European rural landscapes are under pressure due to rural-urban migration, change of livelihood strategies, socio-economic and institutional changes regarding land use, climate change, and invasion of alien species (IAS). Agricultural land abandonment is probably, the most widespread land change process in Europe and affects both croplands and grasslands. Agricultural land abandonment may result in the formation of novel ecosystems with positive but also negative impacts on the environment. Evidence shows that IAS may spread over abandoned agricultural lands (Masemola et al., 2020), resulting in diminishing the ecosystem services and thus, have significant negative implications for human livelihoods and human well-being. Among IAS common in Europe is Giant Hogweed (Heracleum Sosnowskyi), further, H. Sosnowskyi. It was introduced early 20th century in some European countries as an ornamental plant originally from the Caucasus. During the 1950s, it has also been planted as a livestock fodder crop due to the substantive biomass potential of this plant. Following the demise of the Soviet Union, H. Sosnowskyi went out of control and is now rapidly spreading over in Eastern European countries. H. Sosnowskyi aggressively spreads, it is difficult to control H. Sosnowskyi. H. Sosnowskyi is also dangerous to humans and animals. Satellite remote sensing provides an unprecedented source to monitor the state of the land cover land use, particularly with the advent of ESA’s Copernicus Sentinel monitoring program- SAR Sentinel-1 and optical Sentinel-2 satellites, and therefore, extending the availability of optical Landsat observations dating back to 1980s. Our major goal was to document the agricultural land-cover change from 1990 to 2020 in temperate Eastern Europe (with a strong focus on European Russia and neighboring countries), namely transitioning of managed grasslands and croplands to abandoned lands at a different stage of natural succession, but also to reveal the spread of H. Sosnowskyi and urbanization. We tested the suitability of random forest classifier as well as U-Net architecture for convolutional neural networks. Training and validation data have been collected with the use of very-high-resolution satellite imagery available via Google Earth, Planetscope constellations, dense Landsat, Sentinel-2 composites. Work has also been complemented by validation data collection during the field campaigns, attracting trained volunteers and open-access crowdsource information. We employed available Landsat-based land-cover maps circa 1990 from the University of Maryland (Potapov et al., 2014) and circa 2015 from Greenpeace-Russia (Glushkov et al., 2021) to attribute the transitions from managed cropland and grassland to different stages of agricultural land abandonment for a Russia (European part) and neighboring countries. Further, we complemented the analysis with detailed elaboration on land use and land cover by 2020 with the analysis of Sentinel-1 and Sentinel-2 time series and zoomed-in Moscow province of Russia to understand the spread of H. Sosnowskyi. Our broad-scale mapping of land-cover change using Landsat-based from 1990 to 2015 showed a widespread abandonment of croplands and grasslands, particularly in European Russia, albeit with remaining cultivation spots, such as in Vladimir and Kaluga provinces, but also in the forest-steppe zone. Many abandoned lands became naturally afforested, yet the capacity still exists for further natural afforestation. The results of broad-scale analysis correlated well with change observed from the official agricultural statics at the province level. The detailed zoom-in to Moscow province showed that only a small portion of managed grasslands in 1990 remained mown by 2020. Formerly managed croplands and grasslands transitioned to unmanaged grasslands, areas with early shrub succession and young forest, but were also contracted due to urbanization and the spread of H. Sosnowskyi. Out of 1,790 thousand hectares of managed agricultural lands, approximately 8% were encroached by H. Sosnowskyi by 2020. From a remote sensing aspect, Sentinel-2 served as a source of data to develop monthly cloud-free composites. Preliminary analysis showed, while the add-on of Sentinel-1 derivatives did not result in higher classification accuracies, once it has been added to Sentinel-2 composites, nevertheless VV, and VH polarizations were useful to distinguish mowing activities. Feature analysis with random forest showed a contribution of Sentinel-2 SWIR bands and simple seasonal metrics, such as standard deviation calculated from May 1st to October 1st. We also tested and did not find a significant contribution of the Hogweed Index (which is based on the difference between green ad NIR bands) because it is probably represented with uncondensed information already available in the classified layer stack. Our further experiments with U-Net CNN showed the great potential of this classification approach. Yet, the requirement of having a large training set and complexity in the parameterization of -Net CNN leaves room for random forest classifier.
In sum, our study showed a massive agricultural land-use change transformation after 1990, resulting in cropland and grassland abandonment with subsequent shrubs and young trees encroachment. However, alternative trajectories of grassland contraction, as the case study in Moscow province showed, primarily urbanization and invasion of H. Sosnowskyi, additionally reduces available grasslands and meadows in temperate Europe. Our study showed a great advantage of using Sentinel-1 and Sentinel-2 time series, particularly the latter one, to map shrubs and young trees encroachment as well as the spread of H. Sosnowskyi. The invasion of H. Sosnowskyi is worrisome as it already affects many European countries-for instance in Denmark, Germany, France, the Baltic States, Russia and Ukraine. Therefore, our study shows how H. Sosnowskyi and the transition of grasslands can be monitored timely. Last but not least, we postulate, the withdrawal of lands from agricultural land use and restoration of vegetation should be steered rather left for “nature”, as it may result in unintended invasions of alien species, such as H. Sosnowskyi.
Glushkov, I., Zhuravleva, I., McCarty, J.L., Komarova, A., Drozdovsky, A., Drozdovskaya, M., Lupachik, V., Yaroshenko, A., Stehman, S.V., Prishchepov, A.V., 2021. Spring fires in Russia: results from participatory burned area mapping with Sentinel-2 imagery. Environ. Res. Lett. 16, 125005. https://doi.org/10.1088/1748-9326/ac3287
Masemola, C., Cho, M.A., Ramoelo, A., 2020. Sentinel-2 time series based optimal features and time window for mapping invasive Australian native Acacia species in KwaZulu Natal, South Africa. International Journal of Applied Earth Observation and Geoinformation 93, 102207. https://doi.org/10.1016/j.jag.2020.102207
Potapov, P.V., Turubanova, S.A., Tyukavina, A., Krylov, A.M., McCarty, J.L., Radeloff, V.C., Hansen, M.C., 2014. Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sensing of Environment. https://doi.org/10.1016/j.rse.2014.11.027