, we also considered terrain attributes in the analysis.
We conducted the study in a sector of NW Iberian Peninsula, an area where good examples of
chestnut forest stands with different conservation status occur. In order to provide a dataset with
an adequate size to assemble variability in forest stand conditions, we considered an area of
approximately 24 448 km 2 corresponding with the land extent of the Landsat TM scene 204-30
(Fig. 1). Altitudes range from sea level to about 2000 m a.s.l. showing a contrasted relief.
Biogeographically most of the area is included in the Atlantic Region, but a sector of the SE
quadrant of the scene belongs to the Mediterranean region, (European Environmental Agency
2008; Rivas-Martinez and Rivas-Saenz, 2009). Natural vegetation is dominated by different
deciduous forest, mostly dominated by Quercus robur and Quercus petraea, coexisting toward
the west with Betula alba and Fagus sylvatica and interspersed with mixed mesophytic
woodland at lower altitudes. Transition to Continental and Mediterranean environments are
characterised by Quercus pyrenaica forests, while some evergreen or sclerophyllous forest are
restricted to the dryer and warmer locations (Rivas-Martínez, 2007). Chestnut forests appear as
pure stands or interspersed with different deciduous forests or alien species in the area, being
more frequent towards the East.
2. Methodology
Chestnut forest variables were extracted from the Digital Forest Map of Spain at a scale 1:50
000 (Ministerio de Medio Ambiente, 2002). This map is based on digitalisation of forests from
satellite images or aerial orthophotographs and supported by fieldwork and ancillary data.
Minimum mapable area is 2.5 has for forested area and 6.25 has for other land cover types. The
map provides information on tree species composition, overall (in percentage) and per-species
(in 10% intervals) canopy coverage, along with other stand variables. We queried the map to
extract the chestnut forest patches for the study area. At the effects of the present work and as
we are focused on dense forest with a significant share of chestnut in the canopy, we considered
patches with minimum crown coverage of tree species of 60 %, a minimum of 20 % of chestnut
coverage, and the interval 2 to 4 of stage of stand development for chestnut out of a scale from 1
to 4, resulting a total of 1585 chestnut patches. For each patch we retrieved chestnut canopy
coverage in intervals of 10%, coded as a scale variable from 1 (0-10% to 10 (90-100%) along
with the stage of stand development. Then we set three classes of chestnut stand quality based
on the combination of these two variables (cf. table 1), being class 1 the worst and 3 the best
quality. As some of the analysis inputs must be in raster format, we also generated 30 m
resolution raster mask of chestnut patches.
A collection of morphometric parameters were extracted from the selected forest patches by
means of two different approaches (table 2). The first approach was based on the use six
different landscape metrics: patch area (MPS), patch edge (TE), shape index (MSI), perimeter
area ratio (MPAR), fractal dimension (MFRACT), and number of shape characteristic points
(NSCP). All were calculated using the software V-Late (Lang and Tiede 2003), except for
NSCP (Moser et al. 2002). Calculation was made at patch level. We also used the GUIDOS
software (Graphical User Interface for the Description of image Objects and their Shapes)
initially designed for morphological spatial pattern analysis (MSPA) of forest functional
connectivity (Vogt et al., 2009) as an alternative morphometic approach. The output of the
MSPA is a raster layer where patch cells are assigned to seven morphological spatial pattern
classes: edge, core, perforated, islets, bridge, loop and branch. We ran the software using the
default parameters for the computation of MSPA, considering one pixel edge (30 m).
Forest Landscapes and Global Change-New Frontiers in Management, Conservation and Restoration. Proceedings of the IUFRO Landscape Ecology
Working Group International Conference, September 21-27, 2010, Bragança, Portugal. J.C. Azevedo, M. Feliciano, J. Castro & M.A. Pinto (eds.)
2010, Instituto Politécnico de Bragança, Bragança, Portugal.