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Methodology of invasive species mapping using remote sensing approach
Müllerová, Jana ; Bartaloš, T. ; Brůna, Josef ; Dvořák, P. ; Vítková, Michaela
The manual provides instructions on monitoring of four problematic invasive species based on remote sensing. It focuses on low cost and operational unmanned aircraft solution. The manual provides bases for understanding of the problematic, specifies the requirements for the data type and quality in respect to the species phenophases, and describes the data processing and quality assessment. Described methods are particularly suitable for targeted monitoring (in case of unmanned aircraft solution), still can be used at regional scale as well (in case of satellite imagery). Methodology is intended mainly for environmental agencies and other land managers, such as regional authorities and owners due to prevent and manage the introduction and spread of invasive alien species.
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Assessments of forest damage using satellite and LIDAR data
Lihanová, Kristýna ; Štych, Přemysl (advisor) ; Bartaloš, Tomáš (referee)
Assessment of forest damage using satellite and lidar data Abstract The main objective of this thesis is to create a methodical procedure used for the evaluation of forest damage in the chosen area of the National Park Sumava, Czech Republic. In this work were combined the multispectral satellite data and data of airborne laser scanning. The forests in this area are heavily damaged mainly due to bark beetle outbreak. You can find here as healthy so damaged forests. Based on this methodology will be differentiated greater number of classes than I found in the literature. In this work was used pansharpened multispectral image SPOT, multispectral image Landsat and airborne laser scanning data with low density points. Another task was to get height information from ALS data in the form of grid. Forest stands were classified using object-oriented classification, which included at first segmentation and then creation of classification base. In classification entered spectral information and height information obtained from the ALS data. Forests were classified into 5 classes and accuracy of both classifications was evaluated using the error matrix and kappa coefficient. SPOT image classification reached kappa coefficient of 68,5 % and Landsat image classification reached kappa coefficient of 72,3 %. From the...
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