Volumen 18 (2014): Heft 2 (June 2014)
Thematic issue: Geoinformatics

Natural vegetation is complex and its reflectance is not easy to model. The aim of this study was to adjust the Radiative Transfer Model parameters for modelling the reflectance of heterogeneous meadows and evaluate its accuracy dependent on the vegetation characteristics. PROSAIL input parameters and reference spectra were collected during field measurements. Two different datasets were created: in the first, the input parameters were modelled using only field measurements; in the second, three input parameters were adjusted to minimize the differences between modelled and measured spectra. Reflectance was modelled using two datasets and then verified based on field reflectance using the RMSE. The average RMSE for the first dataset was equal to 0.1058, the second was 0.0362. The accuracy of the simulated spectra was analysed dependent on the value of the biophysical parameters. Better results were obtained for meadows with higher biomass value, greater LAI and lower water content.

The goal of the paper is a presentation of field remote sensing methods for the analysis of the trampled plants of a highly protected mountain meadow ecosystem (M&B UNESCO Reserve and one of the most important Polish National Parks). The research area covers a core part of the Western Tatras - the Gąsienicowa Valley and Kasprowy Wierch summit, which are among the most visited destinations of the Polish Tatras. The research method is based on field hyperspectral measurements, using the ASD FieldSpec 3 spectrometer, on the dominant plant species of alpine swards. Sampling sites were located on trampled areas (next to trails) and reference plots, with the same species, but located more than 10 m from the trail (where the probability of trampling was very low, but the same composition of analysed plants). In each case, homogenous plots with a domination of one plant species were investigated. Based on the hyperspectral measurements, spectral characteristics as well as vegetation indices were analysed with the ANOVA statistical test. This indicated a varied resistance to trampling of the studied plant species. The analysis of vegetation indices enabled the selection of those groups which are the most useful for research into mountain vegetation condition: the broadband greenness group; the narrowband greenness group, measuring chlorophyll content and cell structure; and the canopy water content group. The results of the analyses show that vegetation of the High Tatras is characterised by optimal ranges of remote sensing indices. Only plants located nearest to the trails were in a worse condition (chlorophyll and water content was lower for the reference targets). These differences are statistically significant, but the measured values indicate a good condition of vegetation along trampled trails, within the range of optimum plant characteristics.

The paper deals with the evaluation of mountain meadow vegetation condition using in-situ measurements of the fraction of Accumulated Photosynthetically Active Radiation (fAPAR) and Leaf Area Index (LAI). The study analyses the relationship between these parameters and spectral properties of meadow vegetation and selected invasive species with the goal of finding out vegetation indices for the detection of fAPAR and LAI. The developed vegetation indices were applied on hyperspectral data from an APEX (Airborne Prism Experiment) sensor in the area of interest in the Krkonoše National Park. The results of index development on the level of the field data were quite good. The maximal sensitivity expressed by the coefficient of determination for LAI was R² = 0.56 and R² = 0.79 for fAPAR. However, the sensitivity of all the indices developed at the image level was quite low. The output values of in-situ measurements confirmed the condition of invasive species as better than that of the valuable original meadow vegetation, which is a serious problem for national park management.

This research aims to discover the potential of hyperspectral remote sensing data for mapping mountain vegetation ecosystems. First, the importance of mountain ecosystems to the global system should be stressed due to mountainous ecosystems forming a very sensitive indicator of global climate change. Furthermore, a variety of biotic and abiotic factors influence the spatial distribution of vegetation in the mountains, producing a diverse mosaic leading to high biodiversity.

The research area covers the Szrenica Mount region on the border between Poland and the Czech Republic - the most important part of the Western Karkonosze and one of the main areas in the Karkonosze National Park (M&B Reserve of the UNESCO).

The APEX hyperspectral data that was classified in this study was acquired on 10th September 2012 by the German Aerospace Center (DLR) in the framework of the EUFAR HyMountEcos project. This airborne scanner is a 288-channel imaging spectrometer operating in the wavelength range 0.4-2.5 μm.

For reference patterns of forest and non-forest vegetation, maps (provided by the Polish Karkonosze National Park) were chosen. Terrain recognition was based on field walks with a Trimble GeoXT GPS receiver. It allowed test and validation dominant polygons of 15 classes of vegetation communities to be selected, which were used in the Support Vector Machines (SVM) classification. The SVM classifier is a type of machine used for pattern recognition. The result is a post classification map with statistics (total, user, producer accuracies, kappa coefficient and error matrix). Assessment of the statistics shows that almost all the classes were properly recognised, excluding the fern community. The overall classification accuracy is 79.13% and the kappa coefficient is 0.77. This shows that hyperspectral images and remote sensing methods can be support tools for the identification of the dominant plant communities of mountain areas.

Accurate high temporal resolution data is a very important source of information for understanding processes in the landscape. High temporal and spectral resolution data enable the monitoring of dynamic landscape processes. For this reason, since 2008 a receiving station for Metosat, NOAA and Envisat data has been installed at the Department of Applied Geoinformatics and Cartography, Faculty of Science, Charles University in Prague. The aim of this study is to analyse the spectral characteristics of vegetation using MERIS data in the Czech Republic. Spectral characteristics of vegetation were examined both by analysing changes in reflectivity as well as by utilising vegetation indices. Vegetation in forests and agricultural land was evaluated. The results present the spectral characteristics of selected associations of vegetation based on MERIS data and a discussion of the methods of multitemporal classification of land cover.

Remote sensing from unmanned aerial systems (UAS) has been gaining popularity in the last few years. In the field of vegetation mapping, digital cameras converted to calculate vegetation index (DCVI) are one of the most popular sensors. This paper presents simulations using a radiative transfer model (libRadtran) of DCVI and NDVI results in an environment of possible UAS flight scenarios. The analysis of the results is focused on the comparison of atmosphere influence on both indices. The results revealed uncertainties in uncorrected DCVI measurements up to 25% at the altitude of 5 km, 5% at 1 km and around 1% at 0.15 km, which suggests that DCVI can be widely used on small UAS operating below 0.2 km.

Unmanned aerial systems are in many countries one of the most dynamically developing branches of technology. They have also been recognized and are being utilized by scientists who find remote sensing indispensable in their work. Today, it is increasingly common to find research teams utilizing so-called drones in field research. Unmanned systems are becoming ever more important for environment monitoring by, on the one hand, providing data from inaccessible or remote areas, and, on the other hand, reducing the human costs required by traditional large field teams while also increasing the efficiency of the work. This paper presents the possibility of utilizing UAVs for image data collection in woodland areas.

Flights over Głuchów and an Arboretum were performed using two different UAVs (Mentor, AVI). The flights were made in 2010 in the middle of the growing season. Images were taken with Sigma DP2 digital cameras in four spectral channels: R (red), G (green), B (blue) and IR (infrared). Images were saved in 8-bit. The Głuchów forest complex is located in the Głuchów forest district, which forms a part of the Rogów forest division. From the administrative viewpoint, the forest division is located in the Łódzkie province, Skierniewicki Poviat. The Arboretum is a park with a collection of trees and shrubs from different regions of the world. The area is characterized by a high variability of species and trees of varying heights. It is located in the Łódzkie province, Skierniewicki Poviat.

The unique set of physical and chemical properties of asbestos has led to its many industrial applications, such as roof coverings, textiles, rope, cord and yarn, paper, friction and composition materials, household product, acid-resistant filters, packing, insulation, and certain types of lagging, amongst others. In Poland asbestos-containing products were manufactured from raw materials imported mainly from the former Soviet Union, with production launched at the beginning of 20th century. According to Annex 4 to the Act of 19 June 1997 on the prohibition of the use of asbestos-containing products, there were 28 asbestos manufacturing plants in Poland located in 11 provinces throughout the country. The current survey was undertaken to enable asbestos manufacturing plants to be arranged, described and divided in order to contribute to further surveys.

On January 1, 2005 the use of asbestos-containing products was banned in the European Union. According to the Act of 19 June 1997 banning the use of these products, their usage in Poland should be abated by the end of 2032. The whole process is being monitored by the Electronic Spatial Information System for the Monitoring of Asbestos Products Removal. The system design was based on a geodatabase. The research area of the study is the whole territory of Poland at the national, provincial and local level of detail. The monitoring process embraces spatial analysis through the preparation and interpretation of a range of maps. The results obtained from the deployed methods proved that the system has been useful for decision making purposes during the monitoring process. The proposed solutions were appreciated by the EU.