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Mathematical methods of modelling the morphology of spruce trees
Janoutová, Růžena ; Novotný, Jan ; Pivovarník, Marek ; Zemek, František
Radiative transfer (RT) models are simulation tools which can be used to quantify relationships between vegetation canopy properties and observed remotely sensed data. Th is study aims at creating a spruce tree growth model as a key input for use in RT models. Th e spruce tree model is built on data obtained from terrestrial laser scanning of spruce trees. Each tree model is unique. Th is uniqueness is achieved by using L-systems which are able to simulate natural randomness while complying with the given tree parameters. L-systems are established on a theory of grammar that enables rewriting a string of symbols according to specifi ed rewriting rules. In practice, our tree models are generated in Blender visualization soft ware, implementing an algorithm written in Python. Th e algorithm generates the basic parameters of the whole tree and then creates the parameters of the spruce trunk and initial branches. Th e parameters are generated randomly within a range that is calculated from measured data. Th en each branch is grown on the basis of annual increments defi ned by fi eld measurements. Tree needles are distributed with respect to the age of individual branches; therefore, the needles have diff erent colours according to their age. Cones and faces are graphical representations of the spruce model. Branches are represented by cones and needles are represented by faces around the branches. Th e faces are transparent, thus simulating light transmittance in-between the needles. The whole model is highly computationally demanding, especially with respect to computer memory.
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Hyperspectral image segmentation for estimation of biomass at reclaimed heaps
Pikl, Miroslav ; Zemek, František
This paper presents the preliminary results from a study that aims at estimation of above ground biomass and soil carbon content at reclaimed mining heaps in the Sokolov region. Two image segmentation methods are presented. We applied maximal likelihood (ML) and neural network (NN) classifi ers on airborne hyperspectral data. Th e objective of this part of the study was to prepare a land cover classifi cation of the region. Th e main focus was paid to discrimination of six classes with prevailing forest species cover. Th e classifi cation accuracy of the training sites was 93.75 % for NN and 79.12 % for ML respectively. But ML outperformed NN in overall classifi cation accuracy with 61.54 % compared to 40.9 % of NN. Th e more accurate results of the ML classifi er are probably infl uenced by properties of the training samples. Th e larger size of the training samples derived for ML enabled better representation of class histograms. Th e lower overall NN accuracy could result from high spatial resolution of HS data.
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Segmentation of tree crowns from airborne hyperspectral and lidar data: method comparison
Novotný, Jan ; Zemek, František
Structural and spectral information on single trees is needed for diff erent purposes in forest research and its applications. It can help, e.g., to explain the physiological performance of trees, to improve a parameterization of radiative transfer models, to estimate more precisely tree biomass or tree health status. Th is technical note aims at informing about the basic steps in the use of two categories of airborne digital data for tree crown segmentation: 1) passive multispectral (MS) and hyperspectral (HS) data; 2) active laser scanning (LiDAR) data. Basic assumptions of data quality and their pre-processing chains are mentioned for both data categories, followed by an analytical description of the basic steps in crown segmentation: a) detection of individual trees; b) delineation of a projected tree crown. Methods related to each data category and their common use are compared. As a result, synergic application of HS and laser scanning data resulted in the highest precision of tree crown delineation.
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Určení infiltračních oblastí pomocí vodního stresu vegetace na základě dálkového průzkumu Země a pozemních měření
Duffková, R. ; Brom, J. ; Žížala, D. ; Zemek, František ; Procházka, J. ; Nováková, E. ; Zajíček, A. ; Kvítek, T.
Infiltrační oblasti povodí s vysokou půdní propustností jsou kritickými zdrojovými lokalitami plošného zemědělského znečištění. Jejich vymezení je podmínkou cíleného zemědělského managementu v ochranných pásmech vodních zdrojů. Zvýšený odtok vody z propustných půd infiltračních oblastí snižuje aktuální evapotranspiraci a podporuje rychlejší nástup vegetačního vodního stresu ve srovnání s ostatními částmi povodí. Vymezení infiltračních oblastí na základě tohoto předpokladu bylo provedeno na malém (částečně odvodněném) povodí kombinací leteckého snímkování v infračervené a optické části spektra elektromagnetického záření, které vytvořilo základ pro modelování složek povrchové energetické bilance a plodinového vodního stresového indexu (CWSI). Jako nejlepší indikátory vegetačního vodního stresu pro vymezení infiltračních oblastí (orné půdy se zapojeným porostem) se na základě statistické analýzy jevily evaporační frakce, CWSI a teplota povrchu. Výsledky byly podpořeny detailním pedologickým průzkumem.
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Potentials of the hyperspectral remote sensing - Aisa Eagle system and future perspectives
Lukeš, Petr ; Hanuš, Jan ; Kaplan, Věroslav ; Zemek, František
Since 2004 the Global Change Research Centre (CVGZ) operates a hyperspectral airborne imaging system AISA Eagle. A basic introduction to hyperspectral remote sensing and its potential applications in science as well as for commercial and military uses are presented, followed by a description of the system together with an overview of the key data pre-processing steps. The application of a rapidly developing technology, hyperspectral remote sensing, is presented within the field of quantitative remote sensing of plant health status. Finally, the future development of hyperspectral remote sensing at the CVGZ is outlined.
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