The research project focusses on an evaluation of Land Use/Cover Changes related to Climate Change. The main object is to monitor and evaluate Land Use, Land-Use Change and Forestry (LULUCF) using remote sensing data from the Copernicus programme. The Ph.D. project should provide methodologically advanced and cost-effective way of monitoring LULUCF for removals of greenhouse gases from the atmosphere and to estimate greenhouse gas removals and emissions resulting from activities of LULUCF. Main aim is to develop a classification method based on machine learning methods using Sentinel data in the open research application Google Earth Engine. The resulted method should provide a classification of the categories of LULUCF with high accuracy at the (macro)regional scale annually. The method will be testing in the countries of Central Europe. The method and results will be used and tested in the process of LULUCF inventory and reporting. The Ph.D. project will be involved into international activities dealing with Land Use/Land Cover Change and Climate Change with support of Copernicus data, e.g. Project FPCUP - Framework Partnership Agreement on Copernicus User Uptake, the European Union’s Caroline Herschel Framework Partnership Agreement on Copernicus User Uptake (https://www.copernicus-user-uptake.eu).
ŠANDERA, J.; ŠTYCH, P. Selecting Relevant Biological Variables Derived from Sentinel-2 Data for Mapping Changes from Grassland to Arable Land Using Random Forest Classifier. Land 2020, 9, 420. https://doi.org/10.3390/land9110420.
LASTOVICKA, J.; SVEC, P.; PALUBA, D.; KOBLIUK, N.; SVOBODA, J.; HLADKY, R.; STYCH, P. Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation. Remote Sens. 2020, 12, 1914. https://doi.org/10.3390/rs12121914.
MICEK, O.; FERANEC, J.; STYCH, P. Land Use/Land Cover Data of the Urban Atlas and the Cadastre of Real Estate: An Evaluation Study in the Prague Metropolitan Region. Land 2020, 9, 153. https://doi.org/10.3390/land9050153.
Senf, C., Linden, S., Laštovička, J., Okujeni, A., Heurich, M. A generalized regression-based unmixing model for mapping forest cover fractions throughout three decades of Landsat data, Remote Sensing of Environment. Volume 240, April 2020, 111691. https://doi.org/10.1016/j.rse.2020.111691
STYCH, P.; JERABKOVA, B.; LASTOVICKA, J.; RIEDL, M.; PALUBA, D. A Comparison of WorldView-2 and Landsat 8 Images for the Classification of Forests Affected by Bark Beetle Outbreaks Using a Support Vector Machine and a Neural Network: A Case Study in the Sumava Mountains. Geosciences 2019, 9(9), 396; https://doi.org/10.3390/geosciences9090396.
PULCHRA - Science in the City: Building Participatory Urban Learning Community Hubs through Research and Activation, Horizon 2020 (ID 824466), 2019-2022
Framework Partnership Agreement on Copernicus User Uptake (FPCUP), European Commission (ID 275/G/GRO/COPE/17/10042), 2018 - 2022
Ensuring Sustainable Land Management in Selected Areas of Ethiopia on the Basis of Geoscientific Mapping (Czech Development Agency), ET-2019-019-RO-43040, 2019 - 2024
EO4Edu - Earth Observation for Education, Erasmus + (2020-1-UK01-KA201-079007), 2020 - 2023.
Deadline is closed
