Ondřej Pešek

ST_LUCAS is an open-source system designed for providing harmonized space-time aggregated LUCAS data. LUCAS (Land Use and Coverage Area frame Survey) is an activity managed by Eurostat that performs in-situ surveys (points in 2x2km grid) over Europe every three years since 2006. For each LUCAS point, the land cover and land use classes are examined, five photos taken, and various agro-environmental attributes collected. Eurostat is providing data in plain CSV files. LUCAS nomenclature is changing each survey year, some attributes were removed, added or renamed. ST_LUCAS was created with the goal to provide harmonized (each LUCAS survey is translated into common nomenclature) and space-time aggregated (for each LUCAS point, a single location and set of harmonized attributes for each survey year are provided) data. The ST_LUCAS system offers analysis-ready data through the Python API and QGIS plugin (“ST_LUCAS Download Manager”), which minimizes obstacles to use the data by the wider audience. Users may easily access land cover/use information about 1 350 847 points covering 28 EU countries measured from 2006 till 2018 by Eurostat. LUCAS points are retrieved from the ST_LUCAS system based on specified spatial, temporal, attribute, and thematic filters. The Python API and QGIS plugin also allow retrieving photos (one facing photo and four landscape photos in the cardinal compass directions) for each LUCAS point. Additionally, two analytical functions are available: user-defined LUCAS land cover classes aggregation and the possibility to translate LUCAS nomenclature into other nomenclatures. See ST_LUCAS website https://geoforall.fsv.cvut.cz/st_lucas/ for detailed information.

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Other people: Martin Landa

The Model of Living Landscape (MLL) is a set of empirical based tools for land management and landscape planning. It recognizes the complexity of the interactions between humans and the natural environment, and it aims to create a sustainable and resilient landscape that supports the well-being of both people and nature. One of the core MLL components is a process-based model for rainfall-runoff and erosion computation called SMODERP. The model operates on the principle of cell-by-cell mass balance, calculated at each time step. SMODERP (https://github.com/storm-fsv-cvut/smoderp2d) is open-source software implemented in Python language to ensure compatibility with most GIS software solutions. The current implementation supports Esri ArcGIS, GRASS GIS and QGIS. In this contribution, a new QGIS SMODERP plugin linking the hydrologic model outputs to MLL will be presented. The plugin performs the input data preparation on the background using GRASS GIS data provider, computation is done by SMODERP Python package, and results visualised with predefined map symbology in QGIS map canvas. This contribution was supported by grant RAGO - Living landscape (SFZP 085320/2022) and Using remote sensing to assess negative impacts of rainstorms (TAČR - SS01020366).

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Other people: Martin Landa Petr Kavka

Surface runoff is one of the processes with direct impact on water erosion. Surface runoff has two basic components: a) sheet runoff and b) rill runoff. Observation of these phenomena at various scales and then using mathematical models to describe their observations plays a key role for soil protection. One of the models developed to compute these phenomena is SMODERP, used for example in the flexible and adaptive approach to land management and landscape planning called Model of Living Landscape project. Innovative application of the SMODERP model (https://github.com/storm-fsv-cvut/smoderp2d) named SMODERP Line is presented in this contribution. SMODERP Line is accessible through various interfaces including OGC Web Processing Service (WPS) which can be easily integrated into user-defined processing pipelines or web applications. Usage of SMODERP2D Line will be demonstrated in the QGIS environment through a new OWSLib-based QGIS WPS Client Plugin (https://github.com/OpenGeoLabs/qgis-wps-plugin). This contribution was supported by grant RAGO - Living landscape (SFZP 085320/2022) and Using remote sensing to assess negative impacts of rainstorms (TAČR - SS01020366).

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Other people: Martin Landa Petr Kavka