Abstract:In this article we introduce a new method for forest management inventories especially suitable for highly-valued timber where the precise estimation of stem parameters (diameter, form, and tapper) plays the key role for market purposes. The unmanned aerial system (UAS)-based photogrammetry is combined with terrestrial photogrammetry executed by walking inside the stand and the individual tree parameters are estimated. We compare two automatic methods for processing of the point clouds and the delineation of stem circumference at breast height. The error of the diameter estimation was observed to be under 1 cm root mean square error (RMSE) and the height estimation error was 1 m. Apart from the mentioned accuracy, the main advantage of the proposed work is shorter time demand for field measurement; we could complete both inventories of 1 hectare forest stand in less than 2 h of field work.

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Currently, a large part of forest roads with a bituminous surface course constructed in the Czech Republic in the second half of the last century has been worn out. The aim of the study is to verify the possibility and the accuracy of the road wearing course damage detected by four different remote sensing methods: close range photogrammetry, terrestrial laser scanning, mobile laser scanning and airborne laser scanning. At the beginning of verification, cross sections of the road surface were surveyed geodetically and then compared with the cross sections created in the DTMs which were acquired using the four methods mentioned above. The differences calculated between particular models and geodetic measurements show that close range photogrammetry achieved an RMSE of 0.0110 m and the RMSE of terrestrial laser scanning was 0.0243 m. Based on these results, we can conclude that these two methods are sufficient for the monitoring of the asphalt wearing course of forest roads. These methods allow precise and objective localization, size and quantification of the road damage. By contrast, mobile laser scanning with an RMSE of 0.3167 m does not reach the required precision for the damage detection of forest roads due to the vegetation that affects the precision of the measurements. Similar results are achieved by airborne laser scanning, with an RMSE of 0.1392 m. As regards the time needed, close range photogrammetry appears to be the most appropriate method for damage detection of forest roads.

Topographic Exposure and its Practical ApplicationsTopographic exposure is a topographic characteristic representing a degree of protection by a surrounding topography of a certain site. Detailed knowledge of topographic exposure has broad use in a number of applications ranging from studying forest wind damage through research on snow storage dynamics to optimisation in positioning wind power stations. This paper describes a method for creation of topographic exposure on the basis of a digital elevation model (DEM) using GIS. In combination with other climatic data on wind direction and speed, this factor is used to define the degree of terrain ventilation. Low terrain ventilation has, among other things, a significant influence on the creation of valley inversions and related vegetation zoning inversions. By combining the degree of terrain ventilation with DEM and forest vegetation zones in the area of the Training Forest Enterprise Křtiny, a clear relationship between the influence of topographic exposure, or terrain ventilation, and the creation of the vegetation zoning inversion was determined.

Currently, a large part of the forest roads that were built using the bituminous surface technology in the second half of the last century have been worn out. This means that forest owners and forest managers urgently need to determine the amount and extent of this damage and establish a suitable repair plan, which demands both time and staff. The aim of the study is to verify whether it is possible, and with what precision, to detect the damage of the wearing course by means of unmanned aerial systems, which would facilitate and accelerate this process and possibly make it cheaper. A 3D model of a forest road was created using photos of the current state of a damaged part of a forest road. The aerial photographs were taken by an unmanned aircraft. To verify the accuracy of the model, cross sections of the road surface were surveyed tachymetrically and compared with the cross sections created in the 3D model in ArcMap, from photogrammetric pointcloud using aerial photographs from the unmanned aircraft. The RMSE of the values of the control points in the 3D model cross sections compared to the values of the points in the tachymetric measurement of the cross sections reached to within 0.0198 m. The results of the tested road section showed that the unmanned aerial systems can be used to detect the forest road surface damage with the difference in accuracy being up to 2 cm compared with the accuracy of the current tachymetric methods. Based on the results we can conclude that the used method is appropriate for detailed monitoring of the condition of the asphalt wearing course of forest roads and allows for a precise and objective localization and quantification of damage.

Abstract:Traditional land classifications developed on the basis of what was once prevailing expert knowledge have since largely become obsolete. We assessed expert knowledge based landscape-level units delineated in central European temperate forests: Natural Forest Areas (NFA) and Forest Vegetation Zones (FVZ). Our focus was determining to what degree these units reflect vegetation-environmental relationships. After considering as many as 49,000 plots with vegetation and 25,000 plots with environmental data within a territory of the Czech Republic, we analyzed 11,885 plots. We used multivariate statistics to discriminate between the landscape-level units. While NFAs performed extremely well, FVZ results were less successful. Classification of the environment provided better results than classification of vegetation for both the Hercynicum and Carpaticum phytogeographic part of the Czech Republic. Taking into account significance of the environment in our analysis, a delimitation of FVZs and similar vegetation-driven structures worldwide via explicit a priori stratification by tree species without consideration of environmental limits would not be supported by our analysis. We suggest not relying only on vegetation in classification analyses, but also including the significant environmental factors for direct classification of FVZ and units in particular in altered vegetation composition setting such as the central European forests. We propose a novel interpretation of FVZ via appropriate vegetation stratification throughout the environment used in conjunction with the zonal concept. Understanding of coarse-scaled vegetation-environmental relationships is not only fundamental in forest ecology and forest management, but is also essential for improving lower classification levels. Valuable expert knowledge should be combined with formal quantification, which is consistent with recent calls for advanced multidisciplinary ecological classifications in Europe and North America and for forming classifications in Asia.

MIKITA TOMÁŠ, KLIMÁNEK MARTIN, CIBULKA MILOŠ: Evaluation of airborne laser scanning data for tree parameters and terrain modelling in forest environment. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2013, LXI, No. 5, pp. 1339-1347 The aim of this article is to analyse possibilities of airborne laser scanning (ALS) data utilization in forestry, especially for the purposes of terrain modelling and for forest inventory (determination of forest height, diameter breast height and volume -DBH). The accuracy of ALS data in forestry was tested on the area of 1.5 ha. On this area the topography and location of all trees as well as their heights were surveyed in detail by means of total station. Firstly, the altitudinal accuracy of ALS for the creation of digital elevation model (DEM) was evaluated, based on the comparison with relief measurement. The research also evaluated diff erent data sources from various types of scanners with a diff erent point density per m 2 . Further, we compared tree heights determined from ALS data by diff erent ways of interpolation into canopy height model (CHM) with the surveyed data, following calculations of DBH (diameter breast height) and tree volume based on the regressions. The results show suffi cient data accuracy for the creation of DEM. Concerning tree height determination, the data is also useful although the accuracy is slightly lower, there is a slight undervaluation of the tree heights. Concerning using high point density data at full waveform scanner it is also possible to detect skidding tracks and micro-relief details. Anyway we did not fi nd suffi cient accuracy for DBH and tree volume at the scale of individual trees, but ALS data still gives better results for tree height, DBH and timber volume for larger forest stands than usual inventory. airborne laser scanning, tree height assessment, digital elevation model, canopy height model, TerraScan

Leaf area index (LAI) can be measured either directly, using destructive methods, or indirectly using optical methods that are based on the tight relationship between LAI and canopy light transmittance. Third, innovative approach for LAI measuring is usage of remote sensing data, especially airborne laser scanning (ALS) data shows itself as a advisable source for purposes of LAI modelling in large areas. Until now there has been very little research to compare LAI estimated by the two different approaches. Indirect measurements of LAI using hemispherical photography are based on the transmission of solar radiation through the vegetation. It can thus be assumed that the same is true for the penetration of LiDAR laser beams through the vegetation canopy. In this study we use ALS based LiDAR penetration index (LPI) and ground based measurement of LAI obtained from hemispherical photographs as a reference in-situ method. Several regression models describing the corellation LAI and LPI were developed with various coefficients of determination ranging up to 0,81. All models were validated and based on the tests performed, no errors were drawn that would affect their credibility.