This thesis deals with the use of remote sensing data for studying and monitoring vegetation changes. Thanks to archival materials, we can now make extensive studies at the landscape and global level without the need for large-scale old field data. From the Middle Ages, we can rely on different types of maps, for vegetation studies, these are mainly forestry maps. Since the 1930's, aerial photographs have been available in Europe, and satellite imagery was available since the 1970's. Availability and quality of satellite imagery had increased rapidly during my study. The most recent data source are unmanned aerial systems and methods of processing their data, which allow inexpensive detailed mapping of large areas. The presented publications do not only solve ecological research questions, but also contribute to solving current environmental problems in the Czech Republic, from nature conservation in National Parks and protected areas to monitoring of plant invasions. I have used archival forest maps for the reconstruction and analysis of large disturbances (windthrow and subsequent gradations of bark beetle) in forests of Šumava and the Bavarian Forest in 1868-1870. Species composition, as well as environmental factors derived from digital elevation model, were analyzed. The same topic was also...

In recent decades, the night lights data of the Earth’s surface derived from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) have been used to detect the human settlements and human activities, because the DMSP/OLS data is able to supply the information about the urban areas  and non-urban areas on the Earth which means it is more suitable for urban studies than usual satellite imagery data.   The urban development is closed linked to the human society development. Therefore, studies of urban development will help people to understand how the urban changed and predict the urban change. The aim of this study was to detect Beijing’s urban development from 1992 to 2012, and find the contributions to the urban sprawl from socio-economic factors. Based on this objective, the main dataset used in this thesis was night lights images derived from the DMSP/OLS which was detected from  1992 to 2012. Due to the lacking of on-board calibration on OLS, and the over-glow of the lights resources, the information about the night lights cannot be extracted directly. Before any process, the night lights images should be calibrated. There is a method to calibrate the night light images which is called intercalibration. It is a second order regression model based method to find the related digital number values. Therefore, intercalibration was employed, and the threshold values were determined to extract urban areas in this study. Threshold value is useful for diffusing the over-glow effect, and finding the urban areas from the DMSP/OLS data. The methods to determine the threshold value in this thesis are empirical threshold method, sudden jump detection method, statistic data comparison method and k-mean clustering method. In addition, 13 socio-economic factors which included gross domestic product, urban population, permanent population, total energy consumption and so on were used to build the regression model. The contributions from these factors to the sum of the Beijing’s lights were found based on modeling.   The results of this thesis are positive. The intercalibration was successful and all the DMSP/OLS data used in this study were calibrated. And then, the appropriate threshold values to extract the urban areas were figured out. The achieved urban areas were compared to the satellite images and the result showed that the urban areas were useful. During the time certain factors used in this study, such as mobile phone users, possession of civil vehicles, GDP, three positively highest contributed to urban development were close to 23%, 8% and 9%, respectively.

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Indiana University-Purdue University Indianapolis (IUPUI) / Urban forestry is a very important element of urban structures that can improve the environment and life quality within the urban areas. Having an accurate classification of urban forests and grass areas would help improve focused urban tree planting and urban heat wave mitigation efforts. This research project will compare the use of high – resolution aerial imagery and LiDAR data when used to classify canopy and grass areas. The high – resolution image, with 1 – meter resolution, was captured by The National Agriculture Imagery Program (NAIP) on 6/6/2012. Its coordinate system is the North American Datum of 1983 (NAD83). The LiDAR data, with 1.0 – meter average post spacing, was captured by Indiana Statewide Imagery and LiDAR Program from 03/13/2011 to 04/30/2012.The study area is called the Near East Side Community Organization (NESCO) neighborhood. It is located on the east side of downtown Indianapolis, Indiana. Its boundaries are: 65 interstate, East Massachusetts Avenue, East 21st Street, North Emerson Avenue, and the rail road tracks on the south of the East Washington Street. This research will also perform the accuracy assessment based on the results of classifications using high – resolution aerial imagery and LiDAR data in order to determine and explain which method is more accurate to classify urban canopy and grass areas.