## 1 Introduction  
Reptile habitats are described using a variety of indices which can be classified as climatic, topographic and biological. Proper identification of habitat indices associated with a given species is of utmost importance as they are often utilized for the modelling of habitats on local to continental scales. From among a number of animal habitat modelling methods, [PERSON] [1] pointed out the important role of remote sensing and geostatistics. By means of these methods, [PERSON] _et al._[2] found that the presence of reptiles was strongly associated with the energy available in the environment, which could be measured by potential evapotranspiration. Models of reptile habitat suitability were also obtained using a number of environmental variables in a hydrological model [3] and extensive analysis of LiDAR (light detection and ranging) data [4]. Another approach used biological remote sensing indices to model herpetofauna [5] and mammal [6] habitats. These studies show that NDVI (Normalized Difference Vegetation Index) time series can be used for the modelling of fauual habitats, but their results are less reliable compared to the models based on climatic indices. Climatic indices were compared with topographic indices by [PERSON] and [PERSON] [7] for the modelling of habitats of selected reptile species. In this comparison, like in the former examples, models based on climatic indices were found to better represent the real distribution of reptiles. However, climatic indices have one essential drawback: they are assessed on the basis of point measurements at meteorological stations and then spatially interpolated. Therefore, climatic indices are more representative for large areas and may obscure local variability. For smaller spatial scales, alternative indices to assess habitat condition may be needed. An example of a biological index that is very effective in measuring the extent of fauual habitats is the Dynamic Habitat Index [8]. This index is obtained from such remote sensing data sequences as fPAR (fractional Photosynthetically Active Radiation), NDVI or GPP (Gross Primary Productivity) and was utilized for the modelling of avian habitats [9] and for the evaluation of flora and fauna species richness in boreal forests at 1-km grid spacing [10]. The modelling of habitat extent is possible at an even more precise scale using satellite high-resolution data. This was demonstrated by [PERSON] _et al._[11], who successfully modelled avian ranges on the basis of NDVI and ATSAVI (Adjusted Transformed Soil-adjusted Vegetation Index); it should be noted that ground LAI (Leaf Area Index) measurements also constituted an important aspect of their survey. A benefit of the above-mentioned biological indices is that they are associated with climatic and topographical indices, both of which directly influence vegetation. Moreover, indices of this sort can be measured on the ground and then used as input data for spatial models, based, for example, on remote sensing data. In this research we focused on the analysis of reptile habitats as evaluated by LAI index. LAI's strength is the fact that it enables easy ground measurement reflecting local habitat conditions. LAI is also related to solar light interception by the plants [12]; when overstory trees are present, the degree of shading affects the development of lower stratum plants [13], which constitutes the habitat for reptiles surveyed in this research. An important feature of LAI is that this index can be evaluated not only on the basis of ground measurements but also by means of remote sensing, especially in forests [14, 15, 16, 17, 18]. So far, the association of LAI with reptile occurrence has not been extensively examined. Nevertheless, there are reports suggesting the existence of such a relationship. [PERSON] [19] showed that forests damagedby wind were richer in reptile species compared to non-damaged forests in the eastern USA.