Landscapes Forest and Global Change - ESA - Escola Superior ...

R.A. Diaz-Varela et al. 2010. Assessment of conservation status in managed chestnut forest
202
Considering that terrain relief might play a key role in the landscape structure and in vegetation
pattern in general (Hoechstetter, et al. 2008) we selected several terrain features that showed
their potential as predictors for forest distribution in previous works (Lindenmayer et al. 1999).
Terrain features were derived from a raster digital elevation model with a spatial resolution of
30 m. We selected a number of first and second order terrain features widely used in
hydrological, geomorphological, and ecological studies (Wilson and Gallant 2000) along with
other addressing more specifically vegetation and forest assessment (Mcnab 1989; Roberts and
Cooper 1989) as detailed in table 2.
Image texture has shown its potential as predictor for forest stand characteristics (see p.e.
Franklin et al., 2001). In order to assess its value as predictor of chestnut stand quality at patch
level, we computed eight texture features based on kernel grey level co-occurrence matrices as
proposed by Haralick et al., (1973) on a NDVI (greenness index) image, using the software
package PCI TM 9.1 (cf. table 2) setting a direction 45º, displacement of one pixel and a window
size of 5x5. We used a relatively small size for the kernel as some small or narrow patches are
foreseen and to avoid influence from the neighbouring patches.
As terrain relief and texture information are refered at pixel level, we computed mean (MN) and
standard deviation (SD) of these features for each patch in order to obtain information at patch
level. The final dataset comprises 16 texture variables (MN and SD for the eight texture
features), 14 terrain variables (MN and SD for the seven terrain features) and 13 patch
morphology variables (six patch landscape metrics along with the percentages of each of the
seven typologies of the MPSA analysis for a given forest patch). These 43 variables were
analysed by means of a Classification and Regression Tree (CaRT) procedure using the
statistical package SPSS TM 16.0. CaRT is a data mining technique pursuing the recursive binary
partition of a multivariate space of independent or predictor variables into regions for which the
values of the dependent or response (in this case stand quality) variable are approximately equal.
In this application, we use the method for exploring the internal structure of the dataset in order
to assess the potential of the different variables as chestnut patch quality predictors.
3. Results
Figure 2 shows the results of the CaRT application on the 43 potential predictors, pruned to 3
branch levels. The first significant classification variable was mean terrain slope, with a value of
15º that splits a terminal node corresponding with patches of gently slope assigned mainly
(72 %) to the worst class of chestnut stands quality. The second significant classification
variable was the mean patch value of GLCM mean (Mean_MN), a variable sensitive to the
image tone and texture, that could be interpreted in this case as an indicator of the degree of
texture complexity and greenness inside a patch. Values lower that 48 for this variable led to a
third level defined by terrain elevation, spitting at a value of approximately 600 m in two
branches, one dominated by the lower quality classes corresponding to low altitudes and other
dominated by high quality stands, located at higher altitudes. Values of Mean_MN higher that
48 led to another splitting level, where the criterion was patch size. In this case low area patches
correspond with good quality stands, while larger patches correspond to stands containing lower
chestnut canopy coverage and development (qualities 2 and 3).
4. Conclusions
We assessed the potential discrimination power of different kind of variables and chestnut forest
conservation status by means of CaRT data mining. Despite of the relative high degree of
impurity in some of the nodes, the method allowed us to recognise some mayor trends of
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.