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Heterogeneity in forest vegetation monitoring with remote sensing
Kolešová, Petra ; Kolář, Jan (advisor) ; Potůčková, Markéta (referee)
Heterogeneity in forest vegetation monitoring with remote sensing Abtract The main aim of this diploma thesis is to examine the suitability of various classification approaches for forest vegetation categorization using Landsat 8 satellite imagery. Two satellite images acquired during vegetative period (8th March, 27th July 2013) were chosen. The overall goal of the study is to explore the potential of using statistical methods to obtain information about forest heterogeneity in a given territory. Chosen study sites are defined by administrative boundaries of selected municipalities from South and Central Bohemia located within following municipalities with extended powers - Blatná, Milevsko, Písek, Příbram and Sedlčany. Supervised and unsupervised classifications were used based on obtained training areas and orthophoto. The definition of chosen classes (coniferous forests, mixed forests, ecotones, structurally homogeneous deciduous forests and structurally heterogeneous deciduous forests) was identical with the categories used in "Project MT 11425-5/2010 The Mapping of Natural Zoonoses Focal Points, Transferable on Humans in the Czech Republic and Their Changes Affected by the Modification of Climate". Due to large amount of training datasets obtained from field survey, ortophoto and spectral analysis,...
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Forest species determination from satellite data
Launer, Michal ; Kolář, Jan (advisor) ; Brodský, Lukáš (referee)
Forest species determination from satellite data Abstract This thesis examines the species composition of forests from satellite images using the pixel classification. The research was done on 24 forest locations in The Ustecký Region, The Karlovarský Region, The Plzeňský Region and The Central Bohemian Region in the Czech Republic. In this thesis, data from the Landsat-8 and Sentinel-2 satellites from summer season and the Random Forest Classifier method were used. The layer of species composition of forests from map portal LhpoMap was used as reference data. The method of work consisted of a broad literature search to select the most favourable classifier and to choose the most advantageous input parameter values to achieve the highest overall accuracy of the classification. The practical part was focused on creating a software classification process. The accuracy of the individual image values was verified using matrix errors. Based on the literature search, the Random Forest classifier was used to classify the images. Parameter values were used for the Gini criterion, 500 decision trees, and the other parameters were left with default values. The entire classification process was performed in ArcMap and ArcGIS Pro software using Python programming language with the help of the sklearn.ensemble module...
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Forest species determination from satellite data
Launer, Michal ; Kolář, Jan (advisor) ; Kupková, Lucie (referee) ; Brodský, Lukáš (referee)
Forest species determination from satellite data Abstract Examining the species composition of forests from satellite imagery is constantly evolving. The new ways of exploring forests from the satellites make it easier for foresters to maintain a more accurate and up-to-date overview of the state of forests. In this work, the research was made on the forests in the cadastral territories of Osvětimany and Buchlovice in the Chřiby Mountains in the Czech Republic. In this work, data from the Landsat-8 satellite from three seasons and the Maximum Likelihood Classification method were used. The reference maps were used as reference data. The method of work consists in the fact that 6 frames were classified with the help of training sets using Maximum Likehood Classification. Subsequently, the pixels which were at least 4 times out of 6 ranked in the same class after the classification were selected. Based on these pixels, artificial training sets were calculated for each of the 6 frames, and they were used for another classification with the expectation of better results. The accuracy of the individual classification frames was verified by an error matrix on the crop maps. Keywords: remote sensing, forest canopy, forest tree types, forestry map
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Heterogeneity in forest vegetation monitoring with remote sensing
Kolešová, Petra ; Kolář, Jan (advisor) ; Potůčková, Markéta (referee)
Heterogeneity in forest vegetation monitoring with remote sensing Abtract The main aim of this diploma thesis is to examine the suitability of various classification approaches for forest vegetation categorization using Landsat 8 satellite imagery. Two satellite images acquired during vegetative period (8th March, 27th July 2013) were chosen. The overall goal of the study is to explore the potential of using statistical methods to obtain information about forest heterogeneity in a given territory. Chosen study sites are defined by administrative boundaries of selected municipalities from South and Central Bohemia located within following municipalities with extended powers - Blatná, Milevsko, Písek, Příbram and Sedlčany. Supervised and unsupervised classifications were used based on obtained training areas and orthophoto. The definition of chosen classes (coniferous forests, mixed forests, ecotones, structurally homogeneous deciduous forests and structurally heterogeneous deciduous forests) was identical with the categories used in "Project MT 11425-5/2010 The Mapping of Natural Zoonoses Focal Points, Transferable on Humans in the Czech Republic and Their Changes Affected by the Modification of Climate". Due to large amount of training datasets obtained from field survey, ortophoto and spectral analysis,...
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Analysis of turbulent flow over forested terrain
Potužníková, Kateřina ; Sedlák, Pavel ; Šauli, Petra
The aim of the present study is to assess and describe low-frequency oscillations and coherent structures in temporal series of temperature and wind velocity components sampled at three different levels within the forest (z/h = 0.3, 0.5 and 1, where h is the canopy height). The wavelet transform is used as a basic tool for our analysis. The periods of detected structures depend on the temperature gradient in the canopy, and furthermore seem to vary with the different local circulations regimes.
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