|
|
Forest monitoring using the GEDI sensor
Šedová, Adéla ; Potůčková, Markéta (advisor) ; Moudrý, Vítězslav (referee)
The overall objective of this thesis was to explore the use of GEDI and its integration with Airborne Laser Scanning (ALS) for large scale forest monitoring. The study was carried out using a sample of GEDI footprints that fell into the timeline of three available ALS datasets that were acquired during the same year. The study area, located in Southeast of Czechia, is covered with mature 121-year-old forest monoculture of Norway spruce (Picea abies), and due to frequent disturbances caused by infestation is closely monitored as a part of research on forest dynamic. As a result, the forest is highly fragmented, and due to its dynamic character, close dates of acquisitions were preferred to a larger dataset. Canopy gaps and low tree densities are known to pose a challenge for large-footprint full-waveform LiDARs. The specific of GEDI sensor, such as its footprint size, were specially designed to overcome these challenges. The options of optimising GEDI's geolocation accuracy were explored. A tool for integrating GEDI and ALS data, the GEDI Simulator, was used to standardise both data sources and derive elevation height, Relative Height (RH) and Canopy Cover Fraction (CCF). The metrics were derived from real GEDI waveforms, simulated GEDI-like waveforms, and calculated from the ALS point cloud, and...
|
|
|
A correction of the local incidence angle of SAR data: a land cover specific approach for time series analysis
Paluba, Daniel ; Štych, Přemysl (advisor) ; Mouratidis, Antonios (referee)
To ensure the highest possible temporal resolution of SAR data, it is necessary to use all the available acquisition orbits and paths of a selected area. This can be a challenge in a mountainous terrain, where the side-looking geometry of space-borne SAR satellites in combination with different slope and aspect angles of terrain can strongly affect the backscatter intensity. These errors/noises caused by terrain need to be eliminated. Although there have been methods described in the literature that address this problem, none of these methods is prepared for operable and easily accessible time series analysis in the mountainous areas. This study deals with a land cover-specific local incidence angle (LIA) correction method for time-series analysis of forests in mountainous areas. The methodology is based on the use of a linear relationship between backscatter and LIA, which is calculated for each image separately. Using the combination of CORINE and Hansen Global Forest databases, a wide range of different LIAs for a specific forest type can be generated for each individual image. The algorithm is prepared and tested in cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, SRTM digital elevation model, and CORINE and Hansen Global Forest databases. The method was tested...
|
guest ::
