ABSTRACT

Private owned forests occupy 22 % of the total forest and forest land area i.e. approximately 600,000 ha, according to Forest Management Plan for the area of the Republic of Croatia. Some of the private owned forest characteristics are as follows: small cadastral plots and estates, large number of landlords for the same area and great discrepancy of cadastral data. In this research, analyzed data about cadastral plots and landlords are taken from 319 cadastral municipalities (CM) distributed in 5 regions of the Republic of Croatia as well in 53 private owned forest management units. The sample represents 8.74 % of the Croatian area i.e. 15.69 % of the private owned forest area, according to the Forest Management Plan for the area of the Republic of Croatia. In addition to the statistical characteristic of the cadastral plots regarding regions, two cluster analyses are performed for better characterization of the regions. The first cluster analysis refers to the possession variables: average area of the cadastral plots inside CMs included in private-owned forest management plans (P-FMP), average area of the estates inside CMs included in P-FMP and average amount of the landlords in possession sheets by CMs included in P-FMP. Second cluster analysis refers to spatial variables: total area of the CMs, ratio of the forested areas by private-owned forests and ratio of the “non-forest” land according to cadastral land use data included in P-FMP. This research gave us an insight about the most pronounced characteristic of each region and about unique relation of the observed characteristics for each region. This research defines that regions Istria and Kvarner, Slavonija and central Croatia are more differentiated from the other regions in terms of possession variables, whilst the regions of Gorski kotar, Lika and Dalmatia are better explained with spatial variables as differentiation factor from other regions. Few principles are also confirmed as the results of the cluster analysis, for example the relation between the size of the cadastral plots and number of the landlords as well as relation regarding private owned forests ratio and ratio of the „non-forest“ land included in P-FMP.

ABSTRACT

The conservation of gray wolf (Canis lupus) and its coexistence with humans presents a challenge and requires continuous monitoring and management efforts. One of the non-invasive methods that produces high-quality wolf monitoring datasets is camera trapping. We present a novel monitoring approach where camera traps are positioned on wildlife crossing structures that channel the animals, thereby increasing trapping success and increasing the cost-efficiency of the method. In this way we have followed abundance trends of five wolf packs whose home ranges are intersected by a motorway which spans throughout the wolf distribution range in Croatia. During the five-year monitoring of six green bridges we have recorded 28 250 camera-events, 132 with wolves. Four viaducts were monitored for two years, recording 4914 camera-events, 185 with wolves. We have detected a negative abundance trend of the monitored Croatian wolf packs since 2011, especially severe in the northern part of the study area. Further, we have pinpointed the legal cull as probable major negative influence on the wolf pack abundance trends (linear regression, r2 > 0.75, P < 0.05). Using the same approach we did not find evidence for a negative impact of wolves on the prey populations, both wild ungulates and livestock. We encourage strict protection of wolf in Croatia until there is more data proving population stability. In conclusion, quantitative methods, such as the one presented here, should be used as much as possible when assessing wolf abundance trends.

ABSTRACT

While experiments in boundary layer wind tunnels remain to be a major research tool in wind engineering and environmental aerodynamics, designing the modeling hardware required for a proper atmospheric boundary layer (ABL) simulation can be costly and time consuming. Hence, possibilities are sought to speed-up this process and make it more timeefficient. In this study, two artificial neural networks (ANNs) are developed to determine an optimal design of the Counihan hardware, i.e., castellated barrier wall, vortex generators, and surface roughness, in order to simulate the ABL flow developing above urban, suburban, and rural terrains, as previous ANN models were created for one terrain type only. A standard procedureisusedindevelopingthosetwoANNsinorderto further enhance best-practice possibilities rather than to improve existing ANN designing methodology. In total, experimental results obtained using 23 different hardware setups are used when creating ANNs. In those tests, basic barrier height, barrier castellation height, spacing density, and height of surface roughness elements are the parameters that were varied to create satisfactory ABL simulations. The first ANN was used for the estimation of mean wind velocity, turbulent Reynolds stress, turbulence intensity, and length scales, while the second one was used for the estimation of the power spectral density of velocity fluctuations. This extensive set of studied flow and turbulence parameters is unmatched in comparison to the previous relevant studies, as it includes here turbulence intensity and power spectral density of velocity fluctuations in all three directions, as well as the Reynolds stress profiles and turbulence length scales. Modeling results agree well with experiments for all terrain types, particularly in the lower ABL within the height range of the most engineering structures, while exhibiting sensitivity to abrupt changes and data scattering in profiles of wind-tunnel results. The proposed approach allows for quicker and more effective achieving targeted flow and turbulence features of the ABL wind-tunnel simulations as compared to the common trial and error procedures. This methodology is expected to enable wind-tunnel modelers a quick and time-efficient designing of ABL simulations in studies dealing with air pollutant dispersion, wind loading of structures, wind energy, and urban micrometeorology, where atmospheric flow and turbulence play a key role.

ABSTRACT

This study analyzes the relationship between Aerosol Optical Depth (AOD) obtained from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based PM10 mass concentration distribution over a period of 5 years (2008–2012), and investigates the applicability of satellite AOD data for ground PM10 mapping for the Croatian territory. Many studies have shown that satellite AOD data are correlated to ground-based PM mass concentration. However, the relationship between AOD and PM is not explicit and there are unknowns that cause uncertainties in this relationship. The relationship between MODIS AOD and ground-based PM10 has been studied on the basis of a large data set where daily averaged PM10 data from the 12 air quality stations across Croatia over the 5 year period are correlated with AODs retrieved from MODIS Terra and Aqua. A database was developed to associate coincident MODIS AOD (independent) and PM10 data (dependent variable). Additional tested independent variables (predictors, estimators) included season, cloud fraction, and meteorological parameters — including temperature, air pressure, relative humidity, wind speed, wind direction, as well as planetary boundary layer height — using meteorological data from WRF (Weather Research and Forecast) model. It has been found that 1) a univariate linear regression model fails at explaining the data variability well which suggests nonlinearity of the AOD-PM10 relationship, and 2) explanation of data variability can be improved with multivariate linear modeling and a neural network approach, using additional independent variables.

ABSTRACT

The new method for the forecasting hourly concentrations of air pollutants is presented in the paper. The method was developed for a site in urban residential area in city of Zagreb, Croatia, for four air pollutants (NO2, O3, CO and PM10). Meteorological variables and concentrations of the respective pollutant were taken as predictors. A novel approach, based on families of univariate regression models, was employed in selecting the averaging intervals for input variables. For each variable and each averaging period between 1 and 97 h, a separate model was built. By inspecting values of the coefficient of correlation between measured and modelled concentrations, optimal averaging periods for each variable were selected. A new dataset for building the forecasting model was then calculated as temporal moving averages (running means) of former variables. A multi-layer perceptron type of neural networks is used as the forecasting model. Index of agreement, calculated for the entire dataset including the data for model building, ranged from 0.91 to 0.97 for the respective pollutants. As suggested by the analysis of the relative importance of the input variables, different agreements for different pollutants are likely due to different sources and production mechanisms of investigated pollutants. A comparison of the new method with more traditional method, which takes hourly averages of the forecast hour as input variables, showed similar or better performance. The model was developed for the purpose of public-health-oriented air quality forecasting, aiming to use a numerical weather forecast model for the prediction of the part of input data yet unknown at the forecasting time. It is to expect that longer term averages used as inputs in the proposed method will contribute to smaller input errors and the greater accuracy of the model.

ABSTRACT

Within the framework of the 3-year project "Mapping the habitats of the Republic of Croatia" the marine benthic habitats of the entire Croatian maritory were mapped. The supralittoral and the mediolittoral were mapped as a function of the coastal lithology and the presumed levels of human impact (both in scale 1:100,000). The infralittoral was mapped on the basis of spatial modelling (using neural networks as a modelling tool, data about habitats collected by fieldwork as the independent variable for training and testing the model, and the digital bathymetrical model, the distance from coast, the second spectral channel of Landsat ETM+ satellite image and the sea bottom sea temperature, salinity and current magnitude, as dependent variables). The circalittoral and the bathyal were mapped by overlapping and reinterpretation of the existing spatial databases (bathymetry and lithology) within the framework of the raster-GIS.

ABSTRACT

There is a great challenge for marine ecologists to explain and map benthic marine habitats by developing new methods that identify and link environmental processes responsible for those map patterns. Methods that use single factors such as sediment type or water depth for predicting and explaining marine benthic habitats are generally inadequate when applied on a broad scale. Using a combination of factors such as near bottom velocity (tidal and wind induced), temperature and salinity fields in conjunction with sediment type and depth those habitats may be more clearly characterized. In the frame of the project “Mapping the habitats of the Republic of Croatia” (financed by the Ministry of Environmental Protection and Physical Planning of the Republic of Croatia) specific oceanographic variables (sea bottom temperature, sea bottom salinity, sea bottom currents) have to be modelled as an input parameters for neural network models developed for spatial prediction of infralittoral habitat types. The physical model used in this study is based on a three-dimensional finite element method and unstructured grid. By running the model on the high resolution finite element mesh we were able to include the main features of the very complex East Adriatic coast, representing a domain with 77 islands and many narrow channels as well as varied bathymetry. To our knowledge, this study is making the first step in gaining better knowledge and understanding of the complicated relationships of specific bottom variables (model estimates), that are playing a key role for infralittoral habitat distributions, for the whole Adriatic Sea.

ABSTRACT

The paper investigates the possibility of assessing damage (health status) of silver fir (Abies alba Mill.) using multivariate regression models in the function of geomorphological, climatic and stand factors. Research was carried out in beech-fir forests (Abieti-Fagetum), on limestone-dolomite substrate, in the Dinaric part of the silver fir range in the Republic of Croatia. A general linear modelling method was used, where square terms and interaction terms (multiplication products) of original variables were treated as independent linear predictors. Twenty-seven separate models (all of them with significant part of explained fir damage variability) were developed regarding the different subsets of input data, different subsets of independent variables and different model design. Some of these models could be preliminary used for spatial predicting and mapping of fir damage in a frame of raster-GIS, for entire area of Dinaric part of Abieti-Fagetum in Croatia.

ABSTRACT

In this research we investigated response of forest herbs to available light conditions in the forest of silver fir (Abies alba) and beech (Fagus sylvatica) which are most frequent altimontane forest on Croatian karst, using the hemispherical photographs for measuring available light. In addition to this, we tested the possibility of estimation light condition parameters on the large areas using the remotely sensed data, as potential support to the spatial prediction of phytodiversity and particular plant species occurrences. Data about the floristic assemblage were gathered at two spatial resolution at each location consisted of 5 and 12.5 m radius circular plots. Hemispherical photographs of forest canopies were taken in the centre of each plot and analyzed with Gap Light Analyzer software calculating the two canopy characteristics: leaf area index (LAI) and canopy openness. For majority of sampled area satellite Landsat ETM+ image was available which enable comparison of LAI with spectral channels and Normalized Difference Vegetation Index (NDVI). Relations between mentioned variables (floristic, derived from hemispherical photographs and derived from satellite image) were investigated by redundancy analyses (RDA), classification trees (CT), logistic and linear multiple regression. Using the RDA, six herbaceous plant species that are most influenced by the light conditions were detected. According to CT analysis that included altitude, slope, northness (cosine of aspect) and LAI as predictor variables, LAI was important predictor variable for estimating the frequency of particular plant species. Besides CT, predictive logistic regression models were developed for selected species as well. Regression models developed with Landsat ETM+ bands as estimators explained moderate amount of variability in LAI data. Obtained results lead to the future modelling of silver fir–beech forest phytodiversity and spatial distribution of particular plant species as a function of environmental estimators.

ABSTRACT

Croatia is among those European countries without an Atlas of Flora produced till today as a result of constant lack of greater number of active botanists and inconsistency in gathering data in the field. Recently, a standard for collection of data, based on the Central European MTB (abbreviation of German term “Meßtischblätter” that stands for a sheet of topographic map) grid was proposed and tested in the field on the “Medvednica Nature Park” on Medvednica mountain near the city of Zagreb. Using the data collected in 97 MTB/64 quadrants (presence/absence of plant species), we tested the potential of estimating species occurrence at the proposed grid by models in a function of the Digital Elevation Model (DEM)-based variables, namely altitude, terrain slope, terrain aspect, and flow accumulation potential. Because of significant spatial variability of environmental factors within MTB/64 quadrants, each one was represented by descriptive statistics (median, 5-, 25-, 75- and 95-percentiles) of DEM-based variables. Thirty-seven plant species were selected arbitrarily, on the basis of their frequency in the studied area (40–60% of all quadrants). Three methods for development of predictive model were used and compared: discriminant analyses, logistic regression, and classification trees. Yielded results suggest that spatial modelling could be probably applied in flora mapping, which would optimise fieldwork. However, decreasing of mapping unit area is recommended, especially for rare species. For larger areas, inclusion of other environmental predictors (macroclimatic, lithological, landuse) in models is probably needed.

ABSTRACT

Empirical models for seven climatic variables (monthly mean air temperature, monthly mean daily minimum and maximum air temperature, monthly mean relative humidity, monthly precipitation, monthly mean global solar irradiation and monthly potential evapotranspiration) were built using neural networks. Climatic data from 127 weather stations were used, comprising more than 30 000 cases for each variable. Independent estimators were elevation, latitude, longitude, month and time series of respective climatic variable observed at two weather stations (coastal and inland), which have long time-series of climatic variables (from mid last century). Goodness of fit by model was very high for all climatic variables (R>0.98), except for monthly mean relative humidity and monthly precipitation, for which it was somewhat lower (R=0.84 and R=0.80, respectively). Differences in residuals around model were insignificant between months, but significant between weather stations, both for all climatic variables. This was the reason for calculation of mean residuals for all stations, which were spatially interpolated by kriging and used as a model correction. Similarly interpolated standard deviation and standard error of residuals are estimators of the model precision and model error, respectively. Goodness of fit after the averaging of monthly values between years was very high for all climatic variables, which enables construction of spatial distributions of average climate (climatic atlas) for a given period. Presented interpolation models provide reliable, both spatial and temporal estimations of climatic variables, especially useful for dendroecological analysis.

ABSTRACT

The model of spatial distribution of major forest types in Croatia was developed as a function of macroclimatic variables (monthly mean temperature, monthly precipitation, monthly mean global solar irradiation and monthly potential evapotranspiration) and variables derived from digital elevation model (terrain aspect and slope). Neural networks were used as modelling tool. The model was developed within the frame of a raster geographic information system with a spatial resolution of 300 x 300 m, and it was based on a forest vegetation map (in scale of 1:500000) and interpolation macroclimatic models. The agreement between modelled and mapped forest types was very good, which suggests a strong correlation between macroclimate and the main forest types in Croatia and high model reliability. The model was applied to the entire area of Croatia, aiming at the construction of the potential spatial distribution of major forest types. The model could be useful for reforestation planning and for prediction of vegetation succession under assumed climatic changes.

ABSTRACT

The reservoir of the future hydro-electric power plant ‘Novo Virje’ plant on Drava river, Croatia, could increase the absolute mean groundwater level in the nearby flood-plain forest. These changes could result in a decline of some forest species, especially the pedunculate oak (Quercus robur L.), which is very sensitive to the groundwater level disturbance. Dendroecological study was undertaken with the objective of finding acceptable groundwater level regime from the perspective of survival of nearby flood-plain forest. The study was spatially intensive (GIS based) and executed in the following steps: (1) building of the model which describes basal area increment of the pedunculate oak as a function of environmental estimators (hydrologic, soil, climatic and tree competition variables); (2) simulation of increment by the model under different groundwater level regimes and comparison of the simulation results with predefined increment minimum; and (3) designing of the envelopes of the acceptable regimes (simulated increment is larger than predefined minimum). These results are the basis for the design of hydro-technical measures aiming at protecting of flood-plain forests after the hydro-electric power plant is built.

ABSTRACT

This paper introduces a worldwide applicable model for estimating hourly portions of monthly mean daily direct solar radiation intercepted on the horizontal surface, as a function of actual solar elevation, daily maximum solar elevation and latitude. The model fits well (R>0.99) pyranometric data measured at stations from practically entire Northern Hemisphere (0 <φ<70°). Application of the model using independent data sets, including one station from the Southern Hemisphere, leads to the acceptance of the model worldwide. Model is not sensitive to the local climate differences or interannual variability and it represents an ultimate solution for daily topographic solar radiation modelling without site-specific or latitude-specific hourly radiation data.

ABSTRACT

Paper describes application of computer program written in Pascal, mainly intended for reviewing of exercises of one of the basic expert subjects at the study of forestry - dendrometry, that is lectured in the third year of study at Forestry Department at the Faculty of Forestry, University of Zagreb. For that reason, the educational value of paper is all the more significant, with a view to its program character. However, we hope that this paper will be useful in elaboration of forest management plans, since the principles of processing and presentation of dendrometric data are very well defined. Program contains calculation of volume of individual trees, made of double entry tables, numerical fitting of tree height curve, calculation of sample stripes, construction of tariffs, calculation of forest stand volume and volume increment using Meyer's method of tariff difference, Hohenadl's method of sample trees, control method and Krenn's method. All measured data are calculating and presenting in tables and graphs automatically. While selecting the programming language, the simplicity of writing and code checking was crucial. Turbo Pascal 7.0 implementation was chosen. The program was produced by inter-active work of students of two different studies (forestry & mathematics).

ABSTRACT

U radu se opisuje i testira korisnički program “STABLO3D” namijenjen fotogrametrijskoj izmjeri i analizi pojedinačnih stabala u dube}em stanju, korištenjem nezavisnih snimaka. Program je nastao primjenom univerzalnog fotogrametrijskog algoritma, koji ne zahtijeva uporabu mjernih kamera niti fototeodolita. Izmjera dendrometrijskih elemenata stabla temelji se na izradi trodimenzionalnog modela stabla, što se postiže korištenjem dva ili više fotografskih snimaka. Osim izmjere klasičnih dendrometrijskih elemenata stabla, metoda je prvenstveno namijenjena određivanju obujma stabla metodom sekcioniranja, a razmatra se i mogućnost procjene sortimenata. Sve računske radnje su isprogramirana, pa su na taj način ujedinjeni fotogrametrijski i dendrometrijski principi. Rezultati testiranja dokazuju da bi se program, uz određeno usavršenje, mogao upotrijebiti u dendrometijske svrhe, budući je srednja pogreška izmjere promjera 3-6 %, a visina 3-7 %. Program je nastao zajedničkim radom studenata dvaju različitih studija, a temelji se na dosadašnjim iskustvima stečenim na Katedri za geodeziju, Šumarskog fakulteta, te na Zavodu za fotogrametriju, Geodetskog fakulteta Sveučilišta u Zagrebu.

ABSTRACT

This paper is a byproduct of application of close-range photogrammetry for dendrometric purposes. It is based on experiences acquired during the evaluation of computer program "TREE3D", intended for photogrammetric measurement and analysis of standing individual trees, using non-metric camera. Since the basic idea was to adjust photogrammetry for an economical and practical use, and automate most of the operations, the Method of Direct Linear Transformation was chosen. Despite the fact that this is the area of practical science, problems of random errors are simplified on universal level. Results of simulation confirm that accuracy of photogrammetric measurement, using this method, grows with the increase of: number and accuracy of estimation of control points, number of photos, accuracy of digitalization. It is determined that a specially important element in photogrammetric measurement is relation of disposition of control, object points and cameras.