Spatial distribution of main forest soil groups in Croatia as a function of basic pedogenetic factors

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1 Ecological Modelling 170 (2003) Spatial distribution of main forest soil groups in Croatia as a function of basic pedogenetic factors Oleg Antonić a,, Nikola Pernar b, Sven D. Jelaska c a Ru der Bošković Institute, Zagreb, Croatia b Forestry Faculty, Zagreb, Croatia c Oikon Ltd., Zagreb, Croatia Abstract The model of spatial distribution of main forest soil groups in Croatia was developed as a function of basic pedogenetic factors: lithological substratum, macroclimate and relief. Used data about soil group, lithological substratum, terrain slope and aspect were collected on 1881 soil profiles. Macroclimatic data were estimated for each soil profile by spatial interpolation between meteorological stations. Feedforward neural networks were used as modelling tool. The final model has total classification correctness of 63.5% for training data set and 62.3% for independent test data set. The best result (86.4%) was achieved for fluvisols which are strongly spatially correlated with alluvial sediment in a flood plains. The worst result was achieved for luvisol (14.2%) which mainly comprised very old soils, probably developed under pedogenetic factors different from actual. The model was applied on entire Croatian territory aiming at construction of potential spatial distribution of main forest soils (without human impact), which was compared by the potential spatial distribution of major forest types modelled independently Elsevier B.V. All rights reserved. Keywords: Lithological substratum; Macroclimate; Neural networks; Pedogenesis; Raster-GIS; Relief 1. Introduction Explanation of spatial distribution of land cover entities (vegetation, soil) as a function of environmental variables is interesting both from theoretical (correlation between land cover and environment) and practical (environmental management) point of view. Numerous related research in vegetation science, focused on the spatial distribution of vegetation types and/or particular species has been done on national (see e.g. Brzeziecki et al., 1995; Leathwick, 1998; Antonić et al., 2000), and regional scales (e.g. Van de Rijt et al., 1996; Tappeiner et al., 1998; Gottfried Corresponding author. Fax: address: oantonic@oikon.hr (O. Antonić). et al., 1998; Guisan et al., 1998, 1999; Zimmermann and Kienast, 1999). To the contrary, explanation of spatial variability of soils as a function of relevant environmental variables (pedogenetic factors) is less common and mostly oriented to the regional scale (e.g. McKenzie and Austin, 1993; Gessler et al., 1995; McKenzie and Ryan, 1999). These researches are also mostly oriented to the spatial prediction of particular soil properties (e.g. soil profile depth, total soil phosporus and carbon in case of work of McKenzie and Ryan, 1999), rather than to the spatial prediction of soil groups, as pedosystematic units, which describe soil characteristics in total. Starting with this kind of pedological research in Croatia, the country with large macroclimatic, lithological, topographical, edaphical and vegetational spatial variability, we found suitable to try to explain main /$ see front matter 2003 Elsevier B.V. All rights reserved. doi: /s (03)

2 364 O. Antonić et al. / Ecological Modelling 170 (2003) soil spatial variability as a function of basic pedogenetic factors for the entire state territory, before the more detailed research at regional and local scales is undertaken. Therefore, the basic aim of this research was development of the model of spatial distribution of main forest soil groups existing in Croatia, in order to explain this distribution by spatial distributions of pedogenetic factors. Additional aims of the study were (1) construction of potential spatial distribution (without human influence) of main forest soil groups for the entire Croatian territory by the application of the gained model and (2) comparison of yielded soil potential distribution with the potential distribution of major forest types, analogously modelled in previous research. 2. Material and methods 2.1. Soil data Existing soil database (XXX, 1998a), which covers the entire territory of the Republic of Croatia was used as the main data source in research. This database contains number of soil profiles and associated attributes, including soil groups according to the World Reference Base (WRB) for soil resources nomenclature (XXX, 1998b), which were used as dependent variable. Soil group mainly influenced by humans (anthrosol), was not included in the analysis as a non-forest group. The rest of the database contains 11 main natural soil groups. All of these are forest soil groups, due to the fact that the forest is dominant vegetation in Croatia, out of human impact on land cover. Among these 11 soil groups, acrisol, chernozem, podzol and vertisol were omitted from analysis, due to the relative rareness in Croatia, which results in a small number of soil profiles in a database (see Fig. 1). Remaining seven soil groups (cambisol, fluvisol, gleysol, leptosol, luvisol, planosol and regosol), which were included in the analysis, comprise 1881 soil profiles in total. Spatial distribution of these soil profiles in Croatia is shown on Fig. 2. Short descriptions of mentioned seven soil groups (following XXX, 1998b) are given in this paragraph. Cambisol (CM) represents soils which have a horizon of alteration (cambic horizon), or have a mollic horizon overlying a subsoil which has a base saturation of less than 50% in some part within 100 cm from the soil surface; or have one of the following diagnostic horizons within the specified depth from the soil surface: (a) an andic, vertic or vitric horizon start- portion in used database (%) AC CH CM FL GL LP LV PL PZ RG VR soil type (WRB nomenclature) Fig. 1. The main soil classes according to the WRB nomenclature, contained in the used database. Acrisol (AC), chernozem (CH), podzol (PZ) and vertisol (VR) were not included in the analysis as rare soil groups in Croatia, represented in used database with small number of soil profiles (black bars). Other soil groups (grey bars) were included in the analysis (CM, cambisol; FL, fluvisol; GL, gleysol; LP, leptosol; LV, luvisol; PL, planosol; RG, regosol).

3 O. Antonić et al. / Ecological Modelling 170 (2003) Fig. 2. Spatial distribution of soil profiles used in model development and testing in Croatia. ing between 25 and 100 cm, (b) a plintic, petroplintic or salic horizon starting between 50 and 100 cm, in the absence of loamy sand or coarser textures above these horizons. Fluvisols (FL) are soils, frequently hydromorphic, formed on recent alluvial deposits. They have no diagnostic horizons other than a histic, mollic, ochric, takyric, umbric, yermic, salic or sulphuric horizon. Fluvic soil material has to start within 25 cm from the soil surface and continue to a depth of at least 50 cm from the soil surface. Gleysols (GL) are soils which have gleyic properties within 50 cm from the soil surface and no diagnostic horizons other than anthraquic, histic, mollic, ochric, takyric, umbric, andic, calcic, cambic, gypsic, plinthic, salic, sulfuric or vitric horizon within 100 cm from the soil surface. Leptosols (LP) are soils which are limited in depth by continuous hard rock within 25 cm from the soil surface; or overlying material with a calcium carbonate equivalent of more than 40% within 25 cm from the soil surface; or containing less than 10% (by weight) fine earth to a depth of 75 cm or more from the soil surface. They have no diagnostic horizons other than a mollic, ochric, umbric, yermic or vertic horizon. Luvisols (LV) are soils which have an argic horizon with a cation exchange capacity (by 1 M NH 4 OAc) equal to or more than 24 cmol c kg 1 clay throughout. The base saturation is at least 50% in the lower part of the B horizon at less than 125 cm depth. Luvisols have neither a mollic A horizon nor an albic E horizon and do not have an aridic soil moisture regime. Planosols (PL) are soils with an albic horizon having characteristics of hydromorphy (mottles, spots, manganese iron concretions) resting at less than 125 cm depth on a poorly permeable argillic or natric horizon showing an

4 366 O. Antonić et al. / Ecological Modelling 170 (2003) abrupt textural transition, excluding a spodic horizon. Regosols (RG) are non-climatic unweathered mineral soils formed of an unconsolidated material (excluding recent alluvial deposit), generally without diagnostic horizons other than an ochric A horizon. There are no hydromorphy at less than 50 cm depth, nor high salinity, nor vertic or andic features Pedogenetic factors Lithological substratum, relief parameters (terrain slope and aspect) and macroclimate were used as predictors (pedogenetic factors) of spatial distribution of soil groups. The influence of vegetation as pedogenetic factor was not included in the model, under the hypothesis of mainly joint postglacial origin of vegetation and soil on entire Croatian territory, as a function of other environmental factors (following Jenny, 1961). The data about lithological substratum were taken from mentioned soil database, for each soil profile belonging to one of the seven selected main forest soil groups. The original lithological data were simplified to following 13 basic classes of lithological substratum: 1. loess (silts and sands) 2. alluvial deposits 3. diluvial loam and clay 4. colluvial deposits, moraine 5. granite and acid igneous rocks (and gneiss) 6. schist 7. limestone, limestone breccia and conglomerate 8. dolomite and dolomitic breccia 9. sandstone, chert and quartzite (and associated breccia and conglomerate) 10. flysh, marl, marly and other soft limestones 11. shale, clay and loam 12. sand, pebbles (and aeolian sand) 13. neutral and basic rocks This nominal variable was included in the model development as 13 binary ( dummy ) variables. The data about terrain slope and aspect were also taken from soil database. Terrain aspect was originally described as nominal variable, by the use of 16 basic cardinal points (azimuths). They were transformed to ordinal variable with 9 categories, from 1 (north), over 3 (north-east, north-west), 5 (east, west, flat terrain) and 7 (south-east, south-west), to 9 (south), with respective interpolation of other categories (2, 4, 6 and 8). Other pedogenetically relevant relief parameters which could be possibly derived from digital elevation model (DEM), such as topographic solar irradiation (Dubayah and Rich, 1995; Antonić, 1998), soil moisture potential and snow accumulation potential (e.g. Brown, 1994), exposure to wind (Antonić and Legović, 1999) or depth in sink (Antonić et al., 2001a), were assumed as unsuitable for this research because they relate to the finer spatial scale, and consequently to the smaller research area. Four variables were selected as macroclimatic predictors: (1) monthly mean air temperature, (2) monthly precipitation, (3) monthly mean global solar irradiation on horizontal surface at ground level and (4) monthly potential evapotranspiration on horizontal surface. The first two climatic variables were taken directly from the weather station chronicles. The last two were modelled for each weather station as a function of relevant climatic variables observed at the respective station, using the Nikolov and Zeller model (1992) for global solar irradiation and the Priestly and Taylor model (1972, see also Bonan, 1989) for potential evapotranspiration. Spatial distributions of climatic variables were averaged for a period between 1956 and 1995, in spatial resolution of 300 m 300 m, using interpolation models presented in Antonić et al. (2001b). These models were developed using neural networks (NN), based on data from 127 weather stations and on elevation data from DEM (spatial resolution of 300 m 300 m). The basic set of 48 independent macroclimatic estimators (4 variables by 12 months) was reduced to 5 composite estimators (non-linear analogues of principal components) using the five-layered autoassociative NN (see Bishop, 1995), which have 48 neurons in the first and last layer (48 basic estimators), 15 neurons in the second and the fourth layer and 5 neurons in the central layer. Logistic function was used as activation function. Using this NN architecture, 99.79% of total macroclimatic variability was explained. After removing last two layers, this autoassociative NN was used as a input for development of soil group prediction model. Consequently, the soil group prediction model developed in this research was actually driven by all 48 independent macroclimatic estimators, which were only filtered through the autoassociative NN.

5 O. Antonić et al. / Ecological Modelling 170 (2003) Model The initial data set of 1881 cases (soil profiles) was randomly split into three subsets: training (approximately 50% of cases), verification and test set (both approximately 25% of cases). The first set was used for finding of the NN parameters, the second was used to check for overfitting (see e.g. Lawrence et al., 1997), and the third contained fully independent data which were used for testing of different NNs and for evaluation of final model. Total data set used for model development was unbalanced, i.e. particular soil group was represented with different number of cases (pixels), according to its portion in original database. Use of balanced data set, performed in separate control analysis, did not significantly improve model reliability. Prediction model was derived using the feedforward NN with multilayer perceptrons (MLP) which is appropriate for classification problems (see e.g. Bishop, 1995; Patterson, 1996). Logistic function was used as activation function and back-propagation method is used for the network training. During the preliminary research, a number of NN architectures were tested. Each of tested architectures had input layer with 20 independent estimators (13 dummy variables of lithological substratum, terrain slope, terrain aspect and 5 composite macroclimatic estimators) and output layer with 7 main soil groups, but variable number of hidden layers (1 2) and its neurons (3 10). The finally chosen NN architecture (Fig. 3) had one hidden layer with five neurons. Further increase in NN architecture complexity did not yield significant model improvement in a reasonable model training time. Each neuron of the hidden and output layer calculates its output value using the expression: ( ) n β = act a + b i α i (1) i=1 where β is neuron output value, α i is ith neuron input value, n is number of input connections, a and b i are Fig. 3. Graphical visualisation of neural network finally used for the model development. The first (left) layer represents independent variables (predictors). Lithological substratum, as nominal variable with 13 classes (main types of substratum), was included in the model development as 13 binary ( dummy ) variables. Other independent variables are continuous. The second, hidden layer contains neurons which increase complexity of the model and the third (right) layer contains model output, namely probability for each main forest soil group (group with maximum probability is chosen as a result).

6 368 O. Antonić et al. / Ecological Modelling 170 (2003) Table 1 Correctness of classification by the model in total and for particular soil groups. WRB group CM FL GL LP LV PL RG Total Train Verify Test N (total) N (correct) % correct CM FL GL LP LV PL RG k (total) k (train) k (verify) k (test) CM FL GL LP LV PL RG All types WRB group indicates soil group according to the WRB nomenclature (CM, cambisol; FL, fluvisol; GL, gleysol; LP, leptosol; LV, luvisol; PL, planosol; RG, regosol). N (total) is total number of cases (soil profiles) within particular soil groups and within different data sets. N (correct) is number of cases (soil profiles) correctly classified by the model. Grey area represents classification matrix (rows, observed cases, columns, predicted cases, values, number of cases). κ indicates Kappa statistics. empirical parameters (a is neuron threshold and b i is ith input weight), and act is activation function: act(x) = e x (2) 3. Results and discussion The NN model originally calculates the probability of incidence of each soil group for given set of input values. Thus, the model returns seven probabil- Fig. 4. Left: potential spatial distribution of major forest soil groups (predicted by the model), for the entire territory of Republic of Croatia (CM, cambisol; FL, fluvisol; GL, gleysol; LP, leptosol; LV, luvisol; PL, planosol; RG, regosol). Right: potential spatial distribution of major forest vegetation types (predicted by the model presented in Antonić et al., 2000; 1, lowland deciduous forests including flood-plains and swamps; 2, colline and submontane deciduous forests; 3, montane deciduous mesophilic forests; 4, altimontane mixed (coniferous/deciduous) forests; 5, subalpine deciduous and coniferous forests; 6, montane deciduous thermophilic forests; 7, Mediterranean deciduous forests; 8, Mediterranean mixed (evergreen/deciduous) forests; 9, Mediterranean evergreen forests).

7 O. Antonić et al. / Ecological Modelling 170 (2003) ities for each case (soil profile). The soil group with largest probability is used for the classification of the given case, within the particular initialisation of the network. The final model combines results of 11 independent initialisations of the network and selects that soil group which was most frequently selected in particular networks. Classification correctness for particular soil groups is shown in Table 1. The final model has total classification correctness of 63.5% for training data set and 62.3% for independent test data set, and overall Kappa statistics (Monserud and Leemans, 1992) of 0.52 and 0.50, respectively. Consequently, the total agreement between observed and modelled main forest soil groups could be characterised as fair (Landis and Koch, 1977). The best result (86.4% of correctly classified cases) was achieved for fluvisols which are strongly spatially correlated with alluvial sediment in flood plains. According to Kappa statistics result for fluvisol could be characterised as very good. The worst result was achieved for luvisol (14.2%, very poor according to Kappa statistics) which mainly comprised very old soils, probably developed under pedogenetic (especially macroclimatic) factors different from actual (and used in model development). Other soil groups were classified between 55 and 76% of correctness ( fair according to Kappa statistics). Interpretation of unexplained variability, could be addressed to: (1) soil profiles mapping errors, (2) errors in mapping of pedogenetic factors, (3) use of dis- Fig. 5. Correlation between potential spatial distribution of main forest soil groups (yielded in this research) and potential spatial distribution of main forest vegetation types (yielded in research presented in Antonić et al., 2000). Combinations of soil group and vegetation type (x-axis) are denoted according to abbreviations given in description of Fig. 4 (e.g. abbreviation CM-7 represents Mediterranean deciduous forests over cambisol). Lines relate to the left y-axis (bold line indicates observed frequency of combinations and normal line represents frequency of combinations expected by chance) and circles relate to the right y-axis (relative difference between observed and expected frequencies).

8 370 O. Antonić et al. / Ecological Modelling 170 (2003) crete and general soil groups, while natural boundaries between groups are often blurred, (4) local influences of relief (e.g. terrain curvature, flow accumulation, topographic solar radiation and topographic evapotranspiration, exposure to wind) not included in the model and (5) the model error. The model was applied for the entire Croatian territory, aiming at construction of hypothetical (potential, without human impact) spatial distribution of seven main forest soils. This was done within a frame of raster geographic information system (raster-gis), in a spatial resolution of 300 m 300 m, using lithological map of Croatia (1:300,000), terrain slope and aspect derived from DEM and above mentioned spatial interpolations of macroclimatic variables. The result is shown on Fig. 4 (left). Antonić et al. (2000) developed analogous model for predicting spatial distribution of major forest types in Croatia, as a function of the same macroclimatic variables (mean monthly temperature, monthly precipitation, monthly mean global solar irradiation and monthly potential evapotranspiration) and the same DEM derivatives (terrain aspect and slope), while lithological substratum was not included (used major forest types include subtypes on different parent materials). The model was also applied for the entire Croatian territory (using the same spatial resolution of 300 m 300 m), aiming at construction of potential spatial distribution of major forest types (Fig. 4, right). The potential distribution of main forest soil groups yielded in this research and potential distribution of major forest types achieved by Antonić et al. (2000), were overlaid and sampled in raster-gis, aiming at comparison of these two independent results. Chi-square test was used in order to compare absolute frequencies of soil group/vegetation type combinations achieved by the overlap of modelled soil and vegetation map, and theoretical frequencies of combinations expected by chance. Yielded results (chi-square = 368,006.6, d.f. = 62, P< ) clearly suggest that relation between main soil groups and vegetation types is non-random, i.e. that particular vegetation types prefer specific soil groups (Fig. 5), e.g. lowland deciduous forests fluvisol and gleysol, colline and submontane deciduous forests planosol, subalpine deciduous and coniferous forests leptosol and most of the other major forest types cambisol. This result indirectly supports above mentioned hypothesis about mainly joint postglacial origin of vegetation and soil on the entire Croatian territory, as a function of other environmental factors (according to Jenny, 1961). 4. Conclusion Developed model explains fair part of the total spatial variability of the main forest soil groups in Croatia. However, this result has only theoretical meaning, because yielded prediction power of the model is not good enough for the practical use, neither for particular soil groups nor in total. The basic reason for this result lies in a facts that (1) classification of soil in the main groups covers only the part of the real soil variability and (2) pedogenetic factors and processes (especially those connected to relief) are mostly related to the finer spatial resolution (in comparison to ones used in this research). The model improvement in the future could be expected in the following directions: (1) focusing on smaller and consequently less heterogeneous areas, (2) increase in spatial resolution, (3) inclusion of other DEM derivatives (e.g. flow accumulation, depth in sink, terrain exposure to wind, snow accumulation potential), (4) inclusion of human impact (e.g. landuse parameters), (5) prediction of pedosystematic units at finer scale (e.g. subgroups) and (6) prediction of particular pedophysical and pedochemical variables as more objective model output. It is expected that the results yielded by the model improved in this way will be very useful for the forest management, as a part of evaluation of growth conditions and site productivity. Acknowledgements This research has been supported by Croatian Ministry for Research and Technology and by OIKON Ltd., Zagreb. References Antonić, O., Modelling daily topographic solar radiation without site-specific hourly radiation data. Ecol. Model. 113,

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