Thesis (Ph.D.(Environmental Science) North-West University, Mafikeng Campus, 2012

Inaug.-Diss.--Jena.

Thesis (Ph. D.)--Michigan State University. Dept. of Resource Development, 1972. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 510-526).

Thesis (Ph. D.)--Michigan State University. Dept. of Resource Development, 1972. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 510-526).

Dissertation (PhD)--University of Stellenbosch, 2007. / Bibliography. Also available via the Internet.

Thesis (Doctor of Phil.) - University of KwaZulu-Natal, Pietermaritzburg, 2008. / Full text also available online. Scroll down for electronic link.

Forests extend on about 4 billion hectares on the planet Earth, which makes up approximately one third of the total land area and is probably one of the most important environmental elements on the planet.In the Czech Republic forests cover about 33.7% of its land area.Out of Sweden's total land area, which is 41.3 million hectares, there is 23 million hectares of forest land. That is more than half of its total area. Creation of clear-cut areas, loss of the forests natural evolution information, waste majority of artificial forest, storm or fires are just some of the indicators of unsatisfactory conditions of forests. Therefore there is a strong importance for forest protection by means of legal regulation - International, European and especially national law, to be capable of leaving this heritage in a good condition for future generations. In this work I will focus on the comparison of forestry law in the Czech Republic and Sweden - from formal and also material point of view.

Diss. Linköping : Linköpings universitet, 2007.

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

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...