The project will focus on analysis of vegetation change and health in valuable ecosystems (relict arctic-alpine tundra and forests of national parks in Czechia) under environmental pressure (climatic change, damage caused by pests). Various types of remote sensing data will be tested to evaluate an information value of different spatial, spectral and time resolutions. Multitemporal (2019-2021) multispectral (MicaSense RedEdge MX) and hyperspectral (Headwall Nano-Hyperspec) data from UAV will be the main data source for the analysis and upscaling on the levels of hyperspectral aerial data (CASI, SASI sensors) and multispectral satellite data (PlanetScope, Sentinel-2). The main goals of the project will be:
1) To evaluate a potential of MS and HS UAV data for classification of tundra vegetation using advanced classification methods (machine learning) and training polygons provided by botanist for small field plots (100 x 100 m). Influence of various combinations of time series data and training data will be tested.
2) To use an information potential (extremely high spatial resolution – 3 cm) of UAV data and accurate outputs of their classification to upscale from small plots to the landscape level. On the landscape level outputs of UAV data classification will be used as training data for an extensive area classification. MS data from MicaSense Altum camera (acquired by VTOL Atmos Marlyn), HS aerial data and satellite data will be classified on this level and difference in classification accuracy on the both levels will be evaluated.
3) To map succession stages of forests recovering from bark beetle attack. Definition and typology of the successional stages will be proposed in collaboration with foresters.
4) To test a possibility of UAV HS data in combination with other field measurements (LAI, chlorophyll etc.) for vegetation health evaluation and a possibility to upscale to the level of aerial HS and satellite MS data.
An applicant can focus on one of the ecosystems (tundra, forest) or analyze both in her/his work.
Lhotáková, Z.Brodský, L.Kupková, L.Kopačková, V.Potůčková, M.Mišurec, J.Klement, A.Kovářová, M.,Detection of multiple stresses in Scots pine growing at post-mining sites using visible to near-infrared spectroscopy. Environ. Sci.: Processes Impacts, 2013, 15, 2004-2015. DOI:
Suchá, R., Jakešová, L., Kupková, L., Červená, L. (2016): Classification of vegetation above the treeline in the Krkonoše Mts. National Park using remote sensing multispectral data. AUC GEOGRAPHICA, 51, 1, p. 113–129.
Potůčková, M., Červená, L., Kupková, L., Lhotáková, Z., Lukeš, P., Hanuš, J., Novotný, J., Alebrechtová, J. (2016): Comparison of Reflectance Measurements Acquired with a Contact Probe and an Integration Sphere: Implications for the Spectral Properties of Vegetation at a Leaf Level. Sensors, 16, 1801
Kupková, L., Červená, L., Suchá, R., Zagajewski, B., Březina, S., Albrechtová, J. (2017): Classification of Tundra Vegetation in the Krkonoše Mts. National Park Using APEX, AISA Dual and Sentinel-2A Data. European Journal of Remote Sensing. 50:1, p. 29-46.
Kupková L, Potůčková, M, Lhotáková Z and Albrechtová J (2018): Forest cover and disturbance changes, and their driving forces: A case study in the Ore Mountains, Czechia, heavily affected by anthropogenic acidic pollution in the second half of the 20th century. Environmental Research Letters, Volume 13, Number 9.
INTER-ACTION Project (bilateral Czech-USA): Assessment of ecosystem function based on Earth observation of vegetation quantitative parameters retrieved from data with high spatial, spectral and temporal resolution (2019–2022)
Development of methods for monitoring of tundra vegetation changes in the Krkonoše national park using multispectral, hyperspectral and LiDAR UAV data. Operational programme Environment (2019–2023)Apply to the project